Hybrid bus torque distribution method, device and electronic equipment

Abstract

The embodiment of the specification discloses a hybrid bus torque distribution method and device and electronic equipment. The method comprises the following steps: calculating the theoretical electric quantity change curve corresponding to each historical operation data of different buses by a dynamic programming algorithm; determining the predicted electric quantity change curve corresponding to the real-time operation data of the target bus based on an electric quantity change curve prediction model; and finally determining the real-time torque distribution data corresponding to the predicted electric quantity change curve. The real-time torque distribution data is determined through the above-constructed electric quantity change curve prediction model, different real-time operation data is allocated different real-time torque, and the hybrid bus is in the best power source distribution state, the fuel consumption is reduced to the greatest extent, and the use satisfaction of the user is improved.

Description

Technical Field

[0001] This specification relates to the field of hybrid power energy distribution technology, and more particularly to a torque distribution method, apparatus and electronic equipment for a hybrid bus. Background Technology

[0002] Hybrid buses, with two or more power sources, effectively combine the advantages of both pure electric and traditional fuel-powered buses, representing a crucial stage in the transition from traditional fuel-powered vehicles to pure electric buses. Through proper power source allocation, hybrid buses can reduce range anxiety while lowering fuel consumption and optimizing operating costs. However, power source allocation in hybrid buses often relies on torque distribution methods. Existing torque distribution methods frequently use a fixed initial torque allocation scheme set manually based on experience. This results in suboptimal power source allocation during each run, leading to excessive fuel waste and reduced user satisfaction. Summary of the Invention

[0003] This specification provides a torque distribution method, device, and electronic equipment for a hybrid bus, the technical solution of which is as follows: In a first aspect, embodiments of this specification provide a torque distribution method for a hybrid bus, the method comprising: Based on the dynamic programming algorithm, the theoretical power consumption change curves corresponding to the historical operating data of different buses are calculated respectively. The historical operating data includes historical vehicle speed, historical passenger volume and historical usable power. The theoretical power consumption change curve is used to characterize the power consumption change curve corresponding to the minimum theoretical total fuel consumption of the bus. The historical usable power is the difference between the historical remaining power and the battery charging threshold. The battery charging threshold is determined based on the charging habit data of the bus. Based on the power change curve prediction model, the predicted power change curve corresponding to the real-time operation data of the target bus is determined. The power change curve prediction model is constructed by training the theoretical power change curves. Determine the real-time torque distribution data corresponding to the predicted power change curve. The real-time torque distribution data includes the engine torque data and generator torque data corresponding to the target bus when the total equivalent fuel consumption is minimized.

[0004] Secondly, a torque distribution device for a hybrid bus is provided, the device comprising: The calculation module is used to calculate the theoretical power change curves corresponding to various historical operating data of different buses based on the dynamic programming algorithm. The historical operating data includes historical vehicle speed, historical passenger volume and historical usable power. The theoretical power change curve is used to characterize the power change curve corresponding to the minimum theoretical total fuel consumption of the target bus. The historical usable power is the difference between the historical remaining power and the battery charging threshold. The battery charging threshold is determined based on the charging habit data of the bus. The prediction module is used to determine the predicted power change curve corresponding to the real-time operating data of the target bus based on the power change curve prediction model. The power change curve prediction model is trained and constructed from the theoretical power change curves. The determination module is used to determine the real-time torque distribution data corresponding to the predicted power change curve. The real-time torque distribution data includes the engine torque data and generator torque data corresponding to the target bus when the total equivalent fuel consumption is minimized.

[0005] Thirdly, an electronic device is provided, including a device processor and a memory; The device processor is connected to the memory; The memory is used to store executable program code; The device processor runs a program corresponding to the executable program code stored in the memory to perform the steps of the method provided as in the first aspect or any possible implementation thereof.

[0006] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or device processor, cause the computer or device processor to perform the method provided as in the first aspect or any possible implementation thereof.

[0007] The beneficial effects of the technical solutions provided in some embodiments of this specification include at least the following: In one or more embodiments of this specification, a dynamic programming algorithm is used to calculate the theoretical energy change curves corresponding to various historical operating data of different buses. Then, based on the energy change curve prediction model, the predicted energy change curve corresponding to the real-time operating data of the target bus is determined. Finally, the real-time torque allocation data corresponding to the predicted energy change curve is determined. By using the energy change curve prediction model constructed above to determine the real-time torque allocation data, different real-time torques are allocated to different real-time operating data. This ensures that the hybrid bus is in the optimal power source allocation state in real time, minimizing fuel consumption and improving user satisfaction. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart illustrating a torque distribution method for a hybrid bus provided in this specification. Figure 2 This is a schematic diagram of the structure of a torque distribution device for a hybrid bus provided in an embodiment of this specification; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this specification. Detailed Implementation

[0010] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0011] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0012] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this specification. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0013] Please see Figure 1 , Figure 1 A flowchart illustrating an overall process for a torque distribution method for a hybrid bus provided in an embodiment of this specification is shown.

[0014] like Figure 1 As shown, the torque distribution method for hybrid buses may include at least the following steps: Step 101: Based on the dynamic programming algorithm, calculate the theoretical electricity change curves corresponding to each historical operating data of different buses.

[0015] The historical operating data includes historical vehicle speed, historical passenger volume, and historical available power. The theoretical power change curve is used to characterize the power change curve corresponding to the minimum theoretical total fuel consumption of the bus. The historical available power is the difference between the historical remaining power and the battery charging threshold. The battery charging threshold is determined based on the charging habit data corresponding to the bus.

[0016] In the embodiments of this specification, hybrid buses can be used on different bus routes in actual operation. For the operation between two random bus stops on a single bus route, there may be multiple types and brands of hybrid buses running on it. Each type of bus has different charging habits due to different routes. Furthermore, the speed, passenger capacity, and available power of the same type of bus will not be the same between different stops and road sections.

[0017] Therefore, to ensure optimal torque distribution for each bus between any two stops and minimize total fuel consumption, it is necessary to consider three factors simultaneously: actual bus speed, passenger capacity, and available battery power. Specifically, to provide ample training data for the subsequent real-time torque prediction and allocation model, a dynamic programming algorithm is required to pre-calculate the theoretical battery power variation curves for different buses under various historical operating conditions, namely, different historical speeds, different historical passenger capacities, and different historical available battery power.

[0018] Specifically, a dynamic programming algorithm is used to determine the theoretical battery charge change curve for each historical operating condition. This curve represents the battery charge change curve corresponding to the bus's internal battery when the torque is optimally distributed, i.e., the battery charge change curve corresponding to the minimum theoretical total fuel consumption of the bus. Furthermore, because each bus has different charging habits, resulting in different battery charging thresholds, it is necessary to first determine the corresponding battery charging threshold using the charging habit data for that bus, and then calculate the historical usable battery charge by the difference between the historical remaining battery charge and the battery charging threshold.

[0019] In one possible implementation, the method further includes: Based on the bus model information, determine the corresponding charging habit data for the bus; The battery charging threshold corresponding to the charging habit data is determined based on the battery charging threshold prediction model.

[0020] In the embodiments of this specification, different bus models have different battery capacities and different distances to return to their respective charging stations after completing passenger transport, resulting in different charging habit data for each bus. This charging habit data may include the amount charged per charge, the start and end charges for charging, and the energy loss during the round trip. Therefore, the bus model information can be determined first, and then the charging habit data corresponding to that model information can be determined using a pre-built model-charging habit database. Next, the charging habit data is input into a battery charging threshold prediction model to determine the corresponding battery charging threshold. This battery charging threshold prediction model is trained using a large amount of historical data from different bus models, including different charging habit data and their corresponding battery charging thresholds.

[0021] Step 102: Based on the power change curve prediction model, determine the predicted power change curve corresponding to the real-time operating data of the target bus.

[0022] The predicted model for the change in electricity volume is constructed by training each of the theoretical change in electricity volume curves.

[0023] In the embodiments of this specification, since dynamic programming algorithms cannot meet the requirements of real-time online prediction, each input of trial operation data requires a large amount of computation time to obtain the theoretical power change curve. Therefore, in order to ensure that the target bus can obtain the predicted power change curve immediately under actual operation, thereby achieving optimal torque distribution and minimizing total equivalent fuel consumption, the theoretical power change curves corresponding to different buses in each historical operation data can be determined in advance. Then, a power change curve prediction model can be constructed using each theoretical power change curve. Subsequently, when the target bus actually starts working, only real-time operation data needs to be input into the constructed power change curve prediction model to obtain its corresponding predicted power change curve.

[0024] In one possible implementation, the method further includes: By integrating the historical operating data and the theoretical power change curves corresponding to the historical operating data, a historical power change dataset is obtained. An initial power change curve prediction model is constructed, and the initial power change curve prediction model is trained based on the historical power change dataset to obtain a trained power change curve prediction model.

[0025] In the embodiments of this specification, when training and constructing a power change curve prediction model using various theoretical power change curves, the historical operating data and their corresponding theoretical power change curves are first integrated and paired one by one to obtain a historical power change dataset. Next, an initial power change curve prediction model is constructed, using 70% of the obtained historical power change dataset as the training set and the remaining 30% as the test set. Further, the initial power change curve prediction model is trained using the historical operating data from the training set as model input and the corresponding theoretical power change curves as model output. Then, the trained power change curve prediction model is optimized using the test set to obtain a well-trained power change curve prediction model.

[0026] In one possible implementation, determining the predicted power change curve corresponding to the real-time operating data of the target bus based on the power change curve prediction model includes: Obtain real-time operating data of the target bus, including real-time vehicle speed, real-time passenger capacity, and real-time available power. The real-time operating data is input into the trained power change curve prediction model to determine the predicted power change curve of the target bus under the real-time operating data.

[0027] In the embodiments of this specification, when determining the predicted power change curve corresponding to the real-time operating data of the target bus using the power change curve prediction model, the real-time operating data of the target bus can first be obtained through sensors and the bus battery management system. The real-time operating data includes real-time vehicle speed, real-time passenger load, and real-time usable power. Optionally, the average speed of the target bus between the previous one or two stops can be used as the real-time speed, and the difference between the actual vehicle mass and the empty vehicle mass after leaving the stop can be used as the real-time passenger load. Similarly, the real-time usable power is determined using a method for determining historical usable power. First, the real-time remaining power is determined through the battery management system, then the battery charging threshold is determined based on the target bus's charging habits data, and finally, the difference between the real-time remaining power and the battery charging threshold is determined as the real-time usable power.

[0028] Next, the real-time operating data is used as the model input and transmitted to the trained power change curve prediction model to obtain the predicted power change curve of the target bus under the real-time operating data.

[0029] Step 103: Determine the real-time torque distribution data corresponding to the predicted power change curve.

[0030] The real-time torque distribution data includes engine torque data and generator torque data corresponding to the target bus when the total equivalent fuel consumption is minimized.

[0031] In the embodiments of this specification, the predicted energy change curve of the target bus under real-time operating data is determined. This predicted energy change curve can then be combined with an equivalent fuel consumption minimization strategy to determine the corresponding real-time torque distribution data. Specifically, since a hybrid bus can be driven simultaneously by an engine and a generator during operation, the equivalent fuel consumption during generator charging and discharging can be added to the engine's fuel consumption to obtain the total equivalent fuel consumption. Torque distribution is then determined using proportional-integral feedback control or fuzzy control methods, causing the target bus's real-time energy to change according to the determined predicted energy change curve. This yields the engine torque data and generator torque data corresponding to the minimum total equivalent fuel consumption of the target bus under real-time operating data, i.e., the real-time torque distribution data.

[0032] In one possible implementation, determining the real-time torque distribution data corresponding to the predicted power change curve includes: A target equivalent factor is determined based on the predicted power change curve, and the target equivalent factor is used to control the actual power change of the target bus. The real-time torque distribution data corresponding to the target equivalent factor is determined based on the strategy of minimizing total equivalent fuel consumption.

[0033] In the embodiments of this specification, the target equivalent factor that continuously changes during the real-time operation data of the target bus can first be determined by predicting the power consumption change curve. This target equivalent factor is used to equate generator power consumption to fuel consumption, thereby controlling the actual power consumption change of the target bus from the perspective of minimizing total equivalent fuel consumption. This ensures that the actual power consumption change curve of the target bus is as consistent as possible with the predicted power consumption change curve, thus minimizing the total equivalent fuel consumption corresponding to real-time torque distribution according to the predicted power consumption change curve. Next, the real-time torque distribution data corresponding to the determined target equivalent factor is determined based on the strategy of minimizing total equivalent fuel consumption.

[0034] In one possible implementation, determining the target equivalence factor based on the predicted electricity change curve includes: Determine the power change error between the predicted power change curve and the actual power change curve; The target equivalent factor corresponding to the power change error is determined based on the proportional-derivative control method.

[0035] In the embodiments of this specification, when determining the target equivalent factor by predicting the power change curve, the power change error between the actual power change curve and the predicted power change curve can be determined in real time, and the output of the PID controller, i.e. the target equivalent factor, can be determined in real time according to the proportional-derivative control method to suppress or eliminate the power change error as much as possible.

[0036] In one possible implementation, determining the real-time torque distribution data corresponding to the target equivalent factor based on the total equivalent fuel consumption minimization strategy includes: Determine the formula for calculating the total equivalent fuel consumption of the target bus; Based on the total fuel consumption calculation formula and the target equivalent factor, determine the real-time torque distribution data corresponding to the minimum total equivalent fuel consumption.

[0037] In the embodiments of this specification, the formula for calculating the total equivalent fuel consumption of the target bus under the real-time operating data is as follows: in, Indicates total equivalent fuel consumption; Indicates engine fuel consumption; This represents the equivalent fuel consumption during generator discharge. This represents the equivalent fuel consumption during generator charging. This indicates the generator's operating status. When the generator is driving the car, its value is 1; otherwise, it is 0. , These represent the target equivalence factors during driving and charging, respectively; Indicates battery efficiency; This indicates the low calorific value of the fuel. This indicates the generator power.

[0038] Next, the determined target equivalent factor is substituted into the total fuel consumption calculation formula, and constraints are added, including engine and generator speed limits and torque limits, as well as battery operating current and voltage limits. Further, with the constraints fixed, the target equivalent factor, battery efficiency, and fuel low calorific value determined, the generator power corresponding to the minimum total equivalent fuel consumption is determined using an extreme value algorithm. Finally, the engine torque data and generator torque data, i.e., real-time torque distribution data, are derived from the generator power and the total torque demand of the target bus.

[0039] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0040] Please refer to the following. Figure 2 , Figure 2 A schematic diagram of a torque distribution device for a hybrid bus provided in an embodiment of this specification is shown. It should be noted that... Figure 2 The torque distribution device shown in the hybrid bus is used to perform the functions described in this application. Figure 1 The methods shown in the embodiments are for illustrative purposes only, illustrating the parts relevant to the embodiments of this application. For specific technical details not disclosed, please refer to this application. Figure 1 The example shown.

[0041] like Figure 2 As shown, the torque distribution device for the hybrid bus may include at least: The calculation module 201 is used to calculate the theoretical power change curves corresponding to various historical operating data of different buses based on the dynamic programming algorithm. The historical operating data includes historical vehicle speed, historical passenger volume and historical usable power. The theoretical power change curve is used to characterize the power change curve corresponding to the minimum theoretical total fuel consumption of the target bus. The historical usable power is the difference between the historical remaining power and the battery charging threshold. The battery charging threshold is determined based on the charging habit data of the bus. Prediction module 202 is used to determine the predicted power change curve corresponding to the real-time operation data of the target bus based on the power change curve prediction model, wherein the power change curve prediction model is constructed by training the theoretical power change curves. The determination module 203 is used to determine the real-time torque distribution data corresponding to the predicted power change curve. The real-time torque distribution data includes the engine torque data and generator torque data corresponding to the target bus when the total equivalent fuel consumption is minimized.

[0042] In one possible implementation, the computing module 201 is specifically used for: Based on the bus model information, determine the corresponding charging habit data for the bus; The battery charging threshold corresponding to the charging habit data is determined based on the battery charging threshold prediction model.

[0043] In one possible implementation, the prediction module 202 is specifically used for: By integrating the historical operating data and the theoretical power change curves corresponding to the historical operating data, a historical power change dataset is obtained. An initial power change curve prediction model is constructed, and the initial power change curve prediction model is trained based on the historical power change dataset to obtain a trained power change curve prediction model.

[0044] In one possible implementation, the prediction module 202 is further configured to: Obtain real-time operating data of the target bus, including real-time vehicle speed, real-time passenger capacity, and real-time available power. The real-time operating data is input into the trained power change curve prediction model to determine the predicted power change curve of the target bus under the real-time operating data.

[0045] In one possible implementation, the determining module 203 is specifically used for: A target equivalent factor is determined based on the predicted power change curve, and the target equivalent factor is used to control the actual power change of the target bus. The real-time torque distribution data corresponding to the target equivalent factor is determined based on the strategy of minimizing total equivalent fuel consumption.

[0046] In one possible implementation, the determining module 203 is further configured to: Determine the power change error between the predicted power change curve and the actual power change curve; The target equivalent factor corresponding to the power change error is determined based on the proportional-derivative control method.

[0047] In one possible implementation, the determining module 203 is further configured to: Determine the formula for calculating the total equivalent fuel consumption of the target bus; Based on the total fuel consumption calculation formula and the target equivalent factor, determine the real-time torque distribution data corresponding to the minimum total equivalent fuel consumption.

[0048] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0049] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0050] Please refer to the following. Figure 3 , Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0051] like Figure 3As shown, the electronic device 300 may include: at least one device processor 301, at least one network interface 303, user interface 303, memory 305, and at least one communication bus 302.

[0052] The communication bus 302 can be used to realize the connection and communication of the above components.

[0053] The user interface 303 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0054] The network interface 304 may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0055] The device processor 301 may include one or more processing cores. The device processor 301 connects to various parts within the electronic device 300 using various interfaces and lines. It executes various functions and processes data of the electronic device 300 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the device processor 301 may be implemented using at least one hardware form of DSP, FPGA, or PLA. The device processor 301 may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the device processor 301 and may be implemented as a separate chip.

[0056] The memory 305 may include RAM or ROM. Optionally, the memory 305 may include a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned device processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0057] Specifically, the device processor 301 can be used to call the hybrid bus torque distribution application stored in the memory 305 and perform the following operations: Based on the dynamic programming algorithm, the theoretical power consumption change curves corresponding to the historical operating data of different buses are calculated respectively. The historical operating data includes historical vehicle speed, historical passenger volume and historical usable power. The theoretical power consumption change curve is used to characterize the power consumption change curve corresponding to the minimum theoretical total fuel consumption of the bus. The historical usable power is the difference between the historical remaining power and the battery charging threshold. The battery charging threshold is determined based on the charging habit data of the bus. Based on the power change curve prediction model, the predicted power change curve corresponding to the real-time operation data of the target bus is determined. The power change curve prediction model is constructed by training the theoretical power change curves. Determine the real-time torque distribution data corresponding to the predicted power change curve. The real-time torque distribution data includes the engine torque data and generator torque data corresponding to the target bus when the total equivalent fuel consumption is minimized.

[0058] As an optional embodiment of this specification, the method further includes: Based on the bus model information, determine the corresponding charging habit data for the bus; The battery charging threshold corresponding to the charging habit data is determined based on the battery charging threshold prediction model.

[0059] As an optional embodiment of this specification, the method further includes: By integrating the historical operating data and the theoretical power change curves corresponding to the historical operating data, a historical power change dataset is obtained. An initial power change curve prediction model is constructed, and the initial power change curve prediction model is trained based on the historical power change dataset to obtain a trained power change curve prediction model.

[0060] As an optional embodiment of this specification, the step of determining the predicted power change curve corresponding to the real-time operating data of the target bus based on the power change curve prediction model includes: Obtain real-time operating data of the target bus, including real-time vehicle speed, real-time passenger capacity, and real-time available power. The real-time operating data is input into the trained power change curve prediction model to determine the predicted power change curve of the target bus under the real-time operating data.

[0061] As an optional embodiment of this specification, determining the real-time torque distribution data corresponding to the predicted power change curve includes: A target equivalent factor is determined based on the predicted power change curve, and the target equivalent factor is used to control the actual power change of the target bus. The real-time torque distribution data corresponding to the target equivalent factor is determined based on the strategy of minimizing total equivalent fuel consumption.

[0062] As an optional embodiment of this specification, determining the target equivalence factor based on the predicted power change curve includes: Determine the power change error between the predicted power change curve and the actual power change curve; The target equivalent factor corresponding to the power change error is determined based on the proportional-derivative control method.

[0063] As an optional embodiment of this specification, determining the real-time torque distribution data corresponding to the target equivalent factor based on the total equivalent fuel consumption minimization strategy includes: Determine the formula for calculating the total equivalent fuel consumption of the target bus; Based on the total fuel consumption calculation formula and the target equivalent factor, determine the real-time torque distribution data corresponding to the minimum total equivalent fuel consumption.

[0064] This specification also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0065] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0066] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0067] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0068] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0070] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0071] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0072] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. A torque distribution method for a hybrid bus, characterized in that, The method includes: Based on the dynamic programming algorithm, the theoretical power consumption change curves corresponding to the historical operating data of different buses are calculated respectively. The historical operating data includes historical vehicle speed, historical passenger volume and historical usable power. The theoretical power consumption change curve is used to characterize the power consumption change curve corresponding to the minimum theoretical total fuel consumption of the bus. The historical usable power is the difference between the historical remaining power and the battery charging threshold. The battery charging threshold is determined based on the charging habit data of the bus. Based on the power change curve prediction model, the predicted power change curve corresponding to the real-time operation data of the target bus is determined. The power change curve prediction model is constructed by training the theoretical power change curves. Determine the real-time torque distribution data corresponding to the predicted power change curve. The real-time torque distribution data includes the engine torque data and generator torque data corresponding to the target bus when the total equivalent fuel consumption is minimized.

2. The method according to claim 1, characterized in that, The method further includes: Based on the bus model information, determine the corresponding charging habit data for the bus; The battery charging threshold corresponding to the charging habit data is determined based on the battery charging threshold prediction model.

3. The method according to claim 1, characterized in that, The method further includes: By integrating the historical operating data and the theoretical power change curves corresponding to the historical operating data, a historical power change dataset is obtained. An initial power change curve prediction model is constructed, and the initial power change curve prediction model is trained based on the historical power change dataset to obtain a trained power change curve prediction model.

4. The method according to claim 3, characterized in that, The prediction model based on the power consumption change curve determines the predicted power consumption change curve corresponding to the real-time operating data of the target bus, including: Obtain real-time operating data of the target bus, including real-time vehicle speed, real-time passenger capacity, and real-time available power. The real-time operating data is input into the trained power change curve prediction model to determine the predicted power change curve of the target bus under the real-time operating data.

5. The method according to claim 1, characterized in that, The determination of the real-time torque distribution data corresponding to the predicted power change curve includes: A target equivalent factor is determined based on the predicted power change curve, and the target equivalent factor is used to control the actual power change of the target bus. The real-time torque distribution data corresponding to the target equivalent factor is determined based on the strategy of minimizing total equivalent fuel consumption.

6. The method according to claim 5, characterized in that, The determination of the target equivalence factor based on the predicted electricity change curve includes: Determine the power change error between the predicted power change curve and the actual power change curve; The target equivalent factor corresponding to the power change error is determined based on the proportional-derivative control method.

7. The method according to claim 5, characterized in that, The step of determining the real-time torque distribution data corresponding to the target equivalent factor based on the total equivalent fuel consumption minimization strategy includes: Determine the formula for calculating the total equivalent fuel consumption of the target bus; Based on the total equivalent fuel consumption calculation formula and the target equivalent factor, determine the real-time torque distribution data corresponding to the minimum total equivalent fuel consumption.

8. A torque distribution device for a hybrid bus, characterized in that, The device includes: The calculation module is used to calculate the theoretical power change curves corresponding to various historical operating data of different buses based on the dynamic programming algorithm. The historical operating data includes historical vehicle speed, historical passenger volume and historical usable power. The theoretical power change curve is used to characterize the power change curve corresponding to the minimum theoretical total fuel consumption of the bus. The historical usable power is the difference between the historical remaining power and the battery charging threshold. The battery charging threshold is determined based on the charging habit data of the bus. The prediction module is used to determine the predicted power change curve corresponding to the real-time operating data of the target bus based on the power change curve prediction model. The power change curve prediction model is trained and constructed from the theoretical power change curves. The determination module is used to determine the real-time torque distribution data corresponding to the predicted power change curve. The real-time torque distribution data includes the engine torque data and generator torque data corresponding to the target bus when the total equivalent fuel consumption is minimized.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the steps of the method as claimed in any one of claims 1-7.

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