Battery state estimation method and system

Abstract

The invention discloses a battery state estimation method and system, the system comprises a battery module, the battery module comprises a plurality of battery cells arranged in parallel, a plurality of buffer foams and a plurality of pressure sensors, the buffer foams are arranged between every two adjacent battery cells, the pressure sensors are arranged in the buffer foams, and the pressure sensors are arranged in the buffer foams. The method comprises the following steps: determining the measurement pressure of the battery module according to the measurement pressure between battery cells in the battery module; determining the state of charge of the battery module according to the measured temperature of the battery module and the measured pressure of the battery module; and determining the power state of the battery module based on the measured voltage, the measured current, the measured temperature and the state of charge of the battery module. According to the method, on the premise of not additionally occupying the space of the battery module, high-precision and real-time online estimation of the core state parameters of the battery is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, in particular to a battery state estimation method and system. BACKGROUND

[0002] At present, with the rapid development of new energy vehicles, energy storage systems and other fields, the battery management system (BMS) as the "brain" of the battery pack, one of its core functions is to estimate the state of charge (SOC), state of health (SOH) and state of power (SOP) of the battery in real time and accurately.

[0003] At present, the mainstream BMS mainly relies on three parameters of voltage, current and temperature for state estimation, but this technical route has inherent limitations: The battery state of charge estimation has the problems of insufficient accuracy and poor real-time performance, is easily affected by the accuracy of the current sensor and the initial value deviation, leads to cumulative error, and its correction mechanism relies on long-term static battery to obtain stable voltage parameters, which is difficult to meet the real-time online estimation demand, especially for the voltage platform area of the lithium iron phosphate battery, the voltage changes weakly in the middle section of the state of charge interval, which leads to estimation failure and further error amplification.

[0004] The battery health state estimation mainly depends on the full charge and full discharge experiment to calibrate the capacity attenuation, or indirectly infers through the growth of internal resistance. These methods either cannot be performed online in real time, or are highly dependent on data accuracy and model, and have serious hysteresis, making it difficult to reflect the micro aging mechanism inside the battery in a timely and accurate manner, such as lithium precipitation, SEI (Solid-Electrolyte Interphase) thickening, etc.

[0005] Therefore, there is an urgent need in the art to overcome the above-mentioned defects and to realize a higher precision, real-time online and more comprehensive battery state estimation technology. SUMMARY

[0006] In order to improve the accuracy and real-time performance of battery state estimation, the present application provides a battery state estimation method and system.

[0007] In a first aspect, the present application provides a battery state estimation method, comprising: determining the measured pressure of the battery module according to the measured pressure between each battery cell in the battery module; determining the state of charge of the battery module according to the measured temperature of the battery module and the measured pressure of the battery module; determine a power state of the battery module based on the measured voltage, the measured current, the measured temperature, and the state of charge of the battery module.

[0008] In one or some of the optional embodiments of the application, the determining the state of charge of the battery module based on the measured temperature of the battery module and the measured pressure of the battery module comprises: obtaining a preset state of charge-temperature-pressure model; obtaining a first state of charge according to the measured current; querying the preset state of charge-temperature-pressure model according to the first state of charge and the measured temperature to obtain a theoretical pressure; if a difference between the theoretical pressure and the measured pressure of the battery module is greater than a preset pressure threshold, querying the preset state of charge-temperature-pressure model according to the measured temperature and the measured pressure of the battery module to obtain a second state of charge, and determining the state of charge of the battery module based on the first state of charge and the second state of charge; if the difference between the theoretical pressure and the measured pressure is less than or equal to the preset pressure threshold, taking the first state of charge as the state of charge of the battery module.

[0009] In one or some of the optional embodiments of the application, the determining the power state of the battery module based on the measured voltage, the measured current, the measured temperature, and the state of charge of the battery module comprises: obtaining a first power value based on the measured voltage, the measured current, and the measured temperature; obtaining a second power value based on the measured voltage, the measured temperature, and the state of charge; taking a minimum value between the first power value and the second power value as the battery power state.

[0010] In one or some of the optional embodiments of the application, the obtaining the second power value based on the measured voltage, the measured temperature, and the state of charge comprises: obtaining a preset temperature-state of charge-maximum pressure change rate model and a preset temperature-state of charge-proportional coefficient model; querying the preset temperature-state of charge-maximum pressure change rate model according to the measured temperature and the state of charge to obtain a target maximum pressure change rate; querying the preset temperature-state of charge-proportional coefficient model according to the measured temperature and the state of charge to obtain a target proportional coefficient; obtaining a maximum safe current based on the target proportional coefficient and the target maximum pressure change rate; Based on the measured voltage and the maximum safe current, a second power value is obtained.

[0011] In one or some optional embodiments of the application, the method further comprises: obtaining a full voltage force of the battery module and a preset full voltage force- health state model, and querying the preset full voltage force-health state model based on the full voltage force to obtain a battery health state; and / or, obtaining a pressure change amount of the battery module in one-time charging and discharging and a preset pressure change amount-health state model, and querying the preset pressure change amount-health state model based on the pressure change amount to obtain a battery health state.

[0012] In one or some optional embodiments of the application, the method further comprises: obtaining a preset temperature-zero drift model; querying the preset temperature-zero drift model based on the measured temperature to obtain a zero drift pressure; temperature compensating the measured pressure based on the zero drift pressure.

[0013] In a second aspect, an embodiment of the application provides a battery state estimation device, comprising a memory, a processor, and a computer program stored in the memory, and the processor implements the battery state estimation method as described above when executing the computer program.

[0014] In a third aspect, an embodiment of the application provides a battery state estimation system, comprising a battery module and the battery state estimation device according to claim 7; wherein the battery module comprises a plurality of parallel arranged battery cells, a plurality of buffer foams, and a plurality of pressure sensors; the buffer foam is arranged between every two adjacent battery cells; and the pressure sensor is arranged in the buffer foam.

[0015] Optionally, the pressure sensor is connected with the battery state estimation device through a wire harness; the wire harness is located at a preset position of the buffer foam; and the preset position is not adjacent to the battery cell.

[0016] In a fourth aspect, an embodiment of the application provides a computer readable storage medium, which stores a computer program / instruction, and the computer program / instruction is executed by a processor to implement the battery state estimation method as described above.

[0017] In a fifth aspect, an embodiment of the application provides a computer program product, which comprises a computer program / instruction, and the computer program / instruction is executed by a processor to implement the battery state estimation method as described above.

[0018] The above technical solutions provided by the embodiments of the application have at least the following beneficial effects: The battery state estimation method provided by the embodiment of the application introduces the pressure parameter into the battery state estimation system by arranging the pressure sensor between the adjacent battery cells of the battery module, and realizes the collaborative estimation of the multiple physical quantity fusion. Meanwhile, the state of charge of the battery module can be quickly determined according to the measured pressure and the measured temperature, the cumulative error problem caused by the current sensor error and the initial value deviation of the traditional method is effectively overcome, the serious estimation error caused by the insensitive voltage signal in the voltage platform area is avoided, and thus the accuracy and the online real-time performance of the state of charge estimation are greatly improved. In addition, the power state of the battery module is determined by comprehensively measuring the voltage, the current, the temperature and the state of charge, more accurate state information support is provided for the power boundary protection of the battery module, and the comprehensiveness and the safety of the system management are enhanced. In summary, the method realizes the high-precision and real-time online estimation of the core state parameters of the battery without occupying the space of the battery module, and significantly improves the performance and the reliability of the battery management system.

[0019] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as particularly noted.

[0020] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application, and explain the technical solutions of the present application, and do not constitute a limitation on the present application. In the drawings: Figure 1 The flowchart of the battery state estimation method provided by the embodiment of the present application is shown in the figure. Figure 2 The flowchart of the method for determining the state of charge of the battery module provided by the embodiment of the present application is shown in the figure. Figure 3 The flowchart of the method for determining the power state of the battery module provided by the embodiment of the present application is shown in the figure. Figure 4 The structural diagram of the battery state estimation system provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0022] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0023] The inventors found that in the prior art, the conventional battery power state estimation is usually based on the boundary conditions of voltage, current and temperature, and the minimum value among the three is taken as the power limit. However, when the battery is rapidly charged and discharged, intense electrochemical reactions occur inside the battery, accompanied by significant volume expansion and contraction, generating huge mechanical stress. This mechanical stress is also a key factor limiting the power of the battery and affecting its safety and life. The prior art completely ignores this mechanical stress boundary, which may cause the battery to cause internal structure damage due to excessive expansion / contraction rate when the voltage, temperature and current have not exceeded the standard, which poses a safety hazard. Based on this, the inventors have made further research and development to make the present application, which provides a battery state estimation method and system.

[0024] Embodiment one In the embodiment one of the present application, a battery state estimation method is provided, referring to Figure 1 The battery module includes a plurality of parallel arranged battery cells, a plurality of buffer foam and a plurality of pressure sensors, the buffer foam is arranged between every two adjacent battery cells, and the pressure sensor is arranged in the buffer foam. The method can include the following steps S101-S103: S101: Determine the measured pressure of the battery module according to the measured pressure between each battery cell in the battery module.

[0025] S102: Determine the state of charge of the battery module according to the measured temperature of the battery module and the measured pressure of the battery module.

[0026] S103: Determine the power state of the battery module based on the measured voltage, measured current, measured temperature and state of charge of the battery module.

[0027] The embodiment of the present application provides a battery state estimation method, which comprises a battery module, the battery module comprises a plurality of parallelly arranged battery cells, a plurality of buffer foams and a plurality of pressure sensors, the buffer foam is arranged between every two adjacent battery cells, and the pressure sensor is arranged in the buffer foam, the method introduces the pressure parameter into the battery state estimation system by arranging the pressure sensor between the adjacent battery cells of the battery module, and realizes the collaborative estimation of the multi-physical quantity fusion. Meanwhile, the state of charge of the battery module can be quickly determined according to the measured pressure and the measured temperature collected in real time, the cumulative error problem caused by the current sensor error and the initial value deviation of the traditional method is effectively overcome, the serious estimation error caused by the insensitive voltage signal in the voltage platform area is avoided, and therefore the precision and the online real-time performance of the state of charge estimation are greatly improved. Secondly, the power state of the battery module is determined by comprehensively measuring the voltage, the current, the temperature and the state of charge, more accurate state information support is provided for the power boundary protection of the battery module, and the comprehensiveness and the safety of the system management are enhanced. In summary, the method realizes the high-precision, real-time online estimation of the core state parameters of the battery under the premise that the space of the battery module is not additionally occupied, and the performance and the reliability of the battery management system are significantly improved.

[0028] The embodiment of the present application is applied to a battery state estimation system, and the system mainly comprises a battery module and a battery state estimation device for implementing the method.

[0029] The battery state estimation system further comprises a battery management system (BMS), which can be used for collecting the test voltage, the test current and the test temperature of the battery module in real time.

[0030] The battery module comprises a plurality of parallelly arranged battery cells, and the buffer foam is arranged between every two adjacent battery cells and between the battery cell and the module end plate. The buffer foam is made of elastic porous material (for example, PU, PE or EVA foam). A pressure sensor is arranged in each buffer foam, so that an integrated component with mechanical buffering and pressure monitoring functions is formed. The pressure sensor can be a piezoresistive film sensor.

[0031] Since the buffer foam is arranged between the adjacent battery cells in the battery module and between the battery cell and the module end plate, the pressure sensor can directly and timely monitor the expansion force generated by the battery cell in the charging and discharging cycle and the aging process. This structural design can introduce the pressure signal while maintaining the original space layout of the battery module, and lays a hardware foundation for constructing the multi-source information fusion battery state estimation method.

[0032] Meanwhile, to achieve signal transmission, the pressure sensor is connected to the battery state estimation device via a wiring harness. To ensure safety and prevent damage to the wiring harness, it is positioned at a predetermined location within the cushioning foam, and this predetermined location is designed not to be directly adjacent to the battery cell, thereby avoiding compression or wear on the wiring harness during battery cell expansion. The predetermined location can be the side of the cushioning foam.

[0033] In step S101 above, the measurement pressure of the battery module is determined based on the measurement pressure between the individual cells in the battery module. Specifically, this includes the following steps S1011-S1012: S1011: Obtain the measured pressure between each cell in the battery module and perform filtering.

[0034] Specifically, raw pressure data can be acquired in real time using pressure sensors embedded in the buffer foam between the battery cells of the battery module. Due to continuous vibration interference during vehicle operation, this raw pressure data contains a significant amount of high-frequency noise. Therefore, digital filters, such as Kalman filters or low-pass digital filters, are needed to filter the raw signals from each pressure sensor. The purpose is to filter out high-frequency noise interference caused by mechanical vibration, thereby retaining and extracting the low-frequency, slowly varying true expansion force signal caused by the internal chemical reactions of the battery cells.

[0035] Since a battery module contains multiple cells arranged in parallel, a set of filtered measured pressures distributed at different locations will be obtained. For ease of subsequent calculations, these pressure values ​​from different locations can be aggregated, for example, by calculating an arithmetic mean or a weighted average, to obtain a unified test pressure that comprehensively reflects the expansion state of the entire battery module, which can then be used for subsequent battery state estimation.

[0036] S1012: Perform temperature compensation on the filtered test pressure. This specifically includes the following steps: S10121-S10123: S10121: Obtain the preset temperature-zero drift model.

[0037] Specifically, this can be achieved by calling a preset temperature-zero drift model that has been pre-calibrated in the laboratory and stored in the battery management system. The preset temperature-zero drift model is established as follows: In the laboratory, a buffer foam with an integrated pressure sensor is placed in a high and low temperature control chamber. Under the condition of ensuring zero additional pressure, the measured pressure at different temperature points (e.g., -40°C, -20°C, 0°C, 25°C, and 60°C) is recorded, thereby obtaining a curve describing the change of test pressure with temperature, which is the preset temperature-zero drift model.

[0038] S10122: Based on the measured temperature, query the preset temperature-zero drift model to obtain the zero drift pressure.

[0039] Specifically, the temperature value measured by the temperature sensor closest to the battery module, as measured by the battery management system, can be obtained as the measured temperature. Then, using the measured temperature as input, a preset temperature-zero drift model is queried. If the measured temperature is exactly the calibration point temperature in the preset temperature-zero drift model, the corresponding pressure value is directly obtained as the zero drift pressure. If the measured temperature is between two calibration point temperatures, the zero drift pressure corresponding to the measured temperature can be calculated using interpolation.

[0040] S10123: Based on the zero-drift pressure, temperature compensation is applied to the measured pressure to obtain the test pressure of the battery module.

[0041] Specifically, this can be achieved by calculating the difference between the test pressure and the zero-drift pressure to obtain the temperature-compensated test pressure of the battery module, thereby eliminating the zero-point drift of the pressure sensor itself caused by temperature changes. The formula for calculating the temperature-compensated test pressure is as follows:

[0042] In the formula, P_compensated is the test pressure after temperature compensation, U_raw is the test pressure before temperature compensation, and U_zero is the zero-drift pressure.

[0043] In this embodiment of the application, after step S101, the method may further include obtaining the pressure change rate and the pressure relaxation time constant based on the test pressure of the battery module.

[0044] Specifically, for the pressure change rate, the pressure change rate (dP / dt) can be obtained in real time by calculating the difference between the test pressure at the current moment and the previous moment, and dividing it by the corresponding time interval. The pressure change rate reflects the degree of expansion or contraction of the battery cell during charging and discharging. By monitoring the pressure change rate in real time, dynamic boundary conditions reflecting changes in mechanical stress can be provided for estimating the power state of the battery module. This adds a dimension of protection for the mechanical safety state of the battery to the traditional voltage, current, and temperature limits, effectively preventing battery structural damage caused by instantaneous overload.

[0045] For the pressure relaxation time constant, after detecting the cessation or significant decrease of the charging current, monitoring and recording the time required for the test pressure to decay from its peak to a certain percentage (e.g., 90% and 50%) (i.e., T_90 and T_50) begins. Alternatively, the curve of test pressure decaying over time is monitored and recorded. By fitting this curve with an exponential function, an exponential decay curve is obtained. Nonlinear regression analysis is then performed on this exponential decay curve to extract the characteristic parameter representing the rate of pressure decay, i.e., the pressure relaxation time constant. Specifically, the exponential decay curve shown in Formula 2 below can be used for nonlinear regression analysis:

[0046] In the formula, P(t) is the test pressure at time t, and A is the amplitude coefficient. P∞ is the pressure relaxation time constant, exp represents the exponential operation, and P∞ is the steady-state pressure value after the pressure relaxation process ends.

[0047] The pressure relaxation time constant reflects the diffusion kinetics of lithium ions within the electrode material. Its numerical variation is closely related to the battery aging state. By monitoring the evolution trend of the pressure relaxation time constant, early and non-destructive diagnosis of the battery health status can be achieved, providing key characteristic parameters that are difficult to obtain by traditional methods for accurately assessing battery life and aging mechanisms.

[0048] In step S102 above, the state of charge of the battery module is determined based on the measured temperature and measured pressure of the battery module. (Refer to...) Figure 2 As shown, the specific steps include S1021-S1025: S1021: Obtain the preset state of charge-temperature-pressure model.

[0049] Specifically, this can be achieved by calling a pre-calibrated state-of-charge (POC)-temperature-pressure model stored in the non-volatile memory of the battery management system. The method for establishing this pre-calibrated POC-temperature-pressure model is as follows: In a laboratory environment, standard battery module samples are charged and discharged at multiple fixed temperature points (e.g., -20°C, 0°C, 25°C, and 45°C), and the pressure values ​​at which the battery modules reach chemical equilibrium under different POC states are recorded simultaneously and accurately. By organizing this data, a three-dimensional lookup table including POC, temperature, and pressure values ​​is constructed, i.e., the pre-calibrated POC-temperature-pressure model.

[0050] In one specific embodiment, the preset state of charge-temperature-pressure model is shown in Table 1 below: Table 1 Preset state of charge-temperature-pressure model

[0051] In practical applications, bilinear interpolation can also be used to calculate the pressure value corresponding to any state of charge (SOC_real) and temperature (T_real), obtaining a three-dimensional surface fitting function. This three-dimensional surface fitting function is then used as the preset state-of-charge-temperature-pressure model. The specific process is as follows: First, in the temperature dimension, find the two nearest temperature calibration points T_low and T_high of the test temperature T_real. Next, on the state-of-charge-pressure curve corresponding to temperature T_low, calculate the pressure value P_low corresponding to the state of charge (SOC_real) through linear interpolation. Similarly, on the state-of-charge-pressure curve corresponding to temperature T_high, obtain the pressure value P_high corresponding to the state of charge (SOC_real) through linear interpolation. Finally, in the temperature dimension, perform linear interpolation on P_low and P_high to obtain the pressure value corresponding to the actual operating point of the state of charge (SOC_real) and temperature T_real. This interpolation algorithm ensures the continuity and smoothness of the preset state-of-charge-temperature-pressure model across all operating conditions.

[0052] S1022: Obtain the first state of charge based on the measured current.

[0053] Specifically, this can be achieved by acquiring the current value measured by the current sensor of the battery management system as the measured current, integrating the current over a fixed calculation period, and calculating the first state of charge based on the following formula 3:

[0054] In the formula, SOC_ah(t) is the first state of charge at time t, SOC_ah(t-1) is the first state of charge at time t-1, I(t) is the measured current at time t, Δt is the calculation period, and Capacity is the nominal capacity of the battery module. This existing technology provides a simple calculation of the first state of charge, but it suffers from cumulative errors due to current sensor errors, inaccurate coulombic efficiency estimation, and initial value deviations.

[0055] S1023: Based on the first state of charge and the measured temperature, query the preset state of charge-temperature-pressure model to obtain the theoretical pressure.

[0056] Specifically, the first state of charge and the measured temperature obtained in step S1022 can be used as joint inputs to query the preset state of charge-temperature-pressure model obtained in step S1021 to obtain the theoretical pressure. Since the combination of the first state of charge and the measured temperature is likely not at the precise calibration point of the preset state of charge-temperature-pressure model, the theoretical pressure can be calculated using bilinear interpolation.

[0057] S1024: If the difference between the theoretical pressure and the measured pressure of the battery module is greater than the preset pressure threshold, then based on the measured temperature and the measured pressure of the battery module, the preset state of charge-temperature-pressure model is queried to obtain the second state of charge, and the state of charge of the battery module is determined based on the first state of charge and the second state of charge.

[0058] Specifically, this can be done by first calculating the difference between the theoretical pressure and the measured pressure of the battery module. This difference is then compared to a preset dynamic pressure threshold, which can be adaptively adjusted based on the battery's health status or charge / discharge rate. If the difference is greater than the preset dynamic pressure threshold, it indicates a large accumulated error in the first state of charge, requiring correction.

[0059] At this point, the measured temperature and pressure are used as inputs to query the preset state of charge-temperature-pressure model, resulting in the second state of charge. Since the combination of measured temperature and pressure is likely not at the precise calibration point of the preset state of charge-temperature-pressure model, bilinear interpolation can be used to calculate the second state of charge.

[0060] The final state of charge (SOC) of the battery module is determined by a weighted fusion of the first SOC and the second SOC, as shown in Formula 4 below:

[0061] In the formula, SOC_final represents the state of charge of the battery module, SOC_ah represents the first state of charge, SOC_p represents the second state of charge, and w represents the preset weight. The preset weight can be dynamically adjusted based on the difference between the theoretical pressure and the measured pressure of the battery module. The larger the difference, the smaller the preset weight, indicating that the first state of charge has a larger error and lower reliability, and the second state of charge obtained from the pressure sensor should be relied upon more heavily.

[0062] S1025: If the difference between the theoretical pressure and the measured pressure is less than or equal to the preset pressure threshold, then the first state of charge is taken as the state of charge of the battery module.

[0063] Specifically, if the difference between the theoretical pressure and the measured pressure is less than or equal to a preset dynamic pressure threshold, then the first state of charge obtained by the prior art is considered accurate, and its cumulative error is within an acceptable range. In this case, the pressure feedback correction mechanism is not activated, the original estimation strategy is maintained, and the first state of charge calculated in step S1022 is directly used as the state of charge of the battery module. This judgment mechanism ensures that when the second state of charge obtained based on the test pressure matches well with the first state of charge obtained by the prior art, the method prioritizes the first state of charge, which has higher computational efficiency, thus ensuring real-time performance and avoiding unnecessary correction operations that introduce additional interference.

[0064] In step S103 above, the power state of the battery module is determined based on the measured voltage, measured current, measured temperature, and state of charge of the battery module. (Refer to...) Figure 3 As shown, the specific steps include S1031-S1033: S1031: Based on the measured voltage, measured current and measured temperature, the first power value is obtained.

[0065] Specifically, this can be achieved using the power boundary calculation method of a battery management system: First, based on the measured voltage and battery internal resistance, the maximum allowable current in the charging and discharging directions is calculated separately, ensuring that the voltage at the end of the pulse does not exceed the upper charging limit or falls below the lower discharging limit. This maximum allowable current is then used as the voltage-current boundary value. The battery internal resistance is obtained by consulting a preset internal resistance characteristic mapping table based on the state of charge and measured temperature. The method for calculating the maximum allowable current in the charging and discharging directions is existing technology and will not be elaborated upon here.

[0066] Then, obtain the maximum continuous and short-term current limits allowed by the battery system hardware or cell specifications to obtain the current-current boundary values.

[0067] Based on the measured temperature and the battery thermal model built into the battery management system, the maximum allowable charge and discharge current value is calculated in reverse to keep the battery temperature below the safety threshold within a preset control cycle in the future, and this value is used as the temperature-current boundary value.

[0068] Finally, among the voltage-current boundary values, current-current boundary values, and temperature-current boundary values ​​mentioned above, the minimum value is taken as the final current boundary value. This current boundary value is then multiplied by the measured voltage to calculate the first power value.

[0069] S1032: Based on the measured voltage, measured temperature, and state of charge, the second power value is obtained. This specifically includes the following steps S10321-S10325: S10321: Obtain the preset temperature-state of charge-maximum pressure change rate model and the preset temperature-state of charge-proportional coefficient model.

[0070] Specifically, it can be done by calling a preset temperature-state-of-charge-maximum pressure change rate model and a preset temperature-state-of-charge-proportional coefficient model that have been calibrated in the laboratory and stored in the non-volatile memory of the battery management system.

[0071] The method for establishing the preset temperature-state of charge-maximum pressure change rate model is as follows: In a laboratory environment, at multiple fixed temperature points (e.g., -20℃, 0℃, 25℃, and 45℃), mechanical stress limit tests are conducted on standard battery module samples under different states of charge, and the maximum pressure change rate that the battery module structure can withstand without damage is recorded simultaneously and accurately. By organizing this data, a three-dimensional lookup table including temperature, state of charge, and maximum pressure change rate is constructed, i.e., the preset temperature-state of charge-maximum pressure change rate model.

[0072] The method for establishing the preset temperature-state-of-charge-proportionality model is as follows: In a laboratory environment, at multiple fixed temperature points (e.g., -20℃, 0℃, 25℃, and 45℃), a series of known current pulses are applied to standard battery module samples under different states of charge, and the corresponding pressure change rates are recorded synchronously and accurately. The model is calibrated for each temperature and state of charge combination by calculating the proportionality coefficient as "current / pressure change rate". By organizing this data, a three-dimensional lookup table including temperature, state of charge, and proportionality coefficient is constructed, i.e., the preset temperature-state-of-charge-proportionality model.

[0073] S10322: Based on the measured temperature and state of charge, query the preset temperature-state of charge-maximum voltage change rate model to obtain the target maximum pressure change rate.

[0074] Specifically, the measured temperature and the battery module's state of charge (SOC) determined in step S102 can be used as joint inputs. The target maximum pressure change rate can then be obtained by querying the preset temperature-SOC-maximum pressure change rate model acquired in step S10321. When the combination of the measured temperature and the battery module's SOC is not at the precise calibration point of the preset temperature-SOC-maximum pressure change rate model, the target maximum pressure change rate can be calculated using bilinear interpolation.

[0075] S10323: Based on the measured temperature and state of charge, query the preset temperature-state of charge-proportional coefficient model to obtain the target proportional coefficient.

[0076] Specifically, the target proportional coefficient can be obtained by using the measured temperature and the battery module state of charge determined in step S102 as joint inputs, and querying the preset temperature-state of charge-proportional coefficient model obtained in step S10321. When the combination of the measured temperature and the battery module state of charge is not at the precise calibration point of the preset temperature-state of charge-proportional coefficient model, the target proportional coefficient can be calculated using bilinear interpolation.

[0077] S10324: The maximum safe current is obtained based on the target proportional coefficient and the target maximum pressure change rate.

[0078] Specifically, the maximum safe current can be obtained by multiplying the target maximum pressure change rate obtained in step S10322 with the target proportionality coefficient obtained in step S10323. This maximum safe current represents the upper limit of the charging and discharging current allowed under the current operating conditions to ensure that the rate of change of mechanical stress inside the battery does not exceed the safety boundary.

[0079] S10325: Obtain the second power value based on the measured voltage and maximum safe current.

[0080] Specifically, the maximum safe current calculated in step S10324 can be multiplied by the measured voltage to obtain a second power value. This second power value is the upper limit of power determined based on the battery mechanical stress safety boundary.

[0081] S1033: Take the minimum value between the first power value and the second power value as the battery power state.

[0082] Specifically, after obtaining the first power value based on traditional electrothermal constraints in step S1031 and the second power value based on mechanical stress boundaries in step S1032, this method follows the strictest safety principle and takes the minimum of the first and second power values ​​as the final allowable battery power state. This decision mechanism ensures that under any operating condition, the battery power output will not exceed the traditional voltage, current, and temperature limits, nor will it exceed the newly introduced mechanical stress change rate limit, thus achieving comprehensive safety protection through multi-dimensional integration.

[0083] In this embodiment of the application, after step S103, step S104 is further included to obtain the battery health status. Step S104 can be executed in two ways. The first way specifically includes the following steps S1041-S1042: S1041: Obtain the full voltage pressure of the battery module and the preset full voltage pressure-health state model.

[0084] Specifically, it could involve calling a pre-defined full-voltage stress-health state model that has been calibrated in the laboratory and stored in the non-volatile memory of the battery management system.

[0085] The method for establishing the pre-defined full-charge pressure-health state model is as follows: In a laboratory environment, standard battery module samples undergo multiple rounds of standard cycle aging tests. In each cycle, after the battery module is fully charged and allowed to stand until chemical equilibrium is reached, its full-charge standing pressure value is accurately recorded. By organizing the full-charge standing pressure data under different cycle numbers (corresponding to different battery health states), a curve or lookup table describing the mapping relationship between full-charge standing pressure and battery health state is constructed, which is the pre-defined full-charge pressure-health state model. This model reveals the corresponding change law between the full-charge standing pressure value and the battery health state when the battery is in a brand-new state and during the process of battery health state decay with increasing cycle number. Generally, as the battery health state decreases, the full-charge standing pressure value shows a monotonically increasing trend due to the intensification of irreversible gas production and other side reactions.

[0086] S1042: Based on the full-voltage pressure, query the preset full-voltage pressure-health state model to obtain the battery health state.

[0087] Specifically, after the battery module has completed charging and reached a stable state, the pressure value measured by a pressure sensor is obtained as the full-charge pressure. Using this full-charge pressure as input, a preset full-charge static pressure-health state model is queried to obtain the battery health state corresponding to the full-charge pressure. This method utilizes the strong correlation between pressure in the stable state after full charging and irreversible aging products inside the battery to achieve indirect, online assessment of the battery health state.

[0088] The second method of performing step S104 specifically includes the following steps S1043-S1044: S1043: Obtain the pressure change and preset pressure change-health state model of the battery module during a single charge and discharge cycle.

[0089] Specifically, it could involve calling a pre-defined pressure change-health state model that has been calibrated in the laboratory and stored in the non-volatile memory of the battery management system.

[0090] The method for establishing the preset pressure change-health state model is as follows: In a laboratory environment, under standard charge-discharge conditions (such as fixed temperature, current rate, and state of charge variation range), standard battery module samples in different battery health states are subjected to charge-discharge tests, and the pressure change from minimum to maximum value is recorded simultaneously and accurately in each complete charge-discharge cycle. By organizing the pressure change data corresponding to different battery health states, a curve or lookup table describing the mapping relationship between pressure change and battery health state is constructed, i.e., the preset pressure change-health state model. This model reflects that as the battery ages and active lithium ions are lost, under the same or comparable charge-discharge conditions, the electrochemical expansion capacity (i.e., pressure change) corresponding to the battery completing the same amount of charge throughput will show a decreasing trend, indicating that there is a corresponding relationship between pressure change and battery health state.

[0091] S1044: Based on the pressure change, query the preset pressure change-health status model to obtain the battery health status.

[0092] Specifically, after the battery module completes one charge-discharge cycle, the change in pressure sensor readings from minimum to maximum is calculated as the cycle pressure change. Using this cycle pressure change as input, a preset cycle pressure change-health state model is queried to obtain the battery health state corresponding to the cycle pressure change. This method leverages the strong correlation between the pressure change amplitude and the amount of active lithium in the battery during a complete charge-discharge cycle to achieve online, real-time assessment of battery health.

[0093] This application embodiment further provides a multi-level safety early warning mechanism to ensure the safe operation of the battery system. This mechanism primarily relies on real-time monitoring of the absolute pressure value and pressure change rate to set multiple early warning thresholds and execute corresponding protection strategies.

[0094] Specifically, this method continuously monitors the preprocessed absolute pressure value measured by pressure sensors and the calculated real-time pressure change rate. When the absolute pressure value or pressure change rate exceeds a preset first-level warning threshold, but has not yet reached a higher-level warning threshold, a first-level warning is triggered. At this level, the battery management system records the abnormal pressure event in non-volatile memory and can issue a notification to the user via the in-vehicle infotainment system or instrument panel, suggesting an inspection.

[0095] If the absolute value of the pressure or the rate of change of pressure further increases, reaching the preset secondary warning threshold, the pressure anomaly is confirmed, and a secondary warning is triggered. At this time, the battery management system will actively reduce the maximum allowable charging and discharging power of the battery, i.e., enter the so-called "limp mode". To achieve this function, the battery management system immediately sends a reduced peak power output / input capability command to the vehicle control unit (VCU) and motor control unit (MCU) via the controller area network (CAN) bus, forcibly limiting the vehicle's power performance. After receiving the power reduction command from the battery management system, the vehicle control unit executes a series of limiting measures, such as: reducing the power of the air conditioning system to prioritize thermal management, displaying clear warning information on the instrument panel, and limiting the vehicle's acceleration capability.

[0096] When a sudden, abnormal surge in measured pressure is detected, with its value or rate of change far exceeding the model-based prediction range and exhibiting typical early characteristics of thermal runaway, this method will immediately trigger the highest-level Level 3 warning. In this dangerous state, the primary operation of the battery management system is to immediately instruct the high-voltage relay of the battery main circuit to physically disconnect the power supply. Simultaneously, the battery management system will activate the thermal management system, operating at maximum power for forced cooling. Furthermore, the highest-level alarm information will be sent to the cloud monitoring platform via the onboard telematics box (T-Box) so that the remote monitoring center can promptly receive the information and initiate emergency response procedures.

[0097] Through the aforementioned multi-level linkage safety early warning mechanism, the embodiments of this application can realize step-by-step protection from early warning and active performance limitation to final emergency disconnection based on the real-time changes in the internal mechanical stress of the battery, which greatly enhances the safety of the battery system under complex operating conditions.

[0098] Example 2 Based on the same inventive concept, this embodiment of the invention also provides a battery state estimation device, including a memory, a processor, and a computer program stored in the memory. When the processor executes the computer program, it implements the battery state estimation method as described in Embodiment 1 above.

[0099] Example 3 Based on the same inventive concept, embodiments of the present invention also provide a battery state estimation system, referring to... Figure 4As shown, the battery state estimation system includes a battery module 11 and a battery state estimation device 12 according to the above embodiment 2; wherein, the battery module 11 includes a plurality of parallel-arranged battery cells 111, a plurality of buffer foams 112 and a plurality of pressure sensors 113; the buffer foams are disposed between every two adjacent battery cells; the pressure sensors are disposed in the buffer foams.

[0100] The pressure sensor is connected to the battery state estimation device via a wiring harness; the wiring harness is located at a preset position on the buffer foam; the preset position is not adjacent to the battery cell.

[0101] Example 4 Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the battery state estimation method as described in Embodiment 1 above.

[0102] Example 5 Based on the same inventive concept, embodiments of the present invention also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the battery state estimation method as described in Embodiment 1 above.

[0103] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0104] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method of estimating a state of a battery, characterized by, The method comprises: determining a measured pressure of the battery module according to measured pressures between each cell in the battery module; determining a state of charge of the battery module according to a measured temperature of the battery module and the measured pressure of the battery module; determining a power state of the battery module based on a measured voltage, a measured current, a measured temperature and a state of charge of the battery module.

2. The method of claim 1, wherein, The method of determining the state of charge of the battery module according to the measured temperature of the battery module and the measured pressure of the battery module comprises: obtaining a preset state of charge-temperature-pressure model; obtaining a first state of charge according to the measured current; querying the preset state of charge-temperature-pressure model according to the first state of charge and the measured temperature to obtain a theoretical pressure; if a difference between the theoretical pressure and the measured pressure of the battery module is greater than a preset pressure threshold, querying the preset state of charge-temperature-pressure model according to the measured temperature and the measured pressure of the battery module to obtain a second state of charge, and determining the state of charge of the battery module based on the first state of charge and the second state of charge; if the difference between the theoretical pressure and the measured pressure is less than or equal to the preset pressure threshold, taking the first state of charge as the state of charge of the battery module.

3. The method of claim 1, wherein, The method of determining the power state of the battery module based on the measured voltage, the measured current, the measured temperature and the state of charge of the battery module comprises: obtaining a first power value based on the measured voltage, the measured current and the measured temperature; obtaining a second power value based on the measured voltage, the measured temperature and the state of charge; taking a minimum value between the first power value and the second power value as the battery power state.

4. The method of claim 3, wherein, The method of obtaining the second power value based on the measured voltage, the measured temperature and the state of charge comprises: obtaining a preset temperature-state of charge-maximum pressure change rate model and a preset temperature-state of charge-proportional coefficient model; querying the preset temperature-state of charge-maximum pressure change rate model according to the measured temperature and the state of charge to obtain a target maximum pressure change rate; querying the preset temperature-state of charge-proportional coefficient model according to the measured temperature and the state of charge to obtain a target proportional coefficient; obtaining a maximum safe current based on the target proportional coefficient and the target maximum pressure change rate; obtaining a second power value based on the measured voltage and the maximum safe current.

5. The method of claim 1, wherein, The method further comprises: obtaining a full voltage pressure of the battery module and a preset full voltage pressure-health state model, and querying the preset full voltage pressure-health state model based on the full voltage pressure to obtain a battery health state; and / or obtaining a pressure change amount of the battery module during one-time charging and discharging and a preset pressure change amount-health state model, and querying the preset pressure change amount-health state model based on the pressure change amount to obtain a battery health state.

6. The method of claim 1, wherein, The method further comprises: obtaining a preset temperature-zero drift model; querying the preset temperature-zero drift model according to the measured temperature to obtain a zero drift pressure; The measured pressure is temperature-compensated according to the zero-drift pressure.

7. A battery state estimation device comprising a processor and a memory, characterized by, The memory stores computer program instructions capable of being executed by the processor, and the processor executes the computer program instructions to implement the steps of the battery state estimation method according to any one of claims 1 to 6.

8. A battery state estimation system characterized by comprising: Comprising: A battery module and the battery state estimation device according to claim 7; wherein the battery module comprises a plurality of parallel arranged battery cells, a plurality of buffer foam and a plurality of pressure sensors; the buffer foam is arranged between every two adjacent battery cells; the pressure sensors are arranged in the buffer foam.

9. The system of claim 8, wherein, The pressure sensors are connected with the battery state estimation device through a wire harness; the wire harness is located at a preset position of the buffer foam; the preset position is not adjacent to the battery cells.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the battery state estimation method according to any one of claims 1 to 6.

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