Method, device, processor and vehicle for controlling thermal management system of battery

Abstract

The application discloses a control method and device of a battery thermal management system, a processor and a vehicle. The method comprises the following steps: obtaining working condition data of a battery of the vehicle, wherein the working condition data is used to represent the working condition of the battery during the operation of the vehicle; extracting feature data of the battery from the working condition data, wherein the feature data is used to represent the performance condition of the corresponding battery; determining a target performance state of the battery based on the feature data; determining a control strategy of the battery based on the target performance state; and controlling the thermal management system of the battery to perform heating operation or cooling operation on the battery in response to the control instruction corresponding to the control strategy. The application solves the technical problem of low accuracy of heating or cooling of the battery.

Description

Technical Field

[0001] This invention relates to the field of vehicles, and more specifically, to a control method, apparatus, processor, and vehicle for a battery thermal management system. Background Technology

[0002] In related technologies, when the ambient temperature around the battery is too high or too low, the temperature of the battery can be raised or lowered by heating or cooling devices deployed around the battery. However, simple heating or cooling alone cannot accurately control the temperature, which leads to the inability to avoid the loss of battery life and performance caused by temperature in a timely manner. Therefore, there is still a technical problem of low accuracy in heating or cooling the battery.

[0003] There is currently no effective solution to the technical problem of low accuracy in heating or cooling batteries in the aforementioned related technologies. Summary of the Invention

[0004] This invention provides a control method, apparatus, processor, and vehicle for a battery thermal management system, to at least solve the technical problem of low accuracy in heating or cooling batteries.

[0005] According to one aspect of the present invention, a control method for a battery thermal management system is provided. The method may include: acquiring battery operating condition data of a vehicle, wherein the operating condition data represents the battery's operating status during vehicle operation; extracting battery feature data from the operating condition data, wherein the feature data characterizes the corresponding battery performance status; determining a target performance state of the battery based on the feature data; determining a control strategy for the battery based on the target performance state; and controlling the battery thermal management system to perform heating or cooling operations on the battery in response to a control command corresponding to the control strategy.

[0006] Optionally, feature data of the battery can be extracted from the operating condition data, including: feature extraction of the operating condition data to obtain power features, temperature features and / or vehicle features in the operating condition data, wherein the feature data includes, but is not limited to, power features, temperature features and vehicle features.

[0007] Optionally, feature extraction is performed on the operating condition data to obtain power features, temperature features, and / or vehicle features from the operating condition data, including: normalizing the power features, temperature features, and / or vehicle features to obtain the processing results; and determining the processing results as feature data.

[0008] Optionally, before determining the target performance state of the battery based on feature data, the method further includes: acquiring historical operating condition data of the battery of the target vehicle model; determining the performance state of the battery corresponding to the historical operating condition data; and establishing a relationship model between the performance state and the feature data of the historical operating condition data, wherein the relationship model is used to determine the target performance state from the performance state.

[0009] Optionally, determining the battery performance state corresponding to historical operating condition data includes: determining the initial capacity and target capacity of the battery from the historical operating condition data, and determining the performance state based on the initial capacity and target capacity, wherein the initial capacity is used to represent the capacity of the battery before it is charged and discharged after it is taken off the production line; or determining the battery capacity decay, the coefficient of the battery capacity decay model, and the battery energy throughput from the historical operating condition data, and determining the performance state based on the capacity decay, the coefficient, and the energy throughput.

[0010] Optionally, the target performance state of the battery is determined based on feature data, including: selecting the target performance state from the performance states in the relational model based on feature data, and the temperature characteristics corresponding to the target performance state.

[0011] Optionally, based on the target performance state, a control strategy for the battery is determined, including: optimizing the temperature characteristics corresponding to the target performance state to generate a control strategy for the battery.

[0012] According to another aspect of the present invention, a control device for a battery thermal management system is also provided. The device may include: an acquisition unit for acquiring operating condition data of a vehicle's battery, wherein the operating condition data represents the battery's operating status during vehicle operation; an extraction unit for extracting characteristic data of the battery from the operating condition data, wherein the characteristic data characterizes the corresponding battery performance status; a first determination unit for determining a target performance state of the battery based on the characteristic data; a second determination unit for determining a control strategy for the battery based on the target performance state, wherein the control strategy is used to mitigate battery degradation; and a control unit for controlling the battery's thermal management system to perform heating or cooling operations on the battery in response to a control command corresponding to the control strategy.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided. The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform a control method for the thermal management system of the battery according to the embodiments of the present invention.

[0014] According to another aspect of the present invention, a processor is also provided. The processor is used to run a program, wherein the program executes the control method of the battery thermal management system according to the embodiments of the present invention.

[0015] According to another aspect of the present invention, a vehicle is also provided. This vehicle is used to execute the control method of the battery thermal management system according to the embodiments of the present invention.

[0016] In this embodiment of the invention, battery operating condition data of a vehicle is acquired, wherein the operating condition data represents the battery's working status during vehicle operation; feature data of the battery is extracted from the operating condition data, wherein the feature data characterizes the corresponding battery performance status; based on the feature data, a target performance state of the battery is determined; based on the target performance state, a control strategy for the battery is determined; and in response to the control command corresponding to the control strategy, the battery's thermal management system is controlled to perform heating or cooling operations on the battery. In other words, this embodiment of the invention can extract feature data reflecting the battery's performance status from the acquired battery operating condition data, determine the target performance state of the battery based on the feature data, determine a control strategy based on the target performance state, and generate a control command corresponding to the control strategy. Based on the control command, the battery's thermal management system can be controlled to heat or cool the battery. Since the target performance of the battery can be determined through detailed analysis of the feature data, the performance during battery heating or cooling is guaranteed, thereby solving the technical problem of low accuracy in battery heating or cooling and achieving the technical effect of improving the accuracy of battery heating or cooling. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a control method for a battery thermal management system according to an embodiment of the present invention;

[0019] Figure 2 This is a flowchart of another control method for a battery thermal management system according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of a data-driven thermal management strategy optimization according to an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of a control system for a thermal management system according to an embodiment of the present invention;

[0022] Figure 5 This is a schematic diagram of the average discharge power of a battery according to an embodiment of the present invention;

[0023] Figure 6This is a schematic diagram of a power characteristic according to an embodiment of the present invention;

[0024] Figure 7 This is a schematic diagram of a temperature characteristic according to an embodiment of the present invention;

[0025] Figure 8 This is a schematic diagram of a vehicle usage feature according to an embodiment of the present invention;

[0026] Figure 9 This is a schematic diagram of the change value of average remaining power for a single trip according to an embodiment of the present invention;

[0027] Figure 10 This is a schematic diagram of a control device for a battery thermal management system according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] According to an embodiment of the present invention, an embodiment of a control method for a battery thermal management system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] Figure 1This is a flowchart of a control method for a battery thermal management system according to an embodiment of the present invention, such as... Figure 1 As shown, the method may include the following steps:

[0033] Step S102: Obtain the battery operating condition data of the vehicle, wherein the operating condition data is used to represent the battery's operating status during vehicle operation.

[0034] In the technical solution provided by step S102 of the present invention, the operating condition data of the battery in the vehicle can be obtained. This operating condition data can be used to represent the battery's operating status during vehicle operation. The vehicle operation process can include the charging and discharging processes, and can be used to reflect the driver's driving style. The battery can be a power battery. The vehicle can be a pure electric vehicle.

[0035] Optionally, before acquiring battery operating condition data, historical operating condition data of batteries in all target vehicle models can be pre-acquired. This allows for the differentiation of different driving styles and usage habits within the target vehicle models based on historical operating condition data, categorizing them into several different types. When it is necessary to determine the control strategy for the battery, the battery's operating condition data can be acquired to determine which pre-defined category the data belongs to.

[0036] Optionally, a control system for determining the battery's thermal management system can be pre-deployed, which may include a working condition acquisition module, a data processing module, a result output module, a strategy optimization module, a machine learning module, and a strategy output module, etc. The working condition acquisition module can obtain historical working condition data such as the user's driving habits based on big data statistics, or it can obtain working condition data.

[0037] Step S104: Extract the battery feature data from the operating condition data, wherein the feature data is used to characterize the performance status of the corresponding battery.

[0038] In the technical solution provided in step S104 of the present invention, after obtaining the battery's operating condition data, feature data of the battery can be extracted from the operating condition data. The feature data can be used to characterize the performance status of the corresponding battery, and can also be referred to as feature parameters, including power characteristics, temperature characteristics, and vehicle usage characteristics. It should be noted that the specific features included in the above feature data are merely illustrative examples and are not specifically limited here. Any process and features involved in feature extraction from operating condition data are within the protection scope of the embodiments of the present invention.

[0039] Optionally, before acquiring battery operating condition data, feature extraction can be performed on all historical operating condition data to extract the power characteristics, temperature characteristics, and vehicle usage characteristics of each historical operating condition data. Based on the above three feature data, the historical operating condition data can be classified into multiple categories, and each category corresponds to a high degree of similarity in driving style and vehicle usage habits.

[0040] Optionally, after classifying the historical operating condition data, the operating condition data for which the control strategy needs to be determined can be obtained, and the operating condition data can also be feature extracted in the same way as the historical operating condition data to determine the power characteristics, temperature characteristics, and vehicle usage characteristics in the feature data. Thus, the category of the current operating condition data can be determined based on the above three characteristics.

[0041] In this embodiment of the invention, since feature data can have a significant impact on battery performance and lifespan, feature extraction can be performed on operating condition data and historical operating condition data to obtain feature data. This allows for a comprehensive analysis of the battery to which the current operating condition data belongs, thereby ensuring the accuracy of determining the corresponding battery's control strategy. Furthermore, historical operating condition data can be obtained by relying on big data, making the optimization of the control strategy more scientific and reasonable. This achieves the technical effect of improving the accuracy of heating or cooling the battery.

[0042] Step S106: Determine the target performance state of the battery based on the feature data.

[0043] In the technical solution provided by step S106 of the present invention, after extracting the characteristic data of the battery from the operating condition data, the target performance state of the battery can be determined based on the characteristic data. The target performance state can be the optimal state of health (SOH) of the battery, or simply the optimal SOH value.

[0044] Optionally, the optimal SOH value of the battery can be determined based on the power characteristics, temperature characteristics, and vehicle usage characteristics in the feature data.

[0045] Optionally, a mapping relationship can be established between battery health and battery power characteristics, temperature characteristics, and vehicle usage characteristics. The optimal SOH value of the battery can be determined from the mapping relationship. The optimal SOH value is the value with the largest SOH, which also indicates that the battery with the largest SOH has the smallest lifespan degradation.

[0046] In this embodiment of the invention, a relationship between battery SOH and feature data can be established. The target vehicle model corresponding to the optimal SOH and the temperature characteristics of the target vehicle model are used as the control target. The strategy optimization of the thermal management system can be performed, thereby achieving the technical effect of improving the accuracy of heating or cooling the battery.

[0047] Step S108: Determine the battery control strategy based on the target performance state.

[0048] In the technical solution provided by step S108 of the present invention, after determining the target performance state of the battery based on feature data, the control strategy of the battery can be determined based on the target performance state. The control strategy can be called the optimal control strategy, which may include the optimal cooling strategy and the optimal heating strategy.

[0049] Optionally, the temperature characteristics corresponding to the vehicle with the optimal SOH can be used as the control target to optimize the strategy, so that the temperature characteristics of the vehicle corresponding to the current battery are taken into account, thereby obtaining the optimized control strategy.

[0050] Step S110: In response to the control command corresponding to the control strategy, control the battery thermal management system to perform heating or cooling operations on the battery.

[0051] In the technical solution provided by step S110 of the present invention, after determining the battery control strategy based on the target performance state, a control command corresponding to the control strategy can be generated. After receiving the control command, the battery's thermal management system can be controlled to perform heating or cooling operations on the battery. The control command may include information such as the desired battery temperature and control time. It should be noted that the information related to heating or cooling the battery included in the control command is merely illustrative and not intended to be specific.

[0052] Optionally, based on the control strategy, a corresponding control command can be generated and sent to the thermal management system. After the thermal management system receives the control command, it can perform corresponding heating or cooling operations on the battery.

[0053] Controlling battery temperature solely through simple heating or cooling devices still suffers from low accuracy. However, in this invention, feature extraction is performed on battery operating data to obtain power, temperature, and vehicle usage characteristics. This allows for analysis of the battery's current performance and lifespan, determining the optimal State of Harm (SOH) value for the battery, and generating a corresponding control strategy. The battery can then be heated or cooled according to the control commands corresponding to this strategy, thus solving the problem of low accuracy in battery heating or cooling.

[0054] In steps S102 to S110 of this application, the battery's operating condition data is obtained, whereby the operating condition data represents the battery's working status during vehicle operation. Feature data of the battery is extracted from the operating condition data, whereby the feature data characterizes the corresponding battery performance status. Based on the feature data, the target performance state of the battery is determined. Based on the target performance state, a control strategy for the battery is determined. In response to the control command corresponding to the control strategy, the battery's thermal management system is controlled to perform heating or cooling operations on the battery. In other words, this embodiment of the invention can extract feature data reflecting the battery's performance status from the obtained battery operating condition data, determine the target performance state of the battery based on the feature data, determine a control strategy based on the target performance state, and generate a control command corresponding to the control strategy. Based on the control command, the battery's thermal management system can be controlled to heat or cool the battery. Since the target performance of the battery can be determined through detailed analysis of the feature data, the performance during battery heating or cooling is guaranteed, thereby solving the technical problem of low accuracy in battery heating or cooling and achieving the technical effect of improving the accuracy of battery heating or cooling.

[0055] The method described in this embodiment will be further described below.

[0056] As an optional embodiment, step S104 involves extracting battery feature data from the operating condition data, including: extracting features from the operating condition data to obtain power features, temperature features, and / or vehicle features from the operating condition data, wherein the feature data includes, but is not limited to, power features, temperature features, and vehicle features.

[0057] In this embodiment, during the process of extracting battery feature data from operating condition data, feature extraction can be performed on the operating condition data to obtain power features, temperature features and / or vehicle features of the operating condition data, and the power features, temperature features and / or vehicle features can be determined as feature data.

[0058] Optionally, the power characteristic can be average power, average discharge power, the average of the absolute values ​​of charge and discharge power, or a statistical distribution result based on power values. The temperature characteristic can be average temperature, discharge temperature, charging temperature, storage temperature, temperature range, or a statistical distribution result based on temperature values. Vehicle usage characteristics can include information such as battery DOD and daily mileage. It should be noted that the information included in the above power characteristics, temperature characteristics, and vehicle usage characteristics is merely illustrative and not specifically limited here. Any characteristic or data used to reflect the performance status of the battery and to determine the target performance status through that characteristic is within the protection scope of this invention.

[0059] In this embodiment of the invention, a comprehensive analysis can be performed on the battery to which the current operating condition data belongs. For example, feature data that can reflect the battery's performance and lifespan can be extracted from the operating condition data features, thereby ensuring the accuracy of determining the control strategy for the corresponding battery.

[0060] As an optional embodiment, step S104 involves extracting features from the operating condition data to obtain power features, temperature features, and / or vehicle features from the operating condition data, including: normalizing the power features, temperature features, and / or vehicle features to obtain feature data.

[0061] In this embodiment, during the process of determining power characteristics, temperature characteristics, and / or vehicle characteristics as feature data, the power characteristics, temperature characteristics, and / or vehicle characteristics can be normalized to obtain processing results, and the processing results can be determined as feature data.

[0062] Optionally, the power characteristics, temperature characteristics, and vehicle characteristics in the feature data can be normalized to the range of [0,1] by the data processing module of the thermal management system control system using the normalization method. That is, the processing result can be obtained and the processing result can be determined as feature data.

[0063] As an optional embodiment, in step S106, before determining the target performance state of the battery based on feature data, the method further includes: acquiring historical operating condition data of the battery of the target vehicle model; determining the performance state of the battery corresponding to the historical operating condition data; and establishing a relationship model between the performance state and the feature data of the historical operating condition data, wherein the relationship model is used to determine the target performance state from the performance state.

[0064] In this embodiment, before determining the target performance state of the battery based on feature data, historical operating condition data of the battery of the target vehicle model can be obtained, and the performance state corresponding to the historical operating condition data can be determined. A relationship model between the performance state and the feature data of the historical operating condition data can be established, where the performance state can be the battery's health state, also known as the battery's State of Health (SOH). The relationship model can be a relational expression that can be used to determine the target performance state from the performance state.

[0065] Optionally, historical operating condition data of the target vehicle's battery can be obtained. This historical operating condition data can reflect the driver's driving habits and driving conditions, and feature data such as power characteristics, temperature characteristics, and vehicle usage characteristics can be extracted from the historical operating condition data. All of these features can have a significant impact on battery performance and lifespan. Based on the above feature data, the State of Harmony (SOH) of each battery can be determined. A mapping relationship between SOH and battery power characteristics, temperature characteristics, and vehicle usage characteristics can be established: SOH = f(P,T,S), where P can be used to represent power characteristics, T can be used to represent temperature characteristics, and S can be used to represent vehicle usage characteristics.

[0066] Optionally, a predicted value can be obtained using the machine learning module in the thermal management system's control system, employing probability theory, statistics, and approximation theory. This estimate represents the impact of different cooling or heating start-up temperatures on State of Emergency (SOH) under certain user driving habits. In the machine learning model within the module, power characteristics, temperature characteristics, and SOH can all be variables, with SOH as the dependent variable. Historical operating condition data acquired through cloud-based big data is divided into two parts. The first part (60%) serves as the training set to fit a relationship. The second part (40%) serves as the test set, used to test and refine the fitted relationship. Therefore, the greater the user's driving time and mileage, and the more historical operating condition data collected, the higher the accuracy of the relationship, leading to a more accurate determination of SOH. The results output module in the thermal management system's control system can then be used to determine whether the current control strategy needs optimization.

[0067] As an optional embodiment, step S106, determining the battery performance state corresponding to historical operating condition data, includes: determining the initial capacity and target capacity of the battery from the historical operating condition data, and determining the performance state based on the initial capacity and target capacity, wherein the initial capacity is used to represent the capacity of the battery before it is charged and discharged after it is taken offline; or determining the battery capacity decay, the coefficient of the battery capacity decay model, and the battery energy throughput from the historical operating condition data, and determining the performance state based on the capacity decay, the coefficient, and the energy throughput.

[0068] In this embodiment, during the process of determining the battery's performance state corresponding to historical operating condition data, the initial capacity and target capacity of the battery can be determined from the historical operating condition data, and the performance state can be determined based on the initial capacity and target capacity. Alternatively, the battery's capacity decay, the coefficient of the capacity decay model, and the energy throughput can be determined from the historical operating condition data, and the performance state can be determined based on the capacity decay, coefficient, and energy throughput. The initial capacity can be used to represent the battery's capacity before charging and discharging after it has been taken off the production line. The target capacity can be used to represent the battery's current capacity.

[0069] Alternatively, based on the initial capacity and the target capacity, the performance status can be determined using the following formula:

[0070]

[0071] Among them, SOH can be used to represent the performance status; I can be used to represent the battery current; ΔQ can be used to represent the target capacity; Q0 ​​can be used to represent the initial capacity; and ΔSOC can be used to represent the difference between the initial charge and the current charge.

[0072] Alternatively, using Arrhenius theory, based on capacity decay, the coefficients of the capacity decay model, and energy throughput, the performance state can be determined by the following formula:

[0073] InY c =K1 T InX c +B1 T

[0074] Among them, Y c It can be used to represent capacity decay; K1 T and B1 T It can be used to represent the coefficients of the capacity decay model at temperature T; X c It can be used to represent energy throughput.

[0075] As an optional embodiment, step S106, determining the target performance state of the battery based on feature data, includes: selecting the target performance state and the temperature feature corresponding to the target performance state from the performance states in the relational model based on feature data.

[0076] In this embodiment, during the process of determining the target performance state of the battery based on feature data, the target performance state and the temperature feature corresponding to the target performance state can be selected from the performance states in the relational model based on the feature data.

[0077] Optionally, by performing feature analysis on the vehicle's operating data, the vehicle's power characteristics, temperature characteristics, and usage characteristics can be incorporated into the SOH mapping SOH=f(P,T,S) to find the optimal SOH value and the temperature characteristics corresponding to the optimal SOH value.

[0078] As an optional embodiment, step S108, based on the target performance state, determines the control strategy of the battery, including: optimizing the temperature characteristics corresponding to the target performance state to generate the control strategy of the battery.

[0079] In this embodiment, during the process of determining the battery control strategy based on the target performance state, the temperature characteristics corresponding to the target performance state can be optimized to generate the battery control strategy.

[0080] Optionally, the temperature characteristics corresponding to the optimal SOH value can be used as the control target to optimize the control strategy, so that the temperature characteristics of the vehicle move closer to the temperature characteristics corresponding to the optimal SOH value, thereby generating a control strategy for the battery.

[0081] Optionally, the control strategy of the thermal management system can be optimized based on the optimal SOH value and the corresponding temperature characteristics through the strategy optimization module in the control system of the thermal management system. The optimized strategy can then be fed into the machine learning module for calculation to determine whether the optimized control strategy can meet the temperature characteristics corresponding to the optimal lifespan of the battery. If it cannot meet the requirements, it needs to be optimized again. If it can meet the requirements, it can be pushed to the output module to output the current control strategy, and control commands can be generated based on the control strategy.

[0082] In this embodiment of the invention, battery operating condition data of a vehicle is acquired, wherein the operating condition data represents the battery's working status during vehicle operation; feature data of the battery is extracted from the operating condition data, wherein the feature data characterizes the corresponding battery performance status; based on the feature data, a target performance state of the battery is determined; based on the target performance state, a control strategy for the battery is determined; and in response to the control command corresponding to the control strategy, the battery's thermal management system is controlled to perform heating or cooling operations on the battery. In other words, this embodiment of the invention can extract feature data reflecting the battery's performance status from the acquired battery operating condition data, determine the target performance state of the battery based on the feature data, determine a control strategy based on the target performance state, and generate a control command corresponding to the control strategy. Based on the control command, the battery's thermal management system can be controlled to heat or cool the battery. Since the target performance of the battery can be determined through detailed analysis of the feature data, the performance during battery heating or cooling is guaranteed, thereby solving the technical problem of low accuracy in battery heating or cooling and achieving the technical effect of improving the accuracy of battery heating or cooling.

[0083] Example 2

[0084] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0085] Currently, the performance and lifespan of pure electric vehicle power batteries are affected by multiple factors. Whether the State of Harm (SOH) value of the battery can reach the target value when it reaches the calendar life and mileage life specified in the warranty is related to factors such as the rate characteristics, temperature characteristics, SOC usage range, and the ratio of fast and slow charging during user driving. Among these, temperature has the most significant impact on battery life. The lifespan and performance of the power battery can be extended and improved by optimizing the cooling strategy and redetermining the temperature at which cooling or heating is activated.

[0086] In one related technology, a method, apparatus, device, and storage medium for determining the remaining driving range of a new energy vehicle are proposed. The method includes the following steps: acquiring monitoring data of the new energy vehicle collected by a big data platform, including vehicle driving status data and basic vehicle battery data; calculating the battery health status (SOH), actual driving mileage within the state of charge (SOC) range, and power consumption within the SOC range of the new energy vehicle under various operating conditions based on the monitoring data; generating baseline operating condition data for various operating conditions based on the monitoring data, battery SOH, actual driving mileage within the SOC range, and power consumption within the SOC range; and matching the corresponding baseline operating condition data with the real-time operating condition data of the new energy vehicle under test to determine the remaining driving range of the new energy vehicle under test. This method can improve the prediction accuracy of the remaining driving range of new energy vehicles.

[0087] In another related technology, a method, apparatus, and electronic device for determining battery health status are proposed. This method may include: determining the annual battery capacity degradation, annual mileage, and mileage corresponding to the battery's discharge capacity per ampere-hour for the current vehicle based on a preset warranty standard; determining the mileage already driven and the mileage not yet driven for the current vehicle under the preset warranty standard based on the target warranty standard and the annual mileage; determining the first capacity degradation corresponding to the mileage already driven based on the target warranty standard and the annual battery capacity degradation for the current vehicle; determining the second capacity degradation under cycle durability conditions based on the mileage not yet driven and the mileage corresponding to the battery's discharge capacity per ampere-hour; and determining the battery health status of the current vehicle under the target warranty standard based on the first and second capacity degradations. This method accurately yields a battery health status that meets various warranty requirements, facilitating its use as a testing indicator for various application scenarios.

[0088] However, the above methods still have the technical problem of low accuracy in heating or cooling the battery.

[0089] To address the aforementioned issues, this invention proposes a data-driven thermal management strategy optimization method. This method extracts feature data reflecting the battery's performance status from the acquired battery operating data. Based on the feature data, the target performance state of the battery is determined, and a control strategy is determined based on the target performance state. When a control command corresponding to the control strategy is generated, the battery's thermal management system can be controlled to heat or cool the battery based on the control command. Since the target performance of the battery can be determined through detailed analysis of the feature data, the performance during the battery heating or cooling process is guaranteed, thereby solving the technical problem of low accuracy in heating or cooling the battery and achieving the technical effect of improving the accuracy of heating or cooling the battery.

[0090] The embodiments of the present invention will be further described below.

[0091] Figure 2 This is a flowchart of another control method for a battery thermal management system according to an embodiment of the present invention, such as... Figure 2 As shown, the method may include the following steps:

[0092] Step S202: Obtain historical operating condition data.

[0093] In the technical solution provided by step S202 of the present invention, historical operating condition data of the battery in the target vehicle model can be obtained. The historical operating condition data can reflect the driver's driving habits and driving conditions, etc.

[0094] Figure 4 This is a schematic diagram of a control system for a thermal management system according to an embodiment of the present invention, as shown below. Figure 4 As shown, the system may include a working condition acquisition module 401, a data processing module 402, a machine learning module 403, a result output module 404, a strategy optimization module 405, and a strategy output module 406. The working condition acquisition module 401 can acquire historical working condition data such as user driving habits based on big data statistics, or it can acquire current working condition data. The data processing module 402 can perform data processing on historical and current working condition data, such as feature extraction and normalization. The machine learning module 403 can determine the relationship model between SOH and feature data. The result output module 404 can be used to determine the optimal SOH and the corresponding temperature characteristics. The strategy optimization model 405 can be used to optimize the control strategy. The strategy output module 406 can be used to output the final control strategy that meets the requirements and generate corresponding control commands.

[0095] Step S204: Establish a relationship model between SOH and feature data.

[0096] In the technical solution provided by step S204 of the present invention, SOH can be determined first, or feature extraction can be performed on historical operating data to extract feature data, and then a relationship model between SOH and feature data can be established.

[0097] Optionally, feature extraction can be performed on all historical operating condition data to extract the power features, temperature features, and vehicle usage features of each historical operating condition data. Based on the above three feature data, the historical operating condition data can be classified into multiple categories, and the driving style and vehicle usage habits corresponding to each category are highly similar.

[0098] For example, power characteristics can be average power, average discharge power, the average of the absolute values ​​of charge and discharge power, or a statistical distribution result based on power values. Temperature characteristics can be average temperature, discharge temperature, charging temperature, storage temperature, temperature range, or a statistical distribution result based on temperature values. Vehicle usage characteristics can include information such as battery DOD and daily mileage.

[0099] For example, Figure 5 This is a schematic diagram of the average discharge power of a battery according to an embodiment of the present invention, as shown below. Figure 5 As shown, power characteristic data can be extracted by using the average battery discharge power during vehicle operation as a feature, and then sorted by vehicle number. Figure 5 The results are shown.

[0100] As an optional instance, Figure 6 This is a schematic diagram of a power characteristic according to an embodiment of the present invention, such as... Figure 6 As shown, the average discharge power can be classified into different intervals every 10 kW, and those in the same interval are considered to have similar power characteristics.

[0101] For example, Figure 7 This is a schematic diagram of a temperature characteristic according to an embodiment of the present invention, such as... Figure 7 As shown, the highest temperature during vehicle operation can be used as a temperature feature, and different intervals can be classified according to 5°C intervals. Those in the same interval are considered to have similar temperature features.

[0102] For example, Figure 8 This is a schematic diagram of a vehicle usage feature according to an embodiment of the present invention, such as... Figure 8 As shown, the single-trip mileage of a vehicle during its journey can be used as a vehicle usage characteristic, and different intervals can be classified according to every 20km interval. Vehicles in the same interval are considered to have similar vehicle usage characteristics.

[0103] Optionally, the power characteristics, temperature characteristics, and vehicle characteristics in the feature data can be normalized to the range of [0,1] by the data processing module of the thermal management system control system using the normalization method. That is, the processing result can be obtained and the processing result can be determined as feature data.

[0104] Optionally, the initial capacity and target capacity of the battery can be determined from historical operating condition data, and the performance status can be determined based on the initial capacity and target capacity. Alternatively, the battery capacity decay, the coefficient of the capacity decay model, and the energy throughput can be determined from historical operating condition data, and the performance status can be determined based on the capacity decay, the coefficient, and the energy throughput.

[0105] For example, Figure 9 This is a schematic diagram illustrating the change in average remaining battery power over a single trip according to an embodiment of the present invention, such as... Figure 9 As shown, the average SOC change value per trip during vehicle travel can be used as the SOC feature, and different intervals can be classified according to every 5% SOC interval. Intervals with the same SOC are considered to have similar SOC features.

[0106] For another example, based on the initial capacity and the target capacity, the performance status can be determined using the following formula:

[0107]

[0108] Among them, SOH can be used to represent the performance state; I can be used to represent the battery circuit; ΔQ can be used to represent the target capacity; Q0 ​​can be used to represent the initial capacity; and ΔSOC can be used to represent the difference between the initial charge and the current charge.

[0109] As an alternative example, using Arrhenius theory, based on capacity decay, the coefficients of the capacity decay model, and energy throughput, the performance state can be determined by the following formula:

[0110] InY c =K1 T InX c +B1 T

[0111] Among them, Y c It can be used to represent capacity decay; K1 T and B1 T It can be used to represent the coefficients of the capacity decay model at temperature T; X c It can be used to represent energy throughput.

[0112] Optionally, historical operating condition data of the target vehicle's battery can be obtained. This historical operating condition data can reflect the driver's driving habits and driving conditions, and feature data such as power characteristics, temperature characteristics, and vehicle usage characteristics can be extracted from the historical operating condition data. All of these features can have a significant impact on battery performance and lifespan. Based on the above feature data, the State of Harmony (SOH) of each battery can be determined. A mapping relationship between SOH and battery power characteristics, temperature characteristics, and vehicle usage characteristics can be established: SOH = f(P,T,S), where P can be used to represent power characteristics, T can be used to represent temperature characteristics, and S can be used to represent vehicle usage characteristics.

[0113] For example, the machine learning module in the thermal management system's control system uses probability theory, statistics, and approximation theory to obtain a predicted value: the impact of different cooling or heating start-up temperatures on State of Emergency (SOH) under certain user driving habits. In the machine learning model within the module, power characteristics, temperature characteristics, and SOH can all be variables, with SOH as the dependent variable. Historical operating condition data obtained from the cloud is divided into two parts: the first part (60%) serves as the training set to fit a relationship, and the second part (40%) serves as the test set to test and correct the fitted relationship. Therefore, the greater the user's driving time and mileage, and the more historical operating condition data collected, the higher the accuracy of the relationship, and thus the more accurate the SOH determination. The results output module in the thermal management system's control system can then be used to determine whether the current control strategy needs optimization.

[0114] Step S206: Optimize the control strategy based on the relational model.

[0115] In the technical solution provided by step S206 of the present invention, feature analysis of the vehicle's operating data can be performed, and the vehicle's power characteristics, temperature characteristics, and usage characteristics can be brought into the SOH mapping SOH=f(P,T,S) to find the optimal SOH value and the temperature characteristics corresponding to the optimal SOH value.

[0116] Step S208: Iteratively optimize the control strategy.

[0117] In the technical solution provided by step S208 of the present invention, the control strategy of the thermal management system can be optimized by the strategy optimization module in the control system of the thermal management system according to the optimal SOH value and the corresponding temperature characteristics. The optimized strategy can be fed into the machine learning module for calculation to determine whether the optimized control strategy can meet the temperature characteristics corresponding to the optimal life of the battery. Otherwise, if it cannot meet the requirements, it needs to be optimized again. If it can meet the requirements, it can be pushed to the output module to output the current control strategy, and control commands can be generated based on the control strategy.

[0118] Figure 3 This is a schematic diagram illustrating a data-driven thermal management strategy optimization according to an embodiment of the present invention, such as... Figure 3 As shown, historical operating condition data can be acquired and input into the data processing module for feature extraction. For example, power, temperature, and vehicle usage features can be extracted from historical operating conditions. Then, feature data processing and SOH calculation can be performed. The data is then input into a machine learning model for machine learning to obtain variable relationships, i.e., a relational model. Multi-objective cooling strategy optimization can be performed through the strategy optimization module to determine the control strategy and find a cooling or heating strategy that balances lifespan, energy consumption, and performance.

[0119] This invention can extract feature data reflecting the battery's performance status from the acquired battery operating data. Based on the feature data, the target performance state of the battery is determined, and a control strategy can be determined based on the target performance state. When a control command corresponding to the control strategy is generated, the battery's thermal management system can be controlled to heat or cool the battery based on the control command. Since the target performance of the battery can be determined through detailed analysis of the feature data, the performance during the battery heating or cooling process is guaranteed, thereby solving the technical problem of low accuracy in heating or cooling the battery and achieving the technical effect of improving the accuracy of heating or cooling the battery.

[0120] Example 3

[0121] According to an embodiment of the present invention, a control device for a battery thermal management system is also provided. It should be noted that this control device for the battery thermal management system can be used to execute the control method for the battery thermal management system in Embodiment 1.

[0122] Figure 10 This is a schematic diagram of a control device for a battery thermal management system according to an embodiment of the present invention, such as... Figure 10 As shown, the control device 1000 of the thermal management system of the battery may include: an acquisition unit 1002, an extraction unit 1004, a first determination unit 1006, a second determination unit 1008, and a control unit 1010.

[0123] The acquisition unit 1002 is used to acquire the battery operating condition data of the vehicle, wherein the operating condition data is used to represent the battery's operating status during vehicle operation.

[0124] Extraction unit 1004 is used to extract battery feature data from operating condition data, wherein the feature data is used to characterize the performance status of the corresponding battery.

[0125] The first determining unit 1006 is used to determine the target performance state of the battery based on feature data.

[0126] The second determining unit 1008 is used to determine the control strategy of the battery based on the target performance state, wherein the control strategy is used to mitigate battery degradation.

[0127] The control unit 1010 is used to control the battery's thermal management system to perform heating or cooling operations on the battery in response to control commands corresponding to the control strategy.

[0128] Optionally, the extraction unit 1004 may include an extraction module for extracting features from the operating condition data to obtain power features, temperature features, and / or vehicle features in the operating condition data, wherein the feature data includes, but is not limited to, power features, temperature features, and vehicle features.

[0129] Optionally, the extraction module may include a processing submodule for normalizing power characteristics, temperature characteristics, and / or vehicle characteristics to obtain feature data.

[0130] Optionally, the device may further include: an acquisition module for acquiring historical operating condition data of the battery of the target vehicle model; a first determination module for determining the performance state of the battery corresponding to the historical operating condition data; and an establishment module for establishing a relationship model between the performance state and the feature data of the historical operating condition data, wherein the relationship model is used to determine the target performance state from the performance state.

[0131] Optionally, the first determining module may include: a first processing submodule, used to determine the initial capacity and target capacity of the battery from historical operating condition data, and to determine the performance state based on the initial capacity and target capacity, wherein the initial capacity is used to represent the capacity of the battery before it is charged and discharged after it is taken off the production line; and a second processing submodule, used to determine the capacity decay of the battery, the coefficient of the battery capacity decay model, and the energy throughput of the battery from historical operating condition data, and to determine the performance state based on the capacity decay, the coefficient, and the energy throughput.

[0132] Optionally, the first determining unit 1006 may include: a filtering module for feature data, filtering out the target performance state from the performance states in the relational model, and the temperature feature corresponding to the target performance state.

[0133] Optionally, the second determining unit 1008 may include: an optimization module, used to perform strategy optimization on the temperature characteristics corresponding to the target performance state, and generate a control strategy for the battery.

[0134] In this embodiment of the invention, an acquisition unit acquires battery operating condition data of the vehicle, wherein the operating condition data represents the battery's operating status during vehicle operation; an extraction unit extracts battery feature data from the operating condition data, wherein the feature data represents the corresponding battery performance status; a first determination unit determines the target performance state of the battery based on the feature data; a second determination unit determines a battery control strategy based on the target performance state, wherein the control strategy is used to mitigate battery wear; and a control unit, responding to the control command corresponding to the control strategy, controls the battery's thermal management system to perform heating or cooling operations on the battery, thereby solving the technical problem of low accuracy in heating or cooling the battery and achieving the technical effect of improving the accuracy of heating or cooling the battery.

[0135] Example 4

[0136] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein the program executes the control method of the thermal management system of the battery described in Embodiment 1.

[0137] Example 5

[0138] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program executes the control method of the battery thermal management system described in Embodiment 1.

[0139] Example 6

[0140] According to an embodiment of the present invention, a vehicle is also provided, which is used to execute the control method of the thermal management system of the battery according to the embodiment of the present invention.

[0141] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0142] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0146] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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 storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0147] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A control method for a battery thermal management system, characterized in that, include: Acquire the battery condition data of the vehicle, wherein the condition data is used to represent the working status of the battery during the operation of the vehicle; The characteristic data of the battery is extracted from the operating condition data. The characteristic data is used to characterize the performance status of the corresponding battery. The characteristic data includes the power characteristics, temperature characteristics and vehicle usage characteristics of the vehicle. The vehicle usage characteristics are the single-trip mileage of the vehicle during driving. Obtain historical operating condition data of the battery of the target vehicle model, and divide the historical operating condition data into a training set and a test set; The initial capacity and target capacity of the battery are determined from the training set, and the performance state of the battery is determined based on the initial capacity and the target capacity, wherein the initial capacity represents the capacity of the battery before it is charged and discharged after it is taken offline; or The capacity decay of the battery, the coefficients of the capacity decay model of the battery, and the energy throughput of the battery are determined from the training set. Based on the capacity decay, the coefficients, and the energy throughput, the performance state of the battery corresponding to the historical operating data is determined. Establish a relationship model between the performance status and the feature data of the historical operating conditions, and use the test set to test and correct the relationship model; Based on the power characteristics, temperature characteristics, and vehicle usage characteristics, the target performance state of the battery and the temperature characteristics corresponding to the target performance state are selected from the performance states in the relationship model. The temperature characteristics corresponding to the target performance state are iteratively optimized to generate the control strategy for the battery. In response to the control command corresponding to the control strategy, the thermal management system of the battery is controlled to perform heating or cooling operations on the battery.

2. The method according to claim 1, characterized in that, The characteristic data of the battery are extracted from the operating condition data, including: The power characteristics, temperature characteristics, and vehicle usage characteristics are normalized to obtain the characteristic data.

3. A control device for a battery thermal management system, characterized in that, The device includes: An acquisition unit is used to acquire the operating condition data of the vehicle's battery, wherein the operating condition data is used to represent the operating status of the battery during the operation of the vehicle. An extraction unit is used to extract the feature data of the battery from the operating condition data. The feature data is used to characterize the performance status of the corresponding battery. The feature data includes the power characteristics, temperature characteristics, and vehicle usage characteristics of the vehicle. The vehicle usage characteristics are the single-trip mileage of the vehicle during driving. The device is further configured to acquire historical operating condition data of the battery of the target vehicle model, and divide the historical operating condition data into a training set and a test set; determine the initial capacity and target capacity of the battery from the training set, and determine the performance state of the battery based on the initial capacity and the target capacity, wherein the initial capacity is used to represent the capacity of the battery before it is charged and discharged after it is off the production line; or determine the capacity decay of the battery, the coefficient of the capacity decay model of the battery, and the energy throughput of the battery from the training set, and determine the performance state of the battery corresponding to the historical operating condition data based on the capacity decay, the coefficient, and the energy throughput; establish a relationship model between the performance state and the feature data of the historical operating condition data, and use the test set to test and correct the relationship model; The first determining unit is used to filter out the target performance state of the battery and the temperature feature corresponding to the target performance state from the performance states in the relation model based on the power characteristics, the temperature characteristics and the vehicle usage characteristics. The second determining unit is used to perform strategy iterative optimization on the temperature characteristics corresponding to the target performance state to generate a control strategy for the battery, wherein the control strategy is used to mitigate the battery's wear and tear. The control unit is used to control the battery's thermal management system to perform heating or cooling operations on the battery in response to the control command corresponding to the control strategy.

4. A processor, characterized in that, The processor is used to run a program, wherein the program is executed by the processor to perform the method according to any one of claims 1 to 2.

5. A vehicle, characterized in that, Used to perform the method according to any one of claims 1 to 2.

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