Modeling method and device of battery pack, electronic equipment and storage medium

Abstract

The invention provides a modeling method and device for a battery pack, electronic equipment and a storage medium, relates to the technical field of vehicles, is used for modeling the battery pack, and comprises the following steps: carrying out a charging and discharging test based on different working condition parameters by using a battery pack test device; the actual energy data, the actual SOC change range, the actual capacity data and the first incidence relation of the battery pack under the multiple working conditions are obtained; based on the actual energy data under the plurality of working conditions and the pack body temperature of the battery pack under the plurality of working conditions, determining charging and discharging efficiency-temperature data of the battery pack; based on the charging and discharging efficiency-temperature data of the battery pack, determining heating value-temperature data of the battery pack; determining capacity-temperature data of the battery pack based on the actual capacity data under the plurality of working conditions and the pack body temperatures under the plurality of working conditions; determining a second association relationship based on the first association relationship under the plurality of working conditions; and constructing a battery pack model based on the capacity-temperature data, the second association relationship and the calorific value-temperature data.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and more specifically to a method, apparatus, electronic device, and storage medium for modeling a battery pack. Background Technology

[0002] Among the many subsystems of a vehicle, the power battery pack is one of the most critical subsystems of an electric vehicle. Generally speaking, the size of the power battery pack largely determines the vehicle's energy consumption level and driving range, while the battery's charging and discharging capabilities determine the vehicle's fast charging efficiency, power response speed, and continuous output capability under extreme operating conditions. Therefore, the development of the power battery pack has become an important part of the overall vehicle product development process.

[0003] In the development of power battery packs, building a battery pack model often replaces some real-vehicle testing to reduce R&D costs and time. Therefore, how to model the battery pack is a problem that urgently needs to be solved. Summary of the Invention

[0004] One of the objectives of this invention is to provide a method, apparatus, electronic device, and storage medium for modeling a battery pack, so as to realize the modeling of the battery pack.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Firstly, a battery pack modeling method is provided, comprising: using a battery pack testing device to conduct charge-discharge tests based on different operating condition parameters to obtain actual energy data, actual SOC variation range, actual capacity data, and a first correlation relationship of the battery pack under multiple operating conditions; wherein, the first correlation relationship is used to indicate the law of voltage variation of the battery pack with the SOC of the battery pack; the operating condition parameters are used to simulate the operating conditions of the battery pack under different ambient temperatures; based on the actual energy data and the battery pack body temperature under multiple operating conditions, the charge-discharge efficiency-temperature data of the battery pack is determined; based on the charge-discharge efficiency-temperature data, the heat generation-temperature data of the battery pack is determined; based on the actual capacity data and the battery pack body temperature under multiple operating conditions, the capacity-temperature data of the battery pack is determined; based on the first correlation relationship under multiple operating conditions, a second correlation relationship is determined; the second correlation relationship represents the comprehensive law of voltage variation with SOC of the battery pack under different temperature conditions; and a battery pack model is constructed based on the capacity-temperature data, the second correlation relationship, and the heat generation-temperature data.

[0006] The beneficial effects of this application are as follows: This application obtains the actual energy data, actual SOC variation range, actual capacity data, and a first correlation of the battery pack under various operating conditions. Based on this data, it then derives charge / discharge efficiency-temperature data, heat generation-temperature data, capacity-temperature data, and a second correlation. Finally, based on the capacity-temperature data, the second correlation, and the heat generation-temperature data, a battery pack model is constructed. When modeling a battery pack, it is generally necessary to consider its electrical and thermal characteristics. Electrical characteristics reflect the voltage output, capacity changes, and energy conversion patterns of the battery pack under different operating conditions. Thermal characteristics reflect the heat generation, dissipation, and temperature changes of the battery pack during energy conversion. The heat generation-temperature data of this application reflects the change in energy loss converted into heat under different temperature conditions. The capacity-temperature data reflects the influence of temperature on the battery pack's capacity. The second correlation reflects the voltage variation with SOC at different temperatures. Combining these three factors allows for the reflection of the battery pack's electrical and thermal characteristics under different temperature and SOC coupled operating conditions, thus enabling the modeling of the battery pack.

[0007] Furthermore, the actual energy data includes: discharge energy data when the battery pack is in a discharging state and charging energy data when the battery pack is in a charging state; based on the actual energy data under multiple operating conditions and the battery pack body temperature under multiple operating conditions, the charge / discharge efficiency-temperature data of the battery pack is determined, including: for each operating condition of the battery pack, determining the discharge energy data and charging energy data; the discharge energy data includes: first discharge energy, first charging energy, and first net discharge energy; the charging energy data includes: second discharge energy and second charging energy; based on the energy balance equation between the second charging energy, second discharge energy, first discharge energy, first charging energy, and first net discharge energy, the charge / discharge efficiency of the battery pack under each operating condition is determined; based on the charge / discharge efficiency corresponding to the battery pack body temperature under multiple operating conditions, the charge / discharge efficiency-temperature data of the battery pack is determined.

[0008] Based on the above technical means, the first discharge energy in the discharge energy data corresponds to the total discharge output energy under CLTC operating conditions, the first charging energy corresponds to the energy recovered by regenerative braking during the process, and the first net discharge energy is the actual external output energy after deducting the recovered energy; the second charging energy in the charging energy data is the total input energy under standard charging mode, and the second discharge energy is the energy loss during the charging stage. By constructing an energy balance equation that includes the above five types of energy parameters, the charging and discharging efficiency of the battery pack during the charging and discharging process can be accurately calculated.

[0009] Furthermore, the charge and discharge efficiency of the battery pack is determined as follows: based on the ratio of the reference SOC variation range to the actual SOC variation range, the actual energy data under multiple operating conditions are corrected to obtain the corrected actual energy data under multiple operating conditions; based on the corrected actual energy data under multiple operating conditions, the charge and discharge efficiency of the battery pack is determined.

[0010] Based on the aforementioned technical methods, the actual SOC variation range may differ under different temperature conditions. If the charge / discharge efficiency is calculated directly using uncorrected actual energy data, the efficiency results will be biased due to the differences in the SOC variation range, failing to accurately reflect the impact of temperature on battery energy conversion efficiency. By correcting the actual energy data using the ratio of the reference SOC variation range to the actual SOC variation range, the energy data under different operating conditions can be converted to a unified reference SOC variation range. Calculations based on the corrected actual energy data can yield a more accurate charge / discharge efficiency.

[0011] Furthermore, the charge and discharge efficiency of the battery pack satisfies the following relationship: Where Eff represents the charge and discharge efficiency of the battery pack. This indicates the corrected second charging energy. This represents the corrected second discharge energy. This indicates the corrected first charging energy. This represents the corrected first discharge energy. This represents the corrected first net discharge energy.

[0012] Based on the above technical means, by collecting the charging energy under charging conditions, the net discharge energy under charging conditions, the discharge energy during the discharge process, and the energy recovered by regenerative braking, an energy balance relationship between charging energy, discharge energy, and net discharge energy can be established. This allows for the accurate calculation of the battery pack's charging and discharging efficiency, providing a reliable basis for subsequent calculations of heat generation and temperature data.

[0013] Furthermore, based on the actual capacity data under multiple operating conditions and the battery pack body temperature under multiple operating conditions, the capacity-temperature data of the battery pack is determined, including: correcting the actual capacity data under multiple operating conditions based on the ratio of the reference SOC variation range to the actual SOC variation range to obtain the corrected actual capacity data under multiple operating conditions; and obtaining the capacity-temperature data of the battery pack based on the corrected actual capacity data corresponding to the battery pack body temperature under multiple operating conditions.

[0014] Based on the above technical means, the actual SOC variation range may vary in charge-discharge tests under different temperature conditions, which may lead to errors in the directly collected actual capacity data. By correcting the actual capacity data, the interference of inconsistent SOC variation range on the capacity calculation results can be eliminated, thereby improving the accuracy of capacity-temperature data.

[0015] Furthermore, the first correlation is determined in the following way: based on the voltage corresponding to different SOC values ​​of the battery pack, an initial correlation is determined; the initial correlation represents the preliminary law of the battery pack voltage changing with SOC; the initial correlation is corrected based on the correction coefficient to obtain the first correlation, and the correction coefficient is used to characterize the degree of deviation between the predicted net discharge and the actual net discharge of the battery pack.

[0016] Based on the aforementioned technical methods, the initial correlation is obtained by directly using the voltage data corresponding to the SOC node under a single temperature condition to obtain the voltage-SOC curve. This process does not fully consider the energy loss of the battery pack during actual operation. The correction factor is essentially the ratio (or deviation rate) between the predicted net discharge and the actual net discharge. Using the correction factor to calibrate the initial correlation essentially maps the impact of actual energy loss onto the initial correlation, making the initial correlation more closely resemble real-world operating conditions.

[0017] Furthermore, based on capacity-temperature data, the second correlation, and heat generation-temperature data, a battery pack model is constructed, including: an electrical module for the battery pack model based on capacity-temperature data and the second correlation, which is used to characterize the voltage output characteristics and capacity variation of the battery pack under different temperature and SOC conditions; and a heat generation module for the battery pack model based on heat generation-temperature data, which is used to characterize the correspondence between the heat generation of the battery pack and temperature changes.

[0018] Based on the aforementioned technical methods, an electrical module for the battery pack model is constructed using capacity-temperature data and a second correlation. This module can accurately characterize the voltage output pattern and capacity decay characteristics of the battery pack under different temperature and SOC combinations, providing parameter support for battery remaining capacity estimation, terminal voltage prediction, and battery management system algorithm development. Similarly, a heat generation module for the battery pack model is constructed using heat generation-temperature data. This module can quantify the heat generation change trend of the battery pack under different temperature conditions and deduce the temperature-heat generation variation law, providing quantitative basis for determining the triggering timing of cooling / heating strategies and selecting power levels in vehicle thermal management systems.

[0019] Furthermore, the above method also includes: obtaining correction coefficient-temperature data based on the correction coefficient corresponding to the battery pack temperature under multiple operating conditions; and constructing a battery pack model based on capacity-temperature data, the second correlation relationship, and heat generation-temperature data, including: constructing a battery pack model based on correction coefficient-temperature data, capacity-temperature data, the second correlation relationship, and heat generation-temperature data.

[0020] According to the aforementioned technical methods, the correction coefficient is used to correct the first correlation (voltage-SOC curve) at a single temperature. However, temperature changes the internal electrochemical characteristics of the battery, thereby affecting the energy loss amplitude and causing the correction coefficient to change with temperature. If this change is ignored and a fixed correction coefficient is used to adapt to all temperature conditions, the first correlation will deviate, affecting the accuracy of the battery pack model. This application combines the correction coefficient-temperature data to make the simulated voltage values ​​of the second correlation at different temperatures closer to reality, thereby improving the accuracy of the battery pack model.

[0021] Secondly, a battery pack modeling device is provided, comprising: a testing unit for conducting charge-discharge tests using a battery pack testing device based on different operating condition parameters to obtain actual energy data, actual SOC variation range, actual capacity data, and a first correlation relationship of the battery pack under multiple operating conditions; wherein the first correlation relationship is used to indicate the law of voltage variation of the battery pack with the SOC of the battery pack; the operating condition parameters are used to simulate the operating conditions of the battery pack under different ambient temperatures; the multiple operating conditions include: a first operating condition and a second operating condition where the temperature difference is greater than a preset temperature threshold; and a first determining unit for determining the charge-discharge efficiency-temperature data of the battery pack based on the actual energy data and the battery pack body temperature under multiple operating conditions; the charge-discharge efficiency-temperature data represents the charge-discharge efficiency of the battery pack under different temperature conditions. The system comprises five determination units: a first determination unit and a second determination unit. The first unit determines the battery pack's charge / discharge efficiency-temperature data, and the second unit determines the battery pack's heat generation-temperature data. The heat generation-temperature data represents the battery pack's heat generation variation under different temperature conditions. The third unit determines the battery pack's capacity-temperature data based on actual capacity data and battery pack body temperature under multiple operating conditions. The capacity-temperature data represents the battery pack's capacity variation under different temperature conditions. The fourth unit determines the second correlation based on the first correlation under multiple operating conditions. The second correlation represents the comprehensive law of voltage variation with SOC under different temperature conditions. The fifth unit constructs a battery pack model based on the capacity-temperature data, the second correlation, and the heat generation-temperature data.

[0022] Thirdly, an electronic device is provided, comprising: a processor and a memory; the memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the methods of the first aspect and any possible implementation thereof.

[0023] Fourthly, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the methods described in the first aspect and any possible implementation thereof.

[0024] Fifthly, a computer program product is provided, comprising computer instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0025] The beneficial effects of this invention are: (1) By acquiring the actual energy data, actual SOC variation range, actual capacity data, and first correlation of the battery pack under various operating conditions, and then obtaining the charge / discharge efficiency-temperature data, heat generation-temperature data, capacity-temperature data, and second correlation based on these data, a battery pack model is constructed based on the capacity-temperature data, the second correlation, and the heat generation-temperature data. When modeling a battery pack, it is generally necessary to consider the electrical and thermal characteristics of the battery pack. The electrical characteristics can reflect the voltage output, capacity change, and energy conversion patterns of the battery pack under different operating conditions, while the thermal characteristics can reflect the heat generation, dissipation, and temperature change patterns of the battery pack during energy conversion. The heat generation-temperature data of this application can reflect the change pattern of energy loss converted into heat under different temperature conditions, the capacity-temperature data can reflect the influence of temperature on the capacity of the battery pack, and the second correlation can reflect the pattern of voltage change with SOC at different temperatures. Combining these three can reflect the electrical and thermal characteristics of the battery pack under different temperature and SOC coupled operating conditions, thereby completing the modeling of the battery pack.

[0026] (2) The first discharge energy in the discharge energy data corresponds to the total discharge output energy under CLTC operating conditions, the first charging energy corresponds to the energy recovered by regenerative braking during the process, and the first net discharge energy is the actual external output energy after deducting the recovered energy; the second charging energy in the charging energy data is the total input energy under standard charging mode, and the second discharge energy is the energy loss during the charging stage. By constructing an energy balance equation containing the above five types of energy parameters, the charging and discharging efficiency of the battery pack during the charging and discharging process can be accurately calculated.

[0027] (3) The actual SOC variation range may differ under different temperature conditions. If the charge and discharge efficiency is calculated directly using the uncorrected actual energy data, the efficiency results will be biased due to the difference in the SOC variation range, and the effect of temperature on battery energy conversion efficiency cannot be truly reflected. By correcting the actual energy data by the ratio of the reference SOC variation range to the actual SOC variation range, the energy data under different operating conditions can be converted to a unified reference SOC variation range. Calculations based on the corrected actual energy data can yield a more accurate charge and discharge efficiency.

[0028] (4) By collecting the charging energy under charging conditions, the net discharge energy under charging conditions, the discharge energy during the discharge process and the energy recovered by regenerative braking, the energy balance relationship between charging energy, discharge energy and net discharge energy can be established, and the charging and discharging efficiency of the battery pack can be accurately calculated, providing a reliable basis for subsequent calculation of heat generation and temperature data.

[0029] (5) In charge and discharge tests under different temperature conditions, the actual SOC variation range may vary, which may lead to errors in the directly collected actual capacity data. By correcting the actual capacity data, the interference of inconsistent SOC variation range on the capacity calculation results can be eliminated, thereby improving the accuracy of capacity-temperature data.

[0030] (6) The initial correlation is obtained by directly using the voltage data corresponding to the SOC node under a single temperature condition to obtain the voltage-SOC curve. This process does not fully consider the energy loss of the battery pack during actual operation. The essence of the correction coefficient is the ratio (or deviation rate) between the predicted net discharge and the actual net discharge. The correction coefficient is used to calibrate the initial correlation, which essentially maps the influence of actual energy loss onto the voltage-SOC curve, making the slope of the curve more consistent with the real operating conditions.

[0031] (7) By constructing the electrical module of the battery pack model through capacity-temperature data and the second correlation, the voltage output law and capacity decay characteristics of the battery pack under different temperature and SOC combination conditions can be accurately characterized, providing parameter support for battery remaining capacity estimation, terminal voltage prediction, and battery management system algorithm development. By constructing the heat generation module of the battery pack model through heat generation-temperature data, the heat generation change trend of the battery pack under different temperature conditions can be quantified, and the temperature change law with heat generation can be derived, providing quantitative basis for determining the triggering time of cooling / heating strategies and selecting the power level of the vehicle thermal management system.

[0032] (8) The correction coefficient is used to correct the first correlation (voltage-SOC curve) at a single temperature. However, temperature changes the internal electrochemical characteristics of the battery, which in turn affects the energy loss amplitude, causing the correction coefficient to change with temperature. If this change is ignored and a fixed correction coefficient is used to adapt to all temperature conditions, the first correlation will be deviated, affecting the accuracy of the battery pack model. This application combines the correction coefficient-temperature data to make the simulated voltage values ​​of the second correlation at different temperatures closer to reality, thereby improving the accuracy of the battery pack model. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the structure of a battery pack modeling system according to an embodiment of this application; Figure 2 This is a schematic flowchart illustrating a battery pack modeling method according to an embodiment of this application. Figure 3 This is a flowchart illustrating another battery pack modeling method according to an embodiment of this application; Figure 4 This is a schematic diagram of capacity-temperature data according to an embodiment of this application; Figure 5 This is a schematic diagram of energy-temperature data according to an embodiment of this application; Figure 6 This is a schematic diagram illustrating a first association relationship according to an embodiment of this application; Figure 7 This is a schematic diagram of a correction factor-temperature data according to an embodiment of this application; Figure 8 This is a schematic diagram of charge / discharge efficiency-temperature data according to an embodiment of this application; Figure 9 This is a schematic diagram of a battery pack model according to an embodiment of this application; Figure 10 This is a schematic diagram of the structure of a battery pack modeling device according to an embodiment of this application; Figure 11 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0034] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0035] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0036] The modeling method, apparatus, electronic equipment, and storage medium of the battery pack of this application will be described below with reference to the accompanying drawings.

[0037] In some embodiments, when designing a battery thermal management strategy, the temperature control accuracy and energy consumption rationality of the strategy under different temperature conditions can be predicted by modeling and simulating the battery pack, thus obtaining the optimal control logic that adapts to the entire temperature range, without the need to repeatedly conduct real-vehicle tests under extreme environments such as high and low temperatures. Therefore, how to model the battery pack is a problem that urgently needs to be solved.

[0038] However, the modeling process often requires various parameters of the battery pack, including structural and electrochemical parameters. These parameters are usually only fully obtained in the later stages of product development. In early-stage development scenarios such as scheme demonstration and vehicle benchmarking analysis, the lack of detailed battery pack modeling parameters often hinders the progress of the modeling work.

[0039] In some embodiments, such as Figure 1 As shown, the battery pack modeling system includes: battery pack 101 and battery pack testing device 102.

[0040] The battery pack testing device 102 is a dedicated device for conducting charge and discharge tests on battery packs, and typically includes a charge and discharge cabinet, a data acquisition module, and a modeling module.

[0041] The charging and discharging cabinet is used to perform charging and discharging operations on the battery pack according to preset charging and discharging parameters (voltage, current). The data acquisition module is used to collect parameters in real time throughout the charging and discharging process, including battery pack terminal voltage, charging and discharging current, battery pack body temperature, SOC value, etc. The modeling module is used to call preset algorithms to process the collected test data, calculate charging and discharging efficiency-temperature data, heat generation-temperature data, capacity-temperature data and second correlation relationship, and complete the separate modeling of electrical module and heat generation module based on the above data, and finally construct a complete battery pack model.

[0042] In the process of modeling the battery pack, if detailed modeling parameters of the battery pack are lacking, the battery pack testing device of this application can be directly connected to the whole vehicle or the entire battery pack to collect the vehicle's operating parameters or the parameters of the entire battery charging and discharging process mentioned above. Based on these data, the battery pack can be modeled without the need to disassemble the battery pack to obtain detailed structure and electrochemical parameters.

[0043] In some embodiments, after constructing the battery pack model, the battery pack model can be used for vehicle battery management system (BMS) algorithm verification and iteration, vehicle range calibration, battery thermal management strategy optimization, and battery safety risk assessment.

[0044] As one possible approach, the battery pack model can be integrated into a vehicle simulation platform. Based on vehicle operating parameters under different operating scenarios, the electro-thermal response of the battery pack can be simulated to obtain simulation results. The simulation results can then be compared with preset results to obtain deviation analysis results. The preset results represent the set of performance parameters that the battery pack needs to achieve under the same operating scenario. Based on the deviation analysis results, the configuration parameters of the thermal management strategy can be corrected.

[0045] For example, in a fast-charging scenario at low temperatures (-15℃) in winter, the electrical module can calculate the terminal voltage corresponding to different states of charge (SOC) during fast charging, thereby supporting the battery management system to adjust the charging current and avoid triggering overvoltage protection due to voltage abnormalities. Meanwhile, the heat generation module can calculate the heat generated per unit time during fast charging under these low-temperature conditions. Combined with the battery pack's heating system parameters, it determines whether the current heat generation can raise the battery temperature back to a preset operating range (e.g., 10℃), and then decides whether to activate the heating device, thereby verifying and optimizing the battery thermal management strategy.

[0046] In some embodiments, the battery pack modeling method of this application can be applied to the battery pack testing apparatus described above.

[0047] like Figure 2 As shown, the battery pack modeling method of this application includes the following steps: S201. Using a battery pack testing device, charge and discharge tests are conducted based on different operating parameters to obtain the actual energy data, actual SOC variation range, actual capacity data, and first correlation of the battery pack under multiple operating conditions.

[0048] The first correlation is used to indicate the pattern of voltage variation of the battery pack with its State of Charge (SOC), and can be represented as a voltage-SOC curve. In one possible implementation, an initial correlation is determined based on the voltage corresponding to different SOC values ​​of the battery pack; the initial correlation represents the preliminary pattern of voltage variation with SOC; the initial correlation is then corrected based on a correction coefficient to obtain the first correlation, where the correction coefficient characterizes the degree of deviation between the predicted net discharge and the actual net discharge of the battery pack.

[0049] Operating condition parameters are used to simulate the battery pack's operation under different ambient temperatures. In one implementation, multiple operating conditions may include a first operating condition and a second operating condition where the temperature difference is greater than a preset temperature threshold. For example, the multiple operating conditions include a high-temperature condition, a low-temperature condition, and a normal-temperature condition, where the first operating condition is a high-temperature condition, the second operating condition is a low-temperature condition, and the temperature difference between the high-temperature and low-temperature conditions is greater than the preset temperature threshold. By setting the difference between the maximum and minimum temperatures corresponding to multiple operating conditions to be greater than the preset temperature threshold, it is ensured that the temperature range covered by the test is sufficiently wide, avoiding limitations on the applicability of subsequent models due to an excessively narrow temperature range.

[0050] Actual energy data represents the total energy actually charged and released by the battery pack in a single charge-discharge cycle, including: charging energy, discharging energy, and charge-discharge energy.

[0051] The actual state of charge (SOC) range represents the percentage of the battery's remaining capacity relative to its rated capacity. This parameter is the range between the starting and ending values ​​of the battery pack's SOC in a single test, for example, from 100% discharge to 20%, with a range of 80%.

[0052] Actual capacity data represents the actual amount of electricity released by the battery pack under specific operating conditions, including: charging capacity, discharging capacity, and charge / discharge capacity.

[0053] For example, based on the normal temperature discharge condition, the State of Charge (SOC) is used as the independent variable and the voltage U as the dependent variable to perform curve fitting, obtaining the basic OCV curve, i.e., the initial correlation. The fitted curve uses an Nth-order polynomial. The initial value of N is 2. If the accuracy of the fitted curve is less than the preset accuracy, N is adjusted to 3, 4, 5, ..., until the set maximum value is reached.

[0054] Understandably, the initial correlation is derived by directly using the voltage data corresponding to the SOC node under a single temperature condition to obtain the voltage-SOC curve. This process does not fully consider the energy loss of the battery pack during actual operation. The correction factor is essentially the ratio (or deviation rate) between the predicted net discharge and the actual net discharge. Using the correction factor to calibrate the initial correlation essentially maps the impact of actual energy loss onto the voltage-SOC curve, making the slope of the curve more closely resemble real-world operating conditions.

[0055] As one possible approach, after the battery pack testing device is connected to the battery pack under test, a first set of temperature conditions is set. Once the battery pack temperature stabilizes, it operates according to a preset charging and discharging strategy. Data such as voltage, current, SOC, charged / discharged energy, and actual capacity of the battery pack are collected during the test, and a first correlation (voltage-SOC curve) is obtained at that temperature. The test temperature is then adjusted to the second and third sets of operating temperatures, ensuring that the difference between the maximum and minimum temperatures corresponding to multiple operating conditions is greater than a preset temperature threshold. The above steps are repeated to obtain the basic data and the first correlation under multiple temperature conditions.

[0056] It is understandable that by collecting actual energy data, actual SOC variation range, actual capacity data, and the first correlation under the above-mentioned multi-temperature operating conditions, a complete and effective basic dataset can be provided for subsequent calculation of charge and discharge efficiency, derivation of capacity-temperature characteristics, and construction of voltage-SOC-temperature integrated correlation model.

[0057] S202. Based on the actual energy data under multiple operating conditions and the battery pack body temperature under multiple operating conditions, determine the charge and discharge efficiency-temperature data of the battery pack.

[0058] Among them, the charge / discharge efficiency-temperature data represents the variation of the battery pack's charge / discharge efficiency under different temperature conditions. In essence, it is the set of the battery pack's charge / discharge efficiencies at different temperatures.

[0059] It should be noted that the temperature mentioned in the embodiments of this application can represent the average temperature of the battery pack under a certain operating condition.

[0060] The charge / discharge efficiency of a battery pack is actually the conversion ratio of the effective output energy during the discharge phase to the total input energy during the charging phase. Therefore, by collecting the total input energy during the charging phase and the output energy during the discharging phase under various temperature conditions, and combining the principle of energy conservation, the charge / discharge efficiency at the corresponding temperature can be calculated. Then, the correspondence between efficiency and temperature can be established, and finally, charge / discharge efficiency-temperature data can be generated.

[0061] That is, the actual energy data includes: discharge energy data when the battery pack is in a discharging state and charging energy data when the battery pack is in a charging state. As one possible implementation, for each operating condition of the battery pack, discharge energy data and charging energy data are determined; the discharge energy data includes: first discharge energy, first charging energy, and first net discharge energy; the charging energy data includes: second discharge energy and second charging energy; based on the energy balance equation between the second charging energy, second discharge energy, first discharge energy, first charging energy, and first net discharge energy, the charge / discharge efficiency of the battery pack under each operating condition is determined; based on the charge / discharge efficiency corresponding to the battery pack's body temperature under multiple operating conditions, the charge / discharge efficiency-temperature data of the battery pack is determined.

[0062] As another possible implementation, the charge and discharge efficiency of the battery pack is determined by: correcting the actual energy data under multiple operating conditions based on the ratio of the reference SOC variation range to the actual SOC variation range, and obtaining the corrected actual energy data under multiple operating conditions; and determining the charge and discharge efficiency of the battery pack based on the corrected actual energy data under multiple operating conditions.

[0063] It should be noted that the process of correcting the actual energy data under multiple operating conditions based on the ratio of the reference SOC variation range to the actual SOC variation range can be referred to the following formula (3), which will not be elaborated here.

[0064] It is understandable that the actual SOC variation range may differ under different temperature conditions. If the charge / discharge efficiency is calculated directly using uncorrected actual energy data, the efficiency results will be biased due to the differences in the SOC variation range, failing to accurately reflect the impact of temperature on battery energy conversion efficiency. By correcting the actual energy data using the ratio of the reference SOC variation range to the actual SOC variation range, the energy data under different operating conditions can be converted to a unified reference SOC variation range. Calculations based on the corrected actual energy data can yield a more accurate charge / discharge efficiency.

[0065] S203. Based on the charge and discharge efficiency-temperature data of the battery pack, determine the heat generation-temperature data of the battery pack.

[0066] Among them, the heat generation-temperature data represents the change pattern of heat generation of the battery pack under different temperature conditions.

[0067] As one possible approach, the charging and discharging power of the battery pack during the charging and discharging process under various temperature conditions is determined, and the heat generation-temperature data of the battery pack is obtained based on the charging and discharging power and charging and discharging efficiency-temperature data under various temperature conditions.

[0068] In one possible implementation, the heat output-temperature data satisfy the following relationship:

[0069] Where HeatPwr represents heat generation-temperature data, Eff represents charge / discharge efficiency-temperature data, and Pwr represents charge / discharge power.

[0070] S204. Based on the actual capacity data under multiple operating conditions and the battery pack body temperature under multiple operating conditions, determine the capacity-temperature data of the battery pack.

[0071] Among them, the capacity-temperature data represents the capacity change pattern of the battery pack under different temperature conditions, which can reflect the impact of temperature on the battery's energy storage capacity.

[0072] As one possible approach, the actual capacity data under various temperature conditions in S201 is extracted. With temperature as the horizontal axis and actual capacity as the vertical axis, multiple sets of actual capacity data are fitted together. The capacity values ​​of the temperature range not covered by the test are supplemented by the fitting algorithm (such as polynomial fitting or Gaussian fitting) to obtain the capacity-temperature data of the battery pack.

[0073] As another possible implementation, the actual capacity data under multiple operating conditions is corrected based on the ratio of the reference SOC variation range to the actual SOC variation range to obtain the corrected actual capacity data under multiple operating conditions; based on the corrected actual capacity data corresponding to the battery pack body temperature under multiple operating conditions, the capacity-temperature data of the battery pack is obtained.

[0074] In one possible implementation, the corrected actual capacity data is calculated as follows:

[0075] Where, the corrected C1 represents the corrected discharge capacity, C1 represents the discharge capacity, SOC0 represents the initial state of charge, and SOC represents the discharge capacity. end This indicates the termination of the charged state.

[0076] It is understandable that the actual SOC variation range may differ in charge-discharge tests under different temperature conditions, leading to errors in the directly collected actual capacity data. By correcting the actual capacity data, the interference of inconsistent SOC variation range on the capacity calculation results can be eliminated, thereby improving the accuracy of capacity-temperature data.

[0077] S205. Based on the first association relationship under multiple working conditions, determine the second association relationship.

[0078] The second correlation represents the comprehensive law of voltage variation with SOC under different temperature conditions for the battery pack.

[0079] As one possible approach, the first correlation under each temperature condition is first standardized, and the SOC node and corresponding terminal voltage data for each temperature are extracted to form a three-dimensional data set of temperature-SOC-voltage. Then, a polynomial fitting algorithm is used to fit the three-dimensional data set to obtain the second correlation.

[0080] S206. Based on capacity-temperature data, the second correlation, and heat generation-temperature data, a battery pack model is constructed.

[0081] As one possible implementation, an electrical module of the battery pack model is constructed based on capacity-temperature data and a second correlation. The electrical module is used to characterize the voltage output characteristics and capacity variation of the battery pack under different temperature and SOC conditions. A heat generation module of the battery pack model is constructed based on heat generation-temperature data. The heat generation module is used to characterize the correspondence between the heat generation of the battery pack and temperature changes.

[0082] Understandably, the purpose of constructing a battery pack model is to accurately simulate the electrical and thermal dynamic coupling characteristics of the battery pack under different temperatures, SOCs, and charge / discharge conditions. This provides a reliable simulation basis for battery management system algorithm optimization, vehicle range calibration, and thermal management strategies, ultimately improving the battery pack's operational stability, lifespan, and overall vehicle safety performance. By using capacity-temperature data and a second correlation, the electrical module of the battery pack model can accurately characterize the voltage output pattern and capacity decay characteristics of the battery pack under different temperature and SOC combinations, providing parameter support for battery remaining capacity estimation, terminal voltage prediction, and battery management system algorithm development. Similarly, by using heat generation-temperature data, the heat generation module of the battery pack model can quantify the heat generation change trend of the battery pack under different temperature conditions, derive the temperature-heat generation variation law, and provide a quantitative basis for determining the triggering timing and power selection of the cooling / heating strategy in the vehicle thermal management system.

[0083] It should be understood that by modeling the electrical module and the heat generation module separately and linking them together with the temperature of the battery pack, this application can achieve a synergistic response of electrical and thermal characteristics, allowing the model to accurately characterize the characteristics of a single module while also reflecting the coupling effect of the two modules.

[0084] Therefore, this application obtains the actual energy data, actual SOC variation range, actual capacity data, and first correlation of the battery pack under various operating conditions. Based on this data, it then derives charge / discharge efficiency-temperature data, heat generation-temperature data, capacity-temperature data, and a second correlation. Finally, based on the capacity-temperature data, the second correlation, and the heat generation-temperature data, a battery pack model is constructed. When modeling a battery pack, it is generally necessary to consider its electrical and thermal characteristics. Electrical characteristics reflect the voltage output, capacity changes, and energy conversion patterns of the battery pack under different operating conditions. Thermal characteristics reflect the heat generation, dissipation, and temperature changes during energy conversion. The heat generation-temperature data in this application reflects the change in energy loss converted into heat under different temperature conditions. The capacity-temperature data reflects the influence of temperature on the battery pack's capacity. The second correlation reflects the voltage variation with SOC at different temperatures. Combining these three factors allows for the reflection of the battery pack's electrical and thermal characteristics under different temperature and SOC coupled operating conditions, thus enabling the modeling of the battery pack.

[0085] In some embodiments, during charge-discharge experiments on the battery pack, charging is performed under standard charging conditions, and only charging occurs during charging. Discharging is performed under CLTC conditions, and the battery simultaneously charges and discharges due to the inclusion of regenerative braking energy recovery. Therefore, the charge-discharge efficiency of the battery pack satisfies the following relationship:

[0086] Here, Eff represents the charge and discharge efficiency of the battery pack. This indicates the corrected second charging energy. This represents the corrected second discharge energy. This indicates the corrected first charging energy. This represents the corrected first discharge energy. This represents the corrected first net discharge energy.

[0087] Understandable, This indicates the amount of electricity the battery pack has gained during charging. This indicates the amount of electricity discharged by the battery pack during charging (usually 0). This indicates the amount of electricity discharged by the battery pack under discharge conditions. This indicates the amount of electricity the battery pack takes in during discharge. The net discharge energy of the battery pack during charging is the amount of electricity taken in minus the amount of electricity discharged.

[0088] Therefore, by collecting the charging energy, net discharge energy, discharge energy, and regenerative braking energy under charging conditions, and establishing the energy balance relationship between charging energy, discharge energy, and net discharge energy, the charging and discharging efficiency of the battery pack can be accurately calculated, providing a reliable basis for subsequent calculation of heat generation and temperature data.

[0089] In some embodiments, the internal resistance loss and other parameters of the battery pack differ under different temperature conditions, resulting in different degrees of deviation between the predicted net discharge and the actual net discharge. When the temperature of the battery pack changes, the correction coefficient also changes. Therefore, the above method further includes: obtaining correction coefficient-temperature data based on the correction coefficients corresponding to the battery pack temperature under multiple operating conditions.

[0090] Among them, the correction coefficient-temperature data represents the variation law of the deviation between the predicted net discharge and the actual net discharge under different temperature conditions. The correction coefficient-temperature data (curve) can be obtained by fitting the correction coefficient under each temperature condition.

[0091] Therefore, S206 above includes: constructing a battery pack model based on correction coefficient-temperature data, capacity-temperature data, second correlation relationship, and heat generation-temperature data.

[0092] As one possible approach, the voltage-SOC curves of the second correlation at various temperatures are first corrected based on the correction coefficient-temperature data to obtain the corrected second correlation. This corrected correlation is then fused with the capacity-temperature data to construct a battery pack electrical sub-model, accurately outputting voltage, capacity, and charge / discharge power parameters under different temperatures and SOC conditions. Finally, the output parameters of the electrical sub-model are coupled and iterated with the heat generation-temperature data. The accuracy of heat generation calculation is optimized through electrical parameter inversion, and the temperature response characteristics of the electrical parameters are corrected based on the heat generation data, ultimately constructing a complete electro-thermal coupled battery pack model.

[0093] It should be understood that the correction factor is used to correct the first correlation (voltage-SOC curve) at a single temperature. However, temperature changes the internal electrochemical characteristics of the battery, thus affecting the energy loss amplitude and causing the correction factor to change with temperature. If this change is ignored and a fixed correction factor is used to adapt to all temperature conditions, the first correlation will be deviated, affecting the accuracy of the battery pack model. This application combines the correction factor-temperature data to make the simulated voltage values ​​of the second correlation at different temperatures closer to reality, thereby improving the accuracy of the battery pack model.

[0094] The following section uses the simulation modeling of a power battery pack installed in a vehicle as an example to illustrate the battery pack modeling method of this application in more detail. Figure 3As shown, it includes the following steps: Step 1: Determine the applicable operating conditions for the battery pack simulation model. The operating conditions are determined by both ambient temperature and charge / discharge type. Ambient temperature is categorized into three levels: -10℃, 25℃, and 40℃. Operating conditions are categorized into two typical levels: 6.6kW slow charging and CLTC-P full-cycle discharge. Accordingly, six operating conditions are determined: -10℃ 6.6kW slow charging, -10℃ CLTC-P full-cycle discharge, 25℃ 6.6kW slow charging, 25℃ CLTC-P full-cycle discharge, 40℃ 6.6kW slow charging, and 40℃ CLTC-P full-cycle discharge.

[0095] Step 2: Conduct vehicle or battery tests according to the 6 operating conditions defined in Step 1, and obtain test data. The obtained test data includes two types: electrical and thermal. Electrical data includes battery pack voltage U, current I, power Pwr, and state of charge (SOC); thermal data includes temperature T, for a total of 5 types of test data.

[0096] Step 3: Define the discharge current I as negative, and integrate the current I under the six operating conditions to obtain capacity data, including: discharge capacity. Charging capacity and net discharge capacity Specifically, as shown in Table 1. Correspondingly, the power Pwr is negative during discharge. The power Pwr under the six operating conditions is integrated to obtain energy data, including: discharge energy. Charging energy and net discharge energy See also Table 1.

[0097] Table 1 Battery Capacity and Energy (Before Correction)

[0098] The SOC variation range under the six operating conditions is shown in Table 2. Table 2 SOC Variation Range

[0099] Therefore, as shown in Table 3, the corrected discharge capacity under six operating conditions was calculated. Correct charging capacity Corrected net discharge capacity Correcting discharge energy Correct charging energy and corrected net discharge energy .

[0100] Table 3 Battery Capacity and Energy (Revised)

[0101] Step 4: The average battery temperature under 6 operating conditions is shown in Table 4. Table 4 Average Battery Temperature

[0102] With the average battery temperature as the independent variable, curve fitting was performed using the absolute values ​​of the corrected net discharge capacity C3 and the corrected net discharge energy E3 as dependent variables. The fitting curves were obtained using second-order polynomials. The fitting results for the corrected net discharge capacity C3 and the corrected net discharge energy E3 under discharge conditions are as follows: Figure 4 and Figure 5 As shown. Figure 4 In the diagram, the horizontal axis x represents the average battery temperature TMean, and the vertical axis y represents the capacity (actual capacity data), y = -0.0028x. 2 +0.1793x+137.06 represents the capacity-temperature data, specifically the net discharge capacity-temperature data. Figure 5 In the diagram, the horizontal axis x represents the average battery temperature TMean, and the vertical axis y represents the energy (actual energy data), y = -0.0007x. 2 +0.0849x+84.634 represents the energy-temperature data, specifically the net discharge energy-temperature data.

[0103] Step 5: Based on the normal temperature discharge condition, use SOC as the independent variable and voltage U as the dependent variable to perform curve fitting to obtain the first correlation. The fitting curve uses an Nth-order polynomial, with N initially set to 2. The fitting accuracy is set to Adj-R² greater than or equal to 0.95. If the fitting accuracy is not met, N = 3, 4, 5, ..., until the set maximum value of 9 is reached. The entire fitting process is shown in Table 5. When N = 9, the fitting accuracy requirement is met. The basic OCV curve of the fitting is shown in Table 5. Figure 6 As shown. Figure 6 In the diagram, the horizontal axis x represents SOC, and the vertical axis y represents voltage U.

[0104] Table 5. Basic OCV curve fitting process

[0105] Step 6: Correct the basic OCV curve using a correction factor K. The measured current I under normal temperature discharge conditions is used as an external input to the battery pack simulation model for simulation. (Normal temperature correction factor) The calculation process is shown in Table 6. Its initial value is 1. After one iteration, the simulated net discharge is equal to the measured net discharge, which meets the requirements.

[0106] Table 6. Calculation process for K at room temperature

[0107] and The calculation method follows the same logic, calculating the discharge conditions. and The values ​​are 0.9841 and 0.9930, respectively.

[0108] Step 7: Using the average battery temperature TMean under all operating conditions as the independent variable and the correction factor K as the dependent variable, perform curve fitting. The fitting curve uses a second-order polynomial. The fitting result of the correction factor K under discharge conditions is as follows: Figure 7 As shown. The correction factor K is also the correction coefficient mentioned above. Figure 7 In the diagram, the horizontal axis x represents the average battery temperature TMean, and the vertical axis y represents the correction factor K, y = 1E-05x 2 -0.0003x+0.9859 is the correction factor for temperature data.

[0109] Step 8: The corrections E1, E2, and E3 under normal temperature charging conditions and the corrections E1, E2, and E3 under normal temperature discharging conditions are shown in Table 7.

[0110] Table 7 Corrected Energy (Normal Temperature Charging and Discharging Conditions)

[0111] The room-temperature charge-discharge efficiency is calculated by establishing the following energy balance equation. ,

[0112] Calculate the charge / discharge efficiency at room temperature The low-temperature charge / discharge efficiency is 0.9848. and high temperature charge and discharge efficiency The energy balance equation was established and the calculation method was similarly applied to calculate the low-temperature charge-discharge efficiency. and high temperature charge and discharge efficiency The values ​​are 0.9829 and 0.9827, respectively.

[0113] Step 9: Using the average battery temperature TMean under all operating conditions as the independent variable and the charge / discharge efficiency Eff as the dependent variable, perform curve fitting. The fitting curve uses a second-order polynomial, and the fitting result is as follows: Figure 8 As shown. Figure 8 In the diagram, the horizontal axis x represents the average battery temperature TMean, and the vertical axis y represents the charge / discharge efficiency Eff, where y = -1E-05x. 2 +0.0005x+0.9788 represents the charge / discharge efficiency-temperature data.

[0114] Step 10: Independently model the heat generation module (HeatPwr) of the battery pack simulation model, such as... Figure 9 As shown. The modeling parameters for the electrical module include two parts: the fitted capacity and the corrected OCV. The modeling parameter for the heat generation module is the fitted efficiency Eff. The battery power Pwr of the electrical module is input to the heat generation module.

[0115] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the battery pack modeling device or electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] This application embodiment can, according to the above method, exemplarily divide a battery pack modeling device or electronic device into functional modules. For example, the battery pack modeling device or electronic device may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0117] Reference Figure 10 The battery pack modeling device 1000 provided in this application embodiment includes: a testing unit 1001, a first determining unit 1002, a second determining unit 1003, a third determining unit 1004, a fourth determining unit 1005, and a fifth determining unit 1006.

[0118] The test unit 1001 is used to conduct charge and discharge tests based on different operating conditions using a battery pack test device to obtain actual energy data, actual SOC variation range, actual capacity data, and a first correlation relationship of the battery pack under multiple operating conditions; wherein, the first correlation relationship is used to indicate the law of voltage variation of the battery pack with the SOC of the battery pack; the operating conditions parameters are used to simulate the operating conditions of the battery pack under different ambient temperatures.

[0119] The first determining unit 1002 is used to determine the charge / discharge efficiency-temperature data of the battery pack based on the actual energy data under multiple operating conditions and the battery pack body temperature under multiple operating conditions.

[0120] The second determining unit 1003 is used to determine the heat generation-temperature data of the battery pack based on the charge-discharge efficiency-temperature data of the battery pack.

[0121] The third determining unit 1004 is used to determine the capacity-temperature data of the battery pack based on the actual capacity data under multiple operating conditions and the pack body temperature under multiple operating conditions.

[0122] The fourth determining unit 1005 is used to determine the second correlation based on the first correlation under multiple operating conditions; the second correlation represents the comprehensive law of voltage change with SOC of the battery pack under different temperature conditions.

[0123] The fifth determining unit 1006 is used to construct a battery pack model based on capacity-temperature data, the second correlation relationship, and heat generation-temperature data.

[0124] Furthermore, the actual energy data includes: discharge energy data when the battery pack is in a discharging state and charging energy data when the battery pack is in a charging state; the first determining unit 1002 is specifically used to determine the discharge energy data and charging energy data for each operating condition of the battery pack; the discharge energy data includes: first discharge energy, first charging energy, and first net discharge energy; the charging energy data includes: second discharge energy and second charging energy; based on the energy balance equation between the second charging energy, second discharge energy, first discharge energy, first charging energy, and first net discharge energy, the charging and discharging efficiency of the battery pack under each operating condition is determined; based on the charging and discharging efficiency corresponding to the battery pack body temperature under multiple operating conditions, the charging and discharging efficiency-temperature data of the battery pack is determined.

[0125] Furthermore, the charge and discharge efficiency of the battery pack is determined as follows: based on the ratio of the reference SOC variation range to the actual SOC variation range, the actual energy data under multiple operating conditions are corrected to obtain the corrected actual energy data under multiple operating conditions; based on the corrected actual energy data under multiple operating conditions, the charge and discharge efficiency of the battery pack is determined.

[0126] Furthermore, the charge and discharge efficiency of the battery pack satisfies the following relationship: Where Eff represents the charge and discharge efficiency of the battery pack. This indicates the corrected second charging energy. This represents the corrected second discharge energy. This indicates the corrected first charging energy. This represents the corrected first discharge energy. This represents the corrected first net discharge energy.

[0127] Furthermore, the third determining unit 1004 is specifically used to correct the actual capacity data under multiple operating conditions based on the ratio of the reference SOC variation range to the actual SOC variation range, so as to obtain the corrected actual capacity data under multiple operating conditions; and to obtain the capacity-temperature data of the battery pack based on the corrected actual capacity data corresponding to the battery pack body temperature under multiple operating conditions.

[0128] Furthermore, the first correlation is determined in the following way: based on the voltage corresponding to different SOC values ​​of the battery pack, an initial correlation is determined; the initial correlation represents the preliminary law of the battery pack voltage changing with SOC; the initial correlation is corrected based on the correction coefficient to obtain the first correlation, and the correction coefficient is used to characterize the degree of deviation between the predicted net discharge and the actual net discharge of the battery pack.

[0129] Furthermore, the fifth determining unit 1006 is specifically used to construct an electrical module of the battery pack model based on capacity-temperature data and the second correlation. The electrical module is used to characterize the voltage output characteristics and capacity change law of the battery pack under different temperature and SOC conditions. Based on heat generation-temperature data, a heat generation module of the battery pack model is constructed. The heat generation module is used to characterize the correspondence between the heat generation of the battery pack and temperature changes.

[0130] Furthermore, the above-mentioned device also includes: a sixth determining unit, used to obtain correction coefficient-temperature data based on the correction coefficient corresponding to the battery pack body temperature under multiple operating conditions; and a fifth determining unit 1006, specifically used to construct a battery pack model based on the correction coefficient-temperature data, capacity-temperature data, second correlation relationship, and heat generation-temperature data.

[0131] like Figure 11 As shown, the electronic device 1100 provided in this application embodiment includes, but is not limited to, a processor 1101 and a memory 1102.

[0132] The memory 1102 described above is used to store the executable instructions of the processor 1101. It is understood that the processor 1101 is configured to execute instructions to implement the battery pack modeling method in the above embodiments.

[0133] It should be noted that those skilled in the art will understand that Figure 11 The electronic device structure shown does not constitute a limitation on electronic device 1100; electronic devices may include, but are not limited to, those described above. Figure 11 This may indicate more or fewer components, or a combination of certain components, or a different arrangement of components.

[0134] Processor 1101 is the control center of electronic device 1100. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 1102, and by calling data stored in memory 1102, it performs various functions and processes data of electronic device 1100, thereby providing overall monitoring of electronic device 1100. Processor 1101 may include one or more processing units. Optionally, processor 1101 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 1101.

[0135] The memory 1102 can be used to store software programs and various data. The memory 1102 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, processing unit, etc.), etc. Furthermore, the memory 1102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0136] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 1102 including instructions, which can be executed by a processor 1101 of an electronic device 1100 to implement the methods in the above embodiments.

[0137] In actual implementation, Figure 10 The functions of each module can be provided by Figure 11 The processor 1101 calls the computer program stored in the memory 1102 to implement the process. The specific execution process can be found in the method section of the previous embodiment, and will not be repeated here.

[0138] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), a compact disc (CD-ROM), magnetic tape, floppy disk, and a modeling device for photovoltaic cells.

[0139] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 1101 of the electronic device 1100 to perform the methods described above.

[0140] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of an electronic device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0141] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0143] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0145] 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 readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0146] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.

Claims

1. A method for modeling a battery pack, characterized in that, include: Using a battery pack testing device, charge-discharge tests are conducted based on different operating parameters to obtain actual energy data, actual SOC variation range, actual capacity data, and a first correlation relationship of the battery pack under multiple operating conditions; wherein, the first correlation relationship is used to indicate the law of voltage variation of the battery pack with the SOC of the battery pack; the operating parameters are used to simulate the operating conditions of the battery pack under different ambient temperatures; Based on the actual energy data under the multiple operating conditions and the battery pack body temperature under the multiple operating conditions, the charge and discharge efficiency-temperature data of the battery pack is determined; Based on the charge / discharge efficiency-temperature data of the battery pack, the heat generation-temperature data of the battery pack is determined. Based on the actual capacity data under the multiple operating conditions and the battery pack body temperature under the multiple operating conditions, the capacity-temperature data of the battery pack is determined; Based on the first correlation under the multiple operating conditions, a second correlation is determined; the second correlation represents the comprehensive law of voltage variation with SOC of the battery pack under different temperature conditions. A battery pack model is constructed based on the capacity-temperature data, the second correlation, and the heat generation-temperature data.

2. The method according to claim 1, characterized in that, The actual energy data includes: discharge energy data when the battery pack is in a discharging state and charging energy data when the battery pack is in a charging state; The determination of the charge / discharge efficiency-temperature data of the battery pack based on the actual energy data under the multiple operating conditions and the battery pack body temperature under the multiple operating conditions includes: For each operating condition of the battery pack, the discharge energy data and the charging energy data are determined; the discharge energy data includes: first discharge energy, first charging energy, and first net discharge energy; the charging energy data includes: second discharge energy and second charging energy. Based on the energy balance equation between the second charging energy, the second discharging energy, the first discharging energy, the first charging energy, and the first net discharging energy, the charging and discharging efficiency of the battery pack under each operating condition is determined. Based on the charge / discharge efficiency corresponding to the battery pack temperature under the multiple operating conditions, the charge / discharge efficiency-temperature data of the battery pack is determined.

3. The method according to claim 1, characterized in that, The charge / discharge efficiency of the battery pack is determined in the following way: Based on the ratio of the reference SOC variation range to the actual SOC variation range, the actual energy data under the multiple operating conditions are corrected to obtain the corrected actual energy data under the multiple operating conditions. The charge and discharge efficiency of the battery pack is determined based on the corrected actual energy data under the multiple operating conditions.

4. The method according to claim 3, characterized in that, The charge and discharge efficiency of the battery pack satisfies the following relationship: Wherein, Eff represents the charge / discharge efficiency of the battery pack. This indicates the corrected second charging energy. This represents the corrected second discharge energy. This indicates the corrected first charging energy. This represents the corrected first discharge energy. This represents the corrected first net discharge energy.

5. The method according to claim 1, characterized in that, The determination of the battery pack's capacity-temperature data based on the actual capacity data under the multiple operating conditions and the battery pack's body temperature under the multiple operating conditions includes: Based on the ratio of the reference SOC variation range to the actual SOC variation range, the actual capacity data under the multiple operating conditions are corrected to obtain the corrected actual capacity data under the multiple operating conditions. Based on the corrected actual capacity data corresponding to the battery pack body temperature under the multiple operating conditions, the capacity-temperature data of the battery pack is obtained.

6. The method according to claim 1, characterized in that, The first association relationship is determined in the following way: Based on the voltage corresponding to different SOC values ​​of the battery pack, an initial correlation is determined; the initial correlation represents the preliminary pattern of the voltage of the battery pack changing with the SOC. The initial correlation is corrected based on the correction coefficient to obtain the first correlation. The correction coefficient is used to characterize the degree of deviation between the predicted net discharge and the actual net discharge of the battery pack.

7. The method according to claim 1, characterized in that, The construction of the battery pack model based on the capacity-temperature data, the second correlation, and the heat generation-temperature data includes: Based on the capacity-temperature data and the second correlation, an electrical module of the battery pack model is constructed. The electrical module is used to characterize the voltage output characteristics and capacity variation law of the battery pack under different temperature and SOC conditions. Based on the heat generation-temperature data, a heat generation module is constructed for the battery pack model. The heat generation module is used to characterize the correspondence between the heat generation of the battery pack and temperature changes.

8. The method according to claim 1, characterized in that, The method further includes: Based on the correction coefficients corresponding to the battery pack body temperature under the multiple operating conditions, the correction coefficient-temperature data is obtained; The construction of the battery pack model based on the capacity-temperature data, the second correlation, and the heat generation-temperature data includes: The battery pack model is constructed based on the correction coefficient-temperature data, the capacity-temperature data, the second correlation, and the heat generation-temperature data.

9. A modeling apparatus for a battery pack, characterized in that, The device includes: The testing unit is used to conduct charge-discharge tests based on different operating conditions using a battery pack testing device to obtain the actual energy data, actual SOC variation range, actual capacity data, and a first correlation relationship of the battery pack under the multiple operating conditions; wherein, the first correlation relationship is used to indicate the law of voltage variation of the battery pack with the SOC of the battery pack; the operating conditions parameters are used to simulate the operating conditions of the battery pack under different ambient temperatures; The first determining unit is used to determine the charge / discharge efficiency-temperature data of the battery pack based on the actual energy data under the multiple operating conditions and the pack body temperature of the battery pack under the multiple operating conditions. The second determining unit is used to determine the heat generation-temperature data of the battery pack based on the charge-discharge efficiency-temperature data of the battery pack. The third determining unit is used to determine the capacity-temperature data of the battery pack based on the actual capacity data under the multiple operating conditions and the pack body temperature under the multiple operating conditions. The fourth determining unit is used to determine a second correlation based on the first correlation under the multiple operating conditions; the second correlation represents the comprehensive law of voltage variation with SOC of the battery pack under different temperature conditions; The fifth determining unit is used to construct the battery pack model based on the capacity-temperature data, the second correlation relationship, and the heat generation-temperature data.

10. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, When the computer-executable instructions stored in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is capable of performing the method as described in any one of claims 1 to 8.

12. A computer program product, characterized in that, When the computer program product is run in an electronic device, it causes the electronic device to perform the method as described in any one of claims 1 to 8.

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