Torque distribution method and device for hybrid power bus and electronic equipment

Abstract

The embodiment of the invention discloses a torque distribution method and device for a hybrid power bus and electronic equipment. The method comprises the steps that theoretical electric quantity change curves corresponding to historical operation data of different buses are calculated through a dynamic planning algorithm, and then a predicted electric quantity change curve corresponding to real-time operation data of a target bus is determined based on an electric quantity change curve prediction model; and finally determining real-time torque distribution data corresponding to the predicted electric quantity change curve. The real-time torque distribution data is determined through the constructed electric quantity change curve prediction model, different real-time torques are correspondingly distributed according to different real-time operation data, then the hybrid power bus is in the optimal power source distribution state in real time, oil consumption is reduced to the maximum degree, and the use satisfaction degree of users is improved.

Description

Technical Field

[0001] One or more embodiments of this specification relate to the technical field of hybrid power energy distribution, and in particular, to a torque distribution method, device, and electronic device for a hybrid bus. Background Art

[0002] Hybrid buses have two or more power sources, which can well combine the advantages of pure electric buses and traditional fuel buses. It is a very important stage in the transformation from traditional fuel vehicles to pure electric buses. Through reasonable distribution of power sources, hybrid buses can reduce range anxiety while reducing fuel consumption and optimizing operating costs. However, the power source distribution of hybrid buses often adopts a torque distribution method, and the existing torque distribution methods often set an initial fixed torque distribution scheme manually according to experience, resulting in an unsatisfactory power source distribution effect when operating according to the fixed torque distribution scheme each time, leading to excessive fuel consumption waste and reducing user satisfaction. Summary of the Invention

[0003] Embodiments of this specification provide a torque distribution method, device, and electronic device for a hybrid bus. The technical solutions are as follows: In a first aspect, embodiments of this specification provide a torque distribution method for a hybrid bus. The method includes: Based on the dynamic programming algorithm, calculate the theoretical power change curves corresponding to the historical operation data of different buses respectively. The historical operation data includes historical vehicle speed, historical passenger load, and historical available power. The theoretical power change curve is used to represent 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, and the battery charging threshold is determined based on the charging habit data corresponding to the bus; Based on the power change curve prediction model, determine the predicted power change curve corresponding to the real-time operation data of the target bus. The power change curve prediction model is trained and constructed from each of 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 the generator torque data corresponding to the minimum total equivalent fuel consumption of the target bus.

[0004] In a second aspect, a torque distribution device for a hybrid bus is provided. The device includes: A calculation module, configured to calculate, based on a dynamic programming algorithm, theoretical power change curves corresponding to respective historical operation data of different buses, where the historical operation data includes historical vehicle speed, historical passenger capacity, and historical available power, the theoretical power change curve is used to represent a power change curve corresponding to the minimum theoretical total fuel consumption of the target bus, the historical available power is the difference between the historical remaining power and a battery charging threshold, and the battery charging threshold is determined based on charging habit data corresponding to the bus; A prediction module, configured to determine a predicted power change curve corresponding to the real-time operation data of the target bus based on a power change curve prediction model, where the power change curve prediction model is trained and constructed from the respective theoretical power change curves; A determination module, configured to determine real-time torque distribution data corresponding to the predicted power change curve, where the real-time torque distribution data includes engine torque data and generator torque data corresponding to the minimum total equivalent fuel consumption of the target bus.

[0005] In a third aspect, an electronic device is provided, including a device processor and a memory; The device processor is connected to the memory; The memory is configured to store an executable program code; The device processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any one of the possible implementation manners of the first aspect.

[0006] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. Instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer or a device processor, the computer or the device processor is caused to execute the method provided in the first aspect or any one of the possible implementation manners of the first aspect.

[0007] The beneficial effects brought by the technical solutions provided in some embodiments of this specification at least include: In one or more embodiments of this specification, by using a dynamic programming algorithm, theoretical power change curves corresponding to respective historical operation data of different buses are calculated respectively, then a predicted power change curve corresponding to the real-time operation data of the target bus is determined based on a power change curve prediction model, and finally real-time torque distribution data corresponding to the predicted power change curve is determined. By determining the real-time torque distribution data through the above constructed power change curve prediction model, different real-time torques are allocated corresponding to different real-time operation data, thereby enabling the hybrid bus to be in the best power source allocation state in real time, minimizing fuel consumption to the greatest extent, and improving the user satisfaction. Description of the Drawings

[0008] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0009] Figure 1 It is a flowchart of a torque distribution method for a hybrid bus provided by an embodiment of this specification; Figure 2 It is a schematic structural diagram of a torque distribution device for a hybrid bus provided by an embodiment of this specification; Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of this specification. Detailed implementation manners

[0010] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application.

[0011] The terms "first", "second", "third", etc. in the specification, claims and the above accompanying drawings of this specification are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0012] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes can be made to the functions and arrangements of the described elements without departing from the scope of the content of this specification. Various processes or components can be appropriately omitted, substituted or added in each example. For example, the described method can be executed in a different order from the described order, and various steps can be added, omitted or combined. In addition, the features described in some examples can be combined into other examples.

[0013] Please refer to Figure 1 , Figure 1 which shows an overall flowchart of a torque distribution method for a hybrid bus provided by an embodiment of this specification.

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

[0015] Among them, the historical operation data includes historical vehicle speed, historical passenger capacity, and historical available power. The theoretical power change curve is used to represent 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, and the battery charging threshold is determined based on the charging habit data corresponding to the bus.

[0016] In the embodiments of this specification, during the actual operation of hybrid buses, they can be used on different bus lines. For the operation situation between two random bus stops on a single bus line, there may be multiple different types and brands of hybrid buses running on it. Since each bus has a different set operation route, its corresponding charging habit data will also be different. Moreover, for the same bus, the vehicle speed, passenger capacity, and available power corresponding to different sections between stops will not be the same.

[0017] Therefore, in order to ensure the optimal distributed torque corresponding to each bus between any two stops and achieve the purpose of minimizing the total fuel consumption, it is necessary to consider the three factors of vehicle speed, passenger capacity, and available power during the actual driving of the bus at the same time. Among them, in order to provide a large amount of training data for the subsequent real-time torque prediction and distribution scheme model, it is necessary to first use the dynamic programming algorithm to calculate in advance the theoretical power change curves corresponding to different buses under different historical operation data, that is, different historical vehicle speeds, different historical passenger capacities, and different historical available powers.

[0018] Among them, the theoretical power change curve corresponding to each historical operation situation determined by the dynamic programming algorithm is the power change curve of the battery inside the bus corresponding to the optimal distributed torque, that is, the power change curve corresponding to the minimum theoretical total fuel consumption of the bus. And because each bus has a different charging habit, the corresponding battery charging threshold is different. Therefore, it is necessary to first determine the corresponding battery charging threshold through the charging habit data corresponding to the bus, and then calculate the historical available power by calculating the difference between the historical remaining power and the battery charging threshold.

[0019] In an implementable manner, the method further includes: Determine the charging habit data corresponding to the bus based on the model information of the bus; Determine the battery charging threshold corresponding to the charging habit data according to the battery charging threshold prediction model.

[0020] In the embodiments of this specification, since buses of different models have different battery capacities and different distances to return to their respective corresponding charging stations after completing the passenger-carrying work, the charging habit data corresponding to each bus is different. Among them, the charging habit data may include the amount of each charge, the start and end power of charging, and the charging round-trip loss power. Therefore, the model information of the bus can be determined first, and then the charging habit data corresponding to the model information can be determined through a pre-constructed model-charging habit database. Then, the charging habit data is input into the battery charging threshold prediction model to determine the battery charging threshold corresponding to the charging habit data. The battery charging threshold prediction model is trained by a large amount of historical record data of buses of different models, and the historical record data includes 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 operation data of the target bus.

[0022] Among them, the power change curve prediction model is trained and constructed by each of the theoretical power change curves.

[0023] In the embodiments of this specification, since the dynamic programming algorithm cannot meet the requirements of real-time online prediction, and a large amount of computing time is required to obtain the theoretical power change curve every time the trial operation data is input. Therefore, in order to ensure that the target bus can obtain the predicted power change curve immediately under the actual operating state, so as to achieve the optimal torque distribution and the purpose of minimizing the total equivalent fuel consumption, the theoretical power change curves corresponding to the historical operation data of different buses can be determined in advance, and then a power change curve prediction model is trained and constructed by using each of the theoretical power change curves. Subsequently, when the target bus actually starts to work, only the real-time operation data needs to be input into the constructed power change curve prediction model to obtain the corresponding predicted power change curve.

[0024] In an implementable manner, the method further includes: Integrate each of the historical operation data and the corresponding theoretical power change curves of each of the historical operation data to obtain a historical power change data set; Construct an initial power change curve prediction model, and train the initial power change curve prediction model according to the historical power change data set to obtain a trained power change curve prediction model.

[0025] In the embodiments of this specification, when training and constructing a predicted power change curve model through various theoretical power change curves, the historical operation data and the corresponding theoretical power change curves of the historical operation data can be integrated and paired one by one to obtain a historical power change data set. Then, an initial power change curve prediction model is constructed, and 70% of the data set in the obtained historical power change data set is used as the training set, and the remaining 30% of the data set is used as the test set. Further, first use the historical operation data in the training set as the model input and the corresponding theoretical power change curve of the historical operation data as the model output to train the initial power change curve prediction model, and then use the test set to optimize the trained power change curve prediction model to obtain a trained power change curve prediction model.

[0026] In an implementable manner, for the predicted power change curve model to determine the predicted power change curve corresponding to the real-time operation data of the target bus, it includes: Obtain the real-time operation data of the target bus, where the real-time operation data includes real-time vehicle speed, real-time passenger capacity, and real-time available power. Input the real-time operation data into the trained power change curve prediction model to determine the predicted power change curve of the target bus under the real-time operation data.

[0027] In the embodiments of this specification, when determining the predicted power change curve corresponding to the real-time operation data of the target bus through the power change curve prediction model, the real-time operation data of the target bus can be obtained through sensors and the bus battery management system. Among them, the real-time operation data includes real-time vehicle speed, real-time passenger capacity, and real-time available power. Optionally, the average vehicle speed of the target bus between the previous two stations can be used as the real-time vehicle speed, and the difference between the actual vehicle mass and the empty vehicle mass after leaving the platform can be used as the real-time passenger capacity. Similarly, the method for determining the historical available power is used to determine the real-time available power. First, the real-time remaining power is determined through the battery management system, and then the battery charging threshold is determined according to the charging habit data of the target bus. Finally, the difference between the real-time remaining power and the battery charging threshold is determined as the real-time available power.

[0028] Then, use the real-time operation data as the model input and transmit it to the trained power change curve prediction model, and the predicted power change curve of the target bus under the real-time operation data can be obtained.

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

[0030] Among them, the real-time torque distribution data includes the engine torque data and the 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 power change curve of the target bus under real-time operation data is determined, and then the corresponding real-time torque distribution data can be determined by combining the predicted power change curve with the minimum equivalent fuel consumption strategy. Specifically, since a hybrid bus can be driven by both an engine and a generator during driving, the equivalent fuel consumption during the charge and discharge of the generator can be added to the fuel consumption of the engine to obtain the total equivalent fuel consumption, and the torque distribution can be determined by the proportional-integral feedback control method or the fuzzy control method, so that the real-time power of the target bus changes according to the determined predicted power change curve, and then the engine torque data and generator torque data corresponding to the minimum total equivalent fuel consumption of the target bus under real-time operation data are obtained, that is, the real-time torque distribution data.

[0032] In an implementable manner, determining the real-time torque distribution data corresponding to the predicted power change curve includes: Determining a target equivalent factor based on the predicted power change curve, where the target equivalent factor is used to control the actual power change of the target bus; Determining the real-time torque distribution data corresponding to the target equivalent factor according to the minimum total equivalent fuel consumption strategy.

[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 be first determined through the predicted power change curve. Among them, this target equivalent factor is used to equivalent the electric energy consumption of the generator to fuel consumption, and from the perspective of controlling the minimum total equivalent fuel consumption, the actual power change of the target bus is further controlled, so that the actual power change curve of the target bus is as consistent as possible with the predicted power change curve, ensuring that the total equivalent fuel consumption corresponding to the real-time torque distribution of the target bus according to the predicted power change curve is the minimum. Then, the real-time torque distribution data corresponding to the target equivalent factor in the determined case is determined according to the minimum total equivalent fuel consumption strategy.

[0034] In an implementable manner, determining the target equivalent factor based on the predicted power change curve includes: Determining the power change error between the predicted power change curve and the actual power change curve; [[ID=!19]]Determining the target equivalent factor corresponding to the power change error based on the proportional-derivative control method.

[0035] In the embodiments of this specification, when determining the target equivalent factor through the predicted 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 in the PID controller, that is, 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 implementable manner, determining the real-time torque distribution data corresponding to the target equivalent factor according to the total equivalent fuel consumption minimization strategy includes: Determining the calculation formula for the total equivalent fuel consumption of the target bus; Based on the total fuel consumption calculation formula and the target equivalent factor, determining the real-time torque distribution data corresponding to the minimum total equivalent fuel consumption.

[0037] In the embodiments of this specification, the calculation formula for the total equivalent fuel consumption of the target bus under the real-time operation data is determined as follows: Among them, represents the total equivalent fuel consumption; represents the engine fuel consumption; represents the equivalent fuel consumption during generator discharge; represents the equivalent fuel consumption during generator charging; represents the working state of the generator. When the generator drives the vehicle, its value is 1, otherwise it is 0; , respectively represent the target equivalent factors during driving and charging; represents the battery efficiency; represents the low calorific value of fuel; represents the generator power.

[0038] Next, substituting the determined target equivalent factor into the total fuel consumption calculation formula and adding constraint conditions thereto, which may include the speed limits and torque limits of the engine and generator, as well as the working current and voltage limits of the battery. Further, under the condition that the constraint conditions are fixed, the target equivalent factor, the battery efficiency, and the low calorific value of fuel are determined, the generator power corresponding to the minimum total equivalent fuel consumption is determined according to the extreme value algorithm, and finally, the engine torque data and the generator torque data are inversely deduced through the generator power and the total demand torque of the target bus, that is, the real-time torque distribution data.

[0039] The above describes 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 can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain implementations, multitasking and parallel processing are also possible or may be advantageous.

[0040] Next, please refer to Figure 2 , Figure 2 which shows a schematic structural diagram of a torque distribution device for a hybrid bus provided by an embodiment of this specification. It should be noted that Figure 2 the torque distribution device for the hybrid bus shown is used to execute the method of the embodiment of this application Figure 1 shown. For the sake of convenience of description, only the parts related to the embodiment of this application are shown. For those specific technical details not disclosed, please refer to the embodiment Figure 1 shown in this application

[0041] As Figure 2 shown, the torque distribution device for the hybrid bus may at least include: A calculation module 201, configured to calculate, based on a dynamic programming algorithm, theoretical power change curves corresponding to the historical operation data of different buses respectively. The historical operation data includes historical vehicle speed, historical passenger capacity, 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 target bus. The historical available power is the difference between the historical remaining power and the battery charging threshold, and the battery charging threshold is determined based on the charging habit data corresponding to the bus; A prediction module 202, configured to determine a predicted power change curve corresponding to the real-time operation data of the target bus based on a power change curve prediction model, and the power change curve prediction model is trained and constructed from the theoretical power change curves; A determination module 203, configured to determine real-time torque distribution data corresponding to the predicted power change curve, and the real-time torque distribution data includes engine torque data and generator torque data corresponding to the minimum total equivalent fuel consumption of the target bus.

[0042] In an implementable manner, the calculation module 201 is specifically configured to: Determine the charging habit data corresponding to the bus based on the model information of the bus; Determine the battery charging threshold corresponding to the charging habit data according to a battery charging threshold prediction model.

[0043] In an implementable manner, the prediction module 202 is specifically configured to: Integrate the historical operation data and the theoretical power change curves corresponding to the historical operation data to obtain a historical power change data set; Construct an initial power change curve prediction model, and train the initial power change curve prediction model according to the historical power change data set to obtain a trained power change curve prediction model.

[0044] In one implementable manner, the prediction module 202 is further specifically configured to: Obtain the real-time operation data of the target bus, where the real-time operation data includes real-time vehicle speed, real-time passenger capacity, and real-time available power; Input the real-time operation data into the trained power change curve prediction model to determine the predicted power change curve of the target bus under the real-time operation data.

[0045] In one implementable manner, the determination module 203 is specifically configured to: Determine a target equivalent factor based on the predicted power change curve, where the target equivalent factor is used to control the actual power change of the target bus; Determine the real-time torque distribution data corresponding to the target equivalent factor according to the total equivalent fuel consumption minimum strategy.

[0046] In one implementable manner, the determination module 203 is further specifically configured to: Determine the power change error between the predicted power change curve and the actual power change curve; Determine the target equivalent factor corresponding to the power change error based on the proportional derivative control method.

[0047] In one implementable manner, the determination module 203 is further specifically configured to: Determine the total equivalent fuel consumption calculation formula of the target bus; Determine the real-time torque distribution data corresponding to the minimum total equivalent fuel consumption based on the total fuel consumption calculation formula and the target equivalent factor.

[0048] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented by means of software and / or hardware. The "units" and "modules" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), etc.

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

[0050] Next, please refer to Figure 3 , Figure 3 which shows a schematic structural diagram of an electronic device provided by an embodiment of this specification.

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

[0052] Among them, the communication bus 302 can be used to implement the connection and communication of the above-mentioned components.

[0053] Among them, the user interface 303 may include buttons. Optionally, the user interface may further include a standard wired interface and a wireless interface.

[0054] Among them, the network interface 304 can but is not limited to including a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0055] Among them, the device processor 301 may include one or more processing cores. The device processor 301 uses various interfaces and lines to connect all parts within the entire electronic device 300, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, as well as calling data stored in the memory 305, it executes various functions of the electronic device 300 and processes data. Optionally, the device processor 301 can be implemented in at least one of the hardware forms of DSP, FPGA, and PLA. The device processor 301 can integrate one or a combination of several of CPU, GPU, and modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the device processor 301 and can be implemented separately through a single chip.

[0056] Among them, the memory 305 may include RAM and may also include ROM. Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can 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. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area can store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned device processor 301. As Figure 3 shown, the memory 305, 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 torque distribution application program for hybrid buses stored in the memory 305, and specifically perform the following operations: Based on the dynamic programming algorithm, calculate the theoretical power change curves corresponding to the historical operation data of different buses respectively. The historical operation data includes historical vehicle speed, historical passenger capacity, and historical available power. The theoretical power change curve is used to represent 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, and the battery charging threshold is determined based on the charging habit data corresponding to the bus; Based on the power change curve prediction model, determine the predicted power change curve corresponding to the real-time operation data of the target bus. The power change curve prediction model is trained and constructed from the respective 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 the generator torque data corresponding to the minimum total equivalent fuel consumption of the target bus.

[0058] As an option of the embodiment of this specification, the method further includes: Determine the charging habit data corresponding to the bus based on the model information of the bus; Determine the battery charging threshold corresponding to the charging habit data according to the battery charging threshold prediction model.

[0059] As an option of the embodiment of this specification, the method further includes: Integrate the historical operation data and the theoretical power change curves corresponding to the historical operation data to obtain a historical power change data set; Construct an initial power change curve prediction model, and train the initial power change curve prediction model according to the historical power change data set to obtain a trained power change curve prediction model.

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

[0061] As an optional embodiment of this specification, determining the real-time torque distribution data corresponding to the predicted power change curve includes: Determining a target equivalent factor based on the predicted power change curve, wherein 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 according to the total equivalent fuel consumption minimum strategy.

[0062] As an optional embodiment of this specification, determining the target equivalence factor based on the predicted power change curve includes: Determining an electric quantity change error between the predicted electric quantity change curve and the actual electric quantity change curve; A target equivalent factor corresponding to the electric quantity variation error is determined based on a proportional differential control method.

[0063] As an option in the embodiment of this specification, the determining of the real-time torque distribution data corresponding to the target equivalent factor according to the total equivalent fuel consumption minimum strategy includes: Determine a calculation formula for the total equivalent fuel consumption of the target bus; The real-time torque distribution data corresponding to the minimum total equivalent fuel consumption is determined based on the total fuel consumption calculation formula and the target equivalent factor.

[0064] The embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above method. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic or optical card, a nanosystem (including a molecular memory IC), or any other type of medium or device suitable for storing instructions and / or data.

[0065] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0066] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0067] In several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

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

[0069] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0070] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. And the aforementioned memory includes: USB flash drives, read-only memory (ROM), random access memory (RAM), mobile hard disks, magnetic disks, or optical disks, etc., which can store program codes.

[0071] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory. The memory can include: flash drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, etc.

[0072] The foregoing describes particular embodiments of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired result. In certain implementations, multitasking and parallel processing are also 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, calculate the theoretical power change curves corresponding to the historical operation data of different buses respectively. The historical operation data includes historical vehicle speed, historical passenger capacity, 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, and the battery charging threshold is determined based on the charging habit data corresponding to the bus. Based on the power change curve prediction model, determine the predicted power change curve corresponding to the real-time operation data of the target bus. The power change curve prediction model is trained and constructed by each of 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 the 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: Determine the charging habit data corresponding to the bus based on the model information of the bus. Determine the battery charging threshold corresponding to the charging habit data according to the battery charging threshold prediction model.

3. The method according to claim 1, wherein The method further includes: Integrate each of the historical operation data and the theoretical power change curves corresponding to each of the historical operation data to obtain a historical power change data set. Construct an initial power change curve prediction model, and train the initial power change curve prediction model according to the historical power change data set to obtain a trained power change curve prediction model.

4. The method according to claim 3, wherein The determining the predicted power change curve corresponding to the real-time operation data of the target bus based on the power change curve prediction model includes: Obtain the real-time operation data of the target bus. The real-time operation data includes real-time vehicle speed, real-time passenger capacity, and real-time available power. Input the real-time operation data into the trained power change curve prediction model to determine the predicted power change curve of the target bus under the real-time operation data.

5. The method according to claim 1, wherein The determining the real-time torque distribution data corresponding to the predicted power change curve includes: Determine a target equivalent factor based on the predicted power change curve. The target equivalent factor is used to control the actual power change of the target bus. Determine the real-time torque distribution data corresponding to the target equivalent factor according to the minimum total equivalent fuel consumption strategy.

6. The method according to claim 5, characterized in that, The determining the target equivalent 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. Determine the target equivalent factor corresponding to the power change error based on the proportional derivative control method.

7. The method according to claim 5, wherein The determining the real-time torque distribution data corresponding to the target equivalent factor according to the minimum total equivalent fuel consumption strategy includes: Determine the total equivalent fuel consumption calculation formula of the target bus. Determine the real-time torque distribution data corresponding to the minimum total equivalent fuel consumption based on the total fuel consumption calculation formula and the target equivalent factor.

8. A torque distribution device for a hybrid bus, characterized in that, The device includes: A calculation module, configured to calculate theoretical power change curves corresponding to historical operation data of different buses respectively based on a dynamic programming algorithm, where the historical operation data includes historical vehicle speed, historical passenger capacity, and historical available power, the theoretical power change curve is used to represent 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, and the battery charging threshold is determined based on the charging habit data corresponding to the bus; A prediction module, configured to determine a predicted power change curve corresponding to the real-time operation data of the target bus based on a power change curve prediction model, where the power change curve prediction model is trained and constructed from the theoretical power change curves; A determination module, configured to determine real-time torque distribution data corresponding to the predicted power change curve, where the real-time torque distribution data includes engine torque data and generator torque data corresponding to the minimum total equivalent fuel consumption of the target bus.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1-7 are implemented.

10. A computer-readable storage medium, on which a computer program is stored, where instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer or a processor, the computer or the processor is caused to execute the steps of the method according to any one of claims 1-7.

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