Railway upcycling system, method thereof, and production method thereof

Abstract

According to the present invention, a controller: configures a first digital twin model which belongs to a railway system and simulates a subject to be value-improved; adjusts parameters of the first digital twin model by fetching operation data of the subject; configures a second digital twin model which simulates a subject to be updated when the value of the subject is improved by improving the accuracy of the first digital twin model; and enables evaluation for value improvement by comparing the simulation results when the first digital twin model and the second digital twin model operate under the same condition. Therefore, a railway upcycle system is provided which can improve the performance of an upcycle target, such as a facility or software related to the railway system, over that of existing specifications by accurately determining whether upcycling is right, and in addition, repetitively execute this performance improvement.

Description

Technical Field

[0001] The present invention relates to a railway upcycle system, its method, and a production method thereby.

Background Art

[0002] In recent years, as a sustainable society, there is a movement towards a society that does not damage the global environment, does not overuse resources, and enables future generations to continue living richly. As part of this, there is upcycle, which improves the overall performance of the target product (hereinafter also referred to as "target product" or "target") while making it last longer and reducing waste. Even the reduced waste is recycled while continuously changing its quality into a recyclable product that conserves the environment. Thus, a technology for improving the value of products and the like is desired.

[0003] Here, it is known that value can be classified into environmental value, economic value, and social value. Environmental value includes, in addition to resource circulation, in the railway system, low energy consumption, low noise, low vibration, etc. Economic value includes, in addition to low cost, in the railway system, small size and light weight, safety, ease of use from the user's perspective, ease of access, etc. Social value is considered a goal to be aimed for along with the realization of environmental value and economic value, and SDGs (Sustainable Development Goals) etc. are known.

[0004] Patent Document 1 describes a system that acquires field performance data of a vehicle subsystem monitored by sensors mounted on the vehicle during operation, and determines a performance composite index of the vehicle subsystem based on the variance between the simulated performance data and the field performance data. Based on the performance composite index, it is described that the vehicle is controlled during operation or subsequent operation, or preventive maintenance of the vehicle is automatically scheduled based on a health score.

[0005] Non-patent document 1 describes robust adhesion control that appropriately follows the constantly changing adhesion state by applying the framework of the Taguchi method (a stabilization design method that accepts various quality variations that occur in product manufacturing at the design stage in order to eliminate these variations and prevent variations from occurring in the actual product's function) to the parameter design of adhesion control. [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] US 2018 / 0257683A1 [Non-patent literature]

[0007] [Non-Patent Document 1] "Robust Adhesion Control," Proceedings of the 41st Symposium on Cybernetics in Railways, Paper No. 518 (2004-11) [Overview of the project] [Problems that the invention aims to solve]

[0008] To ensure that products remain usable as circular products, providing new value (function, performance, lifespan, etc.), it is necessary to manage and track data such as usage history and quality information at the product and material levels for modularized equipment and components. On the other hand, the railway sector operates under a linear economic model of procurement → production → use → disposal, or a recycling-type economic model that tolerates waste while recycling some products and materials, and has not established a system to track usage history and quality information at the product and material levels.

[0009] Furthermore, to improve the control performance of software functions, the final parameter adjustments and performance verification have traditionally been performed in an actual vehicle environment. However, in an actual vehicle environment, route conditions, passenger conditions, and weather conditions change moment by moment. There is a need for a simulated vehicle environment, such as a simulator, that can repeatedly reproduce the operating conditions in an actual vehicle environment, adjust numerous parameters in response to changes, stably converge the control performance, and complete the decision on its applicability to actual vehicles.

[0010] Thus, in conventional railway systems, it has been difficult to determine whether or not upcycling is appropriate, and it has been difficult to smoothly implement continuous upcycling. The objective of the present invention is to provide a railway upcycling system that can accurately determine whether or not upcycling is appropriate, thereby improving the performance of upcyclable items such as equipment and software related to railway systems beyond their conventional specifications, and furthermore, can repeatedly perform such performance improvements. [Means for solving the problem]

[0011] The present invention, which solves the above problems, comprises a controller and a memory, and the controller and the memory construct a digital twin environment for simulating a railway system, and executes value improvement of the railway system according to a program in the memory, wherein the controller sets up a first digital twin model that simulates the object to be improved in value belonging to the railway system, adjusts the parameters of the first digital twin model by taking operational data of the object, improves the accuracy of the first digital twin model, sets up a second digital twin model that simulates the update target when the object is improved in value, and enables evaluation of value improvement by comparing the simulation results when the first digital twin model and the second digital twin model are operated under the same conditions. [Effects of the Invention]

[0012] According to the present invention, by enabling accurate judgment regarding the appropriateness of upcycling, it is possible to improve the performance of upcyclable items such as equipment and software related to railway systems beyond their conventional specifications, and furthermore, a railway upcycling system is provided that can repeatedly perform such performance improvements. [Brief explanation of the drawing]

[0013] [Figure 1] This is a diagram illustrating the concept of parallel analysis in the railway upcycling system according to Embodiment 1 of the present invention (hereinafter also referred to as "this upcycling system"). [Figure 2] Figure 1 is a functional configuration diagram showing the basic operation of this upcycling system. [Figure 3] Figure 1 is a functional configuration diagram showing an example of the basic operation (simulated vehicle model update) of this upcycling system. [Figure 4] Figure 1 is a functional configuration diagram showing an example of the basic operation (actual vehicle control update) of this upcycling system. [Figure 5] Figure 1 is a schematic diagram illustrating an example of the information transmission unit 3 of this upcycling system. [Figure 6] This is a conceptual diagram illustrating the battery recycling system using the railway upcycling system according to Embodiment 2 of the present invention (also referred to as "this upcycling system"). [Figure 7] Figure 6 is a schematic diagram illustrating the parameter identification process in the cyclical system shown. [Figure 8] This is a flowchart illustrating the procedure for identifying the parameters shown in Figure 7. [Figure 9] This is a schematic diagram illustrating the upcycling evaluation in the circular economy system shown in Figure 6. [Figure 10] Figure 9 is a flowchart illustrating the upcycle evaluation procedure. [Figure 11] Figure 6 is a schematic diagram illustrating the parameter identification (after upcycling) in the cyclical system shown. [Figure 12]It is a flowchart for explaining the procedure of parameter identification (after the upcycle) in FIG. 11. [Figure 13] It is a schematic explanatory diagram for explaining maintenance determination in the circulation system of FIG. 6. [Figure 14] It is a flowchart for explaining the procedure of maintenance determination in FIG. 13. [Figure 15] It is a conceptual explanatory diagram for explaining the function of proposing a maintenance plan in addition to the data management function of products and materials in this upcycle system. [Figure 16] It is a functional block diagram showing the configuration of a modified example in which the actual vehicles 11a and 11b in FIG. 5 are connected in one formation. [Figure 17] FIG. 17 is a functional block diagram showing the configuration of the actual vehicle 11a in FIG. 16 in more detail. [Figure 18] FIG. 1 shows an example of an ID system that defines the hardware / software traceability (traceability) of the parallel analysis system. [Figure 19] It is a conceptual explanatory diagram for explaining the circulation system of a storage battery by the upcycle system of Example 3. [Figure 20] It is a schematic explanatory diagram showing another example of the information transmission unit 3 shown in FIG. 5. [Figure 21] It is a block diagram showing the configuration of the drive system of the actual vehicle 11 when the main circuit storage battery 123 in FIG. 17 consists of a plurality of storage battery systems. [Figure 22] It is a diagram showing the internal configuration of the storage battery system. [Figure 23] (a) is a diagram showing the configuration of a controller that collects data from the storage battery system. (b) is a diagram showing the necessary data required as the quality data of the storage battery. [Figure 24] In Example 3, it is a diagram showing an example of an ID system that enables traceability. [Figure 25] In Example 3, it is a diagram showing an example of an ID system that enables traceability. [Figure 26] This is a step-by-step diagram showing the maintenance and replacement process for a battery storage system. [Figure 27] This is a flowchart illustrating the maintenance and replacement process for a battery storage system. [Figure 28] This diagram shows the configuration of a model (simulation model) that simulates the charging and discharging operation of a battery storage system. [Modes for carrying out the invention]

[0014] Hereinafter, the upcycling system according to embodiments of the present invention will be described with reference to the drawings. Embodiment 1, mainly concerning software, will be described using Figures 1 to 5, and Embodiment 2, mainly concerning hardware (things), will be described using Figures 6 to 18. In each figure, the same reference numerals are used for equivalent parts to avoid redundant explanations. Furthermore, in Figure 6, the relationships between each requirement are clearly shown in the figure based on the stated terms, so reference numerals are omitted. Similarly, reference numerals are omitted in Figures 9, 11, 13, 15, and 18.

[0015] In this embodiment, a railway upcycling system or railway upcycling method is described, which includes a controller and a memory, and which constructs a digital twin environment for simulating a railway system and performs value enhancement of the railway system according to a program in the memory. The controller sets up a first digital twin model that simulates the object to be enhanced in value, which belongs to the railway system, adjusts the parameters of the first digital twin model by taking operational data of the object, improves the accuracy of the first digital twin model, sets up a second digital twin model that simulates the update target when the object is enhanced in value, and enables evaluation of the value enhancement by comparing the simulation results when the first digital twin model and the second digital twin model are operated under the same conditions. Furthermore, a railway upcycling production method is described, which produces a railway system with enhanced value using this railway upcycling system or railway upcycling method.

[0016] Furthermore, the examples illustrate how to create a new digital twin model by updating one or more functions included in an identified and existing digital twin model with new functions.

[0017] Furthermore, in this embodiment, it is explained that a common ID is assigned to the object belonging to the railway system and the corresponding digital twin model, and operational data is linked to this ID.

[0018] Furthermore, in this embodiment, it is explained that the operational data used to adjust the parameters of the digital twin model includes train operation information, operation commands or operations, train configuration information, or vehicle equipment operation.

[0019] Furthermore, the embodiments explain that the operational data used to adjust the parameters of the digital twin model includes, if the target is a drive control device, frequency, input / output current / voltage, or power unit temperature; if the target is an auxiliary power control device, frequency, input / output current / voltage, or power unit temperature; if the target is a storage battery, charge / discharge current, voltage, SOC, SOH, or temperature; if the target is a train information control device, the status of each device, occupancy rate, or ambient temperature; and if the target is a vehicle component, vibration, temperature, or strain.

[0020] Furthermore, the embodiment describes adjusting the parameters of the digital twin model after determining that the operational data of the target belonging to the railway system does not contain any abnormal data caused by an accident or malfunction.

[0021] Furthermore, the embodiment explains how performance degradation is identified through time-history-based simulations to predict the timing of performance deterioration, and how the timing of value improvement is determined and proposed before the deterioration of the object belonging to the railway system begins.

[0022] Furthermore, in this embodiment, it is explained that the operational data for the target belonging to the railway system is data from driving using an automated driving system or a driver assistance system. It is also explained that the driving patterns necessary for adjusting the digital twin model are defined in at least one of the driving patterns for automated driving or the driving patterns for the driver assistance system.

[0023] Furthermore, the embodiment explains that by comparing simulation results that include variable data other than actual vehicle operation data, it becomes possible to evaluate the improvement in value.

[0024] Furthermore, the embodiments will explain that the object of value enhancement is any of the following: the actual vehicle, the storage battery, the drive control device, the auxiliary power control device, the fuel cell system, the air conditioning system, the in-vehicle monitoring device, the train information control device, the passenger information provision device, the seats, the vehicle components, the forward monitoring device, the safety monitoring device, the driver assistance system, the control function for automatic driving, or the control function for ground / on-vehicle wireless information transmission.

[0025] Furthermore, in this embodiment, objects belonging to the railway system are assigned an ID used for individual identification, a medical record is linked to this ID, and the medical record is updated with the same work performed as the change work data.

[0026] Furthermore, the embodiment describes how the parameters of the control software are automatically adjusted on a digital twin model based on operational data, and how the automatically adjusted software is activated in response to a predetermined trigger. It also describes how the parameters of the control software that are automatically adjusted include adhesion control, forward monitoring sensitivity, driver assistance system patterns, automatic train control patterns, hybrid energy management, battery current limiting, battery SOC range limiting, traction force control by acceleration limiting, air brake blending control, or equipment operation changeover point information. [Examples]

[0027] Here, we use control software for train drive systems as an example of railway-related upcycling, but this also includes control software for in-vehicle equipment such as operation management systems, vehicle information control systems, and autonomous driving systems. Example 1 illustrates a parallel analysis system (see Figure 1) that predicts the operation of a railway vehicle drive control system and optimizes its control. In other words, this upcycling system exemplified verifies the advantages and disadvantages of updating the software that implements the railway vehicle drive control system and supports the decision of whether or not to update (upcycle).

[0028] In other words, this upcycling system maintains the drive control of railway vehicles in an optimal state corresponding to the track conditions, passenger conditions, and weather conditions. If it is determined that upcycling is possible based on simulations on a digital twin that simulates the conditions of a real vehicle in real space, then it will be implemented. The search for stable control parameters should preferably be conducted in a simulation environment that realizes an environment as close as possible to that of a real vehicle 11, and the stable control parameters should be searched and determined in this environment.

[0029] The railway vehicle drive control system drives the train by controlling the torque of the main motor 117 mounted on the motor car, which is one of the motor cars and trailer cars that make up the train, using a main converter mounted on the same car or a nearby car.

[0030] Torque control of the main motor 117 is typically implemented using software installed in the controller of the main converter. This software is designed to meet predetermined vehicle performance characteristics, such as acceleration and deceleration characteristics, based on the mechanical properties of the main motor 117, equipment specifications (wheel diameter, gear ratio, etc.), vehicle specifications (vehicle mass, running resistance, load, etc.), and vehicle specifications.

[0031] As described above, the software for the main converter that realizes the vehicle performance is functionally designed based on the equipment and vehicle specifications. The vehicle performance realized by the designed software is finally confirmed to meet the specified performance requirements through actual vehicle driving tests.

[0032] If the equipment and vehicle specifications were exactly equivalent to those of the actual vehicle 11, then the only requirement for the driving test would be to confirm that the designed vehicle performance meets the specified performance requirements. However, in reality, there are always differences in specifications between the equipment and vehicle specifications and the actual vehicle 11, to varying degrees.

[0033] Regarding equipment specifications, there are variations in the characteristic constants of the main motor 117 due to temperature changes and the length of the on-board wiring. Similarly, the design value and the measured value of equipment efficiency will never perfectly match. Regarding vehicle specifications, there are always manufacturing tolerances between the design value and the measured value of the vehicle mass. Furthermore, the running resistance of the actual vehicle 11 varies greatly depending on the body shape, but the design uses a running resistance formula based on JIS standards, etc., and in reality, the two do not necessarily match.

[0034] Thus, the vehicle performance designed based on ideal equipment and vehicle specifications differs from that of the actual vehicle 11. Therefore, it is necessary to recognize the difference between the two through driving tests of the actual vehicle 11 and then adjust the control to achieve the specified performance.

[0035] The vehicle performance aspects that require adjustment include driving performance such as acceleration and deceleration (including evaluation in rainy weather (when the rail surface is wet)). Regarding the basic control of the main motor 117, which is driven by vector control, the difference between the design value and the measured value of the main motor characteristic constant affects stability, so control adjustments are necessary in the actual vehicle 11.

[0036] Furthermore, in some cases, control adjustments may be necessary on the actual vehicle 11 to address the impact on signal equipment due to harmonic return currents and electromagnetic compatibility (EMC). One example of control adjustment on the actual vehicle 11 is the "robust adhesion control" described in Non-Patent Literature 1. This "robust adhesion control" discloses an adhesion control method that suppresses wheel slip, which occurs when the tread force transmitted from the wheel to the rail based on motor torque in a railway vehicle exceeds the friction limit between the wheel and the rail, such as during rainy weather, by reducing the tread force.

[0037] This adhesion control involves the Taguchi method, which searches for stable control parameters that reliably transmit tread force in response to fluctuations in friction limits such as rainfall, and performance verification using an actual vehicle 11 to which the stable control parameters are applied. Generally, wheelspin is distinguished from slippage during acceleration and skidding during deceleration, but here they are collectively referred to as "wheelpin."

[0038] Figure 1 is a configuration diagram illustrating the concept of parallel analysis in this upcycling system. In this upcycling system, the actual vehicle field 1 is an environment equipped with the necessary facilities for the operation of the actual vehicle 11, and includes at least the actual vehicle 11 as well as rails 15 such as tracks (Figures 5 and 6) which are not shown. Furthermore, if the actual vehicle 11 is an electric vehicle that runs on power supplied from outside the vehicle, it includes a power supply unit 16 (Figures 5 and 6) that supplies power to the vehicle 11, such as overhead lines or a third rail.

[0039] The actual vehicle 11 is equipped with a vehicle drive unit 12 consisting of an electric motor, an inverter device, etc. that drives itself. The vehicle drive unit 12 may also be equipped with a power storage unit that temporarily stores regenerative power generated by the electric motor during braking, or power generated by a fuel cell, engine, etc., and supplies it to the electric motor at the appropriate time to obtain driving force.

[0040] The actual vehicle control unit 13 monitors the state of each component of the actual vehicle 11 and controls its operation. The actual vehicle drive unit 12 and the actual vehicle drive control unit 14 have an interface for sending and receiving information necessary for drive control. The actual vehicle drive control unit 14 mainly sends and receives information, and outputs a PWM signal Vp to turn the switching circuit of the inverter device that constitutes the actual vehicle drive unit 12 on / off. As a result of controlling the switching circuit by inputting the PWM signal Vp, the actual vehicle drive unit 12 outputs at least the motor current Im flowing through the motor and the input voltage Ecf of the switching circuit, and inputs them to the simulated vehicle drive control unit 24.

[0041] The simulated vehicle field 2 is an environment equipped with the necessary facilities for the operation of the simulated vehicle 21, and includes at least the simulated vehicle 21 as well as rails 15 such as railway tracks (Figures 5 and 6). Furthermore, if the simulated vehicle 21 is an electric vehicle that runs on power supplied from outside the vehicle, it is equipped with a power supply unit 16 such as an overhead line or a third rail that supplies power to the vehicle 11 (Figures 5 and 6). In this way, the simulated vehicle field 2 replaces the actual vehicle 11 in the actual vehicle field 1 with the simulated vehicle 21. Similarly, the actual vehicle drive unit 12 is replaced with the simulated vehicle drive unit 22, the actual vehicle control unit 13 with the simulated vehicle control unit 23, and the actual vehicle drive control unit 14 with the simulated vehicle drive control unit 24.

[0042] The simulated vehicle 21, simulated vehicle drive unit 22, simulated vehicle control unit 23, and simulated vehicle drive control unit 24 that constitute the simulated vehicle field 2 are simulators on an electronic computer. However, a mixed configuration (Hardware In the Loop Simulator) can also be realized in which one or more of these parts are replaced with real parts, such as the simulated vehicle 21 being replaced with the real vehicle 11, the simulated vehicle drive unit 22 with the real vehicle drive unit 12, the simulated vehicle control unit 23 with the real vehicle control unit 13, and the simulated vehicle drive control unit 24 with the real vehicle drive control unit 14. Here, the real vehicle field 1 and the simulated vehicle field 2 operate on the same time scale, but the absolute time in which they operate does not necessarily have to be the same.

[0043] The simulated vehicle 21 is equipped with a simulated vehicle drive unit 22, which consists of an electric motor, an inverter device, etc., that drives itself. The simulated vehicle drive unit 22 may also be equipped with a power storage unit that temporarily stores regenerative power generated by the electric motor during braking, or power generated by a fuel cell, engine, etc., and supplies it to the electric motor at the appropriate time to obtain driving force.

[0044] The simulated vehicle control unit 23 monitors the state of each component constituting the simulated vehicle 21 and controls its operation. The simulated vehicle drive unit 22 and the simulated vehicle drive control unit 24 are equipped with an interface for sending and receiving information necessary for drive control. Primarily as information sent and received, the simulated vehicle drive control unit 24 outputs a PWM signal Vp that turns the switching circuit of the inverter device constituting the simulated vehicle drive unit 22 on / off. As a result of controlling the switching circuit by inputting the PWM signal Vp, the simulated vehicle drive unit 22 outputs at least the motor current Im flowing through the motor and the input voltage Ecf of the switching circuit, and inputs these to the simulated vehicle drive control unit 24.

[0045] Thus, the simulated vehicle field 2 replaces all or part of the actual vehicle 11, actual vehicle drive unit 12, actual vehicle control unit 13, and actual vehicle drive control unit 14 that constitute the actual vehicle field 1 with a simulator implemented using a computer with a CPU and memory, and enables parallel operation of the actual vehicle field 1 and the simulated vehicle field 2 by mutually receiving data. Here, parallel operation is a control method of the railway system that minimizes the difference between the actual vehicle field 1 and the simulated vehicle field 2 when they are running based on the same driving command, for example, the acceleration, speed, distance traveled, and the current value and voltage value of the vehicle drive unit.

[0046] The state of each part constituting the actual vehicle 11 in the actual vehicle field 1 is transmitted to the simulated vehicle field 2 via the information transmission unit 3. The simulated vehicle 21 constituting the simulated vehicle field 2 is defined as a physical theory model on a simulator realized by an electronic computer. However, unlike the simulated vehicle environment as a physical theory model, the actual vehicle environment has fluctuating elements such as route conditions, passenger conditions, and weather conditions.

[0047] The system includes a vehicle motion analysis unit 231 (not shown) that minimizes the difference between driving data such as vehicle acceleration, speed, distance traveled, and current and voltage values ​​of the vehicle's drive unit when a real vehicle field 1 and a simulated vehicle field 2 are driven based on the same driving command, and modifies the physical theoretical model of the simulated vehicle 21 based on the analysis results.

[0048] According to the simulated vehicle 21 based on the revised physical theory model, the driving data can be expected to be identical when the actual vehicle field 1 and the simulated vehicle field 2 are driven based on the same driving command. In other words, if the control operations of the simulated vehicle drive unit 22 and the simulated vehicle control unit 23 are stable in the simulated vehicle field 2, then the control operations of the actual vehicle drive unit 12 and the actual vehicle control unit 13 can also be expected to be stable in the simulated vehicle field 2. For this reason, the drive control operation data is first modified so that the control operations of the simulated vehicle drive unit 22 and the simulated vehicle control unit 23 are stable in the simulated vehicle field 2.

[0049] Specifically, the simulated vehicle control unit 23 includes an adjustment calculation unit that modifies the drive control operation data that realizes the control operation of the simulated vehicle drive control unit 24 and stabilizes the state of each part that constitutes the simulated vehicle 21. As an example, in adhesion control that prevents wheel slippage / skidding between the wheels and rails, it is ideal that the adhesion state (tangential force coefficient) between the wheels and rails and the vehicle acceleration realized by adhesion control are in a proportional relationship (Y=kX). The drive control operation data is optimized by modifying the drive control operation data so that the vehicle acceleration measured by changing the adhesion state in the simulated vehicle field 2 is in a proportional relationship (Y=kX), and the stability of the control operation is verified.

[0050] The optimized drive control operation data is transmitted to the actual vehicle field 1 via the information transmission unit 3 to update the drive control operation data already implemented in the vehicle control unit 13 or the vehicle drive control unit 14. The updating of the drive control operation data in the vehicle control unit 13 or the vehicle drive control unit 14 is basically performed automatically when predetermined conditions are met, such as when the actual vehicle 11 in the actual vehicle field 1 is stopped or when the vehicle drive control unit 14 is stopped. However, in special circumstances, it is conceivable that the update may be performed manually based on the judgment of the person in charge or based on the judgment of an AI equivalent to that of the person in charge.

[0051] Furthermore, the driving data of the actual vehicle 11, which operates using the optimized drive control operation data of the actual vehicle field 1, is transmitted to the simulated vehicle field 2 via the information transmission unit 3. When the actual vehicle field 1 and the simulated vehicle field 2 operate based on the same driving command, the simulated vehicle drive control unit 24 or the drive control operation data of the physical theory model of the simulated vehicle 21 is re-modified to minimize the difference in driving data, such as the vehicle's acceleration, speed, distance traveled, and the current and voltage values ​​of the vehicle's drive unit. The simulator determines whether to modify the drive control operation data or the physical theory model based on the following criteria.

[0052] When the actual vehicle field 1 and the simulated vehicle field 2 are driven based on the same driving command, the driving data of the actual vehicle 11 is compared with the difference in the driving data of the actual vehicle 11 operated by the drive control operation data before and after optimization, and the following is performed.

[0053] (1) If the above difference is less than a predetermined value that is very close to zero: The drive control operation data will be corrected again. (2) If the above difference is greater than or equal to a predetermined value that is infinitely close to zero: The physical theoretical model is revised again.

[0054] If the environments of the actual vehicle field 1 and the simulated vehicle field 2 are identical, then the difference in driving data of the actual vehicle 11 operating with the drive control operation data before and after optimization is expected to be the same as the difference in driving data of the actual vehicle 11 operating with the drive control operation data before and after optimization. The reason for this difference is presumed to be that the environments of the actual vehicle field 1 and the simulated vehicle field 2 are not identical, and from this, it is judged that the physical theoretical model of the simulated vehicle field 2 needs to be modified. Note that the quality of the control performance of the actual vehicle 11 and the simulated vehicle 21 operating with the drive control operation data before and after optimization is not considered in this judgment.

[0055] Here, the drive control operation data is control logic such as software or configuration parameters of control logic, which are implemented in the actual vehicle drive control unit 14 to control the operation of the actual vehicle drive unit 12, or in the simulated vehicle drive control unit 24 to control the operation of the simulated vehicle drive unit 22.

[0056] With the above configuration, the model accuracy of the simulator constituting the simulated vehicle field 2 can be improved based on the driving data of the actual vehicle field 1. Furthermore, by improving the model accuracy of the simulator constituting the simulated vehicle field 2, the operation when the drive control operation data is changed in the actual vehicle field 1 can be predicted and pre-verified based on the operation when the drive control operation data is changed in the simulated vehicle field 2. In addition, the drive control operation data pre-verified in the simulated vehicle field 2 is transmitted to the actual vehicle field 1 via the information transmission unit 3, and the actual operation can be confirmed by changing the drive control operation data in the actual vehicle field 1.

[0057] In other words, by improving the accuracy of the simulated vehicle 21 so that its movements are the same as those of the actual vehicle 11, adjusting the drive control using the improved accuracy of the simulated vehicle 21, and then applying the adjusted drive control operation data to the actual vehicle 11, a control method for a railway system that autonomously optimizes drive control is realized.

[0058] Figure 2 is a functional configuration diagram showing the basic operation of the upcycling system in Figure 1. In Figure 2, the actual vehicle field 1 and the simulated vehicle field 2 are configured to mutually send and receive information data via the information transmission unit 3. The driving control unit 111 outputs a driving command COM that commands the operation of the actual vehicle 11, such as powering (acceleration), braking (deceleration / stopping), and off (coasting). This corresponds to the master controller operated by the driver, or in the case of automatic operation, the ATO (Automatic Train Operation) device, etc.

[0059] The driving command COM is input to the actual vehicle drive control unit 14, which controls the driving force output by the actual vehicle drive unit 12 (not shown) so that the actual vehicle 11 operates according to the driving command COM. The actual vehicle drive control unit 14 also outputs status information STARr as a result of controlling the driving force output by the actual vehicle drive unit 12 so that the actual vehicle 11 operates according to the driving command COM.

[0060] The status information STARr is physical quantity data recognized by the software calculation of the actual vehicle drive control unit 14, and includes the vehicle's acceleration Acc, velocity Vel, position Pos, rotational speed Fr, current Im, voltage Vm of the main motor 117, DC current Is, DC voltage Ecf of the inverter device, etc. The driving command COM and the status information STARr are input to the simulated vehicle field 2 via the information transmission unit 3.

[0061] The driving command COM is input to the simulated vehicle drive control unit 24, which constitutes the simulated vehicle 21, and controls the driving force output by the simulated vehicle drive unit 22 (not shown) so that the simulated vehicle 21 operates according to the driving command COM. Of the status information STARr, the speed data VELr and position data POSr of the actual vehicle 11 are input to the storage unit (memory) 233.

[0062] The memory unit 233 stores gradient data, curve data, station location data STN, etc., of the route on which the actual vehicle 11 travels. The gradient data is table data that defines the amount of gradient (rate of elevation increase relative to travel distance) at the location of the actual vehicle 11 relative to the travel location data POSr. The gradient data is table data that defines the curvature of the track (reciprocal of the curve radius) relative to the travel location data POSr of the actual vehicle 11. The station location data STN is table data that indicates the location of station facilities located on the route on which the actual vehicle 11 travels relative to the travel location data POSr. Station facilities include stations, stops, signal boxes, etc., and are usually indicated by the kilometer markers that show the center position of each facility.

[0063] The vehicle motion analysis unit 231 receives state information STAs output by the simulated vehicle 21 and state information STARr output by the actual vehicle 11 and transmitted to the simulated vehicle field 2 via the information transmission unit 3. Based on the state information STAs and state information STARr, it calculates a correction parameter ΔMDL for the simulated vehicle 21 that approximates the operation of the actual vehicle 11. The correction parameter ΔMDL is input to the simulated vehicle 21 to modify the configuration parameters of the simulated vehicle 21.

[0064] Similarly, regarding the differences in behavior between the actual vehicle drive unit 12 and the simulated vehicle drive unit 22, the configuration parameters of the simulated vehicle drive unit 22 are modified to follow the behavior of the actual vehicle drive unit 12. The control data analysis unit 232 receives state information STAs as input and analyzes and determines the optimal control parameters that best approximate the state information STAs and the state value STAp which is the target control result.

[0065] This analysis of optimal control parameters is performed after the configuration parameters of the simulated vehicle 21 have been modified so that the state information STAs, which is the result of the operation of the simulated vehicle 21 based on the driving command COM, follows the state information STARr, which is the result of the operation of the actual vehicle 11 based on the driving command COM. After the configuration parameter modification is complete, the simulated vehicle 21 and the simulated vehicle drive unit 22 behave in the same way as the actual vehicle 11 and the actual vehicle drive unit 12 with respect to the driving command COM. Therefore, if the control operations of the actual vehicle control unit 13 and the simulated vehicle control unit 23, and the actual vehicle drive control unit 14 and the simulated vehicle drive control unit 24 are made equal, the behavior of the actual vehicle field 1 and the simulated vehicle field 2 will also be equal.

[0066] Therefore, by applying the optimal control parameters determined by the control data analysis unit 232 of the simulated vehicle field 2 to the actual vehicle control unit 13 and the actual vehicle drive control unit 14 of the actual vehicle field 1, the target control result can be achieved in the actual vehicle field 1. The transmission of the optimal control parameters from the simulated vehicle field 2 to the actual vehicle field 1 is realized by the information transmission unit 3.

[0067] With the above configuration, the model accuracy of the simulator constituting the simulated vehicle field 2 can be improved based on the driving data of the actual vehicle field 1. Furthermore, by improving the model accuracy of the simulator constituting the simulated vehicle field 2, the operation when the drive control operation data is changed in the actual vehicle field 1 can be predicted and pre-verified based on the operation when the drive control operation data is changed in the simulated vehicle field 2. In addition, the drive control operation data pre-verified in the simulated vehicle field 2 is transmitted to the actual vehicle field 1 via the information transmission unit 3, and the actual operation can be confirmed by changing the drive control operation data in the actual vehicle field 1.

[0068] In other words, by improving the accuracy of the simulated vehicle 21 so that its movements are the same as those of the actual vehicle 11, adjusting the drive control using the improved accuracy of the simulated vehicle 21, and then applying the adjusted drive control operation data to the actual vehicle 11, a control method for a railway system that autonomously optimizes drive control is realized.

[0069] Here, the drive control operation data is control logic such as software or configuration parameters of control logic, which are implemented in the actual vehicle drive control unit 14 to control the operation of the actual vehicle drive unit 12, or in the simulated vehicle drive control unit 24 to control the operation of the simulated vehicle drive unit 22.

[0070] Figure 3 is a functional configuration diagram showing an example of the basic operation (simulated vehicle model update) of the upcycling system shown in Figure 1. In Figure 3, the actual vehicle field 1 and the simulated vehicle field 2 are configured to mutually send and receive information data via the information transmission unit 3. The driving command unit 111 outputs a driving command COM that commands the operation of the actual vehicle 11, such as powering (acceleration), braking (deceleration / stopping), and off (coasting). This corresponds to the master controller operated by the driver, or in the case of automatic operation, the ATO device, etc.

[0071] The driving command COM is input to the actual vehicle drive control unit 14, which controls the driving force output by the actual vehicle drive unit 12 (not shown) so that the actual vehicle 11 operates according to the driving command COM. The actual vehicle drive control unit 14 also outputs status information STARr as a result of controlling the driving force output by the actual vehicle drive unit 12 so that the actual vehicle 11 operates according to the driving command COM. This allows for the collection of driving command, position, and speed as actual vehicle data.

[0072] The status information STARr is physical quantity data recognized by the software calculation of the actual vehicle drive control unit 14, and includes the vehicle's acceleration Acc, velocity Vel, position Pos, rotational speed Fr, current Im, voltage Vm of the main motor 117, DC current Is, DC voltage Ecf of the inverter device, etc. The driving command COM and the status information STARr are input to the simulated vehicle field 2 via the information transmission unit 3.

[0073] Of the status information STARr, the speed data VELr and position data POSr of the actual vehicle 11 are input to the storage unit 233. The storage unit 233 stores gradient data GRD, curve data CRV, station position data STN, etc. of the route on which the actual vehicle 11 travels. The gradient data GRD is table data that defines the amount of gradient at a given position (rate of elevation increase relative to the distance traveled) for the actual vehicle 11's travel position data POSr. The gradient data GRD is table data that defines the curvature of the track (reciprocal of the curve radius) for the actual vehicle 11's travel position data POSr.

[0074] The station location data STN is a table data that shows the locations of station facilities located along the route, corresponding to the actual vehicle's travel location data POSr. Station facilities include stations, stops, signal boxes, etc., and are usually indicated by the kilometer distance, which represents the center position of each facility.

[0075] The vehicle motion analysis unit 231 receives state information STAs output by the simulated vehicle 21 and state information STARr output by the actual vehicle 11 and transmitted to the simulated vehicle field 2 via the information transmission unit 3. Based on the state information STAs and state information STARr, it calculates a correction parameter ΔMDL for the simulated vehicle 21 that approximates the operation of the actual vehicle 11 and the operation of the simulated vehicle 21. The correction parameter ΔMDL is input to the simulated vehicle 21 to modify the configuration parameters of the simulated vehicle 21. The vehicle motion analysis unit 231 then determines the speed difference between the simulation by the actual vehicle 11 and the simulated vehicle 21. Finally, the vehicle motion analysis unit 231 corrects the running resistance coefficient to compensate for the speed difference (≒ running resistance error).

[0076] The gradient resistance Rg(POSr) is calculated from the position data POSr and gradient data GRD, which are state information STAs output by the simulated vehicle 21. The gradient resistance Rc(POSr) is calculated from the position data POSr and curve data CRV. In addition, Rr(VELr) is calculated from the speed data VELr and the running resistance formula. The running resistance formula is defined in standards, etc., and for example, the following formula (1) is known.

[0077] R(v)=(1.65+0.0247·V(v))·mM +(0.78+0.0028·V(v))·mT+9.81·[0.028+0.0078·(n-1)]·V2(v)···(1)

[0078] Here, R: running resistance (N) ΔR: Driving resistance correction value (N) V: Train speed (km / h) mM: Load (kN) due to the total mass of the motor cars in the train set mT: Load (kN) due to the total mass of the control cars and trailer cars in the train set. n: Number of cars in the train

[0079] When formula 1 is applied as the running resistance formula, V in formula 1 is Rr(VERr) = R(VERr) at the speed VERr mentioned above. On the other hand, the running resistance Rmsd of the actual vehicle 11 is measured based on the driving command COM, position POS, and speed VERr (with timestamp) of the actual vehicle 11, and the running resistance correction value ΔR is calculated from the difference between this and the running resistance Rr(VERr) obtained from the running resistance formula using the following formula (2).

[0080] ΔR=Rr(VERr)-Rmsd (2) The corrected running resistance Rrev(VERr) is obtained by adding the running resistance correction value ΔR to Rr(VERr) obtained from the running resistance in equation (3) below.

[0081] Rrev(VERr)=Rr(VERr)+ΔR (3)

[0082] By applying this corrected driving resistance Rrev(VERr) to the simulated vehicle field 2, the operation of the simulated vehicle 21 can be made closer to the operation of the actual vehicle 11. With the above configuration, the model accuracy of the simulator that constitutes the simulated vehicle field 2 can be improved based on the driving data of the actual vehicle field 1.

[0083] Furthermore, by improving the model accuracy of the simulator that constitutes the simulated vehicle field 2, it is possible to predict and pre-verify the behavior when the drive control operation data is changed in the actual vehicle field 1 based on the behavior when the drive control operation data is changed in the simulated vehicle field 2.

[0084] Furthermore, the drive control operation data that has been pre-verified in the simulated vehicle field 2 is transmitted to the actual vehicle field 1 via the information transmission unit 3, and the actual operation can be confirmed by modifying the drive control operation data in the actual vehicle field 1.

[0085] In other words, by improving the accuracy of the simulated vehicle 21 so that its movements are the same as those of the actual vehicle 11, adjusting the drive control using the improved accuracy of the simulated vehicle 21, and then applying the adjusted drive control operation data to the actual vehicle 11, a control method for a railway system that autonomously optimizes drive control is realized.

[0086] Here, the drive control operation data is control logic such as software or configuration parameters of control logic, which are implemented in the actual vehicle drive control unit 14 to control the operation of the actual vehicle drive unit 12, or in the simulated vehicle drive control unit 24 to control the operation of the simulated vehicle drive unit 22.

[0087] Figure 4 is a functional configuration diagram showing an example of the basic operation (updating the control of the actual vehicle) of the upcycled system shown in Figure 1. In Figure 4, the driving control unit 111 outputs a driving command COM that commands the operation of the actual vehicle 11, such as powering (acceleration), braking (deceleration / stopping), and off (coasting). This corresponds to the master controller operated by the driver, or in the case of automatic operation, the ATO device, etc.

[0088] The driving command COM is input to the actual vehicle drive control unit 14, which controls the driving force output by the actual vehicle drive unit 12 (not shown) so that the actual vehicle 11 operates according to the driving command COM. The actual vehicle drive control unit 14 also outputs status information STARr as a result of controlling the driving force output by the actual vehicle drive unit 12 so that the actual vehicle 11 operates according to the driving command COM. This allows for the collection of driving command, position, and speed as actual vehicle data.

[0089] The status information STARr is physical quantity data recognized by the software calculation of the actual vehicle drive control unit 14, and includes the vehicle's acceleration Acc, velocity Vel, position Pos, rotational speed Fr, current Im, voltage Vm of the main motor 117, DC current Is, DC voltage Ecf of the inverter device, etc. The driving command COM and the status information STARr are input to the simulated vehicle field 2 via the information transmission unit 3.

[0090] Of the status information STARr, the speed data VELr and position data POSr of the actual vehicle 11 are input to the storage unit 233. The storage unit 233 stores gradient data GRD, curve data CRV, station position data STN, etc., of the route on which the actual vehicle 11 travels. The gradient data GRD is table data that defines the amount of gradient (rate of elevation increase relative to travel distance) at the position of the actual vehicle 11 relative to the travel position data POSr.

[0091] The gradient data GRD is a table data that defines the curvature of the track (the reciprocal of the curve radius) relative to the actual vehicle 11's running position data POSr. The station location data STN is a table data that shows the locations of station facilities located on the route, relative to the actual vehicle 11's running position data POSr. Station facilities include stations, stops, signal boxes, etc., and are usually indicated by the kilometer markers that represent the center position of each facility.

[0092] The vehicle motion analysis unit 231 receives state information STAs output by the simulated vehicle 21 and state information STARr output by the actual vehicle 11 and transmitted to the simulated vehicle field 2 via the information transmission unit 3. Based on the state information STAs and state information STARr, it calculates the operation of the actual vehicle 11 and the operation data of the simulated vehicle 21. Here, the operation data of the actual vehicle 11 and the simulated vehicle 21 refers to data related to vehicle dynamics and includes the following, or a part thereof.

[0093] Acceleration of each car in the train: ((d / dt)Vxv_i, (d / dt)θyv_i, velocity: Vxv_i, θyv_i Acceleration of each vehicle's bogie: (d / dt)Vxt_ji, (d / dt)θyt_ji, Velocity: Vxt_ji, θyt_ji Acceleration of wheel axle of each bogie: (d / dt)Vxw_kji,(d / dt)θyw_kji,Speed:Vxw_kji,θyw_kji Note that Vx is the velocity along the X-axis (rail direction), and θy is the angular velocity along the Y-axis (sleeper direction). (i is the car number i=1,2,3…, j is the bogie of each car j=1,2…, k is the bogie of each car k=1,2…)

[0094] These units calculate and output the operation data of the actual vehicle 11 and the simulated vehicle 21. The control data analysis unit 232 uses the motor current Im and the input voltage Ecf of the switching circuit output by the simulated vehicle control unit 23 as control data, and has a function to search for control parameters that further stabilize the control of the simulated vehicle drive control unit 24 based on the control data. Non-patent document 1 is shown as an example of searching for control parameters that stabilize the control.

[0095] Non-patent document 1 describes adhesion control in railway vehicles, which suppresses wheel slip that occurs when the tread force transmitted from the wheel to the rail based on motor torque exceeds the friction limit between the wheel and the rail, such as in rainy weather, by reducing the tread force. It introduces a method for searching for stable control parameters that stably transmit the tread force in response to fluctuations in the friction limit, such as in rainy conditions, using the Taguchi method, and performance verification using an actual vehicle 11 to which the stable control parameters are applied (a distinction is made between wheel slip during acceleration and sliding during deceleration, but here they are collectively referred to as "wheel slip").

[0096] While it is desirable to explore stable control parameters in a real vehicle environment, the real vehicle environment changes moment by moment due to factors such as route conditions, passenger conditions, and weather conditions, making it difficult to ensure the reproducibility of data collected by changing numerous parameters. For this reason, it is desirable to explore and determine stable control parameters in a simulated vehicle field 2 that provides an environment as close as possible to a real vehicle 11.

[0097] The stability control parameters determined by the above procedure are applied as control parameters to the actual vehicle drive control unit 14 in the actual vehicle field 1 and the simulated vehicle drive control unit 24 in the simulated vehicle field 2, and thereafter control is performed by the stability parameters. The control results by the stability parameters are aggregated in the vehicle motion analysis unit 231 to confirm that the vehicle motion data and control data fall within the target range.

[0098] If the vehicle's operation data and control data are outside the target range, the control data analysis unit 232 searches for control parameters again, calculates stabilization parameters, and repeats the cycle of applying them to the control parameters of the actual vehicle drive control unit 14 in the actual vehicle field 1 and the simulated vehicle drive control unit 24 in the simulated vehicle field 2. The vehicle operation analysis unit 231 determines the speed difference between the simulation using the actual vehicle 11 and the simulated vehicle 21. The control data analysis unit 232 corrects the motor torque command to compensate for the speed difference (≒acceleration error). Note that even if there is a transmission delay, it is not a problem as long as all data is delayed by the same amount of time.

[0099] With the above configuration, the model accuracy of the simulator constituting the simulated vehicle field 2 can be improved based on the driving data of the actual vehicle field 1. Furthermore, by improving the model accuracy of the simulator constituting the simulated vehicle field 2, the operation when the drive control operation data is changed in the actual vehicle field 1 can be predicted and pre-verified based on the operation when the drive control operation data is changed in the simulated vehicle field 2. In addition, the drive control operation data pre-verified in the simulated vehicle field 2 is transmitted to the actual vehicle field 1 via the information transmission unit 3, and the actual operation can be confirmed by changing the drive control operation data in the actual vehicle field 1.

[0100] In other words, by improving the accuracy of the simulated vehicle 21 so that its movements are the same as those of the actual vehicle 11, adjusting the drive control using the improved accuracy of the simulated vehicle 21, and then applying the adjusted drive control operation data to the actual vehicle 11, a control method for a railway system that autonomously optimizes drive control is realized.

[0101] The drive control operation data is a control logic or configuration parameter of the control logic that is implemented in the actual vehicle drive control unit 14 to control the operation of the actual vehicle drive unit 12, or in the simulated vehicle drive control unit 24 to control the operation of the simulated vehicle drive unit 22.

[0102] Figure 5 is a schematic diagram illustrating an example of the information transmission unit 3 of the upcycling system shown in Figure 1. In the actual vehicle field 1, multiple actual vehicles 11 are running on rails or other tracks 15. If the actual vehicles 11a to 11e (11 if distinction is not necessary) are electric vehicles, power is supplied to the drive system and auxiliary equipment 118 such as lighting and air conditioning by a power supply unit 16 such as an overhead line. The configuration of the drive control function of actual vehicle 11a will be described in more detail later using Figures 16 and 17. Furthermore, if the vehicles are not electric vehicles and power can be supplied to the drive system and auxiliary equipment 118 by an engine, a power generation device such as a fuel cell, or a power storage unit such as a battery, the power supply unit 16 is not essential.

[0103] The actual vehicle 11 is not necessarily a single vehicle; it may also be a train composed of multiple vehicles coupled together. Multiple rails 15 may be arranged in parallel to form a double-track section where trains traveling in different directions, such as uphill and downhill, can run simultaneously. The simulated vehicle field 2 transmits and receives at least the status information STARr of the actual vehicle 11 and the control parameter PARA_ctrl of the actual vehicle drive control unit 14 to and from the actual vehicle field 1 via the information transmission unit 3. The configuration of the information transmission unit 3 will be described below.

[0104] The actual vehicle 11 transmits and receives information wirelessly with the wide-area information transmission base station 33. Here, wireless communication can be a mobile communication system that assumes continuous communication while moving through multiple limited communication ranges, such as a fifth-generation mobile communication system (5G). The information transmitted and received by the wide-area information transmission unit 32 is aggregated by the wide-area information transmission unit 32 and connected to the external information interface (not shown) of the simulated vehicle field 2 via the wide-area information transmission unit 32, such as the Internet. This allows information from the actual vehicle field 1 and the simulated vehicle field 2 to be received.

[0105] Furthermore, the actual vehicle 11 transmits and receives information wirelessly with the area information transmission and reception base 36. The area information transmission and reception base 36 is installed at stations, train depots, etc., and enables wireless communication with the parked actual vehicle 11. For this reason, wireless LAN, Bluetooth®, infrared communication, etc., which are suitable for short-range inter-device communication, can be applied. The information transmitted and received at the area information transmission and reception base 36 is aggregated at the wide-area information transmission unit 32 and connected to the external information interface (not shown) of the simulated vehicle field 2 via a wide-area information transmission path 31 such as the Internet. This allows information from the actual vehicle field 1 and the simulated vehicle field 2 to be received.

[0106] With the above configuration, the model accuracy of the simulator constituting the simulated vehicle field 2 can be improved based on the driving data of the actual vehicle field 1. Furthermore, by improving the model accuracy of the simulator constituting the simulated vehicle field 2, the operation when the drive control operation data is changed in the actual vehicle field 1 can be predicted and pre-verified based on the operation when the drive control operation data is changed in the simulated vehicle field 2. In addition, the drive control operation data pre-verified in the simulated vehicle field 2 is transmitted to the actual vehicle field 1 via the information transmission unit 3, and the actual operation can be confirmed by changing the drive control operation data in the actual vehicle field 1.

[0107] In other words, by improving the accuracy of the simulated vehicle 21 so that its movements are the same as those of the actual vehicle 11, adjusting the drive control using the improved accuracy of the simulated vehicle 21, and then applying the adjusted drive control operation data to the actual vehicle 11, a control method for a railway system that autonomously optimizes drive control is realized.

[0108] Here, the drive control operation data is control logic such as software or configuration parameters of control logic, which are implemented in the actual vehicle drive control unit 14 to control the operation of the actual vehicle drive unit 12, or in the simulated vehicle drive control unit 24 to control the operation of the simulated vehicle drive unit 22.

[0109] As described above, the upcycling system of Example 1 transmits measurement data from the actual vehicle environment to a simulation environment, identifies a simulation model that approximates the movement of the actual vehicle 11 based on the measurement data, optimizes the drive control in a simulation environment that simulates driving conditions such as route conditions, passenger conditions, and weather conditions, and transmits the optimized control data to the actual vehicle environment to reflect it in the control of the actual vehicle 11.

[0110] Furthermore, the system will examine the advantages and disadvantages of updating the software that implements the railway vehicle drive control system, and support decisions regarding the appropriateness of updating (upcycling). As a result, this upcycling system can maintain the drive control of railway vehicles in an optimal state that corresponds to track conditions, passenger conditions, and weather conditions. In addition, this upcycling system will contribute to the realization of a sustainable society by promoting carbon neutrality through energy saving effects and a circular economy by extending the lifespan of wheels and other components through slip prevention and reducing waste. [Examples]

[0111] Example 2 illustrates this upcycling system, which verifies the performance improvement when various components used in battery-powered trains and hybrid trains (hereinafter referred to as "battery-powered trains, etc.") are updated, and supports decisions regarding the timing and necessity of such updates. Here, batteries are used as an example of products targeted for upcycling related to railways, but other components installed in railway vehicles, particularly other on-board equipment in battery-powered trains, etc., as well as ground equipment such as signaling systems, may also be used.

[0112] Components mounted on railway vehicles may include drive control devices, auxiliary power control devices, fuel cell systems, air conditioning systems, in-car monitoring devices, TCMS (Train Control Management System), PIS (Passenger Information System), and other on-board equipment, as well as in-car equipment such as seats, vehicle components, forward monitoring devices, safety monitoring devices, driver assistance systems, autonomous driving, and control functions such as ground / on-board wireless information transmission. For example, with batteries, improved performance can lead to energy savings, shorter charging times, increased storage capacity, longer lifespan, and reduced waste. Similarly, with drive systems, energy savings, improved ride comfort, longer lifespan, and reduced waste can be achieved.

[0113] Recent advancements in battery technology have led to remarkable performance improvements. Therefore, when replacing batteries, it is highly effective to compare and verify the degree of degradation before replacement with the performance improvement after replacement using a digital twin. In this way, upcycling products and component modules whose performance continuously improves over time, even if it is merely a preservation act of replacing worn-out parts with replacement parts of equivalent specifications, can optimize the determination of replacement timing.

[0114] Measurement data from actual vehicle environments is being used in a simulation environment. Sending The system identifies a simulation model that approximates the movement of the actual vehicle 11 based on the measurement data, optimizes the drive control in a simulation environment that simulates driving conditions such as route conditions, passenger conditions, and weather conditions, and transmits the optimized control data to the actual vehicle environment to reflect it in the control of the actual vehicle 11.

[0115] Figure 6 is a conceptual diagram illustrating the battery recycling system using the upcycling system of Example 2. The recycling system in Figure 6 will be explained in roughly chronological order. The battery is produced, installed, operated, and then upcycled when it becomes possible to increase its value. Upcycled products reflecting this are then produced and recycled.

[0116] The production process involves planning, design, specification determination, material procurement, mass production, inspection, and shipping. The operation process involves delivery, installation, and operation, but the product is removed and shipped out to enhance its value. The upcycling process involves collecting used batteries, measuring their degradation level, manufacturing / producing them based on upcycled designs, grading and evaluation, and managing their classification to create upcycled products. Note that "collection" refers to the return of used batteries. In contrast, software collection can sometimes refer to a state where the expected effects are not being achieved.

[0117] These upcycled products also circulate by taking advantage of the sales channels for new products. To commercialize and circulate upcycled products, data management of the products and materials is necessary. This data management process involves important aspects such as operational planning, model identification, upcycle evaluation, quality control, and upcycle design guidelines. The primary function of data management is to collect operational data (command and operational data) and provide operational plans.

[0118] Examples of information that needs to be collected for data management include production plans, specifications, required quantities, quality measurement data, and provenance (history such as lifetime data). In addition to operational plans, examples of information that can be provided by the data management function include trust data (quality and lifespan) for the production department and production volume and recipe information for the upcycling department.

[0119] In the upcycling concept shown in Figure 6, the order of events is not necessarily fixed. A battery is produced, installed, operated, collected, upcycled, and then an upcycled product reflecting that design is produced, thus completing the cycle. Furthermore, according to the cycle system in Figure 6, upcycling is only performed if the upcycling evaluation, based on product and material data management, is high. When replacing a battery, comparing the degree of degradation before replacement with the performance improvement after replacement using a digital twin will determine whether replacement is appropriate at that point in time.

[0120] The longer a battery is not replaced, the more its energy efficiency decreases during long-term operation. Therefore, the later the replacement is made, the more dramatic the energy savings can be achieved by simply replacing it with a new one of equivalent specifications, as it will restore the significantly reduced storage capacity. Conversely, replacing it too early results in the waste of discarding a battery with good energy efficiency and high residual value, but the earlier the replacement is made, the less the storage capacity decreases before replacement, resulting in greater energy savings.

[0121] On the other hand, recent batteries show considerable performance improvements within the several years considered to be their replacement cycle. Therefore, simply optimizing the battery replacement timing can extend the lifespan of the entire product, such as battery-powered trains, while improving performance and reducing waste. Because batteries are chemically applied products, the interaction of multiple parameters that determine their operating characteristics is complex. However, systematic mathematical models have been constructed, and correction coefficients and other factors are becoming sophisticated enough to be simulated using computers.

[0122] Furthermore, in this upcycling system, the batteries are transformed into a circular product that reduces waste generation and continuously protects the environment by recycling extractable useful materials. From the perspective of upcycling that thus improves the value of the product, this upcycling system performs beneficial upcycling only when the upcycling evaluation is high, according to the circular system shown in Figure 6. More specific upcycling will be explained below using Figures 7 to 14. Note that this upcycling system executes driving commands through human operation using a driver advisory system (DAS), or through automated driving.

[0123] Figure 7 is a schematic diagram illustrating parameter identification in the cyclical system shown in Figure 6. In Figure 7, this upcycling system executes driving commands on both the current actual vehicle No. 1 (which includes function A installed on the actual vehicle), indicated as "current" before upcycling, and the corresponding current physical model No. 1 (which includes function A on the physical model).

[0124] This upcycling system aims to match the performance of a real vehicle (No. 1) and a physical model (No. 1). It performs a paired comparison of their respective operational data (speed, temperature, vibration, and operating time) and identifies parameters to eliminate the difference ΔMode resulting from the comparison. In other words, parameter identification means adjusting the model parameters so that the real vehicle (No. 1) and the physical model (No. 1) operate identically in response to the same command.

[0125] It is desirable to identify parameters until the operation of the actual vehicle (No. 1) and the physical model (No. 1) match, but differences may be tolerated as long as they guarantee the accuracy required in the subsequent control performance optimization process. Matching (differences below a predetermined value) means that the simulation results are at an acceptable level.

[0126] In other words, the simulation results can be calculated and output to a level where they can be judged. As a result, by executing driving commands, parameters can be identified by predictable driving patterns, thus improving and stabilizing the identification accuracy. However, in reality, it is conceivable to allow a certain degree of difference between the operation of the actual vehicle No. 1 and the physical model No. 1, taking into account the measurement errors of the current and voltage detectors installed in the actual vehicle No. 1 regarding the current and voltage movements of the drive control device.

[0127] The actual vehicle No. 1 will be operated in the actual vehicle 11 (mono) environment, which will be described later in Figure 15. The current physical model No. 1, on the other hand, is a digital twin that reproduces the real space in a virtual (cyber) space for product and material data management, as shown in Figure 15. It utilizes computer simulations to predict and evaluate failure prediction and the results of software and component updates. Next, the procedure will be explained using Figure 8.

[0128] Figure 8 is a flowchart illustrating the parameter identification procedure in Figure 7. This upcycling system constructs a physical model 1 that mimics the actual vehicle 1 (S81). Next, this upcycling system compares the differences in operational data based on the driving commands of the actual vehicle 11 as shown in the following equation (S82).

[0129] ΔModel = Operational data (Physical model 1) - Operational data (Actual vehicle 1) If the comparison (S82) shows a difference between ΔModel|>γ, the upcycled system modifies the physical model according to ΔModel (S83). If the difference disappears when |ΔModel|≦γ, the upcycled system completes the identification of physical model 1. Note that 0 is an example.

[0130] Figure 9 is a schematic diagram illustrating the upcycle evaluation in the cyclical system shown in Figure 6. Here, we will mainly explain the differences from Figure 7. In Figure 9, this upcycle system issues operation commands to both the current physical model 1, which is shown as "Current" before upcycled, and the updated physical model 2, which is shown as "New" after upcycled.

[0131] Physical Model 1 and Physical Model 2 are digital twins for simulation in a virtual (cyber) space for data management of the products and materials shown in Figure 15. Physical Model 1 has completed parameter identification based on the operational data of the actual vehicle 1. Physical Model 2 is a modification of Physical Model 1, with "Function A" changed to "Function A'".

[0132] Regarding the functions A and A' of the two systems, for example, "A" is the current battery module, and "A'" is a battery module with improved cell performance (loss reduction, etc.). In this upcycling system, for the evaluation of upcycling, it is conceivable to measure the degree of degradation using the battery degradation index SOH (State of Health) as an evaluation index. In this case, it is best to set the reference value K as the limit value of SOH at which the decision to replace the battery is made. Low / High threshold determination is made against this set reference value K. At this point, this upcycling system is characterized by not only showing the transition from Low to High, which is the threshold determination, but also clarifying the timing at which the transition occurs. This makes it possible to predict when the limit value will be reached and propose battery upcycling while the battery is still performing at the desired level, rather than making a battery replacement decision based on the result that the aforementioned battery degradation index SOH has reached its limit value. Furthermore, while it is assumed that the reference value K for the limit value will be determined by the designer based on equipment specifications, etc., it may also be determined by an AI system based on past performance, etc. Next, the procedure will be explained using Figure 10.

[0133] Figure 10 is a flowchart illustrating the upcycle evaluation procedure shown in Figure 9. First, function A of physical model 1 is changed to function A' of physical model 2 (S11). Next, physical models 1 and 2 are compared based on the operation command (including fluctuations) as shown in the following equation (S12).

[0134] ΔFunc = Evaluation metric (Physical Model 1) - Evaluation metric (Physical Model 2) If the comparison (S12) results in Figure 9(1), then the upcycling is carried out (S13). On the other hand, if the comparison (S12) results in Figure 9(0), then the upcycling is postponed (S14).

[0135] Figure 11 is a schematic diagram illustrating parameter identification (after upcycling) in the cyclical system shown in Figure 6. Here, we will mainly explain the differences from Figure 7. In Figure 11, this upcycling system executes driving commands on both the updated actual vehicle No. 2 (which includes function A' installed on the actual vehicle), indicated as "new" after upcycling, and the corresponding updated physical model No. 2 (which includes function A' on the physical model). The functions of the two are A', A'.

[0136] The updated vehicle No. 2 will be actually operated in the vehicle 11 (mono) environment, which will be described later in Figure 15. The updated physical model No. 2 is a digital twin for simulation in a virtual (cyber) space for managing product and material data, as shown in Figure 15. Next, the procedure will be explained using Figure 12.

[0137] Figure 12 is a flowchart illustrating the procedure for parameter identification (after upcycling) in Figure 11. First, function A of physical model 1 is changed to function A' of physical model 2 (S21). Next, based on the driving commands of the actual vehicle 11, the differences in operational data are compared as shown in the following equation (S22).

[0138] ΔModel = Operational data (Physical model 2) - Operational data (Actual vehicle 2) If the comparison (S22) shows a difference where |ΔModel| > γ, the physical model is modified according to ΔModel (S23). If the difference disappears as a result where |ΔModel| ≤ γ, the identification of physical model 2 is completed.

[0139] The parameter identification described above is intended to be based on normal operating data, and it is necessary to refrain from using operational data from abnormal situations such as accidents or failures for parameter identification. For this reason, it is necessary to monitor for accidents and failures in parallel with parameter identification, and it is desirable to use the information obtained from this monitoring to make maintenance decisions. Upcycling is an exchange that increases value, while maintenance is a preservation act that replaces deteriorated parts due to age-related use. However, replacing deteriorated parts due to age-related use with a replacement that increases value is considered upcycling.

[0140] Figure 13 is a schematic diagram illustrating maintenance decisions in the cyclical system shown in Figure 6. Here, we will mainly explain the differences from Figure 7. In Figure 13, this upcycling system executes operation commands on both the current actual vehicle No. 1 (including function A installed on the actual vehicle), which is shown as "current" before upcycling, and the corresponding current physical model No. 1 (including function A on the physical model), which is also shown as "current".

[0141] This upcycling system compares the operational data of two functions A and A, for which matching performance is desirable, and determines that there is an anomaly if there is a difference in the results of the comparison. The current actual vehicle No. 1 is actually operated in the actual vehicle 11 (mono) environment described later in Figure 15. The current physical model No. 1, in contrast to this, is a digital twin for simulation in a virtual (cyber) space for managing product and material data, as shown in Figure 15.

[0142] This upcycling system evaluates the functionality of both systems (A and A) using operational data (speed, temperature, vibration, operating time) as evaluation indicators. It determines whether the result is Low / High against a set reference value K, and if the result is high, it issues a signal prompting maintenance. If this upcycling system detects an abnormality, it can warn the driver in the driver's cab.

[0143] In addition, as a feature of railway vehicles, the probability of a failure in the battery module is high and is likely to occur only in a specific vehicle, while the probability of the same failure occurring in other vehicles is low. At this time, the (sensor) failure of the host vehicle is determined based on information from other vehicles. Therefore, it is also possible to disconnect only the specific faulty battery from the vehicle in question, enhance the driving force of other normal vehicles, and continue the operation of the train. As a result, this upcycle system can improve the availability of train operation.

[0144] FIG. 14 is a flowchart for explaining the maintenance determination procedure of FIG. 13. First, data of all in-service actual vehicles No. 1 are collected (S41). Next, the differences in the operation data are compared as follows for multiple vehicles (at least three or more) based on the operation command of the actual vehicle No. 11 (S42).

[0145] ΔModel = Operation data (physical model No. 1) - Operation data (actual vehicle No. 1) As a result of the comparison (S42), if Menteyo = 1 for all vehicles, the physical model is corrected according to ΔModel (S43). Also, as a result of the comparison (S42), if Menteyo = 0 for all vehicles, maintenance is postponed (S44). Further, as a result of the comparison (S42), if Menteyo = 1 for a specific vehicle, maintenance is performed (S45). If Δmodel > K for all vehicles, it is determined that the identification of the physical model is insufficient, and the process returns to the model identification cycle. If Δmodel < K for a specific vehicle, it is abnormally determined that the function A of the vehicle is performance-degraded, and maintenance is performed.

[0146] FIG. 15 is a conceptual explanatory diagram for explaining the function of proposing a maintenance plan in addition to the data management function of products and materials in this upcycle system. As shown in FIG. 15, this upcycle system exchanges information via a network between a management unit that manages product and material data and the actual vehicle No. 11 (mono) environment. The data management side collects operation data from the actual vehicle No. 11 (mono) environment and provides an operation plan and a maintenance plan.

[0147] The actual vehicle environment contains operational information, upcycling information, and production information as its main data sources. This information is connected to a network via its respective data input / output unit and is input and output to the outside. The management unit formulates and executes upcycling and maintenance plans based on simulations using a digital twin.

[0148] Upcycling is performed on either hardware or software, or at least one of them. Example 1 illustrates software upcycling, while Example 2 illustrates hardware upcycling. Upcycling is typically used to provide feedback to design guidelines based on quality control. Furthermore, the upcycling process is appropriately evaluated, data is accumulated, and this feedback is also incorporated into operational plans. This feedback from upcycling is connected to a network via a data input / output unit, allowing for effective use of the information.

[0149] Maintenance plans are implemented for either hardware or software, or at least one of them. Software maintenance includes, for example, software changes accompanying hardware changes, and the application of modification items from other projects. The upcycling and maintenance plans shown in Figure 15 are similar in form, except that the information provided to the actual vehicle environment is replaced from an operational plan to a maintenance plan. To illustrate with an example of a battery, replacing a deteriorated battery with a new one is maintenance as an act of preservation, while replacing it with one that increases its value is upcycling. In either case, the management department formulates and implements an upcycling or maintenance plan based on simulations using a digital twin.

[0150] Figure 16 is a functional block diagram showing a modified configuration in which the actual vehicles 11a and 11b from Figure 5 are coupled together in a single train. Note that parts already explained in Figure 5 are omitted from the explanation, and only the differences are explained. As shown in Figure 16, in the actual vehicle field 1, if it is an electric train, the actual vehicle 11a, which has driving force, operates in a train formation that pulls the actual vehicle 11b, which does not have driving force. In addition, both actual vehicles 11a and 11b may have driving force, or they may be electric locomotives rather than electric trains.

[0151] In the actual vehicle 11a, the current received from the power supply unit 16 by the current collector 114 is input to the primary winding of the main transformer 121, and the current path is formed so that it is returned to the substation via the bogies 112a, 112b (collectively referred to as bogie 112 if distinction is not necessary), wheelsets 113a to 113d (collectively referred to as 113 if distinction is not necessary), grounding device 115, and rails 15. In the actual vehicle 11a, the main on-board equipment, the main transformer 121, drive control unit 122, main circuit battery 123, and auxiliary power supply unit (APS_DC / AC) 124 are controlled by the actual vehicle control unit 13. Note that the AC / DC converter 122a and DC / AC inverter 122b are collectively referred to as the drive control unit 122.

[0152] The AC voltage detector (ACPT) 131 is installed between the current collector 114 and the primary winding of the main transformer 121, and the detected voltage signal is input to the vehicle control unit 13 for control purposes. The AC current detector (ACCT) 132a is input to the vehicle control unit 13 for control purposes.

[0153] The actual vehicle control unit 13 performs energy-saving control to minimize the power received from the power supply unit 16 when the actual vehicle 11a is powered / regenerated. To this end, by appropriately adjusting the power input and output between the main motor 117 (Figure 18) and the main circuit battery 123, as well as the power output to the auxiliary power supply unit (APS_DC / AC) 124 and auxiliary equipment 118 (Figure 18), the power consumption from the secondary winding of the main transformer 121 is minimized, thereby reducing the power received from the power supply unit 16. As a result, the actual vehicle 11a can contribute to the realization of a sustainable society by pursuing further energy savings in addition to the inherent energy efficiency of railways.

[0154] Figure 17 is a functional block diagram showing the configuration of the actual vehicle 11a in Figure 16 in more detail. Note that parts already explained in Figure 5 or Figure 16 will not be explained, and only the differences will be described. A main DC circuit is formed between the AC / DC converter 122a and the DC / AC inverter 122b, and the DC voltage signals detected by the DC voltage detectors 133a and 133b are input to the actual vehicle control unit 13 for control.

[0155] The actual vehicle control unit 13 comprises a train information control device 141, an AC / DC converter control unit 142a, a DC / AC inverter control unit 142b, a battery control device 143, and an auxiliary power control device 144. The actual vehicle control unit 13 is controlled by a higher-level control unit, although it is not shown in the diagram. The higher-level control unit is the driving entity, which includes the operation management system and the driver. The actual vehicle control unit 13 is equipped with control software and exchanges control signals with the higher-level control unit, and controls the lower-level controlled units as intended.

[0156] The main DC circuit is connected to the main circuit battery 123 and the auxiliary power supply unit 124. DC voltage detectors 133c to 133d and DC current detectors 134a to 134c are connected to these, and the DC voltage signals detected by them are input to the vehicle control unit 13 for control. The battery control device 143 controls the main circuit battery 123. The auxiliary power control device 144 controls the auxiliary power supply unit 124 and controls the auxiliary equipment 118. The auxiliary equipment 118 is connected to DC voltage detectors 135a and 135b, and the DC voltage signals detected by them are input to the vehicle control unit 13 for control. This configuration makes it possible to collect the following data: AC / DC converter: Control frequency, input current / voltage, output current / voltage, power unit temperature estimate. DC / AC inverter: Control frequency, input current / voltage, output current / voltage, power unit temperature estimate. Auxiliary power supply: Control frequency, input current / voltage, output current / voltage, power unit temperature estimate Main circuit battery: Charge / discharge current, voltage, SOC, SOH, cell temperature Train information control system: Displays the status of each device, passenger occupancy rate, and ambient temperature. This upcycling system collects this data and has the effect of improving the functionality of the control software.

[0157] Figure 18 shows an example of an ID system that enables hardware / software traceability of the parallel analysis system shown in Figure 1. Traceability refers to tracking a product from raw material procurement to production, consumption, and disposal. The ID system in Figure 18 is implemented as appropriate, meeting the following requirements.

[0158] • Assign IDs to each element—modules, parts, and materials—in a hierarchical structure. • All IDs are assigned and managed in electronic medical record information. • Electronic medical records are the "family register transcripts" of each component, and contain information that allows tracing parent-child relationships (higher-level components, lower-level components). • Record your own history based on actual usage data in preparation for upcycling. This recorded data is expected to be part of a separate big data collection, and the electronic medical record will record "linking data" for accessing this data. In other words, it will enable access to the history for carrying out upcycling. When upcycling, add changes to the parent-child relationships along with your own changes. In other words, when upcycling, record the parent-child relationships that are changed to ensure traceability.

[0159] Thus, this upcycling system achieves the benefits of upcycling by combining the "electronic medical record," "family register transcript," and "resume" provided by the ID system. Furthermore, the ID system in Figure 18 has a parent-child relationship where ID:1-1 Li-ion battery cell ABC type, ID:1-2 module case ABC type, ID:1-3 cell controller ABC type, etc. are positioned below the ID:1 battery module 2022 model. In terms of the positioning of these parent-child relationships, for example, ID:1 (level A) is the final product, ID:1-1 (level B) is the component that makes up the product, and ID:1-1-1 (level C) is the material that makes up the component. These levels are not limited to three stages; depending on the actual configuration of the highest level A, it is possible to have two or fewer stages, or even four or more stages.

[0160] Furthermore, ID:1-1 has parent-child relationships with ID:1-1-1, ..., ID:1-1-3, ..., below it. Similarly, ID:1-2 has parent-child relationships with ID:1-2-1, ..., ID:1-2-3, ..., below it. Similarly, ID:1-3 has parent-child relationships with ID:1-3-1, ..., below it. Therefore, ID:1-1-1, ~, ID:1-3-1, are grandchildren of ID:1-1.

[0161] In the ID system shown in Figure 18, the first child ID: 1-1 for Li-ion battery cell type ABC is stored in the memory unit 233 in a systematic, rewritable, and searchable format. The information may be accessible via communication from memory units other than memory unit 233.

[0162] α. Resume Information: Size, Mass, Characteristics, Usage History β. Parent component (ID:1) Battery module 2022 model γ. Sub-components (children)_(ID:1-1-1) Cell case, Al, / (ID:1-1-2) Cathode material, LiCoO2, 3g / (ID:1-1-3) Anode material, C (graphite), 2g δ. Linked Data: Size: Data_ID1_size / Mass: Data_ID1_mass / Characteristics: Data_ID1_chara / Usage History: Data_ID1_UsageHist

[0163] As explained above, this upcycling system improves the performance and lifespan of the entire product, including software upcycling, while reducing waste. It enhances the value of the product by transforming its quality into a circular product that continuously protects the environment by recycling even the reduced waste.

[0164] As a result, this upcycling system contributes to the realization of a sustainable society by promoting carbon neutrality through energy saving effects and extending the lifespan of storage batteries through upcycling and optimal maintenance of storage batteries, as well as promoting a circular economy by reducing waste through recycling. [Examples]

[0165] Example 3 describes this upcycling system, which verifies performance improvements for storage batteries and supports decisions regarding the timing and necessity of replacement, using an example different from Example 2.

[0166] Figure 19 is a conceptual diagram illustrating the battery recycling system using the upcycling system of Example 3. In this case, the batteries are used by the operator. The operator is the railway company (operations division), which installs the batteries in its vehicles and uses them as a power source. Therefore, the railway company (operations division) can be said to be the recipient of the batteries in this case. The batteries are maintained and replaced in units of battery units, which contain multiple battery modules.

[0167] Battery units are serviced by battery hardware technicians. Based on requests from battery hardware managers, battery hardware technicians perform maintenance by disassembling the battery unit and replacing the battery modules that make up the unit. Figure 19 illustrates this process as battery hardware maintenance. Battery units to be serviced are removed from the vehicle as service recovery units and sent to the battery manager. The battery hardware technician then performs battery hardware maintenance on the service recovery units. After maintenance, the battery units are sent back to the operator as serviced units, reinstalled in the vehicle, and used again.

[0168] Furthermore, the battery units are managed by the battery hardware manager. The battery hardware manager owns the battery units. The battery hardware manager will collect the battery units from the operators as needed (unit collection). The battery hardware manager will also supply battery units to operators as needed (unit supply).

[0169] Battery units owned by battery hardware managers are further collected and supplied by battery hardware suppliers. Battery hardware suppliers produce and upcycle battery units. In "production," battery hardware suppliers collect battery units from battery hardware managers (unit collection) and disassemble them into battery modules (unit disassembly). Meanwhile, they produce battery units using the refurbished battery modules (unit assembly). The produced battery units are supplied to battery hardware managers (unit supply). In "upcycling," battery modules are collected (module collection) and the cell materials are recycled (cell material recycling). This refurbishes the battery modules (module recycling), and the recycled battery modules are supplied for the production of battery units (module supply).

[0170] Furthermore, battery data management is carried out among operators, battery hardware technicians, battery hardware managers, and battery hardware suppliers using upcycling methods that include servers for managing batteries. Battery data management includes, for example, managing data on the battery product and its materials. Specifically, operational and quality data of battery units are exchanged between operators and battery data managers. When a maintenance proposal is made by a battery data manager, the proposal is sent to the operator, and a response regarding the date and time of the maintenance proposal is provided. In addition, information regarding unit maintenance and unit inspections is exchanged between operators and battery hardware technicians. Furthermore, information regarding battery module specifications (module specifications), battery unit specifications (unit specifications), and analysis reports is exchanged between battery hardware suppliers and battery hardware suppliers. Finally, information regarding unit supply, collection, maintenance records, and analysis reports is exchanged between battery hardware managers and battery hardware managers. It should be noted that the battery data management carried out by the upcycling methods is in line with Battery Passport (product and material data management), which records information related to the battery lifecycle from material procurement to recycling. This facilitates information sharing and access to upcycling methods among operators, battery hardware installers, battery hardware managers, and battery hardware suppliers.

[0171] Figure 20 is a schematic diagram illustrating another example of the information transmission unit 3 shown in Figure 5. The following explanation will focus on the differences from Figure 5. Figure 20 shows the schematic diagram in Figure 5, but with the use of a wide-area information transmission / reception unit 32a instead of a wide-area information transmission unit 32. Also, an area-specific information transmission / reception unit 35a is used instead of an area-specific information transmission unit 35. Furthermore, an upcycling means 18 is used instead of a simulated vehicle field 2. Here, the routes are classified into three categories: depot, electrified section, and non-electrified section, and information is transmitted and received from each route. Specifically, the routes are classified into route (depot) 17a, route (electrified) 17b, and route (non-electrified) 17c, and information is transmitted and received from each. Other aspects are the same as in Figure 5.

[0172] The functions of the wide-area information transmission unit 32 and the wide-area information transmission / reception unit 32a are almost the same, but it has been clarified that the wide-area information transmission / reception unit 32a has the function of transmitting and receiving information. Similarly, the functions of the local information transmission unit 35 and the local information transmission / reception unit 35a are almost the same, but it has been clarified that the local information transmission / reception unit 35a has the function of transmitting and receiving information. The upcycling means 18 performs upcycling of hardware and software. Specifically, it receives actual driving data wirelessly from the actual vehicle 11 via the wide-area information transmission / reception unit 32a, the area-specific information transmission / reception unit 35a, and the area-specific information transmission / reception base 36. At this time, actual driving data is received for each route (depot) 17a, route (electrified) 17b, and route (non-electrified) 17c. The received actual driving data (received data) is then compared with a simulation by the simulated vehicle means 181a provided in the data analysis means 181. Using the comparison results, an upcycling evaluation is performed in the upcycling evaluation 181b. Furthermore, based on the upcycling evaluation, hardware upcycling 182, which is upcycling of hardware, and software upcycling 183, which is upcycling of software, are performed. Hardware upcycling 182 is, for example, maintenance or updating of battery hardware. Software upcycling 183 is, for example, updating of control software that controls the battery system or control software that controls the drive system powered by electricity from the battery system.

[0173] Figure 21 is a block diagram showing the configuration of the drive system of an actual vehicle 11 when the main circuit battery 123 in Figure 17 consists of multiple battery systems. The following explanation will focus on the differences from Figure 17. Here, we show the case where the main circuit battery 123 in Figure 17 consists of multiple battery systems: battery system (1) 123a, battery system (2) 123b, and battery system (3) 123c. Note that if these are not distinguished, they may simply be referred to as battery systems below. As a result, the DC current detector 134b in Figure 17 becomes three DC current detectors: 134b1 to 134b3. This section also shows the case where the train information control device 141 in Figure 17 is connected to a display 150 and an antenna 160. The display 150 displays the collected data. This data includes, for example, the control frequency, input current / voltage, output current / voltage, and estimated power unit temperature of the AC / DC converter or DC / AC inverter, as explained in Figure 17. The antenna 160 communicates with the wide-area information transmission / reception unit 32a, the area-specific information transmission / reception unit 35a, and the area-specific information transmission / reception base 36, as explained in Figure 20. Note that the display 150 is not necessarily required for the actual train 11.

[0174] The train information control device 141 comprises a controller 141a and routers 141b1, 141b2, 141b3, and 141b4. Controller 141a controls the AC / DC converter control unit 142a, DC / AC inverter control unit 142b, battery control device 143, and auxiliary power control device 144. Routers 141b1, 141b2, 141b3, and 141b4 collect data from the AC / DC converter control unit 142a, DC / AC inverter control unit 142b, battery control device 143, and auxiliary power control device 144, respectively, and output control signals from controller 141a.

[0175] Figure 22 shows the internal configuration of the battery storage system. A battery storage system is also called a battery box. In a battery storage system, multiple battery units are connected in series, and these series-connected battery units are further connected in parallel. Each battery unit consists of multiple battery modules. Although not shown in the diagram, each battery module consists of multiple battery cells. In Figure 22, multiple series-connected battery units are shown as a series unit U. This also indicates that multiple series units U are connected in parallel. Each series unit U is equipped with a current detection means. A DC voltage detection means (DCPT) detects the output voltage of the battery system (series unit U). A current control means controls the output current of the battery system. The battery system controller controls the current control means based on the current detection means and the DC voltage detection means. This allows for continuous control of the flow and interruption of the output current of the battery system. The current control means may also be a circuit breaker that switches between flowing and interrupting.

[0176] Figure 23(a) shows the configuration of the controller that collects data from the battery storage system. As explained in Figures 21 and 22, the main circuit battery 123 is composed of multiple battery systems. These multiple battery systems are, for example, battery systems (1) 123a to (3) 123c in Figure 21. Each battery system is composed of multiple battery units. Each battery unit is composed of multiple battery modules. Each battery module is composed of multiple battery cells.

[0177] Each battery system is equipped with a box controller. Each battery unit is equipped with a unit controller. Each battery module is equipped with a module controller. Each battery cell is equipped with a cell controller. The box controller, unit controller, module controller, and cell controller are equipped with a communication interface (shown as "communication" in Figure 23(a)) and a tag. The communication interface exchanges data with these controllers. The tag contains the model and part number of the battery system, each battery unit, battery module, and battery cell as tag information, allowing the source of the collected data to be identified. The tag information can also be an ID as shown in Figure 18 and Figures 24 and 25 described later. The data acquired by these controllers is collected by a controller in the main converter, and then acquired by a controller in the vehicle information control device, and sent to the upcycling means 18 using wireless communication as described in Figure 20. Here, the case of using OTA (Over The Air) cloud communication as wireless communication is shown.

[0178] Figure 23(b) shows the necessary data required as quality data for storage batteries. Here, the data shown as "item" corresponding to No.1 to No.10 indicates that it is required data. In other words, the required data includes tag information (model, part number), unit SOH (State of Health: battery degradation index) of the battery unit, module SOH (SOH) of the battery module, unit voltage (voltage of the battery unit), unit current (current of the battery unit), module voltage (voltage of the battery module), module current (current of the battery module), cell voltage (voltage of the battery cell), cell current (current of the battery cell), and cell / module temperature (temperature of the battery cell or battery module). Note that "Remarks" is a remarks column. Here, it means that the tag information (model, part number) is transmitted periodically, and the unit SOH is acquired every 1 second.

[0179] Figures 24 and 25 show an example of an ID system that enables traceability in Example 3. Figure 24 shows an example of an ID system for a battery module or higher. Specifically, Figure 24 shows an example of an ID system for a battery system, a battery unit that makes up the battery system, and a battery module that makes up the battery unit. Figure 25 also shows an example of an ID system below the battery module. In this case, it shows an example of an ID system for the battery module, the battery cells that make up the battery module, the battery module case which is the case for the battery module, the cell controller, the cell case which is the case for the cell, the positive electrode material of the cell, and the negative electrode material of the cell. This ID system has a similar configuration to the ID system shown in Figure 18. In other words, IDs are assigned to each element of the battery system, components, and materials in a hierarchical structure, and electronic medical record information is assigned and managed for all IDs. Furthermore, the electronic medical record includes medical record information (higher-level components, lower-level components) that allows tracing parent-child relationships.

[0180] Specifically, in Figure 24, IDs are assigned to each battery system, each battery unit, and each battery module in a hierarchical structure. In this case, the ID of the battery system is A. The IDs of the battery units that make up this battery system are A-U1, A-U2, A-U3, and so on. Furthermore, for example, the IDs of the battery modules that make up the battery unit with ID A-U1 are A-U1-MD11, A-U1-MD12, A-U1-MD21, ..., and each has an electronic medical record corresponding to these IDs. Each electronic medical record is then assigned medical record information α to δ. This electronic medical record has medical record information β and γ, which trace parent-child relationships. Medical record information β is information that indicates the parent, higher-level component. Medical record information γ is information that indicates the child, lower-level component. Medical record information α is history information that includes size, mass, characteristics, and usage history. Furthermore, the medical record information δ is information that shows linked data recording size, mass, characteristics, and usage history.

[0181] Furthermore, in Figure 25, IDs are assigned in a hierarchical structure to each battery system, each battery unit, and each battery module. IDs are assigned in a hierarchical structure to each battery module, battery cell, battery module case, cell controller, cell case, positive electrode material of the cell, and negative electrode material of the cell. In this case, "*" represents the ID of the battery unit in which the battery module is housed. For example, if the ID of the battery unit is A-U1, the ID *-MD11 shown as the ID of the battery module constituting this battery unit is actually A-U1-MD11. Also, the IDs of the battery cell, battery module case, cell controller, cell case, positive electrode material of the cell, and negative electrode material of the cell are represented here as *-MD11-1, *-MD11-2, *-MD11-3, *-MD11-1-1, *-MD11-1-2, and *-MD11-1-3, respectively.

[0182] Figure 26 is a step-by-step diagram showing the maintenance and replacement process for a battery storage system. Figure 26 illustrates the battery system maintenance and replacement process in three stages: Step 1 to Step 3. It also shows the interactions between the business operator using the battery, the battery hardware maintenance technician who maintains the battery, and the battery data manager who manages the upcycling process.

[0183] Step 1 is the process from when the battery hardware maintenance technician proposes battery maintenance and replacement. In Step 1, operational data is transmitted from the operator to the battery data manager. Next, the battery data manager analyzes the operational data. If the analysis results indicate that battery replacement is necessary, the battery data manager issues an instruction to the battery hardware maintenance technician to perform battery maintenance and replacement. The battery hardware maintenance technician confirms the replacement unit and proposes battery maintenance and replacement to the operator. The replacement unit is a battery unit prepared to replace the battery unit installed in vehicle 11.

[0184] Step 2 is the process from when the battery hardware maintenance provider sends the replacement battery unit. In Step 2, the business operator who has received a battery maintenance and replacement proposal from the battery hardware maintenance provider responds to the provider with a replacement date and time. Next, the battery hardware maintenance provider arranges for the replacement unit. Then, the battery hardware maintenance provider sends the replacement unit from the storage location of the battery unit to the business operator's garage. Finally, the battery hardware maintenance provider replaces the battery unit at the business operator's garage.

[0185] Step 3 is the process until the battery data manager receives the maintenance and replacement completion record. In Step 3, at the operator's garage, the battery hardware technician returns the removed replacement unit to the battery unit storage location. The replacement unit is the battery unit that was installed in vehicle 11 and was removed from vehicle 11 in order to install a replacement unit. Next, the battery hardware technician inspects the replacement unit at the battery unit storage location. The inspection results are presented to the operator, who then confirms the inspection results. Finally, the battery hardware technician sends the maintenance and replacement completion record to the battery data manager. This makes the maintenance and replacement completion record accessible to the battery hardware manager as well.

[0186] Figure 27 is a flowchart showing the maintenance and replacement process for a battery storage system. First, the operator operates the battery (S701). That is, the operator uses the battery unit installed in vehicle 11 as a power source. The operational data generated when the battery is in operation is sent to the battery data manager's upcycling system, and the battery data manager analyzes the operational data using the upcycling system (S702). Then, if the analysis results indicate that the battery needs to be replaced, the battery data manager issues an instruction to the battery hardware maintenance worker to service and replace the battery via the upcycling method. At the same time, the battery data manager also issues an instruction to prepare the replacement / alternative unit (S703). Battery hardware maintenance operators propose battery maintenance and replacement to businesses through upcycling methods (S704).

[0187] The operator sets the date and time for battery replacement using the upcycling method (S705) and informs the battery hardware maintenance operator of the replacement date and time via the upcycling method (S706). The battery hardware maintenance technician receives and confirms the date and time of replacement from the business operator via upcycling methods (S707), and sends a replacement unit from the battery unit warehouse to the customer's (business operator's) garage (S708). Upon receiving the replacement unit at the operator's garage (S709), the battery hardware maintenance worker removes the old replacement unit and installs the new unit at the operator's warehouse (S710).

[0188] Furthermore, the battery hardware maintenance technician returns the replacement unit, which was removed from the operator's warehouse, to the battery unit warehouse (S711). The battery hardware maintenance technician receives the replacement unit at the battery unit warehouse (S712). The battery hardware maintenance technician inspects the received replacement unit (S713), inputs the inspection results into the upcycling system, and presents them to the business operator (S713). The business operator confirms the survey results from the upcycling method (S714) and approves the survey results (completion of work) through the upcycling method (S715). Once the business operator approves the survey results, the battery hardware maintenance provider confirms the completion of battery maintenance and replacement through upcycling methods (S716). The battery hardware maintenance technician sends a maintenance and replacement completion record to the battery data administrator via the upcycling method, and the battery data administrator receives it (S717). This makes the maintenance and replacement completion record viewable by the battery hardware administrator. storage Battery hardware managers can use upcycling methods to check records of supplying and collecting specific units, and to confirm the status of desired units (vehicles in which they are installed, storage locations, etc.).

[0189] Next, the battery data manager, if necessary, instructs the battery hardware maintenance worker to replace the battery module of the replacement unit via upcycling means (S718). The battery hardware technician, having received instructions from the upcycling system, replaces the battery module (S719). The battery hardware technician then inspects the battery unit after replacing the battery module (S720). The battery hardware technician then inputs the inspection record of the battery unit and the fact that the battery module replacement is complete into the upcycling system, and stores the battery unit in the warehouse (S721). The battery data manager then receives a record of the completion of storage of the battery unit from the upcycling process (S722). This makes the battery hardware manager able to view the inspection records of the battery unit and the records indicating that the replacement of the battery module has been completed. storage Battery hardware managers can use upcycling methods to check the maintenance records of designated units and verify the desired unit status (such as the configuration of the battery module).

[0190] Figure 28 shows the configuration of a model (simulation model) that simulates the charging and discharging operation of a battery storage system. First, operational data (Real train operation data) is entered. The input operational data is converted into a predetermined data format by the Simulation Model Interface (Input). Then, based on operational data, a simulation is performed using a vehicle model, and a running profile is calculated. The running profile represents the relationship between time and velocity. This running profile is the result of simulating the change in vehicle speed over time based on operational data. The calculated running profile is compared with the actual vehicle operation, and the vehicle model is identified based on the difference between the two. Meanwhile, based on operational data, a simulation is performed using a battery model to calculate a charging profile. The charging profile represents the current (current) over time. This charging profile is determined for each series unit U shown in Figure 22. This charging profile is the result of simulating the change in the charging current flowing through the series unit U when the vehicle is driven according to the above driving pattern. The calculated charging profile is compared with the actual charging current, and the battery model is identified based on the difference between the two.

[0191] Furthermore, the driving pattern and charging / discharging pattern are converted into a predetermined data format via the Simulation Model Interface (Output). Next, the driving pattern and charge / discharge pattern are analyzed. For example, the State of Health (SOH) for each series unit U is calculated as part of the charge / discharge pattern analysis. This SOH allows for the determination of when to replace the battery unit. Alternatively, the SOH of the entire battery system may be calculated as part of the charge / discharge pattern analysis. Furthermore, by measuring the voltage of the battery modules and battery cells, the SOH of the battery modules and battery cells can also be calculated.

[0192] In Example 3, the target to which the railway upcycling system and railway upcycling method are applied is a storage battery. The first digital twin model simulates the charge and discharge current of the storage battery installed in vehicle 11. This corresponds to the process of determining the charge and discharge pattern in Figure 28. The storage battery installed in vehicle 11 corresponds to the replacement unit in Figures 26 and 27. Furthermore, the second digital twin model simulates the charge and discharge currents of the battery to be replaced in place of the battery installed in vehicle 11. The battery to be replaced in place of the battery installed in vehicle 11 corresponds to the replacement unit shown in Figures 26 and 27. When the replacement unit is a new or refurbished battery unit, a predetermined charge and discharge pattern can be applied to the charge and discharge current of the replacement unit. For battery units that are determined not to require refurbishment, the charge and discharge pattern calculated when they were previously installed in vehicle 11 can be applied. Furthermore, in the railway upcycling system and railway upcycling method of Example 3, for the evaluation of upcycling, for example, similar to Example 2, the degree of battery degradation is measured using SOH as an evaluation index. This SOH can be calculated using the method described in Figure 28. This makes it possible to predict when the SOH will reach its limit and propose upcycling the battery while it is still performing at the desired level, rather than making a battery replacement decision based on the result of the SOH reaching its limit, similar to Example 2.

[0193] In the upcycling system according to Example 3, upcycling and optimal maintenance of storage batteries contributes to carbon neutrality through energy saving effects, extends the lifespan of storage batteries, and promotes a circular economy by reducing waste through recycling, thereby contributing to the realization of a sustainable society. In Example 3, upcycling is performed only when the upcycling evaluation based on product and material data management is high, according to the recycling system described in Figure 19. When replacing a battery, comparing the degree of degradation before replacement with the performance improvement after replacement using a digital twin will determine whether replacement is appropriate at that point. The degree of degradation before replacement can be determined by the State of Health (SOH) described in Figure 28. Furthermore, in Example 3, the upcycling means is accessed by the business operator, battery manager, battery hardware manager, and battery hardware supplier to share information about the battery. This allows for the sharing and manipulation of information regarding the battery's status, maintenance and replacement proposals, and maintenance results. This provides a virtual space for determining the battery's status and future movements. It also enables resource history management compatible with Battery Passport.

[0194] This upcycling system can be summarized as follows, for example: [1] This upcycling system is a railway upcycling system that has a controller and memory (storage unit) and performs upcycling of railway systems according to the program in the memory. In this upcycling system, the memory holds a digital twin model for simulation. The controller takes in operational data of the railway system to be upcycled. The controller sets up a first digital twin model for performing operational simulation of the target. The controller improves the accuracy of the first digital twin model by adjusting the parameters. The controller constructs a second digital twin model for the updated target after upcycling. The controller then compares the simulation results when the first digital twin model and the second digital twin model are operated under the same conditions. In this way, the controller enables evaluation of the upcycling.

[0195] This upcycling system comprises a target product, a digital twin, and a memory unit. It is used to improve the target product by performing simulations using the digital twin on a computer. The target product encompasses railway systems in general, i.e., products related to railways, including software. This upcycling system determines whether or not to upcycle a part of such product, or the software that controls it, based on the simulation results.

[0196] A digital twin consists of at least one of a mathematical model and software that virtually represents the target product, enabling the simulation of the product's operation. The memory unit is a computer memory capable of storing information about the parameters and states related to the target product's operation, and further stores reference values ​​for evaluating the results of the upcycling.

[0197] In this upcycling system, the reference value K is determined by an AI system or a human based on past performance information stored in the memory unit, equipment specifications, or at least one of the other. Upcycling involves updating at least one part of the target product and / or its software. Digital twin simulations are performed both before and after this upcycling process.

[0198] If the simulation evaluation exceeds the baseline value (K in Figure 9), the target product is actually upcycled. Furthermore, this upcycling process involves modifying the parameters of the digital twin based on the operational data of the target product to improve its accuracy. After the digital twin's accuracy is sufficient, the simulation results of the digital twin of the target product and the new digital twin (with some parts of the target product replaced with new equipment or control systems) are compared under the same operating conditions. If the results are favorable, some parts of the target product are replaced with new equipment or control systems.

[0199] This upcycling system has the effect of repeatedly ensuring performance that is better than or equal to the specifications of the original product. In this way, this upcycling system can determine the merits of upcycling by simulating and verifying the pre- and post-update states of each part using a digital twin, including environmental protection, performance improvement, and recycling, and can propose a beneficial update plan.

[0200] Furthermore, this upcycling system allows for further upcycling even after the initial upcycling, and this repeated upcycling process improves the accuracy of the simulations. Improving the performance of actual railway vehicles contributes to creating a sustainable environment, and from this perspective, this upcycling system is also beneficial.

[0201] [2] This upcycling system combines an identified and existing digital twin with a newly created digital twin updated with new functions for each upcyclable unit, whether it be a component or software, into a single digital twin. This allows for evaluation by focusing on the effects of the new functions by changing only the upcycled part (new function) model to the actual vehicle and the identified model (known part).

[0202] [3] A common ID is assigned to objects belonging to the railway system and their corresponding digital twin models, and operational data is linked to the ID.

[0203] [4] Operational data used to adjust the parameters of the digital twin model includes train operation information, operation commands or driving operations, train configuration information, or vehicle equipment operation. This upcycling system assigns an ID (registered data) to the following operational data, enabling traceability of the product, part, or material. At least one of the following operational data is linked to different digital twins of actual vehicles: train operation information (equivalent to timetables), operation commands (driving operations), train configuration information, and vehicle / equipment operation (may vary depending on the model). Regarding usage, there is operational data common to each model (corresponding to the vehicle body, INV, control, etc.) as shown in Figure 18 as α history information, which is as follows.

[0204] In other words, operational data is uniquely linked to each digital twin of a different real vehicle, and includes at least one of the following: CI (frequency, input / output current / voltage, PU (power unit) temperature), APS (Auxiliary Power System frequency, input / output current / voltage, PU (power unit) temperature), battery (charge / discharge current, voltage, SOC, SOH, temperature), TCMS (equipment status, occupancy rate, ambient temperature), and vehicle component information (vibration, temperature, strain). Furthermore, data necessary for model identification and evaluation is linked to IDs (registered data) that enable traceability of products, parts, and materials necessary for upcycling.

[0205] These can be used to decide whether or not to upcycle before the product is finished. Furthermore, if upcycling is chosen, a recipe can be created. This recipe is used when using the component as is, or when combining it with other materials for reproduction. This upcycling system can handle a variety of situations because it identifies, evaluates, and upcycles a digital twin based on the aforementioned operational data.

[0206] Furthermore, TCMS (Train Control and Monitoring System) has become an indispensable management system for trains, with its performance and functionality increasing due to advances in information technology. By coordinating with ground equipment and other means, it enables energy conservation and other improvements by integrating and controlling information for the entire train, not just individual vehicles.

[0207] [5] Operational data used to adjust the parameters of the digital twin model includes frequency, input / output current / voltage, or power unit temperature if the subject is CI; efficiency planning method if the subject is APS; charge / discharge current, voltage, SOC, SOH, or temperature if the subject is a battery; equipment status, occupancy rate, or ambient temperature if the subject is TCMS; and vibration, temperature, or strain if the subject is a vehicle component.

[0208] [6] This upcycling system adjusts the parameters of the digital twin model after determining that the operational data of the target belonging to the railway system does not contain abnormal data caused by an accident or malfunction. As a result, the accuracy of the digital twin model is improved by comparing data from multiple vehicles (within the same train set or from other train sets) with the historical data of a single vehicle to identify abnormal locations and remove the abnormal data.

[0209] [7] This upcycling system predicts the timing of performance degradation by understanding performance degradation through time-history-based simulations, and determines and proposes the timing of value improvement before the degradation of items belonging to the railway system begins. According to this, by improving functionality before transitioning to a state considered to be degraded, a circular economy can be built by providing continuous functionality improvements over a long period of time. Continuous functionality improvement means providing value equal to or greater than that of when it was new, without causing degradation.

[0210] [8] This upcycling system uses operational data from railway systems, specifically data from automated driving or driving assistance systems. This allows for the matching of model parameters with stable (known) driving patterns, enabling efficient, quick, and highly accurate identification of the digital twin. In contrast, manual patterns involve inappropriate operations that may be included in model identification due to differences in timing among operators, resulting in longer identification times and increased variability, thus lowering accuracy.

[0211] [9] This upcycling system defines the driving patterns necessary for adjusting the digital twin pattern in at least one of the driving patterns for autonomous driving or the driving patterns for the driver assistance system. This allows for efficient, quick, and highly accurate identification of the digital twin. This enhances the effects of [7] above.

[0212]

[10] This upcycling system enables evaluation of value improvement by comparing the results of simulations that include variable data in addition to actual vehicle operation data. The variable data is not included in the actual vehicle operation data, but it is within the acceptable range according to the specifications. This allows for a judgment on whether or not to upcycle, taking into account conditions other than normal. By using operational data within a foreseeable range as input, the robustness of the upcycling feasibility judgment (adjustment result in control) is improved.

[0213]

[11] The upcycling system aims to enhance the value of any of the following: the actual vehicle, the battery, the drive control device, the auxiliary power control device, the fuel cell system, the air conditioning system, the in-vehicle monitoring device, the train information control device, the passenger information provision device, the seats, the vehicle components, the forward monitoring device, the safety monitoring device, the driver assistance system, the control function for automatic driving, or the control function for ground / on-vehicle wireless information transmission.

[0214] This upcycling system upcycles products or software. Products to be upcycled include, for example, batteries and module products. Software to be upcycled includes at least one of the following functions, which require customization for each route: adhesion control, forward monitoring sensitivity, DAS patterns, ATO patterns, hybrid energy management, battery current limiting, battery SOC range limiting, tensile force control by acceleration limiting, air brake blending control, and equipment operation changeover point information. Examples of module products, software, and systems to be upcycled include safety monitoring devices, seats, 5G_OTA (5th generation mobile communication wireless transmission and reception: Over-the-air), air conditioning systems, driverless systems, fuel cells, drive units, batteries, TCMS, and PIS.

[0215] This upcycling system provides continuous, long-term functional improvements to software by enhancing its functionality before it reaches a state considered degraded. Furthermore, it separates basic and custom functions in hardware, allowing for customized solutions for specific routes and performance improvements. In short, this upcycling system can achieve at least one of two goals: functional improvements tailored to customer and route conditions (module updates) or the pursuit of peak performance (parameter adjustment). This contributes to the realization of a circular economy.

[0216]

[12] In this upcycling system, objects belonging to the railway system are assigned an ID for individual identification, a record is linked to the ID, and the record is updated with the same work performed as the modification work data. The record is updated with the same work performed as the modification work data (such as replacing a part of the product). This demonstrates a method of ensuring traceability to show that upcycling has been achieved. Furthermore, by achieving upcycling, the grade (quality) of the material can be maintained or improved.

[0217] As a result, it can contribute to quality assurance. Furthermore, "products, parts, and materials of actual vehicles" and "products, parts, and materials in the data space" are linked one-to-one by ID, etc., and even if the configuration changes through upcycling, it is possible to track the changes and ensure that the initial quality is maintained or improved. Note that a change in configuration means rebuilding from the material level.

[0218]

[13] This upcycling system automatically adjusts the parameters of the control software on a digital twin model based on operational data and activates the automatically adjusted software in accordance with predetermined triggers. The predetermined triggers can be manual operations, calendar or timetable updates, maintenance, or any other occasion. Moreover, this upcycling system optimizes control in real time according to the driving conditions using the digital twin function. In this case, as a security measure, it would be good to supplement the operation by ensuring that even if the software is downloaded to the actual vehicle controller memory, it will not be activated unless the person in charge presses the "confirm" button.

[0219] Control functions can be separated into standard functions (no customization required, basic control such as PWM) and custom functions (adjustment required depending on the characteristics of the route, adhesion control, DAS, etc.), and the latter can be automatically identified to reduce the cost of software development. Trains and equipment can also be made subscription-based. A subscription model is a fixed-rate billing method that allows free use only during the contract period. With a subscription model, parameter adjustments according to each route are automated, leading to increased efficiency.

[0220]

[14] The upcycling system has control software that automatically adjusts parameters such as adhesion control, forward monitoring sensitivity, driver assistance system patterns, automatic train control patterns, hybrid energy management, battery current limiting, battery SOC range limiting, traction force control by acceleration limiting, air brake blending control, or equipment operation changeover point information.

[0221]

[15] In this upcycling system, the target is the battery, and the first digital twin model simulates the charge and discharge current of the battery installed in the vehicle 11. In practice, the charge and discharge current is simulated for each series unit U. In this case, the second digital twin model simulates the charge and discharge current of the battery to be replaced in place of the battery installed in the vehicle 11. In this case, for the evaluation of upcycling, for example, the degree of battery degradation is measured using SOH as an evaluation index. This makes it possible to predict when the SOH will reach its limit and propose upcycling the battery while it is still performing at the desired level, rather than making a battery replacement decision based on the result that the SOH has reached its limit.

[0222] The railway upcycling production method according to an embodiment of the present invention (hereinafter referred to as "this production method") can be summarized as follows. Note that in the following description, "this production method" may be read as "upcycling method."

[16] This production method is a method for producing products including railway-related software while upcycling the target product according to the results of simulations. In other words, this production method produces the target product by having the following steps:

[0223] The digital twin is initially constructed using at least one of the following: a mathematical model and software that virtually represents the target product. Furthermore, parameters and status information related to the operation of the railway-related target product are stored in the computer's memory in a readable format. This memory also stores a baseline value for evaluating the success of the upcycling process. This baseline value is determined by an AI system or a human based on at least one of the stored historical performance data and equipment specifications. Once ready, the digital twin is used to simulate the operation of the target product.

[0224] Digital twin simulations are performed both before and after an upcycle that involves updating at least one part of the target product and / or its software. Based on the results of these simulations, the necessity of an upcycle (and, if necessary, the timing of its implementation) is determined. If an upcycle is deemed necessary, the target product is updated at the appropriate time.

[0225] This production method has the effect of repeatedly ensuring performance that is higher than the specifications of the previous target product. Furthermore, since this production method allows for further upcycling even after the initial upcycling, repeated upcycling has the effect of improving the accuracy of simulations. [Explanation of Symbols]

[0226] 1...Actual vehicle field, 2...Simulated vehicle field, 3...Information transmission unit, 11...Actual vehicle, 12...Actual vehicle drive unit, 13...Actual vehicle control unit, 14...Actual vehicle drive control unit, 15...Rail, 16...Power supply unit, 21...Simulated vehicle, 22...Simulated vehicle drive unit, 23...Simulated vehicle control unit, 24...Simulated vehicle drive control unit, 31...Wide-area information transmission line, 32...Wide-area information transmission unit, 33...Wide-area information transmission and reception base, 34...In-area information transmission line, 35...In-area information transmission line, 36...In-area information transmission and reception base, 111...Driving command output unit, 112, 112a, 112b...Bogie, 113, 113a~113d...Wheelset, 114 ...current collector, 115...grounding device, 117...main motor, 118...auxiliary equipment, 121...main transformer, 122a...AC / DC converter, 122b...DC / AC inverter, 123...main circuit battery, 123a,123b,123c...battery system, 124... Auxiliary power supply unit (APS_DC / AC), 131…AC voltage detector (ACPT), 132, 132a~132e…AC current detector (ACCT), 133, 133a~133d, 135a, 135b…DC voltage detector, 134, 134a~134c…DC current detector, 141…Train information control device, 142a…AC / DC converter control unit, 142b…DC / AC inverter control unit, 143…Battery control device, 144…Auxiliary power supply control device, 231…Vehicle motion analysis unit, 232…Control data analysis unit, 233…Storage unit, U…Series unit

Claims

1. A railway upcycling system comprising a controller and memory, wherein the controller and memory construct a digital twin environment for simulating a railway system, and perform value enhancement of the railway system according to a program in the memory, The aforementioned controller, A first digital twin model is set up to simulate an object whose value is to be improved, which belongs to the aforementioned railway system. By acquiring the aforementioned operational data of the target, the parameters of the first digital twin model are adjusted to improve the accuracy of the first digital twin model. A second digital twin model is set up to simulate the update target when the aforementioned target is given increased value. By comparing the simulation results when the first digital twin model and the second digital twin model are operated under the same conditions, it becomes possible to evaluate the value improvement. Railway upcycling system.

2. To create a new digital twin model by updating one or more features included in an identified and existing digital twin model with new features. The railway upcycling system according to claim 1.

3. A common ID is assigned to objects belonging to the railway system and their corresponding digital twin models, and operational data is linked to this ID. The railway upcycling system according to claim 1.

4. The operational data used to adjust the parameters of the digital twin model includes: This includes train operation information, train control or operation information, train configuration information, or vehicle equipment operation. The railway upcycling system according to claim 1.

5. The operational data used to adjust the parameters of the digital twin model includes: If the subject is a drive control device, then the frequency, input / output current / voltage, or power unit temperature are included. If the subject is an auxiliary power control device, then the frequency, input / output current / voltage, or power unit temperature are included. If the subject is a storage battery, then the charge / discharge current, voltage, state of charge (SOC), state of heat (SOH), or temperature are included. If the subject is a train information control device, then the status of each device, the occupancy rate, or the temperature and room temperature will be included. If the subject is a vehicle component, then vibration, temperature, or strain may be included. The railway upcycling system according to claim 1.

6. After determining that the operational data belonging to the aforementioned railway system does not contain any abnormal data caused by an accident or malfunction, the parameters of the digital twin model are adjusted. The railway upcycling system according to claim 1.

7. Performance degradation is identified through time-history-based simulations, the timing of performance deterioration is predicted, and the timing of value improvement is determined and proposed before the deterioration of the objects belonging to the railway system begins. The railway upcycling system according to claim 1.

8. The operational data for the subject belonging to the aforementioned railway system will include data from driving using automated driving or driver assistance systems. The railway upcycling system according to claim 1.

9. The driving patterns necessary for adjusting the digital twin model are defined in at least one of the driving patterns of the autonomous driving system or the driving patterns of the driver assistance system. The railway upcycling system according to claim 8.

10. By comparing the results of the aforementioned simulation, which also incorporates variable data other than actual vehicle operation data, it becomes possible to evaluate the aforementioned value improvement. The railway upcycling system according to claim 1.

11. The object whose value is to be improved is, The actual vehicle, battery, drive control device, auxiliary power control device, fuel cell system, air conditioning system, in-vehicle monitoring device, train information control device, passenger information provision device, seat, vehicle components, forward monitoring device, safety monitoring device, driver assistance system, automatic driving control function, or ground / on-vehicle wireless information transmission control function, The railway upcycling system according to claim 1.

12. Each object belonging to the aforementioned railway system is assigned an ID for individual identification, a medical record is linked to that ID, and the medical record is updated with the same work details as the change work data. The railway upcycling system according to claim 1.

13. Based on operational data, the parameters of the control software are automatically adjusted on the digital twin model, and the automatically adjusted software is activated in response to a predetermined trigger. The railway upcycling system according to claim 1.

14. The parameters automatically adjusted by the control software are adhesion control, forward monitoring sensitivity, driver assistance system pattern, automatic train control pattern, hybrid energy management, battery current limit, battery SOC range limit, traction force control by acceleration limit, air brake blending control, or equipment operation changeover point information. The railway upcycling system according to claim 13.

15. The subject is a storage battery, The first digital twin model simulates the charging and discharging current of the battery installed in the vehicle. The railway upcycling system according to claim 1.

16. The second digital twin model simulates the charge and discharge current of the battery to be replaced in place of the battery installed in the vehicle. The railway upcycling system according to claim 15.

17. A railway upcycling method comprising a controller and a memory, wherein the controller and the memory construct a digital twin environment for simulating a railway system, and performs value enhancement of the railway system according to a program in the memory, The aforementioned controller, A first digital twin model is set up to simulate an object whose value is to be improved, which belongs to the aforementioned railway system. By acquiring the aforementioned operational data of the target, the parameters of the first digital twin model are adjusted to improve the accuracy of the first digital twin model. A second digital twin model is set up to simulate the update target when the aforementioned target is given increased value. By comparing the simulation results when the first digital twin model and the second digital twin model are operated under the same conditions, it becomes possible to evaluate the value improvement. Methods for upcycling railways.

18. To create a new digital twin model by updating one or more features included in an identified and existing digital twin model with new features. The railway upcycling method according to claim 17.

19. A common ID is assigned to objects belonging to the railway system and their corresponding digital twin models, and operational data is linked to this ID. The railway upcycling method according to claim 17.

20. The operational data used to adjust the parameters of the digital twin model includes: This includes train operation information, train control or operation information, train configuration information, or vehicle equipment operation. The railway upcycling method according to claim 17.

21. The operational data used to adjust the parameters of the digital twin model includes: If the subject is a drive control device, then the frequency, input / output current / voltage, or power unit temperature are included. If the subject is an auxiliary power control device, then the frequency, input / output current / voltage, or power unit temperature are included. If the subject is a storage battery, then the charge / discharge current, voltage, state of charge (SOC), state of heat (SOH), or temperature are included. If the subject is a train information control device, then the status of each device, the occupancy rate, or the temperature and room temperature will be included. If the subject is a vehicle component, then vibration, temperature, or strain may be included. The railway upcycling method according to claim 17.

22. After determining that the operational data belonging to the aforementioned railway system does not contain any abnormal data caused by an accident or malfunction, the parameters of the digital twin model are adjusted. The railway upcycling method according to claim 17.

23. Performance degradation is identified through time-history-based simulations, the timing of performance deterioration is predicted, and the timing of value improvement is determined and proposed before the deterioration of the objects belonging to the railway system begins. The railway upcycling method according to claim 17.

24. The operational data for the subject belonging to the aforementioned railway system will include data from driving using automated driving or driver assistance systems. The railway upcycling method according to claim 17.

25. The driving patterns necessary for adjusting the digital twin model are defined in at least one of the driving patterns of the autonomous driving system or the driving patterns of the driver assistance system. The railway upcycling method according to claim 24.

26. By comparing the results of the aforementioned simulation, which also incorporates variable data other than actual vehicle operation data, it becomes possible to evaluate the aforementioned value improvement. The railway upcycling method according to claim 17.

27. The object whose value is to be improved is, The actual vehicle, battery, drive control device, auxiliary power control device, fuel cell system, air conditioning system, in-vehicle monitoring device, train information control device, passenger information provision device, seat, vehicle components, forward monitoring device, safety monitoring device, driver assistance system, automatic driving control function, or ground / on-vehicle wireless information transmission control function, The railway upcycling method according to claim 17.

28. Each object belonging to the aforementioned railway system is assigned an ID for individual identification, a medical record is linked to that ID, and the medical record is updated with the same work details as the change work data. The railway upcycling method according to claim 17.

29. Based on operational data, the parameters of the control software are automatically adjusted on the digital twin model, and the automatically adjusted software is activated in response to a predetermined trigger. The railway upcycling method according to claim 17.

30. The parameters automatically adjusted by the control software are adhesion control, forward monitoring sensitivity, driver assistance system pattern, automatic train control pattern, hybrid energy management, battery current limit, battery SOC range limit, traction force control by acceleration limit, air brake blending control, or equipment operation changeover point information. The railway upcycling method according to claim 29.

31. The subject is a storage battery, The first digital twin model simulates the charging and discharging current of the battery installed in the vehicle. The railway upcycling method according to claim 17.

32. The second digital twin model simulates the charge and discharge current of the battery to be replaced in place of the battery installed in the vehicle. The railway upcycling method according to claim 31.

33. A railway upcycling production method comprising a controller and a memory, wherein the controller and the memory construct a digital twin environment for simulating a railway system, and the method performs value enhancement of the railway system according to a program in the memory, The aforementioned controller, A first digital twin model is set up to simulate an object whose value is to be improved, which belongs to the aforementioned railway system. By acquiring the aforementioned operational data of the target, the parameters of the first digital twin model are adjusted to improve the accuracy of the first digital twin model. A second digital twin model is set up to simulate the update target when the aforementioned target is given increased value. By comparing the simulation results when the first digital twin model and the second digital twin model are operated under the same conditions, it becomes possible to evaluate the value improvement. To produce a railway system that enhances the value of the aforementioned target, Railway upcycling production methods.

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