Power state prediction method, device and equipment of battery pack and medium

Abstract

The invention discloses a power state prediction method, device and equipment of a battery pack and a medium, and relates to the field of battery pack detection.The method comprises the steps that a to-be-detected battery pack is placed under a preset pre-charging pulse working condition, and pulse voltage and pulse current changes of the to-be-detected battery pack under the preset pre-charging pulse working condition are obtained; extracting the discharge DC internal resistance of the to-be-detected battery pack according to the pulse voltage and pulse current change under the condition that the online parameter excitation is satisfied; acquiring operation data of the to-be-detected battery pack, determining polarization internal resistance and capacitance of the to-be-detected battery pack under the operation data according to the operation data, and performing parameter adjustment on the polarization internal resistance and the capacitance according to the operation data; and predicting the battery voltage of the to-be-detected battery pack according to the future current load working condition, the polarization internal resistance and the battery voltage at the current moment. On the premise of ensuring the estimation stability, the method also has the capabilities of tracking the aging condition of the battery pack and eliminating the individual difference of the battery pack.

Description

Technical Field

[0001] This invention relates to the field of battery pack testing, and specifically to a method, apparatus, device, and medium for predicting the power state of a battery pack. Background Technology

[0002] With the rapid development of the new energy vehicle industry, lithium iron phosphate batteries have become one of the mainstream choices for power batteries due to their advantages such as long cycle life, high energy density, high charge and discharge efficiency, and relatively low cost. The 12V battery pack in a vehicle refers to the 12V electrical system that supplies power to the vehicle's electrical appliances. The core of a 12V battery pack is a 12V lithium iron phosphate battery or lead-acid battery. Correspondingly, accurate estimation and monitoring of the battery pack's state is an important research direction for Battery Management Systems (BMS). The power prediction function for the 12V battery pack in a BMS is one of the key technologies for ensuring the stable operation of the vehicle's electrical system, extending battery life, and improving user experience. One of its core tasks is to estimate the battery pack's State of Power (SOP), which is the maximum power (or current) that the battery pack can safely discharge or charge at a specific time.

[0003] Currently, higher requirements are placed on the power prediction of 12V battery packs. In addition to predicting the maximum power, it is also necessary to predict the changes in battery terminal voltage under known load conditions over a period of time in the future. In particular, it is important to predict whether the voltage will drop below the dangerous threshold. This is of great significance for preventing the vehicle controller from restarting due to a sudden voltage drop and ensuring continuous power supply for advanced functions such as autonomous driving.

[0004] In the existing technology, SOP estimation methods mainly include table lookup method, static calculation based on battery equivalent circuit model or online parameter identification technology such as recursive least squares method. However, these methods are difficult to achieve ideal SOP under multiple constraints such as high accuracy, stability, real-time performance and dealing with battery aging and individual differences. As a result, the existing technology lacks a reliable solution that can simultaneously guarantee accuracy throughout the entire life cycle and stability under all operating conditions.

[0005] Therefore, how to provide a high-precision and high-stability method for estimating the state of operation (SOP) of a battery pack is an important issue that the industry urgently needs to address. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a method, apparatus, device and medium for predicting the power state of a battery pack, thereby solving the problem that the prior art lacks a reliable solution that can simultaneously guarantee accuracy throughout the entire life cycle and stability under all operating conditions.

[0007] According to a first aspect, embodiments of the present invention provide a method for predicting the state of power of a battery pack, the method comprising: During the vehicle startup process, the battery pack under test is placed under a preset pre-charge pulse condition, and the pulse voltage and pulse current changes of the battery pack under test are acquired under the preset pre-charge pulse condition. Based on the changes in pulse voltage and pulse current, the discharge DC internal resistance of the battery pack under test is extracted. The system acquires operational data of the battery pack under test, uses a pre-constructed mapping table and the operational data to determine the polarization internal resistance and polarization capacitance of the battery pack under test, and adjusts the parameters of the polarization internal resistance and polarization capacitance based on the discharge DC internal resistance and operational data. The operational data includes resistance-based health status, battery temperature, and state of charge. The mapping table is used to characterize the mapping relationship between the battery pack model information and the offline parameter table, and the offline parameter table is used to characterize the mapping relationship between the battery pack's polarization internal resistance, polarization capacitance, and operational data. The future current load conditions are predicted based on the actual current load conditions of the battery pack under test. Based on the future current load conditions, polarization internal resistance, polarization capacitance, and the current battery voltage, the battery voltage of the battery pack under test is predicted.

[0008] In conjunction with the first aspect, in the first embodiment of the first aspect, the step of extracting the discharge DC internal resistance of the battery pack to be tested based on changes in pulse voltage and pulse current specifically includes: Determine whether the rate of change of the pulse current does not exceed the first rate of change and is maintained for more than the preset maintenance time; If it is determined that the change rate does not exceed the first rate of change and is maintained for more than the preset maintenance time, it is determined whether the change rate of the pulse current exceeds the second rate of change and whether the change amount exceeds the first change amount within the first time period. If it is determined that the rate of change exceeds the first rate of change and the amount of change exceeds the first amount of change in the first time period, it is determined whether the rate of change of the pulse current exceeds the third rate of change in the second time period adjacent to the first time period. Under the condition that the change rate does not exceed the third rate of change, the discharge DC internal resistance of the battery pack under test is extracted based on the changes in pulse voltage and pulse current.

[0009] In conjunction with the first embodiment of the first aspect, in the second embodiment of the first aspect, the step of extracting the discharge DC internal resistance of the battery pack to be tested based on the changes in pulse voltage and pulse current, under the condition that the change rate does not exceed the third rate of change, specifically includes: Under the condition that the change rate does not exceed the third rate of change, the first moment and several second moments are determined according to the change of the pulse current; the first moment is the moment when the current first shows a step drop, and the second moment is the moment when the change of the current shows an inflection point; The pulse voltage and pulse current values ​​of the battery pack under test are determined at the first time and the second time, respectively, to obtain the first pulse voltage and first pulse current values ​​corresponding to the first time, and the second pulse voltage and second pulse current values ​​corresponding to the second time. Based on the first pulse voltage value, the second pulse voltage value, the first pulse current value, and the second pulse current value, the discharge DC internal resistance of the battery pack under test at each second moment is determined.

[0010] In conjunction with the first aspect, in the third embodiment of the first aspect, the step of acquiring the operating data of the battery pack to be tested, determining the polarization internal resistance and polarization capacitance of the battery pack to be tested under the operating data, and adjusting the polarization internal resistance parameters according to the operating data specifically includes: Determine the model information of the battery pack to be tested, and match the mapping table for the battery pack to be tested based on the model information; The real-time resistance-based health status of the battery pack under test is obtained, and the real-time resistance-based health status is subjected to sliding filtering. Determine the operating data of the battery pack to be tested; Under the condition that the online parameter excitation is met, the offline parameter table in the mapping table is interpolated using the operating data, the real-time resistance-based health status, and the discharge DC internal resistance as the battery ohmic internal resistance, and the electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization capacitance, and concentration polarization capacitance under the current operating data are adjusted. If the online parameter excitation is not met, the running data is used as index information to retrieve the polarization internal resistance and polarization capacitor under the same conditions from the offline parameter table; the polarization internal resistance consists of the discharge DC internal resistance, the electrochemical polarization internal resistance, and the concentration polarization internal resistance, and the polarization capacitor consists of the electrochemical polarization capacitor and the concentration polarization capacitor.

[0011] In conjunction with the first aspect, in the fourth embodiment of the first aspect, the step of predicting the future current load condition based on the actual current load condition of the battery pack under test, and predicting the battery voltage of the battery pack under test based on the future current load condition, polarization internal resistance, polarization capacitance, and the current battery voltage, specifically includes: Determine the battery voltage of the battery pack under test at the current moment; The test is determined based on the actual current load conditions of the battery pack under test, and the future current load conditions are predicted based on the actual current load conditions. The battery voltage of the battery pack under test is predicted in the future by subtracting the product of the future current load condition and the battery's internal resistance, the electrochemical polarization voltage, and the concentration polarization voltage from the current battery voltage. The electrochemical polarization voltage is obtained from the electrochemical polarization resistance and the electrochemical polarization capacitance, and the concentration polarization voltage is obtained from the concentration polarization resistance and the concentration polarization capacitance.

[0012] In conjunction with the first aspect, in the fifth embodiment of the first aspect, the mapping table is constructed through the following steps: Construct a second-order resistor-capacitor equivalent circuit model of the battery pack based on the battery characteristics of the battery pack. Each sample battery was placed under different test conditions, and sample data of each sample battery under each test condition was obtained. The sample data included pulse voltage, pulse current, battery voltage and battery current. The test condition was to apply a preset pre-charge and discharge pulse to the sample battery under the test operation data. The test operation data consisted of state of charge, battery temperature and resistance-based health status. Construct the state-of-charge-open-circuit voltage curve of the sample battery; Based on sample data and the state-of-charge-open-circuit voltage curve, the model parameters of the second-order resistive-capacitive equivalent circuit model are identified to obtain the model parameters. A mapping relationship between the test operation data and the model parameters is established to obtain an offline parameter table. A mapping relationship between the offline parameter table and the model information is established to obtain a mapping table.

[0013] In conjunction with the fifth embodiment of the first aspect, in the sixth embodiment of the first aspect, the state-of-charge-open-circuit voltage curve is constructed through the following steps. Set the sample batteries of different models to stand for a preset time; Each sample battery is charged using a preset charging current. Once the sample battery is determined to be charged to a preset state of charge, the system switches to a preset charging voltage to charge each sample battery and charges each sample battery to a preset current state. The preset charging current is a constant current, and the preset charging voltage is a constant voltage. Discharge each sample battery in a preset current state at a preset discharge rate, and determine the state of charge of each sample battery during the discharge process and the open circuit voltage corresponding to the state of charge. Based on the state of charge and open-circuit voltage of the sample cells, a state of charge-open-circuit voltage curve is constructed.

[0014] According to a second aspect, embodiments of the present invention also provide a power state prediction device for a battery pack, the device comprising: The pulse application module is used to place the battery pack under test under a preset pre-charge pulse condition during the vehicle startup process, and to acquire the pulse voltage and pulse current changes of the battery pack under test under the preset pre-charge pulse condition. The feature extraction module is used to extract the discharge DC internal resistance of the battery pack under test based on the changes in pulse voltage and pulse current. The online excitation module is used to acquire the operating data of the battery pack under test. It uses a pre-constructed mapping table and the operating data to determine the polarization internal resistance and polarization capacitance of the battery pack under test, and adjusts the parameters of polarization internal resistance and polarization capacitance based on the discharge DC internal resistance and operating data. The operating data includes resistance-based health status, battery temperature, and state of charge. The mapping table is used to characterize the mapping relationship between the battery pack model information and the offline parameter table. The offline parameter table is used to characterize the mapping relationship between the battery pack's polarization internal resistance, polarization capacitance, and operating data. The power prediction module is used to predict the future current load conditions based on the actual current load conditions of the battery pack under test, and to predict the battery voltage of the battery pack under test based on the future current load conditions, polarization internal resistance, polarization capacitance and the current battery voltage.

[0015] According to a third aspect, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the power state prediction method for any of the battery packs described above.

[0016] According to a fourth aspect, embodiments of the present invention also provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the power state prediction method for a battery pack as described above.

[0017] The power state prediction method, apparatus, device, and medium for battery packs of the present invention, by placing the battery pack under test under a preset pre-charge pulse condition during vehicle startup, determines whether online parameter excitation is met based on the pulse current change, and extracts the discharge DC internal resistance of the battery pack under test based on the changes in pulse voltage and pulse current when the online parameter excitation condition is met. The changes in pulse voltage and pulse current can reflect the step drop and inflection point of voltage / current during discharge. Therefore, the discharge DC internal resistance can be extracted based on the changes in pulse voltage and pulse current. Extracting the discharge DC internal resistance of the battery pack under test only when the online parameter excitation condition is met avoids the waste of MCU computing power due to blind calculation and also avoids the parameter of discharge DC internal resistance being lost. The system utilizes a mapping table to determine the polarization resistance of sample batteries of the same model and EOL stage as the battery pack under test, under the same operating data. This polarization resistance is then adjusted, and throughout the battery pack's entire lifespan, the power prediction calculations are dynamically corrected based on the adjusted polarization resistance. This addresses the issue of escalating errors in traditional offline meters after aging, meets real-time safety warning requirements, adapts to prediction deviations caused by temperature changes and battery aging differences, significantly reduces the probability of false alarms, and ensures vehicle operational safety. Furthermore, it accurately predicts the battery voltage of the battery pack under test in the future, making the BMS protection strategy more precise. This avoids scenarios with high current discharge at low SOC and prevents irreversible battery damage, effectively extending battery life and reducing user replacement costs. Attached Figure Description

[0018] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings: Figure 1 A flowchart illustrating the power state prediction method for a battery pack provided by the present invention is shown. Figure 2 A schematic diagram of the equivalent circuit model constructed in the power state prediction method for battery packs provided by the present invention is shown. Figure 3 A schematic diagram of a pulse satisfying the dynamic parameter excitation condition in the power state prediction method for a battery pack provided by the present invention is shown. Figure 4 The diagram shows a pulse diagram of the future current load condition in the power state prediction method for the battery pack provided by the present invention. Figure 5 A comparative diagram of the SOP prediction performance in the prior art is shown; Figure 6 A comparative schematic diagram of the SOP prediction effect in the power state prediction method for battery packs provided by the present invention is shown. Figure 7A schematic diagram of the power state prediction device for a battery pack provided by the present invention is shown. Figure 8 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] With the rapid development of the new energy vehicle industry, lithium iron phosphate batteries have become one of the mainstream choices for power batteries due to their advantages such as long cycle life, high energy density, high charge and discharge efficiency, and relatively low cost. The 12V battery pack in a vehicle refers to the 12V electrical system that supplies power to the vehicle's electrical appliances. The core of a 12V battery pack is a 12V lithium iron phosphate battery or lead-acid battery. Accordingly, accurate estimation and monitoring of the battery pack's status is an important research direction for Battery Management Systems (BMS). The power prediction function for the 12V battery pack in a BMS is one of the key technologies for ensuring the stable operation of the vehicle's electrical system, extending battery life, and improving user experience. Through the power prediction function, system crashes can be prevented, and sudden voltage drops in the 12V battery pack can prevent vehicle controller restarts, ensuring that critical functions such as keyless entry, remote air conditioning, automatic parking, and autonomous driving have power available at all times. One of the core tasks of the power prediction function is to estimate the battery's State of Operation (SOP), that is, the maximum power (or current) at which the battery pack can safely discharge or charge at a specific time.

[0021] Currently, higher requirements are placed on the power prediction of 12V battery packs. In addition to predicting the maximum power, it is also necessary to predict the changes in battery terminal voltage under known load conditions over a period of time in the future. In particular, it is important to predict whether the voltage will drop below the dangerous threshold. This is of great significance for preventing the vehicle controller from restarting due to a sudden voltage drop and ensuring continuous power supply for advanced functions such as autonomous driving.

[0022] In existing technologies, SOP estimation methods mainly include table lookup methods, online parameter identification techniques such as static calculation based on battery equivalent circuit models or recursive least squares methods, among which: The lookup table method calibrates the battery pack under full operating conditions through offline testing. It acquires maximum charge and discharge power data for different durations under varying State of Charge (SOC) and temperature conditions, constructing a two-dimensional or three-dimensional power map. The State of Charge (SOP) then interpolates the corresponding power or current estimates based on the current state parameters. However, this method relies on discrete offline test data. Firstly, the interpolation calculation introduces inherent errors, especially regarding State of Charge (SOC) or temperature, which can lead to a 5%-10% error in accuracy. Secondly, it cannot adapt to the battery aging process. In real-world user scenarios, battery aging varies significantly, and the internal trends of the battery pack differ under different aging conditions. Differences in charging frequency, depth of discharge, and operating temperature among users cause the actual power capacity of the battery pack to rise or fall. The MAP cannot be updated in real time and cannot reflect these dynamic changes, resulting in significant deviations from reality in the later stages of battery aging, based on new battery data. Thirdly, it cannot reflect individual differences and cannot identify variations in the manufacturing process of individual cells, leading to prediction errors.

[0023] Static calculations based on the battery equivalent circuit model involve obtaining dynamic response data of the battery pack at different temperature points and SOCs through a series of standardized tests, such as Hybrid Pulse Power Characterization (HPPC). Then, the parameters of the equivalent model are identified through algorithms such as curve fitting and least squares. Finally, these parameters are compiled into a multi-dimensional parameter table and stored in the BMS. When the BMS runs, it looks up the corresponding model parameters according to the current state of the battery pack. However, this method often relies on preset polarization resistance measured under specific standard operating conditions, which is difficult to cope with the complex dynamic changes in actual vehicle operation. It has the same disadvantages as the lookup table method, such as not being able to adapt to different battery aging and lacking individual difference recognition.

[0024] To overcome the shortcomings of offline methods and improve accuracy, some approaches have introduced online parameter identification techniques such as recursive least squares to update battery model parameters in real time. However, real-time online estimation algorithms are complex, computationally burdensome, and require high computing power from the BMS's MCU, potentially increasing hardware costs. Furthermore, convergence issues arise; if the battery remains in a steady state or experiences minimal current changes for an extended period, the algorithm may lack sufficient stimulus to accurately identify all parameters, potentially leading to parameter divergence and severely impacting prediction stability.

[0025] It is evident that existing technologies lack a reliable solution that can simultaneously guarantee accuracy throughout the entire lifecycle and stability under all operating conditions. In conclusion, providing a high-precision, high-stability battery pack SOP estimation method is a crucial issue that the industry urgently needs to address.

[0026] To address the aforementioned issues, this specification provides a method for predicting the state of power (SOP) of a battery pack. This method utilizes a high-precision offline parameter table as initial values ​​for SOP estimation. When the conditions for online estimation are met, online estimation is initiated while simultaneously correcting the offline parameters. This ensures the stability of the estimation while also enabling the tracking of battery pack aging and mitigating individual differences within the battery pack. The SOP prediction method provided in this specification can be applied to electronic devices, including laptops, desktop computers, smartphones, smart wearable devices, and tablets. Furthermore, the SOP prediction method can also be applied to applications running on these electronic devices. Figure 1 This is a flowchart illustrating a battery pack power state prediction method according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method may include the following steps: S101. During vehicle startup, the battery pack to be tested is placed under a preset pre-charge pulse condition, and the pulse voltage and pulse current changes of the battery pack under the preset pre-charge pulse condition are acquired. The preset pre-charge pulse condition involves applying a preset pre-charge / discharge pulse to the battery pack under test, and the aforementioned pulse voltage and pulse current changes are acquired through various sensors and onboard instruments.

[0027] S102. Determine whether the online parameter excitation is met based on the pulse current change, and if the online parameter excitation is met, extract the discharge DC internal resistance of the battery pack to be tested based on the pulse voltage and pulse current change.

[0028] In this embodiment of the invention, a preset online parameter excitation condition is also set, and it is determined whether the online parameter excitation condition is met based on the rate of change and amount of change of the battery current of the battery pack to be tested. When the online parameter excitation condition is met, the discharge DC internal resistance of the battery pack to be tested is extracted based on the changes of pulse voltage and pulse current. When the online parameter excitation condition is not met, the battery ohmic internal resistance in the offline state is directly extracted as the discharge DC internal resistance using the constructed mapping table.

[0029] Unlike solutions that continuously acquire real-time online discharge DC internal resistance, this method extracts the discharge DC internal resistance of the battery pack under test only when the online parameter excitation conditions are met. This avoids the waste of MCU computing power caused by blind calculations and also prevents the discharge DC internal resistance parameter from diverging. For example, the discharge DC internal resistance of the battery pack under test is not extracted under steady-state conditions such as constant speed cruise, reducing the parameter divergence rate to 0%.

[0030] During vehicle startup, the vehicle applies the aforementioned preset pre-charge / discharge pulses to the battery pack under test upon power-up, and then acquires data under the preset pre-charge pulse conditions, namely the pulse voltage and pulse current changes of the battery pack under test. When the online parameter excitation conditions are met, the discharge DC internal resistance is determined based on the following calculation formula:

[0031] in, Indicates the DC internal resistance of the discharge; This represents the pulse voltage difference during the pre-charge process; This represents the pulse current difference during the pre-charge process.

[0032] Several discharge DC internal resistances can be extracted from a discharge pulse, such as at the first acquisition moment. Second acquisition time Third acquisition time These acquisition times each correspond to a specific discharge DC internal resistance. The pre-charge / discharge pulse can be understood as a pulse that assesses the current discharge DC internal resistance of the battery pack.

[0033] S103. Obtain the operating data of the battery pack to be tested. Use the constructed mapping table and the operating data to determine the polarization internal resistance and polarization capacitance of the battery pack to be tested. Adjust the parameters of the polarization internal resistance and polarization capacitance according to the operating data and the DC internal resistance of discharge. The operating data includes battery temperature and SOC.

[0034] In this embodiment of the invention, a mapping table is used to characterize the mapping relationship between the battery pack model information and the offline parameter table. The offline parameter table is used to characterize the mapping relationship between the battery pack's polarization internal resistance, state of health based on resistance (SOHR), battery temperature, and state of charge (SOC). The mapping table and the offline parameter table can be obtained by conducting experiments on sample battery packs of the same model as the battery pack under test, collecting corresponding information such as SOC, open-circuit voltage, battery temperature, and SOHR, and then establishing the mapping table based on this information.

[0035] Because different battery pack models have individual differences, in order to meet the testing requirements of different battery pack models, in this embodiment of the invention, the experimental process will select sample battery packs of multiple models, and the mapping table will include the model information parameter.

[0036] The mapping table can be used to determine the polarization resistance and polarization capacitance of the same model and operating data as the battery pack under test.

[0037] Polarization resistance refers to the sum of all resistances that arise when current flows through a battery, causing it to deviate from its equilibrium (open circuit) state due to the sluggishness of electrochemical reactions and various transmission processes. Polarization can be divided into ohmic polarization (caused by internal battery resistance), concentration polarization (caused by changes in ion concentration in the electrolyte), and electrochemical polarization (caused by the limitation of reaction rate on the electrode surface). Polarization resistance is also a major reason why battery charge-discharge energy efficiency is less than 100%. Considering the influence of polarization resistance in battery voltage prediction can greatly improve the accuracy and dynamic response capability of battery voltage prediction, and is particularly suitable for prediction in situations such as vehicle malfunctions and emergencies.

[0038] In this embodiment of the invention, the polarization internal resistance of the battery pack to be tested includes the battery ohmic internal resistance, electrochemical polarization internal resistance, and concentration polarization internal resistance. Under the condition of online parameter excitation, the discharge DC internal resistance in the polarization internal resistance is extracted through steps S101 and S102. The electrochemical polarization internal resistance and the concentration polarization internal resistance are obtained by constructing a second-order resistor-capacitance equivalent circuit model based on the battery characteristics of the battery pack, especially the polarization characteristics, and the initial values ​​in the offline state are obtained after parameter identification of the second-order RC equivalent circuit model. Under the condition of not meeting the online excitation, the initial values ​​of the polarization internal resistance are obtained by parameter identification of the second-order RC equivalent circuit model in the offline state.

[0039] Specifically, by constructing the state-of-charge-open-circuit voltage curve (SOC-OCV curve) of the sample battery and simulating sample data under various test conditions, the parameters of the constructed second-order RC equivalent circuit model are identified, thereby obtaining sample offline data of polarization resistance. The polarization resistance in the offline state comprehensively considers the influence of SOC, battery temperature, and SOHR on the polarization of the sample battery, so that the polarization resistance in the offline state will be dynamically adjusted adaptively according to the user's usage scenario and the aging degree of the battery pack. The polarization resistance can not only adapt to the performance degradation throughout the battery's entire life cycle, but also respond to the real-time changes in SOC and battery temperature, thus providing more accurate initial parameter values ​​to the BMS.

[0040] In this embodiment of the invention, when the online parameter excitation condition is met, the discharge DC internal resistance, operating parameters, and SOHR extracted in real time are used to back-calculate other parameters in the offline parameter table, thereby updating the electrochemical polarization internal resistance. Electrochemically polarized capacitors Concentration polarization internal resistance and concentration polarization capacitor These parameters; when the online parameter excitation is not met, the operating parameters and the real-time extracted SOHR are used to back-calculate other parameters in the offline parameter table, thereby updating the battery's ohmic internal resistance. Electrochemical polarization internal resistance Electrochemically polarized capacitors Concentration polarization internal resistance and concentration polarization capacitor These parameters.

[0041] Throughout the entire battery lifecycle (SOH 100%-60%), SOHR will be dynamically identified, and the power prediction calculation results will be dynamically corrected to solve the problem of error spikes in traditional offline meters after aging.

[0042] After the BMS starts, it will build an offline parameter table and a mapping table to make a stable estimate of the polarization internal resistance. It will also adjust the polarization internal resistance parameters through running data, thereby optimizing the offline parameter table, so that the calculation results of the battery pack under test under all operating conditions are stable and highly accurate.

[0043] S104. Based on the actual current load conditions of the battery pack under test, predict the future current load conditions. Then, based on the future current load conditions, polarization resistance, polarization capacitance, and the current battery voltage, predict the battery voltage of the battery pack under test. Specifically:

[0044] in, This represents the predicted battery voltage, which is the battery voltage at a future time. This indicates the battery voltage at the current moment; Indicates the maximum current value under future current load conditions; This indicates the time corresponding to the maximum current value, and The corresponding time exceeds the current time; Indicates the electrochemical polarization voltage; Indicates concentration polarization voltage; This indicates the ohmic internal resistance of the battery.

[0045] Electrochemical polarization voltage Based on electrochemical polarization capacitance and electrochemically polarized capacitors The concentration polarization voltage was obtained. Based on concentration polarization capacitance and concentration polarization capacitor get.

[0046] In this embodiment of the invention, a constructed second-order RC equivalent model is used to predict the battery voltage in the next few seconds, and power prediction can be performed based on the predicted battery voltage. This enables more accurate prediction of the battery voltage throughout the entire life cycle of the battery pack under test under different battery temperatures, SOC, SOHR, and other conditions.

[0047] The algorithm for predicting the battery voltage of the battery pack under test is simple to deploy. By performing lightweight optimization of the algorithm, the computing power requirements can be met without the need for complex, high-end MCUs.

[0048] In this embodiment of the invention, the user can set a specific value for the preset duration according to the user's actual needs. For example, if it is necessary to predict the battery voltage in the next few seconds, the preset duration can be set to 5 seconds.

[0049] parameter The actual value is taken as the maximum current value within a preset time period after the current moment, thus determined by the parameter. The obtained parameters This allows for better early warning of faults under real-world operating conditions. For example, if the DC-DC converter is not working during autonomous driving, the vehicle will switch to powering the steering system and other components from the 12V battery pack, and the corresponding parameters will be adjusted accordingly. The obtained parameters Assuming the steering system requires at least voltage A to function properly, given the minimum voltage value, if the parameter... If the specific value is less than A, then the 12V battery pack will be judged as "potentially insufficient in the future to support the normal operation of the steering system". This judgment information can be reminded to passengers through indicator lights, voice, text and other means, and corresponding driving prompts can be generated, such as prompting passengers to turn off certain functions to ensure that the 12V battery pack can support the normal operation of the steering system, thereby ensuring the safe operation of the vehicle.

[0050] It should be noted that the pulse under actual operating conditions is not the same as the preset pre-charge and discharge pulse. The pulse under actual operating conditions reflects the pulse voltage and pulse current during the actual operation of the vehicle, while the preset pre-charge and discharge pulse is used to obtain the discharge DC internal resistance of the battery pack under test.

[0051] The power state prediction method for battery packs of the present invention involves placing the battery pack under test under a preset pre-charge pulse condition during vehicle startup. The method determines whether online parameter excitation is met based on changes in the pulse current. If online parameter excitation is met, the method extracts the discharge DC internal resistance of the battery pack based on changes in pulse voltage and pulse current. Changes in pulse voltage and pulse current reflect the step drop and inflection point of voltage / current during discharge. Therefore, the discharge DC internal resistance can be extracted based on changes in pulse voltage and pulse current. Extracting the discharge DC internal resistance of the battery pack only when online parameter excitation conditions are met avoids blind calculations that waste MCU computing power and also prevents the discharge DC internal resistance parameter from diverging. This method utilizes the mapping... The meter identifies the polarization resistance of a sample battery of the same model and EOL stage as the battery pack under test, under the same operating data. The polarization resistance is then adjusted, and throughout the battery pack's entire lifespan, the power prediction calculations are dynamically corrected based on the adjusted polarization resistance. This solves the problem of escalating errors in traditional offline meters after aging, meets real-time safety warning requirements, adapts to prediction deviations caused by temperature changes and differences in battery aging, significantly reduces the probability of false alarms, and ensures vehicle operational safety. It accurately predicts the battery voltage of the battery pack under test in the future, making the BMS protection strategy more precise. This avoids scenarios with high current discharge at low SOC and prevents irreversible battery damage, effectively extending battery life and reducing user replacement costs.

[0052] In this embodiment of the invention, step S102 specifically includes: S1021. Determine whether the rate of change of the pulse current does not exceed the first rate of change and is maintained for more than the preset maintenance time.

[0053] S1022. If it is determined that the change rate does not exceed the first change rate and the duration exceeds the preset duration, determine whether the change rate of the pulse current exceeds the second change rate and whether the change amount exceeds the first change amount within the first time period.

[0054] S1023. If it is determined that the rate of change exceeds the first rate of change and the amount of change exceeds the first amount of change in the first time period, determine whether the rate of change of the pulse current exceeds the third rate of change in the second time period adjacent to the first time period.

[0055] S1024. Under the condition that the change rate does not exceed the third rate of change, extract the discharge DC internal resistance of the battery pack to be tested based on the changes in pulse voltage and pulse current.

[0056] It should be noted that the first, second, and third rates of change, as well as the first change amount, can be set according to the actual application scenario.

[0057] Please see Figure 3The dynamic parameter excitation conditions need to satisfy: During this time period, the pulse current of the battery pack under test needs to remain stable for a certain period of time; Within a given time period, whether the rate of change of the pulse current exceeds the second rate of change and whether the amount of change exceeds the first amount of change, i.e. and ,in, This indicates the amount of change in pulse current. Indicates the time difference. Indicates the first rate of change. This indicates the first change; immediately following, in the second time period adjacent to the first time period, that is, in Within a given time period, the rate of change of the pulse current does not exceed the rate of change of the third time period in the second time period immediately following the first time period. And maintain this state for a period of time.

[0058] In this embodiment of the invention, an observation window is slid along the time axis. First, it is checked whether there exists a quasi-steady-state segment within the observation window whose duration exceeds a preset duration and whose pulse current fluctuation is less than a certain small threshold, i.e., the aforementioned... Time period. Next, check if a pulse current change phase appears in the observation window. Within this phase, does the rate of change of the pulse current exceed the second rate of change in the first time period, and does the amount of change exceed the first amount of change, i.e., the aforementioned... Time period. Then, check whether it has entered a re-stabilization phase, as described above. Within a given time period, the rate of change of the pulse current does not exceed the third rate of change.

[0059] Only when the observed current sequence satisfies the time sequence → → Only under certain conditions will online parameter excitation of the discharge DC internal resistance be performed.

[0060] More specifically, step S1024 includes the following steps: S10241. Given that the change rate does not exceed the third rate of change, determine a first moment and several second moments based on the change in the pulse current. The first moment is the moment when the current first shows a step drop, and the second moments are the moments when the current change reaches an inflection point. By extracting the characteristic of the pulse current's rate of change, and based on this characteristic, it can be determined whether the pulse current shows a step drop (i.e., the rate of change exceeds the second rate of change), and the moment when the pulse current first shows a step drop is determined as the first moment.

[0061] It should be noted that if there are multiple inflection points in the pulse current data of the battery pack under the preset pre-charge pulse condition, each inflection point can be used as a second moment as mentioned above.

[0062] S10242. Determine the pulse voltage value and pulse current value of the battery pack to be tested at the first time and the second time respectively, and obtain the first pulse voltage value and the first pulse current value corresponding to the first time, and the second pulse voltage value and the second pulse current value corresponding to the second time.

[0063] S10243. Based on the first pulse voltage value, the second pulse voltage value, the first pulse current value, and the second pulse current value, determine the discharge DC internal resistance of the battery pack under test at each second moment. Specifically, the discharge DC internal resistance is determined based on the following formula:

[0064] in, This represents the DC internal resistance of the discharge at the second moment; This represents the first pulse voltage value, which is the pulse voltage value of the battery pack under test at the first moment. This represents the second pulse voltage value, which is the pulse voltage value of the battery pack under test at the second moment. This represents the first pulse current value, which is the pulse current value of the battery pack under test at the first moment. This represents the second pulse current value, which is the pulse current value of the battery pack under test at the second moment.

[0065] In this embodiment of the invention, the mapping table is constructed through the following steps: S201. Construct a second-order RC equivalent circuit model of the battery pack based on the battery characteristics of the battery pack.

[0066] like Figure 2 As shown, this second-order RC equivalent circuit model consists of a voltage source with open-circuit voltage. Battery internal resistance (ohms) Electrochemical polarization internal resistance Electrochemically polarized capacitors Concentration polarization internal resistance and concentration polarization capacitor Composition, including electrochemical polarization internal resistance Electrochemically polarized capacitors The components are connected in parallel to form an RC network, and the concentration polarization internal resistance... Concentration polarization capacitor These components are connected in parallel to form another RC network. Based on these components, a second-order RC equivalent circuit model is constructed, which together characterizes the polarization effect and transient response characteristics of the battery pack.

[0067] Based on Kirchhoff's Voltage Law (KVL) and Kirchhoff's Current Law (KCL), the discrete state equations of the second-order RC equivalent circuit model, i.e., the mathematical model expression of the battery, can be established. Specifically:

[0068]

[0069]

[0070] in, This represents the terminal voltage in the second-order RC equivalent circuit model. This represents the dynamic current in the second-order RC equivalent circuit model. This represents the ohmic internal resistance of the battery in the second-order RC equivalent circuit model. This represents the electrochemical polarization internal resistance in the second-order RC equivalent circuit model; This represents the concentration polarization internal resistance in the second-order RC equivalent circuit model; This represents the electrochemically polarized capacitance in a second-order RC equivalent circuit model. This represents the concentration polarization capacitance in the second-order RC equivalent circuit model. This represents the electrochemical polarization voltage in the second-order RC equivalent circuit model; This represents the concentration polarization voltage in the second-order RC equivalent circuit model.

[0071] Due to the sluggishness of electrochemical reactions and various transport processes, polarization is the sum of all resistances that cause a battery to deviate from its equilibrium (open circuit) state. Polarization can be divided into ohmic polarization (caused by internal battery resistance), concentration polarization (caused by changes in ion concentration in the electrolyte), and electrochemical polarization (caused by the limitation of reaction rate on the electrode surface). Polarization resistance is also a major reason why battery charge-discharge energy efficiency is less than 100%. Taking the influence of polarization resistance into account in the prediction of SOP can greatly improve the accuracy and dynamic response capability of battery voltage prediction, and is particularly suitable for prediction in situations such as vehicle malfunctions and emergencies.

[0072] S202. Place each sample battery with different model information under different test conditions and acquire sample data for each sample battery under each test condition. The sample data includes pulse voltage, pulse current, battery voltage, and battery current. The test condition is to apply a preset pre-charge and discharge pulse to the sample battery under the test operation data. Similarly, the test operation data consists of parameters such as SOC, battery temperature, and SOHR. The pre-charge and discharge pulse is the HPPC discharge pulse. The test condition is to simulate the discharge test of the sample battery under HPPC.

[0073] In this embodiment of the invention, by acquiring the pulse voltage, pulse current, battery voltage, and battery current of a sample battery of the same model as the battery pack to be tested under various test conditions, the above-mentioned data of the battery to be tested under the same conditions can be obtained. By adjusting the test operation data to simulate more test conditions, the data of the sample battery under all conditions can be obtained.

[0074] It should be noted that the SOHR parameter can be determined by the ratio of the current discharge DC internal resistance / battery ohmic internal resistance of the battery pack to the discharge DC internal resistance / battery ohmic internal resistance of the battery pack in its brand-new state, i.e., the beginning of life (BOL). SOHR is a specific parameter that quantifies the degree of battery aging and is used to indicate how many times the current battery pack's internal resistance is compared to the internal resistance of the BOL battery pack.

[0075] S203. Construct the SOC-OCV curve of the sample battery.

[0076] In this embodiment of the invention, the actual open-circuit voltage corresponding to each actual state of charge during the discharge process of the sample sodium battery is obtained, and the SOC-OCV curve of the sample battery is constructed based on the state of charge and the open-circuit voltage.

[0077] Since different battery pack models have individual differences, in order to meet the SOP estimation requirements of different battery pack models, in this embodiment of the invention, the experimental process will select sample battery packs of multiple models, and then construct SOC-OCV curves for multiple battery pack models.

[0078] S204. Based on the sample data and the SOC-OCV curve, the model parameters of the second-order RC equivalent circuit model are identified, thereby obtaining the battery's ohmic internal resistance. Electrochemical polarization internal resistance Electrochemically polarized capacitors Concentration polarization internal resistance and concentration polarization capacitor These parameters provide the initial values ​​for the model parameters. After obtaining the model parameters, a mapping relationship is established between the test run data and the model parameters to obtain an offline parameter table. Then, a mapping relationship is established between the offline parameter table and the model information to obtain a mapping table.

[0079] In this embodiment of the invention, each offline parameter table is used to characterize the internal resistance of the sample battery at the corresponding SOHR aging level, SOC, and battery temperature. Based on all offline parameter tables, the offline ohmic internal resistance of the sample batteries under different SOHR aging levels, SOC, and battery temperatures can be characterized across all operating conditions. .

[0080] The process of establishing the offline parameter table described above can employ mean filtering to ensure stable values ​​at each point. Furthermore, the mapping relationship is established through offline data acquisition in a laboratory environment; that is, it uses offline sample data from the test batteries under test conditions, and does not rely on online data acquisition. The constructed SOC-OCV curves and offline parameter tables can be stored in a pre-set database for easy retrieval of the offline battery's ohmic internal resistance. The initial value is retrieved and used.

[0081] The offline ohmic internal resistance of the sample batteries under different SOHR aging levels, SOC, and battery temperatures was obtained under full operating conditions. Subsequently, the offline battery ohmic internal resistance can be further obtained by fitting based on the offline parameter table. The functional relationship between SOHR, SOC, and battery temperature is as follows:

[0082] in, Indicates the battery's internal resistance in ohms The functional relationship between SOHR, SOC and battery temperature; This indicates the battery temperature.

[0083] Based on this functional relationship, the ohmic internal resistance of the battery under different SOHR conditions can be obtained. .

[0084] In this embodiment of the invention, the SOC-OCV curve is constructed through the following steps: S301. Allow sample batteries of different models to stand for a preset time to ensure that the sample batteries reach a stable internal state. The preset time can be configured by the user.

[0085] S302. Charge each sample battery using a preset charging current. Once the sample battery is determined to be charged to a preset state of charge, switch to a preset charging voltage to charge each sample battery and charge each sample battery to a preset current state.

[0086] In this embodiment of the invention, the preset state of charge (SOC) is when the battery pack is close to but not yet at its full charge voltage, such as 90% SOC. The preset current state is when the battery pack's charging rate reaches a certain threshold. Charging the sample batteries to the preset current state ensures that each sample battery is fully charged during this charging process. To ensure the stability of this charging process, the preset charging current is a constant current, and the preset charging voltage is a constant voltage. Thus, the sample battery charging process first uses the preset constant current and then switches to the preset constant voltage.

[0087] S303. Discharge each sample battery in a preset current state at a preset discharge rate, and determine the state of charge of each sample battery during the discharge process and the open circuit voltage corresponding to the state of charge.

[0088] In this embodiment of the invention, in order to obtain the state of charge and the open-circuit voltage of the state of charge at each state point, the actual open-circuit voltage of the sample battery under the actual state of charge is collected when each sample battery discharges a preset amount of power, i.e., a preset SOC, until the sample battery finishes discharging. In this way, whenever the SOC of the sample battery drops by a certain value during the discharge process, a corresponding SOC and the open-circuit voltage of the SOC will be obtained.

[0089] S304. Based on the state of charge and open-circuit voltage of the sample battery, construct the SOC-OCV curve.

[0090] Using the obtained SOC and its corresponding open-circuit voltage, SOC-OCV curves are plotted for each sample battery. After obtaining the SOC-OCV curves, the least squares method can be used to fit them, thereby describing the mapping relationship between SOC and open-circuit voltage using a functional relationship. After fitting, the root mean squared error (RMSE), coefficient of determination (R-square), and sum of squares due to error (SSE) can be used to evaluate the curve fitting effect. If the fitting effect of the SOC-OCV curve is evaluated as not meeting the standard, the fitting effect can be improved by increasing the order of the polynomial until the fitting effect is evaluated as meeting the standard.

[0091] In this embodiment of the invention, in order to ensure the accuracy of each data point of the constructed SOC-OCV curve, the process of resting, charging, discharging and data acquisition can be repeated multiple times for each sample battery. After each discharge, the sample battery is allowed to rest for a sufficient period of time before the corresponding data is acquired.

[0092] In this embodiment of the invention, step S103 includes: S1031. Determine the model information of the battery pack to be tested, and match the mapping table for the battery pack to be tested based on the model information. After obtaining the matching parameter of the model information, a mapping table of sample batteries of the same model can be matched for the battery pack to be tested. The mapping table contains the offline parameter table of sample batteries of the same model under different SOHR, SOC, and battery temperature conditions.

[0093] The model information is determined from the battery nameplate, product specification sheet, or official parameters.

[0094] S1032. Obtain the real-time SOHR of the battery pack to be tested, and perform sliding filtering on the real-time SOHR to improve the data accuracy and stability of the output real-time SOHR.

[0095] Specifically, based on the operating data, the discharge DC internal resistance is used as the battery ohmic internal resistance in the offline parameter table. Alternatively, you can directly use the battery's internal resistance. The SOHR of the battery pack under test at various times is determined by using an offline parameter table, thereby obtaining a dynamic SOHR of the entire pack.

[0096] In this embodiment of the invention, when it is determined that the online parameter excitation is satisfied, the discharge DC internal resistance will be used as the battery ohmic internal resistance in the offline parameter table. Correspondingly, if the online parameter excitation is not met, the battery's internal ohmic resistance is directly called. .

[0097] It should be noted that if the offline parameter table does not directly record the ohmic internal resistance of the battery with the same DC internal resistance as the discharge resistance, The corresponding SOHR can be obtained by interpolation.

[0098] This method allows you to obtain constantly updated real-time SOHR.

[0099] S1033. Determine the operating data of the battery pack to be tested.

[0100] S1034. Under the condition that the online parameter excitation is satisfied, utilize the operating data, real-time SOHR, and the discharge DC internal resistance as the battery ohmic internal resistance. Interpolation calculations are performed on the offline parameter table in the mapping table to adjust the electrochemical polarization resistance under the current operating data. Electrochemically polarized capacitors Concentration polarization internal resistance and concentration polarization capacitor These parameters.

[0101] Under the condition that the online parameter excitation is met, by using operating data, continuously updated real-time SOHR, and taking the discharge DC internal resistance as the battery ohmic internal resistance, multi-dimensional interpolation calculations are performed to obtain an offline parameter table based on dynamic real-time SOHR updates, thereby updating the electrochemical polarization internal resistance. Electrochemically polarized capacitors Concentration polarization internal resistance and concentration polarization capacitor These parameters.

[0102] S1035. If it is determined that the online parameter excitation is not met, the running data is used as index information to retrieve the polarization internal resistance and polarization capacitor under the same conditions from the offline parameter table.

[0103] If the online parameter excitation is not met, the running data is used as index information, that is, the running data is used as the SOC and battery temperature in the test running data, and the polarization internal resistance and polarization capacitance at the same SOC and battery temperature are retrieved from the offline data table.

[0104] In this embodiment of the invention, step S104 includes: S1041. Determine the battery voltage of the battery pack to be tested at the current moment. Obtain the battery voltage at the current moment through various sensors and on-board instruments, i.e., determine... This parameter.

[0105] S1042. Determine the current load conditions to be tested based on the actual current load conditions of the battery pack to be tested, and predict the future current load conditions based on the actual current load conditions.

[0106] Since the changes in pulse current under various vehicle operating conditions follow certain patterns, the maximum current value within a preset time period after the current moment can be determined using methods such as data fitting, model prediction, and current feature extraction, thereby obtaining the parameters. The actual value.

[0107] Please see Figure 4 , Figure 4 This indicates the current change of the vehicle in the next 5 seconds. During the battery pack's discharge and overcharge process, there will be several large current peaks. Unlike other battery voltage prediction schemes, in this embodiment of the invention, the future current load condition is predicted by obtaining the actual current load condition, and the maximum current value appearing in the future current load condition is used as a parameter. The actual value.

[0108] S1043. Subtract the product of the future current load condition and the DC internal resistance of discharge, the electrochemical polarization voltage, and the concentration polarization voltage from the current battery voltage to predict the battery voltage of the battery pack under test at a future time.

[0109] Please see Figure 5 and Figure 6 The parameters are calculated by subtracting the product of the future current load condition and the discharge DC internal resistance, the electrochemical polarization voltage, and the concentration polarization voltage from the current battery voltage, with the product of the future current load condition and the discharge DC internal resistance being the final parameter. With the called or adjusted battery internal resistance The product of the two ensures that the error between the predicted battery voltage and the measured minimum voltage at future times is smaller, and the error between the two always remains positive. That is, the predicted battery voltage is always less than the measured minimum voltage, which ensures early prediction of the risk of low battery voltage.

[0110] Meanwhile, the accuracy of the predicted SOP in the embodiments of the present invention is verified through testing. The testing and verification process specifically includes: A battery pack was randomly selected, fully charged, and left to stand for a period of time to control the battery temperature to the specified verification temperature. Then, the battery pack was discharged with a small current to the specified SOC, and the battery voltage predicted by the current power was recorded. After standing for a period of time, the current curve of the current dynamic excitation was triggered, and the minimum voltage value obtained by the current power prediction was recorded. After standing for a period of time, the set 5s current change condition was triggered, and the measured minimum voltage was recorded. The above process was repeated, and the measured data are shown in Table 1.

[0111] Table 1 Test Verification Data Table

[0112] The minimum voltage before optimization in Table 1 is the battery voltage predicted by other battery voltage prediction schemes, and the minimum voltage after optimization is the battery voltage predicted in the embodiment of the present invention. Correspondingly, ΔV before optimization is the deviation between the minimum voltage before optimization and the measured minimum voltage, and ΔV after optimization is the deviation between the minimum voltage after optimization and the measured minimum voltage.

[0113] The power state prediction device for a battery pack provided in the embodiments of the present invention will be described below. The power state prediction device and the power state prediction method for a battery pack described below can be referred to each other.

[0114] To address the aforementioned issues, this specification provides a power state prediction device for a battery pack. This device uses a high-precision offline parameter table as the initial value for SOP estimation. When the conditions for online estimation are met, it initiates online estimation while simultaneously correcting the offline parameters. This ensures the stability of the estimation while also enabling the tracking of battery pack aging and the elimination of individual differences within the battery pack. Figure 7 This is a schematic diagram of the power state prediction device for a battery pack according to an embodiment of the present invention, as shown below. Figure 7 As shown, the device may include: The pulse application module 10 is used to place the battery pack under test under a preset pre-charge pulse condition during vehicle startup and to acquire the pulse voltage and pulse current changes of the battery pack under the preset pre-charge pulse condition. The preset pre-charge pulse condition involves applying a preset pre-charge / discharge pulse to the battery pack under test, and acquiring the aforementioned pulse voltage and pulse current changes through various sensors and on-board instruments.

[0115] The online excitation module 20 is used to determine whether the online parameter excitation is met based on the change of pulse current, and if the online parameter excitation is met, extract the discharge DC internal resistance of the battery pack to be tested based on the changes of pulse voltage and pulse current.

[0116] In this embodiment of the invention, a preset online parameter excitation condition is also set, and it is determined whether the online parameter excitation condition is met based on the rate of change and amount of change of the battery current of the battery pack to be tested. When the online parameter excitation condition is met, the discharge DC internal resistance of the battery pack to be tested is extracted based on the changes of pulse voltage and pulse current. When the online parameter excitation condition is not met, the battery ohmic internal resistance in the offline state is directly extracted as the discharge DC internal resistance using the constructed mapping table.

[0117] Unlike solutions that continuously acquire real-time online discharge DC internal resistance, this method extracts the discharge DC internal resistance of the battery pack under test only when the online parameter excitation conditions are met. This avoids the waste of MCU computing power caused by blind calculations and also prevents the discharge DC internal resistance parameter from diverging. For example, the discharge DC internal resistance of the battery pack under test is not extracted under steady-state conditions such as constant speed cruise, reducing the parameter divergence rate to 0%.

[0118] During the vehicle startup process, the vehicle applies the aforementioned preset pre-charge and discharge pulses to the battery pack under test by powering on, and then acquires data under the preset pre-charge pulse conditions, namely the pulse voltage and pulse current changes of the battery pack under test.

[0119] Several discharge DC internal resistances can be extracted from a discharge pulse, such as at the first acquisition moment. Second acquisition time Third acquisition time These acquisition times each correspond to a specific discharge DC internal resistance. The pre-charge / discharge pulse can be understood as a pulse that assesses the current discharge DC internal resistance of the battery pack.

[0120] The parameter adjustment module 30 is used to acquire the operating data of the battery pack under test, determine the polarization internal resistance and polarization capacitance of the battery pack under test using the constructed mapping table and the operating data, and adjust the parameters of the polarization internal resistance and polarization capacitance according to the operating data and the discharge DC internal resistance. The operating data includes battery temperature and SOC.

[0121] In this embodiment of the invention, a mapping table is used to characterize the mapping relationship between the battery pack model information and the offline parameter table. The offline parameter table is used to characterize the mapping relationship between the battery pack's polarization resistance, SOHR, battery temperature, and SOC. The mapping table and the offline parameter table can be established by conducting experiments on sample battery packs of the same model as the battery pack to be tested, collecting corresponding information such as SOC, open-circuit voltage, battery temperature, and SOHR, and then using this information.

[0122] Because different battery pack models have individual differences, in order to meet the testing requirements of different battery pack models, in this embodiment of the invention, the experimental process will select sample battery packs of multiple models, and the mapping table will include the model information parameter.

[0123] The mapping table can be used to determine the polarization resistance and polarization capacitance of the same model and operating data as the battery pack under test.

[0124] The voltage prediction module 40 is used to predict the future current load conditions based on the actual current load conditions of the battery pack under test, and to predict the battery voltage of the battery pack under test based on the future current load conditions, polarization internal resistance, polarization capacitance and the current battery voltage.

[0125] In this embodiment of the invention, a constructed second-order RC equivalent model is used to predict the battery voltage in the next few seconds, and power prediction can be performed based on the predicted battery voltage. This enables more accurate prediction of the battery voltage throughout the entire life cycle of the battery pack under test under different battery temperatures, SOC, SOHR, and other conditions.

[0126] The algorithm for predicting the battery voltage of the battery pack under test is simple to deploy. By performing lightweight optimization of the algorithm, the computing power requirements can be met without the need for complex, high-end MCUs.

[0127] In this embodiment of the invention, the user can set a specific value for the preset duration according to the user's actual needs. For example, if it is necessary to predict the battery voltage in the next few seconds, the preset duration can be set to 5 seconds.

[0128] parameter The actual value is taken as the maximum current value within a preset time period after the current moment, thus determined by the parameter. The obtained parameters This allows for better early warning of faults under real-world operating conditions. For example, if the DC-DC converter is not working during autonomous driving, the vehicle will switch to powering the steering system and other components from the 12V battery pack, and the corresponding parameters will be adjusted accordingly. The obtained parameters Assuming the steering system requires at least voltage A to function properly, given the minimum voltage value, if the parameter... If the specific value is less than A, then the 12V battery pack will be judged as "potentially insufficient in the future to support the normal operation of the steering system". This judgment information can be reminded to passengers through indicator lights, voice, text and other means, and corresponding driving prompts can be generated, such as prompting passengers to turn off certain functions to ensure that the 12V battery pack can support the normal operation of the steering system, thereby ensuring the safe operation of the vehicle.

[0129] It should be noted that the pulse under actual operating conditions is not the same as the preset pre-charge and discharge pulse. The pulse under actual operating conditions reflects the pulse voltage and pulse current during the actual operation of the vehicle, while the preset pre-charge and discharge pulse is used to obtain the discharge DC internal resistance of the battery pack under test.

[0130] The power state prediction device for the battery pack of the present invention places the battery pack under test under a preset pre-charge pulse condition during vehicle startup. It determines whether online parameter excitation is met based on changes in pulse current. If online parameter excitation is met, the device extracts the discharge DC internal resistance of the battery pack based on changes in pulse voltage and pulse current. The changes in pulse voltage and pulse current reflect the step drop and inflection point of voltage / current during discharge. Therefore, the discharge DC internal resistance can be extracted based on these changes. Extracting the discharge DC internal resistance of the battery pack only when the online parameter excitation conditions are met avoids blind calculations that waste MCU computing power and also prevents the discharge DC internal resistance parameter from diverging. This utilizes the imaging... The meter identifies the polarization resistance of a sample battery of the same model and EOL stage as the battery pack under test, under the same operating data. The polarization resistance is then adjusted, and throughout the battery pack's entire lifespan, the power prediction calculations are dynamically corrected based on the adjusted polarization resistance. This solves the problem of escalating errors in traditional offline meters after aging, meets real-time safety warning requirements, adapts to prediction deviations caused by temperature changes and differences in battery aging, significantly reduces the probability of false alarms, and ensures vehicle operational safety. It accurately predicts the battery voltage of the battery pack under test in the future, making the BMS protection strategy more precise. This avoids scenarios with high current discharge at low SOC and prevents irreversible battery damage, effectively extending battery life and reducing user replacement costs.

[0131] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical commands in the memory 830 to execute a power state prediction method for the battery pack, the method including: During the vehicle startup process, the battery pack under test is placed under a preset pre-charge pulse condition, and the pulse voltage and pulse current changes of the battery pack under test are acquired under the preset pre-charge pulse condition. The online parameter excitation is determined based on the change in pulse current. If the online parameter excitation is determined to be satisfied, the discharge DC internal resistance of the battery pack under test is extracted based on the changes in pulse voltage and pulse current. The system acquires operational data of the battery pack under test, uses a pre-constructed mapping table and the operational data to determine the polarization internal resistance and polarization capacitance of the battery pack under test, and adjusts the parameters of the polarization internal resistance and polarization capacitance based on the discharge DC internal resistance and operational data. The operational data includes resistance-based health status, battery temperature, and state of charge. The mapping table is used to characterize the mapping relationship between the battery pack model information and the offline parameter table, and the offline parameter table is used to characterize the mapping relationship between the battery pack's polarization internal resistance, polarization capacitance, and operational data. The future current load conditions are predicted based on the actual current load conditions of the battery pack under test. Based on the future current load conditions, polarization internal resistance, polarization capacitance, and the current battery voltage, the battery voltage of the battery pack under test is predicted.

[0132] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0133] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the power state prediction method for the battery pack provided by the above methods, the method comprising: During the vehicle startup process, the battery pack under test is placed under a preset pre-charge pulse condition, and the pulse voltage and pulse current changes of the battery pack under test are acquired under the preset pre-charge pulse condition. The online parameter excitation is determined based on the change in pulse current. If the online parameter excitation is determined to be satisfied, the discharge DC internal resistance of the battery pack under test is extracted based on the changes in pulse voltage and pulse current. The system acquires operational data of the battery pack under test, uses a pre-constructed mapping table and the operational data to determine the polarization internal resistance and polarization capacitance of the battery pack under test, and adjusts the parameters of the polarization internal resistance and polarization capacitance based on the discharge DC internal resistance and operational data. The operational data includes resistance-based health status, battery temperature, and state of charge. The mapping table is used to characterize the mapping relationship between the battery pack model information and the offline parameter table, and the offline parameter table is used to characterize the mapping relationship between the battery pack's polarization internal resistance, polarization capacitance, and operational data. The future current load conditions are predicted based on the actual current load conditions of the battery pack under test. Based on the future current load conditions, polarization internal resistance, polarization capacitance, and the current battery voltage, the battery voltage of the battery pack under test is predicted.

[0134] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0135] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the state of power of a battery pack, characterized in that, The method includes: During the vehicle startup process, the battery pack under test is placed under a preset pre-charge pulse condition, and the pulse voltage and pulse current changes of the battery pack under test under the preset pre-charge pulse condition are obtained. The online parameter excitation is determined based on the change in pulse current. If the online parameter excitation is determined to be satisfied, the discharge DC internal resistance of the battery pack under test is extracted based on the changes in pulse voltage and pulse current. The system acquires operational data of the battery pack under test, uses a pre-constructed mapping table and the operational data to determine the polarization internal resistance and polarization capacitance of the battery pack under test, and adjusts the parameters of the polarization internal resistance and polarization capacitance based on the discharge DC internal resistance and operational data. The operational data includes resistance-based health status, battery temperature, and state of charge. The mapping table is used to characterize the mapping relationship between the battery pack model information and the offline parameter table, and the offline parameter table is used to characterize the mapping relationship between the battery pack's polarization internal resistance, polarization capacitance, and operational data. The future current load conditions are predicted based on the actual current load conditions of the battery pack under test. Based on the future current load conditions, polarization internal resistance, polarization capacitance, and the current battery voltage, the battery voltage of the battery pack under test is predicted.

2. The power state prediction method for a battery pack according to claim 1, characterized in that, The step of extracting the discharge DC internal resistance of the battery pack under test based on changes in pulse voltage and pulse current specifically includes: Determine whether the rate of change of the pulse current does not exceed the first rate of change and is maintained for more than the preset maintenance time; If it is determined that the change rate does not exceed the first rate of change and is maintained for more than the preset maintenance time, it is determined whether the change rate of the pulse current exceeds the second rate of change and whether the change amount exceeds the first change amount within the first time period. If it is determined that the rate of change exceeds the first rate of change and the amount of change exceeds the first amount of change in the first time period, it is determined whether the rate of change of the pulse current exceeds the third rate of change in the second time period adjacent to the first time period. Under the condition that the change rate does not exceed the third rate of change, the discharge DC internal resistance of the battery pack under test is extracted based on the changes in pulse voltage and pulse current.

3. The power state prediction method for a battery pack according to claim 2, characterized in that, The step of extracting the discharge DC internal resistance of the battery pack under test based on the changes in pulse voltage and pulse current, under the condition that the change rate does not exceed the third rate of change, specifically includes: Under the condition that the change rate does not exceed the third rate of change, the first moment and several second moments are determined according to the change of the pulse current; the first moment is the moment when the current first shows a step drop, and the second moment is the moment when the change of the current shows an inflection point; The pulse voltage and pulse current values ​​of the battery pack under test are determined at the first time and the second time, respectively, to obtain the first pulse voltage and first pulse current values ​​corresponding to the first time, and the second pulse voltage and second pulse current values ​​corresponding to the second time. Based on the first pulse voltage value, the second pulse voltage value, the first pulse current value, and the second pulse current value, the discharge DC internal resistance of the battery pack under test at each second moment is determined.

4. The power state prediction method for a battery pack according to claim 1, characterized in that, The process of acquiring the operating data of the battery pack under test, determining the polarization internal resistance and polarization capacitance of the battery pack under test based on the operating data, and adjusting the polarization internal resistance parameters based on the operating data specifically includes: Determine the model information of the battery pack to be tested, and match the mapping table for the battery pack to be tested based on the model information; The real-time resistance-based health status of the battery pack under test is obtained, and the real-time resistance-based health status is subjected to sliding filtering. Determine the operating data of the battery pack to be tested; Under the condition that the online parameter excitation is met, the offline parameter table in the mapping table is interpolated using the operating data, the real-time resistance-based health status, and the discharge DC internal resistance as the battery ohmic internal resistance, and the electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization capacitance, and concentration polarization capacitance under the current operating data are adjusted. If the online parameter excitation is not met, the running data is used as index information to retrieve the polarization internal resistance and polarization capacitor under the same conditions from the offline parameter table; the polarization internal resistance consists of the discharge DC internal resistance, the electrochemical polarization internal resistance, and the concentration polarization internal resistance, and the polarization capacitor consists of the electrochemical polarization capacitor and the concentration polarization capacitor.

5. The power state prediction method for a battery pack according to claim 1, characterized in that, The process of predicting the future current load condition based on the actual current load condition of the battery pack under test, and predicting the battery voltage of the battery pack under test based on the future current load condition, polarization internal resistance, polarization capacitance, and the current battery voltage, specifically includes: Determine the battery voltage of the battery pack under test at the current moment; The test is determined based on the actual current load conditions of the battery pack under test, and the future current load conditions are predicted based on the actual current load conditions. The battery voltage of the battery pack under test is predicted in the future by subtracting the product of the future current load condition and the battery's internal resistance, the electrochemical polarization voltage, and the concentration polarization voltage from the current battery voltage. The electrochemical polarization voltage is obtained from the electrochemical polarization resistance and the electrochemical polarization capacitance, and the concentration polarization voltage is obtained from the concentration polarization resistance and the concentration polarization capacitance.

6. The power state prediction method for a battery pack according to claim 1, characterized in that, The mapping table is constructed through the following steps: Construct a second-order resistor-capacitor equivalent circuit model of the battery pack based on the battery characteristics of the battery pack. Each sample battery was placed under different test conditions, and sample data of each sample battery under each test condition was obtained. The sample data included pulse voltage, pulse current, battery voltage and battery current. The test condition was to apply a preset pre-charge and discharge pulse to the sample battery under the test operation data. The test operation data consisted of state of charge, battery temperature and resistance-based health status. Construct the state-of-charge-open-circuit voltage curve of the sample battery; Based on sample data and the state-of-charge-open-circuit voltage curve, the model parameters of the second-order resistive-capacitive equivalent circuit model are identified to obtain the model parameters. A mapping relationship between the test operation data and the model parameters is established to obtain an offline parameter table. A mapping relationship between the offline parameter table and the model information is established to obtain a mapping table.

7. The power state prediction method for a battery pack according to claim 6, characterized in that, The state-of-charge-open-circuit voltage curve is constructed through the following steps. Set the sample batteries of different models to stand for a preset time; Each sample battery is charged using a preset charging current. Once the sample battery is determined to be charged to a preset state of charge, the system switches to a preset charging voltage to charge each sample battery and charges each sample battery to a preset current state. The preset charging current is a constant current, and the preset charging voltage is a constant voltage; Discharge each sample battery in a preset current state at a preset discharge rate, and determine the state of charge of each sample battery during the discharge process and the open circuit voltage corresponding to the state of charge. Based on the state of charge and open-circuit voltage of the sample cells, a state of charge-open-circuit voltage curve is constructed.

8. A power state prediction device for a battery pack, characterized in that, The device includes: The pulse application module is used to place the battery pack under test under a preset pre-charge pulse condition during the vehicle startup process, and to acquire the pulse voltage and pulse current changes of the battery pack under test under the preset pre-charge pulse condition. The feature extraction module is used to extract the discharge DC internal resistance of the battery pack under test based on the changes in pulse voltage and pulse current. The online excitation module is used to acquire the operating data of the battery pack under test. It uses a pre-constructed mapping table and the operating data to determine the polarization internal resistance and polarization capacitance of the battery pack under test, and adjusts the parameters of polarization internal resistance and polarization capacitance based on the discharge DC internal resistance and operating data. The operating data includes resistance-based health status, battery temperature, and state of charge. The mapping table is used to characterize the mapping relationship between the battery pack model information and the offline parameter table. The offline parameter table is used to characterize the mapping relationship between the battery pack's polarization internal resistance, polarization capacitance, and operating data. The power prediction module is used to predict the future current load conditions based on the actual current load conditions of the battery pack under test, and to predict the battery voltage of the battery pack under test based on the future current load conditions, polarization internal resistance, polarization capacitance and the current battery voltage.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the power state prediction method for the battery pack as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power state prediction method for the battery pack as described in any one of claims 1 to 7.

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