Battery management method and device of electric bicycle, electronic equipment and storage medium

Abstract

The invention provides a battery management method and device of an electric bicycle, electronic equipment and a storage medium. The method comprises the steps of obtaining battery performance attenuation data of a vehicle-mounted battery from vehicle-mounted BMS data; determining a battery real-time state of health percentage of the in-vehicle battery based on the battery performance degradation data; determining a working condition stress coefficient of the vehicle-mounted battery based on the high-rate discharge impact event and the deep discharge cycle event of the target electric bicycle; and performing scrap management on the vehicle-mounted battery based on the product of the working condition stress coefficient and the real-time health state percentage of the battery. The working condition stress coefficient is determined through the determined battery real-time health state percentage, the high-rate discharge impact event and the deep discharge cycle event, the complex working condition influence in the battery use process is comprehensively determined, the situation that a high-value battery is scrapped in advance due to a single factor or one-sided judgment is avoided, the use efficiency of the battery is improved, and the service life of the battery is prolonged. And optimal configuration of resources is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, and in particular to a battery management method and device for electric scooters, electronic equipment and storage medium. BACKGROUND

[0002] For short-term rental electric scooters or shared electric scooters, the battery has a non-uniform aging problem caused by high-frequency charging and discharging and multiple user abuses.

[0003] Due to the non-uniform aging of the battery of the electric scooter, it is difficult to assess the health of the battery. The existing battery scrap management process of the electric scooter is a rough batch processing, and after reaching the service time, it is uniformly scrapped. The existing battery management method of the electric scooter will cause high-value batteries to be prematurely scrapped. SUMMARY

[0004] The present application provides a battery management method and device for electric scooters, electronic equipment and storage medium, to solve the problem that the existing battery scrap management process of the electric scooter is a rough batch processing, and after reaching the service time, it is uniformly scrapped, resulting in high-value batteries being prematurely scrapped. The present application realizes the fine management process of the battery.

[0005] The present application provides a battery management method for electric scooters, comprising the following steps: Obtain the vehicle-mounted battery management system (BMS) data of the target electric scooter, and obtain the battery performance attenuation data of the vehicle-mounted battery from the vehicle-mounted BMS data, wherein the battery performance attenuation data includes cycle count data, battery temperature curve data and battery internal resistance change data; Determine the real-time health status percentage of the vehicle-mounted battery based on the battery performance attenuation data; Determine the working condition stress coefficient of the vehicle-mounted battery based on the high-rate discharge impact event and the deep discharge cycle event of the target electric scooter; Perform scrap management on the vehicle-mounted battery based on the product of the working condition stress coefficient and the real-time health status percentage of the battery.

[0006] According to the battery management method for electric scooters provided by the present application, the deep discharge cycle event is determined based on the state of charge (SOC) value of the vehicle-mounted battery being less than a preset SOC value; The high-rate discharge impact event is determined based on the discharge current of the vehicle-mounted battery exceeding a preset multiple of the rated capacity of the battery and the duration exceeding a preset duration.

[0007] According to the battery management method for electric scooters provided by the present application, determining the working condition stress coefficient of the vehicle-mounted battery based on the high-rate discharge impact event and the deep discharge cycle event of the target electric scooter comprises: Based on the vehicle-mounted BMS data, the number of high-rate discharge impact events and the number of deep discharge cycle events in the use process of the target electric bicycle are obtained; The number of high-rate discharge impact events and the number of deep discharge cycle events are weighted to obtain weighted data, and the weighted data is mapped to a coefficient interval to obtain the working condition stress coefficient of the vehicle-mounted battery.

[0008] According to the battery management method of the electric bicycle provided by the application, the battery performance attenuation data is input into a prediction model to obtain the battery real-time health state percentage output by the prediction model. The battery performance attenuation data is input into a prediction model to obtain the battery real-time health state percentage output by the prediction model. The prediction model is trained based on battery performance attenuation sample data and the battery health state percentage label corresponding to the battery performance attenuation sample data.

[0009] According to the battery management method of the electric bicycle provided by the application, the working condition stress coefficient and the battery real-time health state percentage are multiplied to manage the vehicle-mounted battery. Based on the numerical relationship between the product and the preset range, the vehicle-mounted battery is classified and scrapped. The preset range is determined based on the energy storage supply and demand data in the battery scrapping process.

[0010] According to the battery management method of the electric bicycle provided by the application, the working condition stress coefficient and the battery real-time health state percentage are multiplied to manage the vehicle-mounted battery. In the case where the product is greater than the preset range, the vehicle-mounted battery is used for low-speed electric bicycles. In the case where the product is in the preset range, the vehicle-mounted battery is disassembled into an energy storage module. In the case where the product is less than the preset range, the vehicle-mounted battery is disassembled and recycled.

[0011] The application also provides a battery management device for an electric bicycle, comprising the following modules: The data acquisition module is used for acquiring the vehicle-mounted battery management system (BMS) data of a target electric bicycle, and acquiring the battery performance attenuation data of the vehicle-mounted battery from the vehicle-mounted BMS data. The battery health determination module is used for determining the battery real-time health state percentage of the vehicle-mounted battery based on the battery performance attenuation data. A coefficient determination module is configured to determine a working condition stress coefficient of the vehicle-mounted battery based on high-rate discharge impact events and deep discharge cycle events of the target electric bicycle. A battery management module is configured to perform retirement management on the vehicle-mounted battery based on a product of the working condition stress coefficient and the real-time health state percentage of the battery.

[0012] The application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the battery management method of the electric bicycle according to any one of the above when executing the program.

[0013] The application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and the computer program implements the battery management method of the electric bicycle according to any one of the above when executed by a processor.

[0014] The application further provides a computer program product comprising a computer program, and the computer program implements the battery management method of the electric bicycle according to any one of the above when executed by a processor.

[0015] The battery management method, device, electronic device, and storage medium of the electric bicycle provided by the application can truly reflect the current performance of the battery by obtaining battery performance attenuation data and accurately determining the real-time health state percentage. Meanwhile, the working condition stress coefficient is determined based on high-rate discharge impact events and deep discharge cycle events, and the complex working condition influence in the use process of the battery is comprehensively determined. Finally, whether the battery is retired is determined by a comprehensive index of the product of the two, which avoids the high-value battery from being prematurely retired due to a single factor or one-sided judgment, improves the use efficiency of the battery, reduces the operation cost, and optimizes the allocation of resources. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0017] Figure 1 is a flowchart of the battery management method of the electric bicycle provided by the application.

[0018] Figure 2 is a structural schematic diagram of the battery management device of the electric bicycle provided by the application.

[0019] Figure 3 is a structural schematic diagram of the electronic device provided by the application. DETAILED DESCRIPTION

[0020] The technical solutions and advantages of the present application will be described in detail below with reference to the drawings of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0021] Figure 1 The flowchart of the battery management method of the electric bicycle provided by the present application is shown in FIG. 1, which comprises the following steps: Figure 1 Step 110, obtaining the on-board battery management system (BMS) data of the target electric bicycle, and obtaining the battery performance attenuation data of the on-board battery from the on-board BMS data, wherein the battery performance attenuation data comprises cycle count data, battery temperature curve data and battery internal resistance change data; Step 120, determining the real-time health state percentage of the on-board battery based on the battery performance attenuation data; Step 130, determining the working condition stress coefficient of the on-board battery based on the high-rate discharge impact event and the deep discharge cycle event of the target electric bicycle; Step 140, performing scrap management on the on-board battery based on the product of the working condition stress coefficient and the real-time health state percentage of the battery.

[0022] The execution subject of the battery management method of the electric bicycle provided by the present application can be an electronic device, a component in the electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. For example, the mobile electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), and the non-mobile electronic device can be a server, a network attached storage (NAS), or a personal computer (PC), without specific limitation.

[0023] The technical solutions of the present application will be described in detail below with reference to the drawings of the present application.

[0024] ​In step 110, the vehicle-mounted battery management system (BMS) data of the target electric bicycle is obtained, and the battery performance degradation data of the vehicle-mounted battery is obtained from the vehicle-mounted BMS data.

[0025] The vehicle-mounted BMS data of the target electric bicycle can be obtained based on the data collection equipment set in the target electric bicycle. These devices or software establish a communication connection with the BMS system of the electric bicycle. The common communication methods include CAN bus communication. Through a specific communication protocol, the stored data in the BMS is read. After obtaining the battery performance degradation data, it is regularly uploaded to the cloud server. After the cloud server receives the regularly uploaded battery performance degradation data, it is used for subsequent analysis.

[0026] From the obtained BMS data, the data related to battery performance degradation is screened out. Among them, the battery performance degradation data specifically includes cycle count data, battery temperature curve data, and battery internal resistance change data.

[0027] Cycle count data: BMS records the number of charge and discharge cycles of the battery. Each complete charging and discharging process is counted as one cycle. By counting this data, the use intensity of the battery can be understood. Because with the increase of cycle count, the chemical reaction inside the battery will change irreversibly, causing the battery performance to gradually degrade.

[0028] Battery temperature curve data: The battery generates heat during charging and discharging, and temperature changes will affect the performance and life of the battery. BMS monitors the temperature of the battery in real time and records the temperature curve over time. For example, in a high-temperature environment, the chemical reaction speed of the battery increases, but at the same time, the aging of the internal materials of the battery is accelerated, causing the battery capacity to decrease.

[0029] Battery internal resistance change data: Battery internal resistance is an important indicator of battery performance. With the use of the battery, the internal resistance will gradually increase. BMS can obtain the internal resistance data of the battery through specific measurement methods such as alternating current impedance method or direct current internal resistance measurement method, and record its change. The increase of internal resistance will cause the energy loss of the battery during charging and discharging to increase, and the output power to decrease.

[0030] In step 120, based on the battery performance degradation data, the real-time health status percentage of the vehicle-mounted battery is determined.

[0031] Based on the battery performance degradation data, an evaluation model of the State of Health (SOH) of the battery is established in advance. A data-driven method such as a machine learning algorithm can be used. The cycle number, battery temperature curve, and battery internal resistance change are used as input features, and the SOH values of the battery in different states obtained by laboratory tests are used as output labels to train a regression model. For example, using a neural network algorithm, a large amount of historical data is used for training, so that the model can accurately predict the SOH of the battery according to the input data.

[0032] The real-time battery performance degradation data is input into the trained evaluation model to obtain the real-time health status percentage of the battery. This percentage reflects the current performance of the battery relative to the performance of a new battery. For example, if the calculated SOH is 80%, it means that the current performance of the battery is only 80% of that of a new battery.

[0033] In step 130, based on the high-rate discharge impact event and the deep discharge cycle event of the target e-bike, the working condition stress coefficient of the vehicle-mounted battery is determined.

[0034] Through the BMS data, the high-rate discharge impact event and the deep discharge cycle event of the target e-bike can be counted. The high-rate discharge impact event usually refers to the discharge of the battery at a large current in a short time, such as when the e-bike accelerates rapidly. The deep discharge cycle event refers to the discharge of the battery to a low State of Charge (SOC) level, such as discharge to below 20% of the battery SOC.

[0035] According to the influence degree of the high-rate discharge impact event and the deep discharge cycle event on the battery performance, a calculation model of the working condition stress coefficient is established. For example, the weight of the high-rate discharge impact event can be set as a, and the weight of the deep discharge cycle event can be set as b, and then the weighted calculation is performed according to the number of events, weighted result = a×high-rate discharge impact event number + b×deep discharge cycle event number. The weights a and b can be determined by experiments or empirical data. Based on the weighted result, the working condition stress coefficient is determined. The determined working condition stress coefficient can be a value between 0 and 1.

[0036] The product of the working condition stress coefficient and the real-time health status percentage of the battery is taken as a comprehensive index. This comprehensive index takes into account the performance degradation of the battery and the influence of the actual use working condition. For example, if the working condition stress coefficient is 0.8 and the real-time health status percentage of the battery is 80%, then the comprehensive index is 0.8×80% =64%.

[0037] In step 140, based on the product of the working condition stress coefficient and the real-time health status percentage of the battery, the vehicle-mounted battery is managed for retirement.

[0038] The battery is scrapped according to the comprehensive index. When the comprehensive index is lower than a certain set threshold, it is determined that the battery needs to be scrapped.

[0039] For example, the threshold is set to 70%, and when the comprehensive index is lower than 70%, it means that the performance of the battery has failed to meet the normal use requirements of the electric bicycle, and needs to be scrapped. At the same time, the battery can be managed according to the value of the comprehensive index, and for the battery close to the scrapping standard, early warning and maintenance are carried out.

[0040] The battery management method of the electric bicycle provided by the application can truly reflect the current performance of the battery by obtaining battery performance degradation data and accurately determining the real-time health state percentage. At the same time, the working condition stress coefficient is determined based on the high-rate discharge impact event and the deep discharge cycle event, and the complex working condition influence of the battery in the use process is comprehensively determined. Finally, the comprehensive index of the product of the two is used to judge whether the battery is scrapped, which avoids the high-value battery from being scrapped in advance due to a single factor or one-sided judgment, improves the use efficiency of the battery, reduces the operation cost, and realizes the optimal allocation of resources.

[0041] In one embodiment, the deep discharge cycle event is determined based on the state of charge SOC value of the vehicle-mounted battery being less than a preset SOC value; and the high-rate discharge impact event is determined based on the discharge current of the vehicle-mounted battery exceeding a preset multiple of the rated capacity of the battery and the duration exceeding a preset duration.

[0042] When the battery is deeply discharged, the internal chemical reaction deviates from the normal range, causing changes in the structure of the electrode material and accelerated loss of active substances. The preset SOC value is determined by experiments on the vehicle-mounted battery of the target electric bicycle. When the battery is discharged to below this value, it means that the battery has entered a deep discharge state. For example, if the preset SOC value is 20%, when the battery SOC is lower than this value, the problems such as uneven distribution of electrolyte concentration and electrode polarization in the battery will be more prominent, and long-term use will seriously shorten the battery life.

[0043] The high-rate discharge impact event is determined according to that the discharge current exceeds the preset multiple of the rated capacity of the battery and the duration exceeds the preset duration. The high-rate discharge can generate a large amount of heat inside the battery, causing the temperature of the battery to rise sharply and accelerating the aging of the battery. At the same time, the large current can also accelerate the ion migration speed inside the battery, affecting the safety and performance of the battery. By setting the current multiple and duration threshold, the high-rate discharge impact event that is potentially harmful to the battery can be effectively identified, so that measures can be taken in time to protect the battery and ensure the normal operation of the electric bicycle. The specific conditions can be determined based on experiments on the on-board battery of the target electric bicycle. For example, when the discharge current continuously exceeds x times (for example, x = 1.5, that is, I > 1.5C) of the rated capacity C of the on-board battery and the duration exceeds y seconds (for example, y = 2 seconds), it can be determined that the on-board battery is in a high-rate discharge impact event.

[0044] In one embodiment, based on the high-rate discharge impact event and the deep discharge cycle event of the target electric bicycle, the working condition stress coefficient of the on-board battery is determined, including: based on the on-board BMS data, the number of high-rate discharge impact events and the number of deep discharge cycle events during the use of the target electric bicycle are obtained; the number of high-rate discharge impact events and the number of deep discharge cycle events are weighted to obtain weighted data, and the weighted data is mapped to a coefficient interval to obtain the working condition stress coefficient of the on-board battery.

[0045] After obtaining the number of high-rate discharge impact events and the number of deep discharge cycle events during the use of the target electric bicycle, the number of events is weighted and mapped to a coefficient interval, which can reflect the influence of the actual working condition on the battery.

[0046] Different events have different degrees of damage to the battery, and weighting processing can highlight the influence of key events. Mapping to a coefficient interval converts complex event data into intuitive coefficients, which is convenient for subsequent combination with the battery health state to evaluate the battery state. The key factors in the use process of the battery are comprehensively considered, which can more accurately quantify the working condition stress.

[0047] Optionally, the determination of the working condition stress coefficient can be accumulated for all high-rate discharge impact events and deep discharge cycle events during the use of the target electric bicycle.

[0048] The evaluation value S_power of the high-rate discharge impact event is determined as follows: S_power = k_p x (|I_avg - I_threshold|) 2 x Δt; Wherein, k p is the power impact coefficient, a weight constant that can be calibrated; I avg is the average current during the use of the electric bicycle; I threshold: current threshold triggering the event (such as 1.5C); Δt is the duration of the event.

[0049] An exponential decay function is introduced to accumulate all high-rate discharge impact events and deep discharge cycle events, and a Sigmoid function is used to map the stress to a reasonable coefficient interval, obtaining the working condition stress coefficient.

[0050] In one embodiment, based on the battery performance degradation data, the battery real-time health state percentage of the vehicle-mounted battery is determined, including: inputting the battery performance degradation data into a prediction model to obtain the battery real-time health state percentage output by the prediction model; wherein the prediction model is trained based on battery performance degradation sample data and the battery health state percentage label corresponding to the battery performance degradation sample data.

[0051] Based on a large amount of battery performance degradation sample data and the battery health state percentage labels corresponding to these sample data, training is performed. The sample and the corresponding label can be obtained based on laboratory-level pulse testing.

[0052] The battery performance degradation sample data covers key information such as cycle number, temperature change curve, and internal resistance change, and the corresponding battery health state percentage label is accurately determined by professional testing means. By continuously adjusting the model parameters, the model learns the internal relationship between the performance degradation data and the health state percentage.

[0053] In actual application, the battery performance degradation data obtained in real time is input into the trained prediction model, and the battery real-time health state percentage is output. This percentage reflects the current performance of the battery relative to the state of a new battery, providing an important basis for the management and maintenance of the battery. For example, when the health state percentage is below a certain threshold, the battery can be maintained or replaced in time to avoid safety problems caused by battery failure.

[0054] In one embodiment, based on the product of the working condition stress coefficient and the battery real-time health state percentage, the vehicle-mounted battery is managed for retirement, including: based on the numerical relationship between the product and a preset range, the vehicle-mounted battery is managed for graded retirement; wherein the preset range is determined based on the energy storage supply and demand data in the battery retirement process.

[0055] Based on the numerical relationship between the product and the preset range, the vehicle battery is subject to graded scrapping management, including: when it is determined that the product is greater than the preset range, the vehicle battery is used for a low-speed electric motorcycle; when it is determined that the product is within the preset range, the vehicle battery is disassembled into energy storage modules; when it is determined that the product is less than the preset range, the vehicle battery is disassembled and recycled.

[0056] The graded scrapping management method uses the product of the operating stress coefficient and the real-time health status percentage of the battery as the key indicator. The product comprehensively considers the operating conditions and actual performance degradation during battery use.

[0057] The preset range is determined based on the energy storage supply and demand data during the battery scrapping process, which makes the scrapping standard more in line with actual needs and avoids resource waste and safety hazards caused by blind scrapping or excessive use.

[0058] When the product is greater than the preset range, it indicates that while the battery has experienced some use and operating conditions, its overall performance still meets the requirements of low-speed electric motorcycles. In this case, reusing the vehicle's battery for low-speed electric motorcycles achieves a second-life battery system. Low-speed electric motorcycles place relatively low performance requirements on batteries, allowing these batteries to continue functioning in new usage scenarios, extending their lifespan and improving resource utilization.

[0059] If the product is within a preset range, the battery's performance is no longer sufficient for normal use in devices like electric motorcycles. However, the energy storage elements within the battery still have some energy storage capacity. Disassembling the battery into energy storage modules can enable it to be used in energy storage scenarios where performance requirements are less stringent, such as home energy storage and small distributed energy storage systems. This disposal method further taps into the battery's residual value and reduces the environmental impact of battery disposal.

[0060] When the product falls below a preset range, it indicates that the battery's performance has severely degraded and can no longer be effectively reused or used as an energy storage module. Disassembling the vehicle battery to recover rare metals and recycle the valuable metals and materials within the battery not only meets environmental requirements but also reduces the cost of raw materials for battery production.

[0061] The following describes the battery management device for an electric motorcycle provided by the present invention. The battery management device for an electric motorcycle described below and the battery management method for an electric motorcycle described above can be referenced to each other.

[0062] like Figure 2 As shown, the device includes: The data acquisition module 210 is configured to acquire battery management system (BMS) data of a target electric bicycle, and acquire battery performance attenuation data of a battery from the BMS data, wherein the battery performance attenuation data comprises cycle count data, battery temperature curve data and battery internal resistance change data. The battery health determination module 220 is configured to determine a real-time battery health percentage of the battery based on the battery performance attenuation data. The coefficient determination module 230 is configured to determine a working condition stress coefficient of the battery based on a high-rate discharge impact event and a deep discharge cycle event of the target electric bicycle. The battery management module 240 is configured to perform retirement management on the battery based on a product of the working condition stress coefficient and the real-time battery health percentage.

[0063] The battery management device for the electric bicycle provided by the application can accurately determine the real-time health percentage by acquiring the battery performance attenuation data, and can truly reflect the current performance of the battery. The working condition stress coefficient is determined based on the high-rate discharge impact event and the deep discharge cycle event, and the complex working condition influence in the use process of the battery is comprehensively determined. Finally, whether the battery is retired is determined by the comprehensive index of the product of the two, which avoids the high-value battery from being retired in advance due to a single factor or one-sided judgment, improves the use efficiency of the battery, reduces the operation cost, and realizes the optimal allocation of resources.

[0064] In one embodiment, the coefficient determination module 230 is specifically configured to: determine the deep discharge cycle event based on a state of charge (SOC) value of the battery being less than a preset SOC value; determine the high-rate discharge impact event based on a discharge current of the battery exceeding a preset multiple of a rated capacity of the battery and a duration exceeding a preset duration.

[0065] In one embodiment, the coefficient determination module 230 is further specifically configured to: determine the working condition stress coefficient of the battery based on the high-rate discharge impact event and the deep discharge cycle event of the target electric bicycle, comprising: acquiring, based on the BMS data, a number of high-rate discharge impact events and a number of deep discharge cycle events in a use process of the target electric bicycle; weighting the number of high-rate discharge impact events and the number of deep discharge cycle events to obtain weighted data, and mapping the weighted data to a coefficient interval to obtain the working condition stress coefficient of the battery.

[0066] In one embodiment, the battery health determination module 220 is specifically configured to: determining, based on the battery performance degradation data, a battery real-time state of health percentage of the vehicle-mounted battery, includes: inputting the battery performance degradation data into a prediction model to obtain a battery real-time state of health percentage output by the prediction model; The prediction model is trained based on battery performance degradation sample data and the battery state of health percentage label corresponding to the battery performance degradation sample data.

[0067] In one embodiment, the battery management module 240 is specifically used for: The product of the working condition stress coefficient and the battery real-time state of health percentage is used to manage the vehicle-mounted battery for scrapping, including: Based on the numerical relationship between the product and the preset range, the vehicle-mounted battery is managed for graded scrapping; The preset range is determined based on energy storage supply and demand data during battery scrapping.

[0068] In one embodiment, the battery management module 240 is specifically used for: The product of the working condition stress coefficient and the battery real-time state of health percentage is used to manage the vehicle-mounted battery for graded scrapping, including: In the case where the product is greater than the preset range, the vehicle-mounted battery is used for low-speed electric bicycles; In the case where the product is within the preset range, the vehicle-mounted battery is disassembled into energy storage modules; In the case where the product is less than the preset range, the vehicle-mounted battery is disassembled and recycled.

[0069] Figure 3 An example of an electronic device entity structure diagram is shown in Figure 3 As shown, the electronic device can include a processor 310, a communications interface 320, a memory 330, and a communications bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other through the communications bus 340. The processor 310 can invoke the logical instructions in the memory 330 to execute the battery management method of the electric bicycle, which includes: obtaining vehicle-mounted battery management system (BMS) data of a target electric bicycle, and obtaining battery performance degradation data of the vehicle-mounted battery from the vehicle-mounted BMS data, the battery performance degradation data including cycle count data, battery temperature curve data, and battery internal resistance change data; determining, based on the battery performance degradation data, a battery real-time state of health percentage of the vehicle-mounted battery, includes: determine a working condition stress coefficient of the vehicle-mounted battery based on the high-rate discharge impact event and the deep discharge cycle event of the target e-bike; manage the vehicle-mounted battery for scrapping based on a product of the working condition stress coefficient and the real-time health state percentage of the battery.

[0070] In addition, the logical instructions in the memory 530 described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a number of 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 method described in various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0071] On the other hand, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, so that the computer can execute the battery management method of the e-bike provided by the above-mentioned method, the method comprises: obtaining vehicle-mounted battery management system BMS data of a target e-bike, and obtaining battery performance attenuation data of a vehicle-mounted battery from the vehicle-mounted BMS data, the battery performance attenuation data includes cycle count data, battery temperature curve data and battery internal resistance change data; determine a real-time health state percentage of the battery based on the battery performance attenuation data; determine a working condition stress coefficient of the vehicle-mounted battery based on the high-rate discharge impact event and the deep discharge cycle event of the target e-bike; manage the vehicle-mounted battery for scrapping based on a product of the working condition stress coefficient and the real-time health state percentage of the battery.

[0072] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements a battery management method for an electric bicycle provided by any of the above methods, the method comprising: obtaining vehicle-mounted battery management system (BMS) data of a target electric bicycle, and obtaining battery performance degradation data of a vehicle-mounted battery from the vehicle-mounted BMS data, the battery performance degradation data comprising cycle count data, battery temperature curve data, and battery internal resistance change data; Based on the battery performance degradation data, determining a battery real-time health state percentage of the vehicle-mounted battery; Based on a high-rate discharge impact event and a deep discharge cycle event of the target electric bicycle, determining a working condition stress coefficient of the vehicle-mounted battery; Based on a product of the working condition stress coefficient and the battery real-time health state percentage, performing retirement management on the vehicle-mounted battery.

[0073] The device embodiments described above are merely illustrative, wherein the units illustrated as separate components can or can not be physically separated, and the components illustrated as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0074] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in terms of the contribution to the prior art, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0075] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A battery management method for an electric motorcycle, characterized in that: include: Obtaining the target motorcycle's onboard battery management system (BMS) data, and obtaining the onboard battery performance degradation data from the onboard BMS data, wherein the battery performance degradation data includes cycle number data, battery temperature curve data, and battery internal resistance change data; Determining a real-time battery health status percentage of the vehicle battery based on the battery performance degradation data; Determining the operating stress coefficient of the on-board battery based on the high-rate discharge shock event and deep discharge cycle event of the target electric motorcycle; The vehicle battery is scrapped based on the product of the working condition stress coefficient and the real-time health status percentage of the battery.

2. The battery management method for an electric motorcycle according to claim 1, characterized in that: The deep discharge cycle event is determined based on the state of charge (SOC) value of the vehicle battery being less than a preset SOC value; The high-rate discharge shock event is determined based on the discharge current of the vehicle battery exceeding a preset multiple of the battery rated capacity and lasting longer than a preset duration.

3. The battery management method for an electric motorcycle according to claim 1, characterized in that: The determining of the operating stress coefficient of the on-board battery based on the high-rate discharge shock event and the deep discharge cycle event of the target electric motorcycle includes: Based on the onboard BMS data, the number of high-rate discharge shock events and the number of deep discharge cycle events during the use of the target motorcycle are obtained; The number of high-rate discharge impact events and the number of deep discharge cycle events are weighted to obtain weighted data, and the weighted data is mapped to a coefficient interval to obtain the operating stress coefficient of the vehicle battery.

4. The battery management method for an electric motorcycle according to claim 1, characterized in that: The determining, based on the battery performance degradation data, the real-time health status percentage of the vehicle battery includes: Inputting the battery performance degradation data into a prediction model to obtain a real-time health status percentage of the battery output by the prediction model; The prediction model is obtained by training based on battery performance degradation sample data and battery health status percentage labels corresponding to the battery performance degradation sample data.

5. The battery management method for an electric motorcycle according to claim 1, characterized in that: The scrapping management of the vehicle battery based on the product of the working condition stress coefficient and the real-time health status percentage of the battery includes: Based on the numerical relationship between the product and a preset range, performing graded scrapping management on the vehicle battery; The preset range is determined based on energy storage supply and demand data during the battery scrapping process.

6. The battery management method for an electric motorcycle according to claim 5, characterized in that: The performing graded scrapping management on the vehicle battery based on the numerical relationship between the product and a preset range includes: If it is determined that the product is greater than a preset range, using the on-board battery for a low-speed electric motorcycle; If it is determined that the product is within the preset range, disassembling the vehicle battery into energy storage modules; When it is determined that the product is smaller than the preset range, the vehicle-mounted battery is dismantled and recycled.

7. A battery management device for an electric motorcycle, characterized in that: include: A data acquisition module is used to obtain the target motorcycle's onboard battery management system (BMS) data and obtain the battery performance degradation data of the onboard battery from the onboard BMS data. The battery performance degradation data includes cycle number data, battery temperature curve data, and battery internal resistance change data; a battery health determination module, configured to determine a real-time battery health status percentage of the vehicle battery based on the battery performance degradation data; A coefficient determination module, configured to determine the operating stress coefficient of the on-board battery based on a high-rate discharge shock event and a deep discharge cycle event of the target electric motorcycle; A battery management module is used to manage the vehicle battery for scrapping based on the product of the working condition stress coefficient and the real-time health status percentage of the battery.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the battery management method for the electric motorcycle as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the battery management method for an electric motorcycle as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the battery management method for an electric motorcycle as claimed in any one of claims 1 to 6 is implemented.

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