State of charge determination method, electronic device, vehicle, storage medium, and product
By using the extended Kalman filter method and a second-order RC equivalent circuit model, combined with the SOC-OCV relationship, the single cell at the voltage extreme in the battery module is selected, which solves the problem of global accuracy in estimating the state of charge of the battery module and achieves low-cost and high-efficiency SOC estimation, which is particularly suitable for manganese-based lithium batteries.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BYD AUTO IND CO LTD
- Filing Date
- 2025-01-02
- Publication Date
- 2026-07-03
AI Technical Summary
Existing technologies struggle to accurately estimate the state of charge (SOC) of battery modules under various operating conditions, leading to inaccurate SOC estimation.
By employing the extended Kalman filter method combined with a second-order RC equivalent circuit model, the state of charge (SOC) of the target individual cell is calculated by selecting the cells with the highest and lowest voltage in the battery module and combining the SOC-OCV relationship. Finally, the global SOC estimation of the battery module is achieved through the gain matrix and error covariance estimation.
It enables accurate estimation of the state of charge of battery modules across the entire domain, reducing computational load and cost, and improving the accuracy and efficiency of estimation, especially in the application of manganese-based lithium batteries.
Smart Images
Figure CN122330741A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery technology, and more specifically, to a method for determining the state of charge, electronic devices, vehicles, computer-readable storage media, and computer program products. Background Technology
[0002] State of Charge (SOC) is a crucial parameter for Battery Management System (BMS) management, determining the remaining battery capacity. Current technologies estimate SOC using methods such as open-circuit voltage, open-circuit voltage, and ampere-hour integration. However, these methods have limitations and are not applicable to all operating conditions of the battery module. Therefore, a method capable of comprehensively estimating the SOC of the battery module is needed. Summary of the Invention
[0003] This application provides a method for determining the state of charge, an electronic device, a vehicle, a computer-readable storage medium, and a computer program product.
[0004] This application provides a method for determining the state of charge (SOC) for a battery module, the battery module comprising multiple individual cells, the method comprising:
[0005] Based on the target parameters, the state of charge of a target number of individual battery cells is determined, wherein the target number is less than the number of individual battery cells in the battery module;
[0006] The state of charge (SOC) of the battery module is determined based on the SOC of the target individual battery cell.
[0007] Thus, in the method for determining the state of charge, electronic device, vehicle, computer-readable storage medium, and computer program product of the embodiments of this application, the overall state of charge of the battery module is determined based on the state of charge of at least two individual cells in the battery module. The calculation of the state of charge of the battery module can be achieved with less computation, and the computation time and cost are both low.
[0008] In some implementations, determining the state of charge of a target number of target individual cells based on target parameters includes:
[0009] The target number of target individual cells is determined based on the voltage of the individual cells in the battery module.
[0010] The state of charge of the target single cell is determined based on the target parameters.
[0011] In this way, it is possible to determine whether a cell is a target cell based on its voltage. If a cell is identified as a target cell, its state of charge (SOC) is calculated to obtain the SOC of all target cells.
[0012] In some embodiments, determining the state of charge of the target single cell based on target parameters includes:
[0013] Based on the extended Kalman filter method, the state of charge of the target single cell is determined according to the terminal voltage and current of the target single cell.
[0014] In this way, the state of charge (SOC) of the target cell can be calculated based on the terminal voltage and current of the target cell using the Kalman filter method, thereby reducing the amount of data stored when calculating the SOC and enabling the calculation of the SOC across the entire domain.
[0015] In some implementations, determining the state of charge of the target single-cell battery based on the extended Kalman filter method according to the terminal voltage and current of the target single-cell battery includes:
[0016] Based on the equivalent circuit model of the target single cell and the extended Kalman filter method, the state of charge of the target single cell is determined according to its terminal voltage and current.
[0017] Thus, based on the second-order RC equivalent circuit and the Kalman filter method, and by obtaining the terminal voltage and current of the target single cell, the state of charge of the target single cell can be determined.
[0018] In some embodiments, determining the state of charge of the target single cell based on its terminal voltage and current includes:
[0019] The state of charge (SOC) of the target cell is determined based on preset parameters, the terminal voltage and current of the target cell, and the SOC-open circuit voltage relationship.
[0020] Thus, based on the extended Kalman filter method and the equivalent circuit model of the target cell, the state of charge of the target cell can be determined according to the preset parameters, the terminal voltage and current of the target cell, and the SOC-OCV relationship, thereby realizing the determination of the state of charge.
[0021] In some embodiments, determining the state of charge (SOC) of the target cell based on preset parameters, the terminal voltage and current of the target cell, and the SOC-open-circuit voltage relationship includes:
[0022] The preset parameters are determined based on the previous state of charge of the target single cell.
[0023] Based on the preset parameters, the terminal voltage and current of the target single cell, determine the estimated state of charge;
[0024] Based on the previous error covariance, determine the sum error covariance estimate;
[0025] The gain matrix is determined based on the error covariance and the state-of-charge-open-circuit voltage relationship;
[0026] The state of charge of the target single cell is determined based on the gain matrix and the estimated state of charge.
[0027] In this way, the state of charge (SOC) of a target battery cell can be calculated and determined using the extended Kalman filter method without needing to determine the voltage plateau period of the target battery cell, and the SOC calculation can be performed across the entire charge and discharge domain.
[0028] In some embodiments, the determining method further includes:
[0029] The error covariance is determined based on the gain matrix and the error covariance estimate, and the error covariance is used to calculate the state of charge in the next round.
[0030] Thus, based on the gain matrix and the error covariance estimate, the error covariance is determined, allowing the calculation of the state of charge for the next cycle to be performed based on the error covariance.
[0031] In some embodiments, determining the target number of target individual cells based on the voltage of the individual cells in the battery module includes:
[0032] The target single cell is determined based on the single cell with the highest voltage and the single cell with the lowest voltage in the battery module.
[0033] In this way, the target cell can be determined based on the cell with the highest voltage and the cell with the lowest voltage, thereby determining the cell that can be used to characterize the state of charge of the battery module.
[0034] In some embodiments, the single battery cell includes a manganese-based lithium battery.
[0035] Thus, the method for determining the state of charge in the embodiments of this application can determine the state of charge of the manganese-based lithium battery across the entire range by determining the state of charge of the battery module of the manganese-based lithium battery.
[0036] This application provides an electronic device that includes one or more processors and a memory. The memory stores a computer program that, when executed by the processor, implements the steps of the method as described in any of the above embodiments.
[0037] This application provides a vehicle that includes electronic devices as described in the above embodiments.
[0038] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.
[0039] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above embodiments.
[0040] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0041] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein:
[0042] Figure 1 This is a flowchart illustrating the determination method for certain embodiments of this application;
[0043] Figure 2 This is a flowchart illustrating the determination method for certain embodiments of this application;
[0044] Figure 3 This is a flowchart illustrating the determination method for certain embodiments of this application;
[0045] Figure 4 This is a flowchart illustrating the determination method for certain embodiments of this application;
[0046] Figure 5 This is a schematic diagram of the equivalent circuit model of some embodiments of this application;
[0047] Figure 6 This is a flowchart illustrating the determination method for certain embodiments of this application;
[0048] Figure 7 This is a schematic diagram of the state of charge-open circuit voltage relationship in some embodiments of this application;
[0049] Figure 8 This is a flowchart illustrating the determination method for certain embodiments of this application;
[0050] Figure 9 This is a flowchart illustrating the determination method for certain embodiments of this application;
[0051] Figure 10This is a flowchart illustrating the determination method for certain embodiments of this application;
[0052] Figure 11 This is a schematic diagram of the SOC estimation value of the battery module during charging in some embodiments of this application;
[0053] Figure 12 This is a schematic diagram of current measurement values during the charging process of a battery module according to certain embodiments of this application. Detailed Implementation
[0054] The embodiments of this application are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.
[0055] State of Charge (SOC) is a crucial parameter for Battery Management System (BMS) management, determining the remaining battery capacity. Current technologies estimate SOC using methods such as open-circuit voltage, open-circuit voltage, and ampere-hour integration. However, these methods have limitations and are not applicable to all operating conditions of the battery module. Therefore, a method capable of comprehensively estimating the SOC of the battery module is needed.
[0056] Based on the above-mentioned issues that need to be resolved, please refer to Figure 1 This application provides a method for determining the state of charge (SOC) in a battery module, which includes multiple individual cells. The method includes:
[0057] 01: Based on the target parameters, determine the state of charge of the target number of individual cells. The target number is less than the number of individual cells in the battery module.
[0058] 02: Determine the state of charge (SOC) of the battery module based on the SOC of the target individual battery cell.
[0059] This application provides an electronic device including one or more processors and a memory. The memory stores a computer program that can be executed by the processor. The processor can be used to determine the state of charge (SOC) of a target number of individual battery cells based on target parameters, wherein the target number is less than the number of individual battery cells in a battery module; and to determine the SOC of the battery module based on the SOC of the target individual battery cells.
[0060] This application provides a determining device, which includes a first determining module and a second determining module. The first determining module is used to determine the state of charge (SOC) of a target number of target individual battery cells based on target parameters; the second determining module is used to determine the SOC of a battery module based on the SOC of the target individual battery cells.
[0061] Specifically, the target parameters include the terminal voltage, current, and polarization capacitance of the target individual cell. Based on the target parameters, the state of charge of the target number of target individual cells can be determined.
[0062] The target number is an integer less than the total number of individual battery cells in the battery module. For example, if the battery module contains 10 individual battery cells, the target number is an integer less than or equal to 9. If the battery module contains 5 individual battery cells, the target number can be 1, 2, 3, or 4.
[0063] The state of charge (SOC) of the battery module can be determined based on the SOC of the target number of individual target cells. For example, the average SOC of the target number of individual target cells can be used as the SOC of the battery module. Alternatively, weights can be pre-set, and the SOC of the battery module can be determined based on the SOC of each individual target cell and its corresponding weight.
[0064] By determining the state of charge (SOC) of the entire battery module based on the SOC of a small number of individual cells, it eliminates the need to calculate the SOC of all individual cells in the battery module, greatly reducing the computational workload.
[0065] Thus, in the method for determining the state of charge, electronic device, vehicle, computer-readable storage medium, and computer program product of the embodiments of this application, the overall state of charge of the battery module is determined based on the state of charge of at least two individual cells in the battery module. The calculation of the state of charge of the battery module can be achieved with less computation, and the computation time and cost are both low.
[0066] Please see Figure 2 In some implementations, step 01, determining the state of charge of a target number of target individual cells based on target parameters, includes:
[0067] 011: Determine the target number of target individual cells based on the voltage of the individual cells in the battery module;
[0068] 012: Determine the state of charge of the target single cell based on the target parameters.
[0069] In some implementations, the processor can be used to determine a target number of target individual cells based on the voltage of the individual cells in the battery module; and to determine the state of charge of the target individual cells based on target parameters.
[0070] In some embodiments, the first determining module includes a first determining sub-module and a second determining sub-module. The first determining sub-module can be used to determine a target number of target individual cells based on the voltage of the individual cells in the battery module; the second determining sub-module can be used to determine the state of charge of the target individual cells based on target parameters.
[0071] Specifically, the terminal voltage of each individual cell in the battery module is obtained in advance, and the target individual cell is determined based on the terminal voltages of multiple individual cells. Then, the state of charge of each determined target individual cell is calculated separately.
[0072] The terminal voltage of a single battery cell can be obtained through the Battery Management System (BMS).
[0073] For example, the cell with the highest terminal voltage and the cell with the lowest terminal voltage among all individual cells can be selected as the target cells. That is, the target number is 2.
[0074] After identifying the target individual cell, the BMS can calculate its state of charge (SOC) to obtain the SOC of all target individual cells. Then, based on the SOC of all target individual cells, the SOC of the battery module is determined.
[0075] In this way, it is possible to determine whether a cell is a target cell based on its voltage. If a cell is identified as a target cell, its state of charge (SOC) is calculated to obtain the SOC of all target cells.
[0076] Please see Figure 3 In some embodiments, step 012, determining the state of charge of the target single cell based on the target parameters, includes:
[0077] 0121: Based on the extended Kalman filter method, the state of charge of the target single cell is determined according to the terminal voltage and current of the target single cell.
[0078] In some implementations, the processor can be used to determine the state of charge of a target cell based on the terminal voltage and current of the target cell using an extended Kalman filter method.
[0079] In some implementations, the second determining sub-module includes a determining module. This determining module can be used to determine the state of charge of the target cell based on the terminal voltage and current of the target cell, using an extended Kalman filter method.
[0080] Specifically, the Extended Kalman Filter (EKF) method uses a recursive approach to continuously refine the estimate using observations to obtain the optimal estimate. The EKF method only needs to store the data value of the previous time step, and does not need to store the state of charge at all time steps, thus requiring less storage.
[0081] Furthermore, the EKF method is not limited by the voltage plateau period when calculating the state of charge (SOC) of a battery. Therefore, the EKF method can be used to calculate the SOC of batteries with short voltage plateau periods. For example, the EKF method can be used to calculate the SOC of manganese-based lithium batteries.
[0082] The terminal voltage and current of the target cell can be acquired using a BMS, and then substituted into the prediction and observation equations of the extended Kalman filter method to calculate the state of charge of the target cell.
[0083] In this way, the state of charge (SOC) of the target cell can be calculated based on the terminal voltage and current of the target cell using the Kalman filter method, thereby reducing the amount of data stored when calculating the SOC and enabling the calculation of the SOC across the entire domain.
[0084] Please see Figure 4 and Figure 5 In some implementations, step 0121, based on the extended Kalman filter method, determines the state of charge of the target single cell according to its terminal voltage and current, including:
[0085] 01211: Based on the equivalent circuit model of the target single cell and the extended Kalman filter method, the state of charge of the target single cell is determined according to the terminal voltage and current of the target single cell.
[0086] In some implementations, the processor can be used to determine the state of charge of the target cell based on the equivalent circuit model of the target cell and the extended Kalman filter method, according to the terminal voltage and current of the target cell.
[0087] In some implementations, the determining module includes a first determining submodule. The first determining submodule can be used to determine the state of charge of the target single-cell battery based on its equivalent circuit model and extended Kalman filter method, according to the terminal voltage and current of the target single-cell battery.
[0088] Specifically, please refer to Figure 5 The equivalent circuit model of the target single cell includes an N-order RC equivalent circuit model, where N is 1, 2, 3, etc. A suitable equivalent circuit model can be selected based on actual needs and combined with the extended Kalman filter method to determine the state of charge of the target single cell. This embodiment uses a second-order equivalent circuit model for illustration.
[0089] The second-order equivalent circuit model of the target single cell mainly consists of two capacitors and two resistors. Based on zero-input response and the least squares method, the state equation of the second-order equivalent circuit model can be obtained as follows:
[0090]
[0091] Where U1 represents the voltage across capacitor C1, which characterizes the electrochemical polarization voltage of the battery; U2 represents the voltage across capacitor C2, which characterizes the concentration gradient polarization voltage of the battery; U0 represents the terminal voltage; C1 represents the electrochemical polarization capacitance; C2 represents the concentration gradient polarization capacitance; and R... C1 R represents the electrochemical polarization resistance. C2 The value represents the concentration difference polarization internal resistance, I is the current, and E is the open-circuit voltage. It can characterize the relationship between the battery's electromotive force and state of charge.
[0092] Combining the second-order RC equivalent circuit model, the State of Charge (SOC) can be used as one of the state variable parameters in the EKF algorithm, and the voltages of the two polarization capacitors in the second-order RC equivalent circuit model can be estimated using the EKF algorithm. Based on the EKF method, the prediction equation of the system state model of the target single cell is:
[0093]
[0094] Based on the state equations of the second-order equivalent circuit model, the observation equations of the EKF method can be obtained:
[0095]
[0096] Where Δt is the system sampling period, C is the battery calibrated capacity, τ1 and τ2 are the battery response times, SOC(k) is the state of charge of the target single cell at time k, and U p1 and U p2 It is the battery polarization voltage, U 0ocv This is the open-circuit voltage.
[0097] A second-order RC equivalent circuit can simulate the voltage and current changes of a battery during charging and discharging. Using a second-order RC equivalent circuit combined with the Kalman filter method provides a methodological basis for determining the state of charge of a target individual battery cell.
[0098] Thus, based on the second-order RC equivalent circuit and the Kalman filter method, and by obtaining the terminal voltage and current of the target single cell, the state of charge of the target single cell can be determined.
[0099] Please see Figure 6In some embodiments, steps 0121, 01211, and 012111, determining the state of charge of the target single cell based on its terminal voltage and current, includes:
[0100] 01212: Determine the state of charge of the target cell based on preset parameters, the terminal voltage and current of the target cell, and the state of charge-open circuit voltage relationship.
[0101] In some implementations, the processor can be used to determine the state of charge of a target cell based on preset parameters, the terminal voltage and current of the target cell.
[0102] In some implementations, the determining module further includes a second determining submodule. The second determining submodule can be used to determine the state of charge of the target single cell based on preset parameters, the terminal voltage and current of the target single cell.
[0103] Specifically, the state of charge (SOC)-open-circuit voltage (OCV) relationship is obtained in advance. Methods such as battery static testing can be used to establish the SOC-OCV relationship curve (e.g., ...). Figure 7 As shown in the figure, the relationship between open-circuit voltage and state of charge can be determined based on the SOC-OCV curve.
[0104] The preset parameters include the battery's rated capacity, system sampling period, and battery response time. These preset parameters are then substituted into the prediction and observation equations of the target cell to further determine its state of charge.
[0105] The Kalman gain coefficient K can be determined from the SOC-OCV relationship curve. K :
[0106] K K =P K H K T [H K P K - H K T +V K ] -1 +R K (6)
[0107] Among them, P K H represents the error covariance of the estimated value. K It is determined based on the SOC-OCV relationship curve. The error covariance P of the estimated value. K for:
[0108]
[0109] Among them, Q KThis is the noise matrix.
[0110] Combining the above formulas, we can obtain the equations for determining the optimal estimate and its error covariance of the EKF method:
[0111]
[0112] Based on the optimal estimate of the EKF method and the equation for determining its error covariance, the state of charge of the target single cell can be determined by substituting the preset parameters, the terminal voltage and current of the target single cell, and the SOC-OCV relationship into the equation.
[0113] Thus, based on the extended Kalman filter method and the equivalent circuit model of the target cell, the state of charge of the target cell can be determined according to the preset parameters, the terminal voltage and current of the target cell, and the SOC-OCV relationship, thereby realizing the determination of the state of charge.
[0114] Please see Figure 8 In some embodiments, step 01212, determining the state of charge (SOC) of the target cell based on preset parameters, the terminal voltage and current of the target cell, and the SOC-open circuit voltage relationship, includes:
[0115] 012121: Determine the preset parameters based on the previous state of charge of the target single cell;
[0116] 012122: Determine the estimated state of charge based on preset parameters, the terminal voltage and current of the target single cell;
[0117] 012123: Based on the previous error covariance, determine the estimated value of the sum error covariance;
[0118] 012124: Determine the gain matrix based on the error covariance and the state-of-charge-open-circuit voltage relationship;
[0119] 012125: Determine the state of charge of the target single cell based on the gain matrix and the estimated state of charge.
[0120] In some implementations, the processor may be used to: determine preset parameters based on the previous state of charge of the target cell; determine a state of charge estimate based on the preset parameters, the terminal voltage and current of the target cell; determine an error covariance estimate based on the previous error covariance; determine a gain matrix based on the error covariance; and determine the state of charge of the target cell based on the gain matrix and the state of charge estimate.
[0121] In some embodiments, the second determining submodule includes a second determining unit, a third determining unit, a fourth determining unit, and a fifth determining unit. Specifically, the first determining unit can be used to determine preset parameters based on the previous state of charge of the target single cell; the second determining unit can be used to determine an estimated state of charge based on the preset parameters, the terminal voltage of the target single cell, and the current; the third determining unit can be used to determine an estimated error covariance based on the previous error covariance; the fourth determining unit can be used to determine a gain matrix based on the error covariance; and the fifth determining unit can be used to determine the state of charge of the target single cell based on the gain matrix and the estimated state of charge.
[0122] Specifically, based on the previous state of charge (SOC(k-1) of the target single cell, at least some preset parameters can be determined.
[0123] By substituting the preset parameters, terminal voltage, and current into the prediction equations of the system state model of the target single cell as shown in equations (3) and (4), the estimated state of charge (SOC(k)) of the target single cell can be determined. - .
[0124] Based on the previous error covariance P k-1 Combining equation (7), the sum and error covariance estimate P is determined. k - The previous error covariance refers to the error covariance determined at time k-1.
[0125] Based on the error covariance estimate P k - Combining equation (6), the gain matrix K can be determined. K .
[0126] Based on the gain matrix, the estimated state of charge, and relevant preset parameters, combined with equation (8), the state of charge (SOC) of the target single cell can be determined.
[0127] In this way, the state of charge (SOC) of a target battery cell can be calculated and determined using the extended Kalman filter method without needing to determine the voltage plateau period of the target battery cell, and the SOC calculation can be performed across the entire charge and discharge domain.
[0128] In some implementations, the determination method further includes:
[0129] 03: Determine the error covariance based on the gain matrix and the estimated error covariance. The error covariance is used to calculate the state of charge in the next round.
[0130] In some implementations, the processor can be used to determine the error covariance based on the gain matrix and the error covariance estimate.
[0131] In some embodiments, the determining device further includes a third determining module. The third determining module can be used to determine the error covariance based on the gain matrix and the error covariance estimate.
[0132] Specifically, in the extended Kalman filter method, the state equation for each observation is determined based on the previous observation state. Therefore, after calculating and determining the state of charge for the current round, in order to continue calculating the state of charge in the next round, it is necessary to further determine the error covariance P of the current round. k .
[0133] Based on the gain matrix and the error covariance estimate P k - By combining equation (9), the error covariance P can be determined. k .
[0134] Thus, based on the gain matrix and the error covariance estimate, the error covariance is determined, allowing the calculation of the state of charge for the next cycle to be performed based on the error covariance.
[0135] Please see Figure 9 In some embodiments, step 011, determining the target number of target individual cells based on the voltage of the individual cells in the battery module, includes:
[0136] 0111: Determine the target single cell based on the single cell with the highest voltage and the single cell with the lowest voltage in the battery module.
[0137] In some implementations, the processor can be used to determine a target cell based on the cell with the highest voltage and the cell with the lowest voltage in the battery module.
[0138] In some implementations, the first determining submodule includes a third determining submodule. The third determining submodule can be used to determine the target single cell based on the single cell with the highest voltage and the single cell with the lowest voltage in the battery module.
[0139] Specifically, after acquiring the terminal voltage of a single cell in the battery module, the label of the single cell with the highest terminal voltage and the label of the single cell with the lowest terminal voltage can be obtained and identified as the target single cell.
[0140] In other words, the target quantity is 2 at this point.
[0141] In one embodiment, all individual cells are manganese-based lithium batteries. The BMS collects the terminal voltage of the manganese-based lithium batteries and selects the manganese-based lithium batteries with the highest and lowest terminal voltages as target individual cells. At the same time, the current of the target individual cells is collected, and their SOC-OCV relationship curves are obtained. Based on preset parameters, the terminal voltage, current, and state-of-charge-open-circuit voltage relationship of the target individual cells, combined with equations (3) to (9), the state of charge of the target individual cells is determined.
[0142] In this way, the target cell can be determined based on the cell with the highest voltage and the cell with the lowest voltage, thereby determining the cell that can be used to characterize the state of charge of the battery module.
[0143] Please see Figure 10 In some implementations, step 02, determining the state of charge (SOC) of the battery module based on the SOC of the target individual battery cell, includes:
[0144] 021: Determine the state of charge (SOC) of the target cell by taking the average of the SOC of the cell with the highest voltage and the cell with the lowest voltage in the battery module.
[0145] In some implementations, the processor can be used to determine the state of charge (SOC) of a target cell based on the average SOC of the cell with the highest voltage in the battery module and the cell with the lowest voltage.
[0146] In some embodiments, the second determining module further includes a third determining sub-module. The third determining sub-module can be used to determine the state of charge (SOC) of a target cell based on the average SOC of the cell with the highest voltage in the battery module and the cell with the lowest voltage.
[0147] Specifically, the state of charge (SOCmax) of the target cell is determined by the average of the SOCmax of the cell with the highest voltage and the SOCmin of the cell with the lowest voltage in the battery module.
[0148] In other words, the state of charge of the target single cell can be: SOC = (SOCmax + SOCmin) / 2.
[0149] In one embodiment, in a manganese-based lithium battery module, if the BMS determines that the state of charge (SOC) of the single cell with the highest terminal voltage is 80% and the SOC of the single cell with the lowest terminal voltage is 78%, then the SOC of the manganese-based lithium battery module can be determined to be 79%.
[0150] Thus, the state of charge (SOC) of the target cell can be determined by the average of the SOC of the cell with the highest voltage and the cell with the lowest voltage in the battery module. The calculation logic is simple and the accuracy is high.
[0151] In some implementations, the single cell includes a manganese-based lithium battery.
[0152] Specifically, in related technologies, compared with commonly used lithium iron phosphate batteries, manganese-based lithium batteries have a shorter plateau period and a significantly different plateau period, making it difficult to calculate the state of charge (SOC) of manganese-based lithium batteries using commonly used methods such as open-circuit voltage method, ampere-hour integration method, and closed-circuit voltage method. The only possible approach is to combine these methods and occasionally perform a calibration on the manganese-based lithium battery, with a low probability of triggering the calibration.
[0153] This application estimates the state of charge (SOC) of a manganese-based lithium battery using the Kalman filter method and obtains the optimal value as the determined SOC through multiple iterations, thus realizing the determination of the SOC of the manganese-based lithium battery. The determination of the SOC of the manganese-based lithium battery is not limited by the voltage plateau period and can achieve SOC calculation over the entire voltage range.
[0154] Furthermore, the state of charge (SCC) of the battery module can be determined using only a small number of individual cells. The calculation method is simple and accurate, with minimal computational complexity, thus reducing computational costs.
[0155] In one embodiment, the SOC of the battery module is estimated according to the determination method of the present application, and simultaneously estimated according to the ampere-hour integration method, with an initial SOC deviation set to 20%. The SOC curves estimated by the two methods are compared, and the comparison results are as follows: Figure 11 and Figure 12 As shown, the SOC curve estimated by the determination method according to the embodiments of this application converges rapidly, and the total error is within 2%.
[0156] Thus, the method for determining the state of charge in the embodiments of this application can determine the state of charge of the manganese-based lithium battery across the entire range by determining the state of charge of the battery module of the manganese-based lithium battery.
[0157] This application provides a vehicle that includes electronic devices as described in the above embodiments.
[0158] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.
[0159] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above embodiments.
[0160] In the description of this specification, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with an embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, without contradiction, those skilled in the art can combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples.
[0161] Furthermore, the term "connection" should be interpreted broadly. For example, it can include fixed connections, detachable connections, or integral connections; it can include direct connections or indirect connections through an intermediate medium; and it can also include internal communication between two elements. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.
[0162] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0163] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, fragment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as will be understood by those skilled in the art to which embodiments of this application pertain.
[0164] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.
Claims
1. A method for determining the state of charge, used in a battery module, characterized in that, The battery module comprises multiple individual battery cells, and the method for determining them includes: Based on the target parameters, the state of charge of a target number of individual battery cells is determined, wherein the target number is less than the number of individual battery cells in the battery module; The state of charge (SOC) of the battery module is determined based on the SOC of the target individual battery cell.
2. The determination method according to claim 1, characterized in that, The step of determining the state of charge of a target number of target individual cells based on target parameters includes: The target number of target individual cells is determined based on the voltage of the individual cells in the battery module. The state of charge of the target single cell is determined based on the target parameters.
3. The determination method according to claim 2, characterized in that, Determining the state of charge of the target single cell based on the target parameters includes: Based on the extended Kalman filter method, the state of charge of the target single cell is determined according to the terminal voltage and current of the target single cell.
4. The determination method according to claim 3, characterized in that, The extended Kalman filter-based method for determining the state of charge of the target single-cell battery based on its terminal voltage and current includes: Based on the equivalent circuit model of the target single cell and the extended Kalman filter method, the state of charge of the target single cell is determined according to its terminal voltage and current.
5. The determination method according to claim 4, characterized in that, Determining the state of charge of the target single cell based on its terminal voltage and current includes: The state of charge (SOC) of the target cell is determined based on preset parameters, the terminal voltage and current of the target cell, and the SOC-open circuit voltage relationship.
6. The determination method according to claim 5, characterized in that, The step of determining the state of charge (SOC) of the target cell based on preset parameters, the terminal voltage, current, and SOC-open-circuit voltage relationship of the target cell includes: The preset parameters are determined based on the previous state of charge of the target single cell. Based on the preset parameters, the terminal voltage and current of the target single cell, determine the estimated state of charge; Based on the previous error covariance, determine the sum error covariance estimate; The gain matrix is determined based on the error covariance and the state-of-charge-open-circuit voltage relationship; The state of charge of the target single cell is determined based on the gain matrix and the estimated state of charge.
7. The determination method according to claim 6, characterized in that, The determination method further includes: The error covariance is determined based on the gain matrix and the error covariance estimate, and the error covariance is used to calculate the state of charge in the next round.
8. The determination method according to claim 2, characterized in that, The step of determining the target number of target individual cells based on the voltage of the individual cells in the battery module includes: The target single cell is determined based on the single cell with the highest voltage and the single cell with the lowest voltage in the battery module.
9. The determining method according to any one of claims 1-8, characterized in that, The individual battery includes a manganese-based lithium battery.
10. An electronic device, characterized in that, The electronic device includes one or more processors and a memory, the memory storing a computer program that, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 9.
11. A vehicle, characterized in that, The vehicle includes the electronic device as described in claim 10.
12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 9.
13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.