Battery parameter determination method and device, equipment and storage medium

Abstract

The invention relates to a battery parameter determination method and device, equipment and a storage medium. The method comprises the following steps: determining terminal voltage, current, battery temperature and state of charge quantity of the battery at the current moment in the charging and discharging process of the battery; determining the open-circuit voltage of the battery at the current moment based on the battery temperature and the charge state quantity; obtaining an actual voltage difference at the current moment according to the terminal voltage and the open-circuit voltage, and correcting an intermediate parameter at the previous moment according to the actual voltage difference and a predicted voltage difference to obtain an intermediate parameter at the current moment; the predicted voltage difference is obtained by predicting a current moment based on an intermediate parameter of a previous moment; obtaining battery parameters of the battery at the current moment based on the intermediate parameters at the current moment and the parameter mapping relation; the parameter mapping relation is used for describing multiple groups of corresponding relations between the intermediate parameters and the battery parameters. By adopting the method, the identification precision of the battery parameters can be improved.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a method, apparatus, device and storage medium for determining battery parameters. Background Technology

[0002] As a core component of electric vehicles and energy storage systems, the accurate modeling and parameter identification of power batteries are key technological challenges for improving the performance of Battery Management Systems (BMS), ensuring system safety, and promoting battery development. Related technologies rely on equivalent circuit models, electrochemical models, or data-driven methods for online battery parameter estimation, which can reflect the dynamic characteristics of the battery and provide support for estimating its state of charge and state of health. However, the accuracy of battery parameter identification in these technologies is relatively low. Summary of the Invention

[0003] Based on this, this application addresses the aforementioned technical problems by providing a battery parameter determination method, apparatus, device, and storage medium that can improve the accuracy of battery parameter identification.

[0004] Firstly, this application provides a method for determining battery parameters, including:

[0005] During the charging and discharging process of the battery, determine the battery's terminal voltage, current, battery temperature, and state of charge at the current moment;

[0006] Determine the open-circuit voltage of the battery at the current moment based on the battery temperature and state of charge.

[0007] The actual voltage difference at the current moment is obtained based on the terminal voltage and open-circuit voltage. The intermediate parameters at the previous moment are corrected based on the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters at the current moment. The predicted voltage difference is predicted for the current moment based on the intermediate parameters at the previous moment.

[0008] Based on the intermediate parameters and parameter mapping relationships at the current moment, the battery parameters at the current moment are obtained; the parameter mapping relationships are used to describe multiple sets of correspondences between intermediate parameters and battery parameters.

[0009] In the above-mentioned method for determining battery parameters, the open-circuit voltage is determined based on battery temperature and state of charge (SOC). Temperature compensation can be introduced to ensure the accuracy of the open-circuit voltage. The actual voltage difference at the current moment is obtained based on the terminal voltage and the open-circuit voltage. The intermediate parameters are corrected based on the actual voltage difference and the predicted voltage difference. The battery parameters are obtained based on the intermediate parameters and the mapping relationship. This constitutes an adaptive closed-loop identification mechanism based on error feedback, which improves the real-time tracking capability and anti-interference robustness of battery dynamic characteristic changes. It can accurately identify battery parameters that change with operating conditions, thereby improving the identification accuracy of battery parameters.

[0010] In an optional embodiment of the first aspect, the intermediate parameters of the previous time step are corrected based on the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters of the current time step, including: determining the voltage difference error of the current time step based on the actual voltage difference and the predicted voltage difference; determining the gain of the current time step based on the forgetting factor of the previous time step and the regression vector of the current time step; the forgetting factor of the previous time step is determined based on the voltage difference error of the previous time step; the regression vector of the current time step is obtained based on the current, the actual voltage difference of the previous time step, and the current of the previous time step; and the intermediate parameters of the previous time step are corrected based on the gain of the current time step and the voltage difference error of the current time step to obtain the intermediate parameters of the current time step.

[0011] In this optional embodiment, the intermediate parameters of the previous moment are corrected based on the gain and voltage difference error at the current moment. By introducing an adaptive mechanism to dynamically adjust the confidence in historical data, the tracking capability can be enhanced when parameters change rapidly, and the estimation can be kept smooth in steady state, which is beneficial to the identification accuracy of battery parameters.

[0012] In an optional embodiment of the first aspect, determining the voltage difference error at the current moment based on the actual voltage difference and the predicted voltage difference includes: determining the intermediate parameters of the previous moment, and obtaining the predicted voltage difference based on the intermediate parameters of the previous moment and the regression vector at the current moment; and obtaining the voltage difference error at the current moment based on the difference between the actual voltage difference and the predicted voltage difference.

[0013] In this optional embodiment, based on the intermediate parameters of the previous time step and the regression vector of the current time step, the predicted voltage difference can be accurately determined using the battery model state of the previous time step, and the voltage difference error of the current time step can be obtained by combining the actual voltage difference. This can accurately quantify the degree of mismatch between the prediction and the actual battery state, thereby effectively tracking the actual battery parameters based on the voltage difference error, which is beneficial to improving the identification accuracy of battery parameters.

[0014] In an optional embodiment of the first aspect, determining the open-circuit voltage of the battery at the current moment based on the battery temperature and state of charge includes: obtaining an open-circuit voltage mapping relationship, which includes multiple sets of correspondences between battery temperature, state of charge and open-circuit voltage; and determining the open-circuit voltage of the battery at the current moment based on the mapping relationship between battery temperature, state of charge and open-circuit voltage.

[0015] In this optional embodiment, battery temperature is incorporated into the open-circuit voltage mapping relationship. The open-circuit voltage can be corrected using battery temperature, which can obtain a more accurate open-circuit voltage under different temperature conditions, reducing the initial error when identifying battery parameters and thus improving the accuracy of battery parameter identification.

[0016] In an optional embodiment of the first aspect, the battery parameter determination method further includes determining the equivalent circuit of the battery, the equivalent circuit including ohmic internal resistance, polarization internal resistance and polarization capacitance; determining the transfer function relationship between the open-circuit voltage and current of the battery and the equivalent circuit; obtaining a parameter mapping relationship based on the transfer function relationship; the parameter mapping relationship includes multiple sets of correspondences between intermediate parameters and the values ​​of ohmic internal resistance, polarization internal resistance and polarization capacitance.

[0017] In this optional embodiment, battery temperature is incorporated into the open-circuit voltage mapping relationship. The open-circuit voltage can be corrected using battery temperature, which can obtain a more accurate open-circuit voltage under different temperature conditions, reducing the initial error when identifying battery parameters and thus improving the accuracy of battery parameter identification.

[0018] In an optional embodiment of the first aspect, the battery parameter determination method further includes: determining the equivalent circuit of the battery, the equivalent circuit including an ohmic internal resistance, a polarization internal resistance, and a polarization capacitor; determining the transfer function relationship between the open-circuit voltage and current of the battery and the equivalent circuit; obtaining a parameter mapping relationship based on the transfer function relationship; the parameter mapping relationship includes multiple sets of correspondences between intermediate parameters and the values ​​of the ohmic internal resistance, the polarization internal resistance, and the polarization capacitor.

[0019] In this optional embodiment, the problem of identifying nonlinear battery parameters, which is difficult to solve directly online, is transformed into the problem of estimating linear time-varying intermediate parameters online. By utilizing pre-derived and determined parameter mapping relationships, the estimated values ​​of intermediate parameters can be mapped back to battery parameters, thereby achieving efficient and automated battery parameter identification processing.

[0020] In an optional embodiment of the first aspect, the battery parameter determination method further includes: obtaining the battery parameters at each moment during the charging and discharging process; and, under the constraints of the battery's physical characteristics, performing interpolation processing on the battery parameters at each moment to obtain a high-density parameter table of the battery; the high-density parameter table includes the battery parameters corresponding to the battery under different operating conditions.

[0021] In this optional embodiment, interpolation is performed under the constraints of the battery's physical characteristics. This allows for the embedding of mandatory physical rule checks during the generation of the high-density parameter table, ensuring that the data in the high-density parameter table conforms to the basic physical laws of the battery, thereby improving the physical rationality and application safety of the high-density parameter table.

[0022] In an optional embodiment of the first aspect, under the constraints of the battery's physical characteristics, interpolation processing is performed on the battery parameters at each time point to obtain a high-density parameter table of the battery, including: under the constraints of the battery's physical characteristics, interpolation processing is performed on the first battery parameters at each time point according to a preset state of charge threshold to obtain a high-density parameter table of the battery; the first battery parameters include the values ​​of the ohmic internal resistance and / or polarization internal resistance in the battery's equivalent circuit.

[0023] In this optional embodiment, for the physical characteristics of the first battery parameter of internal resistance, interpolation is performed under the constraints of physical characteristics according to the preset state of charge threshold. This can maintain the shape preservation and local monotonicity of the data, avoid overshoot or oscillation that violate physical common sense, and thus improve the accuracy and physical rationality of the high-density parameter table.

[0024] In an optional embodiment of the first aspect, under the constraints of the battery's physical characteristics, interpolation processing is performed on the battery parameters at each time point to obtain a high-density parameter table of the battery. This includes: under the constraints of the battery's physical characteristics, interpolation processing is performed on the second battery parameters at each time point according to a preset state of charge threshold, based on a linear interpolation and extrapolation processing mechanism, to obtain a high-density parameter table of the battery; the second battery parameters include the value of the polarization capacitance in the battery's equivalent circuit.

[0025] In this optional embodiment, for the second battery parameter of the capacitor type, its value changes relatively smoothly and continuously with SOC, and is locally approximately linear. By using linear interpolation, the computational complexity can be reduced while ensuring accuracy. By using an extrapolation processing mechanism to make reasonable predictions based on the trend of nearby known data, the data completeness of the high-density parameter table can be guaranteed.

[0026] Secondly, this application also provides a battery parameter determination device, comprising:

[0027] The working parameter determination module is used to determine the battery's terminal voltage, current, battery temperature, and state of charge at the current moment during the battery's charging and discharging process.

[0028] The open-circuit voltage determination module is used to determine the open-circuit voltage of the battery at the current moment based on the battery temperature and state of charge.

[0029] The correction processing module is used to obtain the actual voltage difference at the current moment based on the terminal voltage and open circuit voltage, and to correct the intermediate parameters of the previous moment based on the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters of the current moment; the predicted voltage difference is predicted for the current moment based on the intermediate parameters of the previous moment.

[0030] The parameter mapping module is used to obtain the battery parameters at the current moment based on the intermediate parameters and parameter mapping relationships at the current moment; the parameter mapping relationships are used to describe multiple sets of correspondences between intermediate parameters and battery parameters.

[0031] Thirdly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0032] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0033] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above aspects.

[0034] Regarding the beneficial effects of any of the technical solutions in the second to fifth aspects mentioned above, refer to the beneficial effects of the corresponding technical solutions in the first aspect; repeated examples will not be listed here. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a schematic diagram of an optional process for determining battery parameters in one embodiment;

[0037] Figure 2 This is a schematic diagram of an optional data format for the open-circuit voltage mapping relationship in one embodiment.

[0038] Figure 3 This is a schematic diagram of an optional process for determining parameter mapping relationships in one embodiment;

[0039] Figure 4 This is a schematic diagram of an optional structure of the equivalent circuit in one embodiment;

[0040] Figure 5 This is a schematic diagram of an optional process for determining battery parameters in another embodiment;

[0041] Figure 6 This is a schematic diagram of an optional structure of the battery parameter determination device in one embodiment;

[0042] Figure 7 This is a schematic diagram of an optional internal structure of an electronic device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0044] The terms "first," "second," etc., used in this application may be used to describe various elements, but these elements are not limited by these terms. These terms are used only to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0045] In related technologies, an equivalent RC (resistance-capacitor) circuit model of a battery can be established, and battery parameters can be identified online using a series of RLS (Recursive Least Squares Algorithms) algorithms. However, under multi-source heterogeneous data, such as HPPC (Hybrid Pulse Power Characterization) test results at different temperatures and rates, the battery parameter results identified online are difficult to directly integrate and apply due to differences in SOC (State of Charge) intervals and dimensions. Manual alignment is inefficient and introduces subjective bias, and a single interpolation strategy has low accuracy for battery parameters with different characteristics. Furthermore, during online identification of battery parameters, abnormal data such as negative values ​​may occur. Offline interpolation of battery parameters lacks physical constraints, which may lead to the simulation model receiving non-physical parameter combinations (such as negative resistance, abnormal time constants), thereby causing voltage prediction instability or even simulation collapse. In other words, the identification accuracy of battery parameters in related technologies is low. Based on this, this application provides a battery parameter determination method, apparatus, device, and storage medium that can improve the accuracy of battery parameter identification.

[0046] In one exemplary embodiment, such as Figure 1 As shown, a method for determining battery parameters is provided. Taking the application of this method to an electronic device as an example, the method includes the following steps S101 to S104. Wherein:

[0047] Step S101: During the charging and discharging process of the battery, determine the battery's terminal voltage, current, battery temperature, and state of charge at the current moment.

[0048] The battery parameter determination method can be executed by electronic devices, which may include at least one of various devices such as servers, terminals, and vehicle controllers. The battery can be a power battery in electric vehicles or hybrid vehicles, and its internal parameters (such as internal resistance and capacitance) dynamically change with operating conditions and environmental conditions. The charging and discharging process is the working phase of the battery outputting electrical energy (discharging) or absorbing electrical energy from the outside (charging). For example, when an electric vehicle is driving (discharging) or connected to a charging station (charging), the battery is in the charging and discharging process; similarly, applying a series of pulse currents containing charging and discharging during the Hybrid Pulse Power Characteristic (HPPC) test is also a standardized charging and discharging process used to stimulate the battery's dynamic characteristics.

[0049] The current moment is the point in time when battery parameters are identified, and this current moment can correspond to a discrete sampling time. For example, if the sampling period of the battery management system is 100 milliseconds, then the current moment... It could be 10.0 seconds, the next moment. The duration is 10.1 seconds. Terminal voltage is the voltage value actually measured across the positive and negative terminals of the battery during the charging and discharging process; it can be denoted as... , such as the current moment The terminal voltage can be written as Terminal voltage is the electrical port characteristic of a battery, directly reflecting the difference between the open-circuit voltage and various internal voltage drops (ohmic voltage drop, polarization voltage drop). Terminal voltage can be derived from a high-precision voltage sensor connected to the battery terminals. Current is the current flowing into or out of the battery during charging and discharging, and can be denoted as... , such as the current moment The current can be written as During battery charging, the current can be defined as positive, and during discharging, it can be defined as negative. The current is the excitation signal that drives the changes in the internal state of the battery, and it is also the main reason for the change in terminal voltage. The current can come from a current sensor (such as a Hall sensor or shunt) connected in series in the battery circuit.

[0050] Battery temperature is the measured temperature of the battery itself or its key components (such as the cell surface and terminals) during the charging and discharging process. It can be denoted as... , such as the current moment The battery temperature can be recorded as Battery temperature is a key environmental variable affecting the battery's electrochemical parameters (such as open-circuit voltage and internal resistance). Battery temperature can be derived from temperature sensors, such as NTC (Negative Temperature Coefficient) thermistors, located on the battery module or cell. In some embodiments, battery temperature may include the average value of multiple temperature sensors within the battery, and may also include the value of the highest temperature point, representing the overall thermal state of the battery at the current moment. State of charge (SCC) is a state quantity representing the proportion of remaining usable charge to the total capacity of the battery during charging and discharging. It is typically expressed as a percentage, ranging from 0% to 100%. SCC can be denoted as... , such as the current moment The state of charge can be denoted as: .For example, This indicates that the remaining battery capacity is approximately half of its nominal capacity. The state of charge (SCC) is a key indicator of the battery's internal chemical state and is strongly correlated with open-circuit voltage. SCC can be estimated in real-time using the ampere-hour integration method, open-circuit voltage method, or other state estimation algorithms.

[0051] For example, during the charging and discharging process of a battery, electronic devices (such as a BMS controller) can interact with various sensors installed on the battery to determine the battery's terminal voltage, current, battery temperature, and state of charge (SOC). The terminal voltage can be measured by a voltage sensor, the current by a current sensor, and the battery temperature by a temperature sensor. The electronic devices can determine the SOC based on state estimation algorithms, such as an ampere-hour integral method or a model-based state observer algorithm. In some embodiments, the electronic devices can read the parameters measured by each sensor at a preset sampling period (e.g., 10 ms or 100 ms) to obtain the battery's terminal voltage, current, battery temperature, and SOC at the current moment.

[0052] Step S102: Determine the open-circuit voltage of the battery at the current moment based on the battery temperature and state of charge.

[0053] The open-circuit voltage is the potential difference between the positive and negative electrodes of a battery after it has been left to stand for a sufficiently long time and the internal electrochemical reactions have reached equilibrium. It can be denoted as... , such as the current moment The open-circuit voltage can be written as Open-circuit voltage can be related to the battery's state of charge (SOC) and battery temperature. The correspondence between open-circuit voltage and SOC and battery temperature can be established in advance, such as open-circuit voltage... The relationship between open-circuit voltage, state of charge (SCC), and battery temperature can be pre-obtained by fitting experimental data with a high-order polynomial function. For example, electronic devices can control the battery to discharge from a fully charged state to a depleted state at different constant temperature points (e.g., -10°C, 25°C, 40°C), and at multiple specified... Points (e.g., every 5%) Let it stand until the voltage stabilizes, record the voltage value at that point, and then process these discrete values. Data points are fitted mathematically to obtain open-circuit voltage mapping relationships, which may include multiple sets of correspondences between battery temperature, state of charge, and open-circuit voltage.

[0054] Optionally, the electronic device determines the battery's open-circuit voltage at the current moment by combining battery temperature and state of charge. For example, the electronic device can be based on... For numerical pairs, query the pre-established open-circuit voltage mapping relationship. The open-circuit voltage of the battery at the current moment is obtained based on the open-circuit voltage mapping relationship. For example, the current moment. If the battery temperature is 25℃ and the state of charge is 50%, the electronic device can query the pre-stored open-circuit voltage mapping relationship to directly read or calculate the open-circuit voltage at the current moment. For example, 3.65V.

[0055] In some embodiments, the open-circuit voltage mapping relationship can be presented in the form of a three-dimensional lookup table. The electronic device can use the current battery temperature and state of charge (SOC) as input keys and perform bilinear interpolation in the lookup table to obtain an accurate open-circuit voltage value. In some embodiments, the open-circuit voltage mapping relationship can also be presented in the form of polynomial coefficients. The electronic device can substitute the current battery temperature and SOC into the polynomial formula for real-time calculation to obtain a scalar form of the open-circuit voltage corresponding to the current operating condition. .

[0056] Step S103: Obtain the actual voltage difference at the current moment based on the terminal voltage and open circuit voltage, and correct the intermediate parameters of the previous moment based on the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters of the current moment; the predicted voltage difference is predicted for the current moment based on the intermediate parameters of the previous moment.

[0057] The actual voltage difference is a real-time observed value, calculated by the electronic device at the current moment, reflecting the internal polarization state of the battery. The actual voltage difference characterizes the instantaneous deviation between the battery's actual terminal voltage and its ideal open-circuit voltage under load. This deviation is affected by the ohmic internal resistance voltage drop and the polarization voltage. The actual voltage difference can be obtained based on the difference between the terminal voltage and the open-circuit voltage; for example, the actual voltage difference can be denoted as... ,but Then at the current moment Actual voltage difference , The terminal voltage at the current moment. This represents the open-circuit voltage at the current moment. For example, the terminal voltage measured by the electronic device at the current moment. Given a current SOC of 60% and a battery temperature of T = 25°C, determine the open-circuit voltage as 4.15V. The actual voltage difference calculated by the electronic device is 3.90V. =4.15V-3.90V=0.25V. This positive difference indicates that the battery is in a charging state and the terminal voltage is higher than the open circuit voltage.

[0058] The predicted voltage difference is an estimate of the voltage difference at the current moment made by an electronic device based on intermediate parameters from the previous moment. The previous moment can be a sampling period immediately preceding the current moment, such as the current moment... Then, corresponding to the previous time, it can be The predicted voltage difference represents the expectation of the current observed value (actual voltage difference) based on historical information. The deviation between the predicted voltage difference and the actual voltage difference (i.e., the prediction error) directly reflects the accuracy of the battery parameter estimation and can be used for drive parameter correction. In some embodiments, the electronic device can utilize intermediate parameters from the previous time step, combined with the current time step and the input current sequence from the previous time step (…). ), and the actual voltage difference at the previous moment. Predictive calculations are performed to obtain the predicted voltage difference for the current moment.

[0059] Intermediate parameters are intermediate variables introduced during the battery parameter identification process of electronic devices. These intermediate parameters may include discrete-time model parameters that have a definite mapping relationship with battery parameters after model discretization (e.g., bilinear transformation). It can include , , In some embodiments, the data form of intermediate parameters may include a parameter vector, such as Intermediate parameters act as a bridge connecting observable input-output data (current, voltage difference) with the battery parameters to be determined. By identifying intermediate parameters, the problem of identifying nonlinear physical parameters can be transformed into a linear parameter estimation problem that is easy to solve recursively, thereby achieving battery parameter identification. For example, battery parameters can be obtained based on the parameter mapping relationship between intermediate parameters and battery parameters.

[0060] For example, an electronic device can obtain the actual voltage difference at the current moment based on the difference between the terminal voltage and the open-circuit voltage. For instance, it can be based on... Calculate the current time Actual voltage difference , The terminal voltage at the current moment. This represents the open-circuit voltage at the current moment. Electronic devices can determine the predicted voltage difference, which can be predicted for the current moment based on intermediate parameters from the previous moment. For example, an electronic device can obtain the predicted voltage difference for the current moment based on intermediate parameters from the previous moment and the regression vector at the current moment. regression vector Based on the previous moment Actual voltage difference Current at the previous moment and the current at the current moment The electronic device can correct the intermediate parameters from the previous moment based on the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters for the current moment. For example, the electronic device can correct the intermediate parameters from the previous moment based on the deviation between the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters for the current moment.

[0061] Step S104: Based on the intermediate parameters and parameter mapping relationship at the current moment, obtain the battery parameters at the current moment; the parameter mapping relationship is used to describe multiple sets of correspondences between the intermediate parameters and the battery parameters.

[0062] The parameter mapping relationships can be pre-built to describe multiple sets of correspondences between intermediate parameters and battery parameters. These parameter mapping relationships are used to determine the relationships between intermediate parameters and battery parameters. The battery parameters are determined by reverse engineering. Battery parameters are model constants with clear physical meaning that describe the dynamic electrical characteristics inside the battery. For example, in an equivalent circuit model, battery parameters may include ohmic internal resistance (denoted as...). ), polarization resistance (denoted as ) and polarization capacitor (denoted as At least one of the following parameters, together, determines the battery's terminal voltage response characteristics under load. Battery parameters dynamically change with factors such as the battery's state of charge, battery temperature, and current rate; therefore, battery parameter identification and processing involves online and accurate tracking of these dynamic changes.

[0063] Optionally, the electronic device can acquire a predetermined parameter mapping relationship, which can describe multiple sets of correspondences between intermediate parameters and battery parameters. In some embodiments, the parameter mapping relationship can be represented as a calculation formula or a lookup table. When represented as a calculation formula, the parameter mapping relationship can include an algebraic expression; when represented as a lookup table, the parameter mapping relationship can include a data structure storing multiple sets of corresponding records (intermediate parameters, battery parameters). The electronic device can determine the battery parameters at the current moment based on the intermediate parameters and the parameter mapping relationship at the current moment, thereby realizing battery parameter interpretation processing. In some embodiments, when the parameter mapping relationship includes a calculation formula, the electronic device can substitute the intermediate parameters at the current moment for calculation to obtain the battery parameters at the current moment. When the parameter mapping relationship includes a lookup table, the electronic device can perform lookup and interpolation operations based on the intermediate parameters at the current moment to obtain the battery parameters at the current moment. For example, the electronic device can query the index point closest to the intermediate parameters at the current moment in the lookup table, and then calculate the battery parameters at the current moment through a linear or bilinear interpolation algorithm.

[0064] In the above-mentioned method for determining battery parameters, the open-circuit voltage is determined based on battery temperature and state of charge (SOC). Temperature compensation can be introduced to ensure the accuracy of the open-circuit voltage. The actual voltage difference at the current moment is obtained based on the terminal voltage and the open-circuit voltage. The intermediate parameters are corrected based on the actual voltage difference and the predicted voltage difference. The battery parameters are obtained based on the intermediate parameters and the mapping relationship. This constitutes an adaptive closed-loop identification mechanism based on error feedback, which improves the real-time tracking capability and anti-interference robustness of battery dynamic characteristic changes. It can accurately identify battery parameters that change with operating conditions, thereby improving the identification accuracy of battery parameters.

[0065] In an exemplary embodiment, intermediate parameters from the previous time step are corrected based on the actual voltage difference and the predicted voltage difference to obtain intermediate parameters for the current time step. This includes: determining the voltage difference error for the current time step based on the actual voltage difference and the predicted voltage difference; determining the gain for the current time step based on the forgetting factor from the previous time step and the regression vector for the current time step; the forgetting factor from the previous time step is determined based on the voltage difference error from the previous time step; the regression vector for the current time step is obtained based on the current, the actual voltage difference from the previous time step, and the current from the previous time step; and correcting the intermediate parameters from the previous time step based on the gain and the voltage difference error for the current time step to obtain intermediate parameters for the current time step.

[0066] The voltage difference error is the deviation between the actual voltage difference calculated from the actual measurement data at the current moment and the predicted voltage difference based on the previous moment. The magnitude and sign of the voltage difference error reflect the accuracy of the prediction at the current moment. A voltage difference error close to zero indicates that the current state of the battery can be well fitted; a large voltage difference error indicates that the dynamic characteristics of the battery may have changed (such as sudden temperature changes or large current surges). Based on the voltage difference error, intermediate parameters can be corrected and the strength of historical data memory can be controlled to achieve intelligent self-adaptation in the identification process.

[0067] The forgetting factor is used to dynamically adjust the weights of historical and current data in parameter estimation using the Recursive Least Squares Algorithm (RLS). The principle behind the forgetting factor is: The closer the value of the forgetting factor is to 1, the stronger the "memory" of past data, and the smoother and more stable the estimation results, but the weaker the ability to track parameter mutations; Appropriately reducing the value of the forgetting factor will enhance the sensitivity to the latest data and accelerate the tracking of parameter changes, but may increase the volatility of the estimation results. The value can range from 0 to 1, such as 0.985 to 0.999; forgetting factor The value can be any value that varies with the time step. Dynamically changing variables, such as the current moment. The forgetting factor is The previous moment The forgetting factor is The forgetting factor of the previous moment can be determined based on the voltage difference error of the previous moment.

[0068] The gain is used to determine how the current voltage difference error is translated into a correction to the estimated intermediate parameters. Current time Gain It can be obtained based on the forgetting factor from the previous time step and the regression vector from the current time step, or by estimating the uncertainty (such as the covariance matrix) based on the parameters from the previous time step. The gain at the current time step is obtained by taking the forgetting factor from the previous time step and the regression vector at the current time step.

[0069] Optionally, the electronic device can obtain the voltage difference error at the current moment based on the difference between the actual voltage difference and the predicted voltage difference. For example, the electronic device can base its calculations on... Calculate the current time Voltage difference error , The actual voltage difference at the current moment. This is the predicted voltage difference for the current time based on the intermediate parameters from the previous time step. After completing the prediction from the previous time step (the... After estimating the parameters at a given time (time), the voltage difference error at the previous time can be obtained. Based on the voltage difference error at the previous time, the forgetting factor at the previous time can be obtained. For example, an electronic device can map the voltage difference error at the previous time according to a preset mapping relationship to obtain the forgetting factor at the previous time. The electronic device can obtain the regression vector at the current time based on the current, the actual voltage difference at the previous time, and the current at the previous time, and determine the gain at the current time based on the forgetting factor at the previous time and the regression vector at the current time. The electronic device can correct the intermediate parameters at the previous time using the gain at the current time and the voltage difference error at the current time to obtain the intermediate parameters at the current time. For example, the electronic device can... Calculate the current time intermediate parameters , These are the intermediate parameters from the previous time step. The gain at the current moment. This represents the voltage difference error at the current moment; and All are estimated values.

[0070] In this exemplary embodiment, the intermediate parameters of the previous moment are corrected based on the gain at the current moment and the voltage difference error at the current moment. By introducing an adaptive mechanism to dynamically adjust the confidence in historical data, the tracking capability can be enhanced when parameters change rapidly, and the estimation can be kept smooth in steady state, which is beneficial to the identification accuracy of battery parameters.

[0071] In an exemplary embodiment, determining the voltage difference error at the current moment based on the actual voltage difference and the predicted voltage difference includes: determining the intermediate parameters of the previous moment, and obtaining the predicted voltage difference based on the intermediate parameters of the previous moment and the regression vector at the current moment; and obtaining the voltage difference error at the current moment based on the difference between the actual voltage difference and the predicted voltage difference.

[0072] The regression vector is used to organize current and historical external measurable signals of the battery (such as current and voltage difference) into a mathematical vector. The regression vector, along with the intermediate parameters to be estimated, can predict the voltage difference at the current moment. The regression vector acts as a bridge connecting the measured data and the internal parameters. The regression vector at the current moment can be a vector containing multiple elements, such as the regression vector at the current moment being based on the actual voltage difference at the previous moment, the current at the previous moment, and the current at the current moment. For example, the regression vector at the current moment... regression vector , For the previous moment The actual voltage difference The current at the previous moment, Let be the current at the current moment.

[0073] For example, the electronic device can determine the intermediate parameters of the previous time step, which can be estimated values. The electronic device can then obtain the predicted voltage difference based on the intermediate parameters of the previous time step and the regression vector of the current time step. For instance, the electronic device can determine the regression vector of the current time step based on the actual voltage difference of the previous time step, the current of the previous time step, and the current of the current time step, and perform matrix multiplication between the regression vector of the current time step and the intermediate parameters of the previous time step to obtain the predicted voltage difference. In some embodiments, it can be based on... The predicted voltage difference is calculated. Let this be the regression vector at the current moment. , For the previous moment The actual voltage difference The current at the previous moment, The current at the current moment; These are the intermediate parameters obtained from the previous time step. Electronic devices can determine the voltage difference error at the current time step based on the difference between the actual voltage difference and the predicted voltage difference.

[0074] In this exemplary embodiment, based on the intermediate parameters of the previous moment and the regression vector of the current moment, the predicted voltage difference can be accurately determined using the battery model state of the previous moment, and the voltage difference error of the current moment can be obtained by combining the actual voltage difference. This can accurately quantify the degree of mismatch between the prediction and the actual battery state, thereby effectively tracking the actual battery parameters based on the voltage difference error, which is beneficial to improving the identification accuracy of battery parameters.

[0075] In some embodiments, a parameter mapping relationship can be pre-constructed based on multiple sets of correspondences between intermediate parameters and battery parameters. This allows for the identification of battery parameters by recognizing intermediate parameters and then combining them with the parameter mapping relationship. For example, intermediate parameters... It can include , , Based on intermediate parameters By mapping the parameters to a pre-built parameter map, the battery parameters can be determined. These battery parameters can include... , , ;in, The ohmic internal resistance of the battery The estimated value, The polarization internal resistance of the battery Estimated value The polarization capacitor of the battery The estimated value. During the battery parameter identification process, intermediate parameters can be calculated by real-time acquisition of the battery's terminal voltage and current. Then, by combining the parameter mapping relationship, the battery parameters can be derived.

[0076] Electronic devices can accurately identify battery parameters based on parameter identification regression equations using the Adaptive Finite Feedback Recursive Least Squares (AFFRLS) method, which employs a terminal voltage error feedback mechanism. The AFFRLS algorithm flow is as follows:

[0077]

[0078] in, Indicates the first RLS gain at time t; Indicates the first The covariance matrix at time t; Indicates the first The forgetting factor at any given time adjusts the weight of past information in the current estimation. It is set within the range of 0.9-1. For power batteries, The lower limit is above 0.95. A fixed forgetting factor may lead to problems such as parameter update lag and covariance matrix degradation, reducing parameter identification accuracy or even causing failure. By introducing an adaptive forgetting factor strategy based on terminal voltage error feedback, the forgetting factor can be dynamically calculated and updated in real time according to the terminal voltage error feedback, improving the tracking performance and identification accuracy of the algorithm. This is essential in operating conditions where parameters change relatively drastically, such as at low temperatures. For the first The regression vector at time step; For the first The forgetting factor of time; For the estimated first Intermediate parameters at time points; For the estimated first Intermediate parameters at time points; For the first The actual voltage difference at any given moment; For the first The covariance matrix at time t.

[0079] In some embodiments, the forgetting factor adaptive strategy based on terminal voltage error feedback is as follows:

[0080]

[0081] in, Representing the Voltage difference error at time, Indicates the first The actual voltage difference at any given moment. Indicates the first The predicted voltage difference at any given time; Indicates the error adjustment weights. This represents the voltage error reference value. This represents the sensitivity coefficient for the forgetting factor regulation, and its value range can be 0.35~0.65; This represents the lower limit of the forgetting factor, such as around 0.985; This represents the upper limit of the forgetting factor, such as 0.99. Indicates the first The forgetting factor of time.

[0082] Battery modeling (such as a second-order RC equivalent circuit model) can provide the structural basis for battery parameter identification, determine the composition of the regression vector and the dimension of the parameters to be estimated. AFFRLS relies on this model form for online identification.

[0083] When achieving accurate battery parameter identification based on the parameter identification regression equation and using the Adaptive Forgotten Factor Recursive Least Squares (AFFRLS) method with a terminal voltage error feedback mechanism, battery modeling (such as a second-order RC equivalent circuit model) can provide the structural basis for battery parameter identification, determining the composition of the regression vector and the dimensions of the parameters to be estimated. AFFRLS relies on this model form for online identification. The input parameters for AFFRLS iterative processing can include:

[0084] Current battery terminal voltage The current is acquired by the BMS sensor. ,temperature , ;

[0085] Regression vector (information matrix): Regression variables constructed based on the selected battery equivalent circuit model (second-order RC equivalent circuit model), including historical current. Historical terminal voltage ;

[0086] Intermediate parameters of the previous time step Estimated value;

[0087] The covariance matrix at the previous time step : A matrix characterizing the uncertainty of parameter estimation, used to calculate the gain;

[0088] Voltage error reference value A preset or online calibrated reference error value used to normalize the voltage difference error at the current moment;

[0089] Adaptive parameters: This represents the sensitivity coefficient for the forgetting factor regulation (with a value ranging from 0.35 to 0.65). This represents the lower limit of the forgetting factor (e.g., a value of 0.985). This represents the upper limit of the forgetting factor (e.g., a value of 0.99).

[0090] Input parameters can be filtered, denoised, and synchronized before being iteratively identified using AFFRLS, ensuring input quality. In some embodiments, intermediate parameters and covariance matrices can be based on offline identification or typical parameter settings as the starting point for recursion.

[0091] The output parameters of the AFFRLS iterative process may include:

[0092] Intermediate parameters at the current time The estimated value ,include , , The latest estimate;

[0093] Covariance matrix at the current time This reflects the current confidence level of the estimate;

[0094] Forgetting factor at the current moment The weights of new and old data are used for dynamic adjustment.

[0095] Voltage difference error at the current moment It is used to evaluate the model's fit and drive the adaptive mechanism.

[0096] In some embodiments, at the current time (i.e., the first) (Time), a complete iterative process based on AFFRLS can include:

[0097] Calculate the current time Voltage difference error (Error Feedback): This voltage difference error is the core driving signal for subsequent adaptive adjustment; For the current moment The actual voltage difference, based on Calculations show that This is the open-circuit voltage;

[0098] Normalization error: Normalize the original error to eliminate the influence of dimensions and facilitate unified adjustment; It can be obtained through experimental statistics, such as from the mean absolute error under DST conditions;

[0099] Adaptive calculation of forgetting factor: When the error adjustment weight When the value is large (the system changes drastically), the exponential term... Decrease → Approaching This means reducing the weight of historical data to speed up response; when the error adjustment weight... When the value is small (system is stable) near It emphasizes data stationarity and improves estimation stability; through The angle ∈(0.35,0.65) controls the steepness of the adjustment curve, which can reduce oscillations;

[0100] Calculate the current time RLS gain : ; by using the covariance matrix of the previous time step and the regression vector at the current moment Calculate Kalman gain; forgetting factor It appears in the denominator and is used to control the "decay rate" of covariance updates;

[0101] Update current time intermediate parameters Gain can be estimated based on the weighted correction parameters of the prediction error; This determines the magnitude of the correction;

[0102] Update current time covariance matrix The covariance matrix reflects the uncertainty of parameter estimation; by introducing To achieve "old information decay" and prevent the covariance matrix from "expanding" or "degrading";

[0103] State passing: passing the updated intermediate parameters The updated covariance matrix Forgetting factor at the current moment Voltage difference error Save as the next moment (the first moment) The initial value at time step () is used to proceed to the next iteration to determine the intermediate parameters for the next time step. .

[0104] For the intermediate parameters at the current moment It can be determined , , ,in The battery parameters at the current moment are calculated by combining the parameter mapping relationship. (Battery's internal resistance) (estimated value) (Battery polarization internal resistance) (estimated value) and (Battery polarization capacitor) The estimated value is used to identify the battery parameters at the current moment.

[0105] In this exemplary embodiment, an adaptive forgetting factor strategy based on voltage difference error feedback (AFFRLS) is introduced, which can intelligently and dynamically adjust the forgetting factor based on the voltage difference error feedback at the current moment. Under extreme operating conditions with rapidly changing parameters (such as low-temperature high-current charging and discharging), it enhances the sensitivity to the latest data and quickly tracks parameter changes; under steady-state operating conditions, it maintains the memory of historical data, ensures identification stability, and improves the identification accuracy and robustness of battery parameters under extreme operating conditions such as low temperatures.

[0106] In an exemplary embodiment, determining the open-circuit voltage of the battery at the current moment based on the battery temperature and state of charge includes: obtaining an open-circuit voltage mapping relationship, which includes multiple sets of correspondences between battery temperature, state of charge, and open-circuit voltage; and determining the open-circuit voltage of the battery at the current moment based on the mapping relationship between battery temperature, state of charge, and open-circuit voltage.

[0107] The open-circuit voltage mapping relationship is used to record the inherent characteristics of the battery, that is, the variation of the battery's open-circuit voltage with its state of charge under different temperature conditions. This can include multiple sets of correspondences between battery temperature, state of charge, and open-circuit voltage, covering the battery's expected operating range. (e.g., 0% to 100%) and temperature (e.g., -20°C to 60°C). In some embodiments, the open-circuit voltage mapping relationship can be represented as a three-dimensional lookup table or a continuous surface model defined by a mathematical function. If the open-circuit voltage mapping relationship is represented as a three-dimensional lookup table, its data structure can include a multidimensional array or matrix, where the two index dimensions correspond to the state of charge and battery temperature, respectively, and the values ​​of the array elements are the corresponding open-circuit voltages. If the open-circuit voltage mapping relationship is represented as a function model, its structure can contain a polynomial function with specific coefficients, such as... .

[0108] For example, electronic devices can acquire pre-built open-circuit voltage mapping relationships, which include multiple sets of correspondences between battery temperature, state of charge (SOC), and open-circuit voltage. These open-circuit voltage mapping relationships can be acquired during battery development or factory calibration by performing HPPC tests or static tests on samples of the same battery model at multiple temperature points (e.g., -20°C, 0°C, 25°C, 45°C) in a laboratory environment, collecting a large amount of data. The data points are then fitted (e.g., polynomial surface fitting) to construct the open-circuit voltage. The electronic device can determine the battery's open-circuit voltage at the current moment based on the mapping relationship between battery temperature, state of charge (SCC), and open-circuit voltage. In some embodiments, when the open-circuit voltage mapping relationship includes a three-dimensional lookup table, the electronic device can perform a lookup operation. If the input battery temperature and SCC are completely consistent with the nodes in the three-dimensional lookup table, the corresponding open-circuit voltage is directly returned; if they are inconsistent, the electronic device can initiate a two-dimensional interpolation algorithm (e.g., bilinear interpolation) to calculate the corresponding open-circuit voltage using the values ​​of adjacent nodes in the three-dimensional lookup table. In some embodiments, when the open-circuit voltage mapping relationship includes a function model, the electronic device can directly call the function model, substituting the battery temperature and SCC into it to calculate the corresponding open-circuit voltage.

[0109] In battery parameter identification, accuracy is affected by temperature. At low temperatures, battery polarization intensifies, and the identification algorithm is susceptible to interference, leading to drastic parameter changes and low accuracy under extreme conditions. Furthermore, the amount of battery data collected under different HPPC conditions (temperature / rate) varies, resulting in inconsistent data for online battery parameters. This inconsistency hinders fusion and cross-validation of parameters across different HPPC conditions. Additionally, online identification algorithms are susceptible to measurement noise, leading to large errors in terminal voltage estimation and drastic parameter fluctuations. This can result in negative values ​​and other abnormal data, further reducing the accuracy of battery parameter identification.

[0110] In some embodiments, the electronic device can pre-construct an open-circuit voltage mapping relationship. For example, the electronic device can preprocess the collected battery data (voltage, temperature, SOC) to construct the open-circuit voltage mapping relationship. For instance, the electronic device can measure the open-circuit voltage of the battery at different SOC points (e.g., 0%, 10%, ..., 100%) under different temperature conditions (e.g., -20℃, 0℃, 25℃, 45℃). This can be achieved by allowing the battery to stand still until the voltage stabilizes. The electronic device can fit the collected discrete data points (including different battery temperatures, different SOCs, and corresponding open-circuit voltages) using a high-order polynomial function, and calculate the open-circuit voltages of different SOCs using the fitted polynomial coefficients. By combining the corresponding open-circuit voltages, the open-circuit voltage mapping relationship can be obtained. For example... Figure 2 As shown, the open-circuit voltage mapping relationship can include a three-dimensional surface of open-circuit voltage-battery temperature-SOC: ,in, Represents the open-circuit voltage of the battery. Represents a higher-order polynomial function. This represents the state of charge (SOC) of the battery. This represents battery temperature. By establishing a three-dimensional surface relationship between open-circuit voltage, state of charge (SOC), and battery temperature, the nonlinear and global influence of temperature on battery electrochemical characteristics is fully considered from the modeling stage. This makes the model parameters (such as open-circuit voltage) inherently robust to temperature changes, significantly improving the model's prediction accuracy across the entire temperature range (especially extreme temperatures). This lays a solid foundation for the safe and efficient management of batteries in extreme environments such as cold.

[0111] In this exemplary embodiment, battery temperature is incorporated into the open-circuit voltage mapping relationship. The open-circuit voltage can be corrected using battery temperature, resulting in a more accurate open-circuit voltage under different temperature conditions. This reduces the initial error during battery parameter identification and thus improves the accuracy of battery parameter identification.

[0112] In one exemplary embodiment, such as Figure 3 As shown, the battery parameter determination method also includes a parameter mapping relationship determination process, comprising steps S301 to S303. Wherein:

[0113] Step S301: Determine the equivalent circuit of the battery, which includes ohmic internal resistance, polarization internal resistance and polarization capacitance.

[0114] The equivalent circuit is a simplified electrical model used to simulate the complex electrochemical processes inside a battery. Instead of directly describing microscopic ion migration and chemical reactions, the equivalent circuit uses combinations of basic circuit elements (such as resistors and capacitors) to characterize the macroscopic electrical characteristics exhibited by the battery, such as terminal voltage response and internal resistance. In some embodiments, the equivalent circuit can be obtained based on battery modeling. This equivalent circuit can include a second-order RC equivalent circuit model, which abstracts the battery as a series structure of a voltage source and a resistor-capacitor network, used for state estimation and simulation analysis in a battery management system (BMS).

[0115] For a second-order RC equivalent circuit model, the equivalent circuit includes ohmic internal resistance, polarization internal resistance, and polarization capacitance. Ohmic internal resistance (denoted as...) or An ohmic internal resistance is a series resistive element in the equivalent circuit of a battery. It characterizes the voltage drop within the battery caused by the inherent physical structure of the electrode materials, electrolyte, separator, and connecting components, and is synchronized with the instantaneous change in current. Flow through ohmic internal resistance It will produce an instantaneous voltage drop Polarization resistance (denoted as...) or The polarization resistance (or polarization capacitance) is a resistive element connected in parallel with the polarization capacitance in the equivalent circuit of a battery. Polarization resistance characterizes the time-varying voltage drop caused by dynamic processes within the battery, such as electrochemical polarization (e.g., charge transfer) and concentration polarization (e.g., ion diffusion). The polarization resistance reflects the resistance to the battery's dynamic response process, and its magnitude varies with the battery's state of charge, temperature, and current history. The polarization capacitance (denoted as...) or The polarization capacitance (DC) is a capacitor element connected in parallel with the polarization resistance in the equivalent circuit of a battery. It characterizes the battery's ability to store and release polarization charge, simulating the dynamic characteristics of the double-layer effect and the accumulation / consumption process of matter within the battery. The DC and polarization resistance together determine the response speed of the polarization process, i.e., the time constant. .

[0116] Optionally, the electronic device can determine an equivalent circuit for battery modeling, which may include ohmic internal resistance, polarization internal resistance, and polarization capacitance, and the values ​​of each of these parameters can be used as battery parameters to be identified. In some embodiments, such as Figure 4 As shown, the equivalent circuit of the battery can be a second-order RC equivalent circuit model that considers the effect of temperature, where, For current, Terminal voltage, Open circuit voltage, For ohmic internal resistance, For polarization internal resistance, For polarized capacitors, the battery parameter identification process is based on measurable terminal voltage. and current Identify the ohmic internal resistance of the battery Polarization internal resistance Or polarized capacitor Processing of at least one parameter.

[0117] Step S302: Determine the transfer function relationship between the battery's open-circuit voltage and current and the equivalent circuit.

[0118] The transfer function, in the complex frequency domain (such as the s-domain or z-domain), is a mathematical expression describing the dynamic relationship between the output and input quantities of the battery's equivalent circuit. For example, the transfer function may include a relationship based on current... The transfer function takes the input as input and the difference between the terminal voltage and the open-circuit voltage (i.e., the variable reflecting the internal voltage drop) as output. The transfer function relationship is derived from the topology of the equivalent circuit (i.e., the connection method of the ohmic internal resistance, polarization internal resistance, and polarization capacitor) and Kirchhoff's laws, establishing an externally measurable electrical quantity (current). Terminal voltage It serves as a bridge between the internal parameters to be determined (ohmic resistance, polarization resistance, and polarization capacitance).

[0119] For example, an electronic device can determine the transfer function relationship between the open-circuit voltage and current of the battery and the equivalent circuit based on the battery's open-circuit voltage and current. In some embodiments, the electronic device can determine the time-domain differential equation of the equivalent circuit; under the assumption of zero initial conditions, it performs a Laplace transform on the time-domain differential equation to obtain a continuous transfer function in the s-domain, which clearly expresses the input current in the s-domain. Output deviation ( The dynamic relationship between the parameters is defined, and the relationship contains the parameters to be determined. , , To implement this in a digital system, the electronic device can discretize the continuous transfer function (e.g., using the bilinear transform method). The discretization process introduces system sampling time. As known quantities, the discrete transfer function, expressed in terms of the delay operator z⁻¹, is finally obtained, and its form can be: Thus, the transfer function relationship that can be directly processed from time series data was obtained.

[0120] Step S303: Obtain the parameter mapping relationship based on the transfer function relationship; the parameter mapping relationship includes multiple sets of correspondences between the intermediate parameters and the values ​​of the ohmic internal resistance, the polarization internal resistance, and the polarization capacitance.

[0121] Optionally, the electronic device can obtain a parameter mapping relationship based on the transfer function relationship. This parameter mapping relationship can include multiple sets of correspondences between intermediate parameters and the values ​​of ohmic internal resistance, polarization internal resistance, and polarization capacitance. In some embodiments, the electronic device can use the coefficients of the discrete transfer function in the transfer function relationship. , , These are defined as intermediate parameters, which can be the standard form coefficients of the transfer function. Electronic devices can be analyzed by comparing the coefficients of the discrete transfer function with those derived from the standard form. , , , The original discretized model coefficients are expressed, resulting in a set of mapping equations. Electronic devices can solve these mapping equations to inversely solve for the intermediate parameters. , , and known sampling time To represent physical parameters , , The analytical formula yields the parameter mapping relationship. This parameter mapping relationship can be pre-stored; once intermediate parameters are identified online, the electronic device can calculate the required battery parameters using this mapping relationship. For example, it can calculate... , , At least one of them.

[0122] In this exemplary embodiment, the problem of identifying nonlinear battery parameters, which is difficult to solve directly online, is transformed into the problem of estimating linear time-varying intermediate parameters online. By utilizing pre-derived and determined parameter mapping relationships, the estimated values ​​of intermediate parameters can be mapped back to battery parameters, thereby achieving efficient and automated battery parameter identification processing.

[0123] In some embodiments, the electronic device may employ a second-order RC equivalent circuit model (including ohmic internal resistance). Polarization internal resistance Polarized capacitors Model the battery, including its terminal voltage (the actual measured terminal voltage). That is, the battery's terminal voltage equals its open-circuit voltage. (Voltage without current) minus polarization voltage (Voltage drop during dynamic process), then subtract the voltage drop due to the ohmic internal resistance. ).

[0124] Among them, polarization voltage Differential equations satisfying a first-order RC circuit That is, polarization voltage. rate of change ( ) by current excitation ( ) and its own decay ( This is jointly determined. When the current changes, It will gradually approach a new steady-state value. It is a current excitation term (positive contribution), representing the operating current. polarization capacitor The charging effect. The greater the current (during charging) (Positive) The faster the capacitor charges, the higher the polarization voltage. The faster it rises; during discharge The value is negative, indicating capacitor discharge. Decrease. Coefficient The "charging efficiency" of a capacitor is reflected in its capacitance: the smaller the capacitor, the more drastic the voltage change under the same current. The term represents the self-attenuation (negative contribution), indicating the polarization voltage. Through polarization internal resistance The discharge effect. The higher the polarization voltage, the higher the discharge current. The larger the value, the faster the capacitor discharges. The more pronounced the attenuation, the higher the coefficient. Reflecting the "decrease rate": Polarization internal resistance Larger polarization capacitance The smaller the value, the faster the decay.

[0125] The open-circuit voltage of the battery (which varies with the state of charge) Battery temperature (variable higher-order polynomial functions); The polarization voltage of the battery (the dynamic voltage drop generated by the polarization circuit). This represents the battery current (positive for charging, negative for discharging). Polarization voltage Time derivative (describing polarization voltage) (Dynamic rate of change); battery temperature By changing The value of indirectly affects the prediction of the terminal voltage of the entire second-order RC equivalent circuit model—at low temperatures. Decrease, increase polarization ( Enlarge (Reduce), the second-order RC equivalent circuit model can be obtained through This characteristic is captured by nonlinear mapping.

[0126] Electronic devices can derive parameter mapping relationships based on the transfer function relationship between the battery's open-circuit voltage and current and the equivalent circuit, such as by defining... →Continuous transfer function → Discretization → Intermediate parameter correlation → Back-calculation of battery parameters to obtain parameter mapping relationships. This includes the following steps:

[0127] 1. Define the terminal voltage deviation variable:

[0128] make , Essentially, it is the difference between the terminal voltage and the open-circuit voltage, reflecting the combined effects of internal polarization (ohmic polarization + concentration polarization) and current in the battery.

[0129] 2. Derivation Continuous transfer function:

[0130]

[0131] By analyzing the time-domain differential equation ( Perform a Laplace transform to obtain the continuous transfer function in the s-domain;

[0132] 3. Discretization using bilinear transform, and correlation of intermediate parameters. , , :

[0133] Bilinear transformation (to Domain mapping to (Domain, to avoid frequency aliasing) , Indicates the sampling time;

[0134] Substituting the continuous transfer function into the bilinear transform and rearranging, we obtain the discrete transfer function: , , , These are the intermediate parameters to be identified;

[0135]

[0136] in, (Ohmic internal resistance); (Polarization internal resistance); (Polarized capacitor);

[0137] By transforming continuous battery parameters into discrete intermediate parameters, it becomes possible to obtain data through "time series data" ( and Identify these intermediate parameters using historical values;

[0138] 4. Identify regression equations and estimate intermediate parameters online:

[0139] To facilitate online identification using the recursive least squares (RLS) method, the discrete transfer function is transformed into a linear regression form: , For the first The actual voltage difference at any given moment. ; For the first The regression vector (input feature) at time step. (including the previous moment) (The current at the current moment and the current at the previous moment). Represents a parameter vector, the intermediate parameters to be identified. ;

[0140] The optimal value of the intermediate parameter can be estimated in real time using the adaptive forgetting factor recursive least squares (AFFRLS) method. (Indicates an estimated value) , ;

[0141] 5. Parameter estimates obtained from identification , , The parameter mapping relationship can be determined, and the required battery parameters can be calculated online based on the parameter mapping relationship.

[0142] The mathematical formulas for parameter mapping relationships can include: ;in, Indicates the sampling time; For Ohm internal resistance The estimated value; Polarization internal resistance Estimated value; Polarized capacitor The estimated value.

[0143] In this exemplary embodiment, based on a temperature-compensated online identification strategy, a high-precision, high-density battery parameter table (such as the mapping relationship between ohmic internal resistance, polarization parameters, etc., and changes in SOC and battery temperature) can be generated directly and efficiently. This avoids the cumbersome manual parameter fitting or low-precision interpolation process, significantly shortening the time cycle for battery model development, BMS algorithm verification, and vehicle system integration, and improving overall R&D efficiency. This parameter table can be directly and seamlessly integrated into the development process of the battery management system (BMS), providing high-fidelity underlying data support for key applications such as battery thermal management simulation, real-time optimization of charge and discharge strategies, state estimation (such as SOC), and lifetime prediction, reducing reliance on a large number of repetitive experiments and complex post-processing.

[0144] In an exemplary embodiment, the battery parameter determination method further includes: obtaining the battery parameters at each moment during the charging and discharging process; and, under the constraints of the battery's physical characteristics, performing interpolation processing on the battery parameters at each moment to obtain a high-density parameter table of the battery; the high-density parameter table includes the battery parameters corresponding to the battery under different operating conditions.

[0145] Physical characteristic constraints are the mandatory constraints derived from the fundamental electrochemical principles of the battery itself, which must be followed during the generation of the battery parameter table. These constraints are not derived from statistical data but rather reflect the physical essence of the battery, ensuring that the processed battery parameters have true physical meaning and avoiding values ​​that violate common scientific sense. In some embodiments, physical characteristic constraints may include a set of inequalities or boundary conditions, such as nonnegativity constraints, requiring ohmic internal resistance... Polarization internal resistance Because internal resistance is a physical quantity that dissipates energy, its negative value is physically inexplicable; another example is the time constant constraint, which requires a certain polarization time constant. ,like The time constant can be 30 seconds. The constraint stems from the fact that the relaxation time of the battery polarization process has a physical upper limit. An excessively large time constant means that the dynamic response is extremely slow, which does not conform to the actual battery characteristics.

[0146] Interpolation is the process of estimating battery parameter values ​​at unmeasured points (such as target high-density SOC intervals) using known battery parameter data obtained at finite discrete points (e.g., specific SOC points) through specific mathematical methods. Its purpose is to transform sparse, non-uniform raw identification data into a continuous, uniform, and complete data sequence or surface for model querying. In some embodiments, interpolation may not be a single method but a hybrid strategy; for example, different interpolation algorithms may be used for different types of battery parameters based on their different physical characteristics. For instance, for internal resistance parameters (… , The change in SOC may exhibit non-monotonicity and inflection points. Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) can be used to ensure that the interpolation curve is smooth and maintains the shape trend of the original data (such as monotonicity). For capacitance parameters ( The changes are usually relatively gradual, and can be estimated by using more efficient linear interpolation and combining extrapolation in the data boundary region.

[0147] A high-density parameter table can include battery parameters corresponding to different operating conditions. These different operating conditions can include different ambient temperatures and load current modes (rates), such as "-10℃, 1C pulse discharge condition," which describes a specific operating condition. The high-density parameter table describes the precise mapping relationship between battery parameters and state variables (battery temperature and state of charge). The high density is reflected in the very small sampling interval of the state variables, making the mapping relationship nearly continuous. In some embodiments, the high-density parameter table can include a two-dimensional or three-dimensional data table. For example, taking a parameter table at a fixed temperature as an example, its rows (or indices) are uniformly dense SOC values ​​(e.g., 0.0%, 0.1%, 0.2%, ..., 100.0%), and its columns are corresponding... , , The numerical values. If multiple temperatures are considered, multiple two-dimensional tables or a single three-dimensional data table can be formed, with SOC and temperature as a joint index.

[0148] Optionally, during the charging and discharging process of the battery, battery parameters are identified at each moment to obtain the battery parameters for that specific moment. For example, the electronic device can read the time-series data stream of the battery during charging and discharging, either in real time or offline, including the terminal voltage, current, battery temperature, and state of charge (SCC) estimated by an algorithm at each moment. For each moment, the electronic device can calculate the actual voltage difference at the current moment based on the intermediate parameters of the previous moment, combined with the measured data at the current moment and the open-circuit voltage obtained from the battery temperature and SCC. By comparing the actual voltage difference with the predicted voltage difference, the intermediate parameters of the previous moment can be corrected to obtain the intermediate parameters of the current moment, and the battery parameters at the current moment can be obtained by combining the parameter mapping relationship. By cyclically performing battery parameter identification in each sampling cycle, the battery parameters for each moment can be obtained.

[0149] Electronic devices can determine the physical characteristic constraints of the battery and use these constraints to interpolate the battery parameters at various times, thereby obtaining a numerically and physically reasonable parameter sequence at dense SOC points. This sequence is then formatted according to a preset format (e.g., SOC column). List, List, Organizing the data into columns (or columns) yields a high-density parameter table, which includes battery parameters under different operating conditions. Electronic devices can output this high-density parameter table as a file in a specific format (e.g., .csv, .mat). This high-density parameter table can be directly accessed by other algorithm modules in the Battery Management System (BMS) (such as state estimators, thermal management controllers, and power predictors) as input for a precise battery model, thereby achieving more accurate SOC estimation, safer charge / discharge control, and more efficient energy management.

[0150] In this exemplary embodiment, interpolation is performed under the constraints of the battery's physical characteristics. This allows for the embedding of mandatory physical rule checks during the generation of the high-density parameter table, ensuring that the data in the high-density parameter table conforms to the basic physical laws of the battery, thereby improving the physical rationality and application safety of the high-density parameter table.

[0151] In an exemplary embodiment, under the constraints of the battery's physical characteristics, interpolation processing is performed on the battery parameters at each time point to obtain a high-density parameter table for the battery. This includes: under the constraints of the battery's physical characteristics, interpolation processing is performed on the first battery parameters at each time point according to a preset state of charge threshold to obtain a high-density parameter table for the battery. The first battery parameters include the values ​​of the ohmic internal resistance and / or polarization internal resistance in the battery's equivalent circuit.

[0152] The state of charge (SOC) threshold is a pre-defined uniform sampling interval for the battery's SOC when constructing a high-density parameter table. The SOC threshold can be pre-set according to actual needs. It can include percentage values ​​to define an ordered sequence of SOC points, determining the density and regularity of the data points in the high-density parameter table. For example, if the SOC threshold is 0.1%, the generated high-density parameter table will contain 1001 discrete data points, starting with 0% SOC and increasing in 0.1% increments up to 100%. By using the SOC threshold, online identification parameter results from different test conditions (temperature, rate) and with different original SOC sampling points can be uniformly aligned to a standard, high-density SOC coordinate axis, thus solving the problem of direct fusion and application of multi-source heterogeneous data.

[0153] The first battery parameter refers to the battery parameters that require a specific interpolation algorithm during interpolation to preserve their physical characteristics. The first battery parameter may include the ohmic internal resistance and / or polarization internal resistance in the battery's equivalent circuit. The first battery parameter exhibits relatively monotonic or regular changes with SOC (e.g., monotonically increasing or decreasing within a certain SOC range), rather than random and drastic fluctuations. During interpolation, an algorithm that preserves this monotonicity or shape characteristic is required to avoid introducing non-physical oscillations or distortions.

[0154] Optionally, the electronic device can obtain a preset state of charge (SOC) threshold (e.g., 0.1%) and generate a high-density SOC vector, for example, SOC_vector=[0,0.1,0.2,...,99.9,100](%). The high-density SOC vector can serve as an index for a high-density parameter table. For the first battery parameter (e.g., ohmic internal resistance and / or polarization internal resistance), the electronic device can perform interpolation processing according to the SOC threshold under the constraints of the battery's physical characteristics to obtain a high-density parameter table for the battery. For example, the electronic device can call the piecewise cubic Hermitian interpolation (PCHIP) algorithm, which considers the function value and its trend (derivative) at each original data point, constructs a cubic polynomial curve between adjacent data points, and ensures that the entire interpolation curve maintains the same monotonicity as the original data in shape. For example, if the original... The data shows an increasing SOC from 30% to 40%, so the interpolated points at 30.1%, 30.2%,...39.9%... The values ​​will also increase. After completing the interpolation processing at the target SOC point, the electronic device can associate the final value of the first battery parameter at each SOC point with the corresponding SOC value and battery temperature, and organize it into a structured high-density parameter table. The high-density parameter table provides a continuous, smooth, and physically reliable relationship between the first battery parameter and SOC at a specific battery temperature.

[0155] In this exemplary embodiment, for the physical characteristics of the first battery parameter of internal resistance, interpolation is performed under physical characteristic constraints according to a preset state of charge threshold. This can maintain the shape preservation and local monotonicity of the data, avoid overshoot or oscillation that violates physical common sense, and thus improve the accuracy and physical rationality of the high-density parameter table.

[0156] In an exemplary embodiment, under the constraints of the battery's physical characteristics, interpolation processing is performed on the battery parameters at each time point to obtain a high-density parameter table for the battery. This includes: under the constraints of the battery's physical characteristics, interpolation processing is performed on the second battery parameters at each time point according to a preset state of charge threshold, based on a linear interpolation and extrapolation processing mechanism, to obtain a high-density parameter table for the battery. The second battery parameters include the value of the polarization capacitance in the battery's equivalent circuit.

[0157] The second battery parameter can include capacitance parameters in the battery's equivalent circuit, such as the value of the polarization capacitance. The extrapolation mechanism is a computational strategy used for parameter estimation in data boundary regions. When parameter values ​​need to be estimated outside the range of the existing data sequence (e.g., below the minimum SOC observation point or above the maximum SOC observation point), the extrapolation mechanism can extend predictions beyond the boundary based on the changing trends (such as slope) shown by the existing data sequence, according to certain mathematical rules, to generate parameter estimates for these missing locations. Based on the extrapolation mechanism, the boundary gap problem of high-density parameter tables caused by experimental limitations or incomplete coverage of online identification data can be mitigated. For example, online identification data may only cover the SOC range from 10% to 90%, but the battery management system (BMS) simulation model may require parameters covering the full range of SOC from 0% to 100%. Therefore, the extrapolation mechanism can be used to extrapolate the boundary intervals of 0%-10% and 90%-100%.

[0158] For example, for the second battery parameter in the battery parameters, the electronic device can perform interpolation processing on the second battery parameter at each time point according to the state of charge threshold, under the constraints of the battery's physical characteristics, based on linear interpolation and extrapolation processing mechanisms, to obtain a high-density parameter table of the battery. For example, for a standard SOC point located within the SOC interval of the data point with known second battery parameters, the electronic device uses linear interpolation for calculation. For example, given that SOC = 50.5%, the second battery parameter... =1180F, SOC=50.7%, second battery parameters =1170F, we need to calculate the parameters of the second battery when SOC=50.6%. Electronic devices can assume that the second battery parameters fall within this small interval. The parameters of the second battery are calculated by linear interpolation formula as the SOC changes linearly. (50.6%)≈1175F, this method is efficient and meets the parameters of the second battery. The change is gradual. For standard SOC points (i.e., boundary regions) that are below the minimum known SOC point or above the maximum known SOC point, linear interpolation cannot be directly applied. Electronic devices can use an extrapolation mechanism to select the known data segment closest to the boundary (such as the two lowest known SOC points), calculate the linear trend of that segment, and extend this trend line to the target SOC point outside the boundary, thereby estimating the second battery parameter at that point. .

[0159] Interpolation and extrapolation processes can be performed synchronously. When generating each interpolated or extrapolated second battery parameter, the electronic device can verify it under the physical characteristic constraints of the battery, either in real-time or after batch processing. Physical characteristic constraints may include parameter non-negativity (…). >0), or with other parameters (such as The time constant constraint that is satisfied by all parties. If a newly generated... If a value violates a constraint (e.g., extrapolating to a negative value), the electronic device can correct it to a reasonable boundary value (e.g., correcting it to 0 or a preset minimum positive value). Through the aforementioned linear interpolation, extrapolation, and physical characteristic constraint verification processes (loop or iterative processing), the electronic device can assign a second battery parameter value that conforms to physical laws to each preset, fixed-interval SOC point. Based on the battery's operating conditions and the second battery parameters, a high-density parameter table covering the entire SOC range with dense and uniform data points can be generated. This high-density parameter table can be output in a specific format (e.g., a .csv file) for direct use by subsequent battery simulation, state estimation, and other modules.

[0160] In this exemplary embodiment, for the second battery parameter of the capacitor type, its value changes relatively smoothly and continuously with SOC, and is locally approximately linear. By using linear interpolation, the computational complexity can be reduced while ensuring accuracy. By using an extrapolation processing mechanism to make reasonable predictions based on the trend of nearby known data, the data completeness of the high-density parameter table can be guaranteed.

[0161] In some embodiments, electronic devices can employ a hybrid interpolation strategy to interpolate online identification results, achieving unified alignment of data volumes from different identification results and avoiding the cumbersome manual parameter fitting or low-precision interpolation processes of traditional methods. The hybrid interpolation strategy is used for online identification results (output by the AFFRLS algorithm). , , The key step in transforming data (based on changes in SOC / T) into a high-density, physically reasonable parameter table is to address the pain point of inconsistent data volume and difficulty in parameter fusion and verification under different temperature / rate HPPC operating conditions. This is achieved through fixed intervals and differentiated parameter processing, transforming discrete online identification results into continuous, uniform parameter mappings that conform to the physical characteristics of the battery. , , The process of generating a high-density parameter table may include:

[0162] 1. Fixed SOC interval, uniform data volume

[0163] The online identification results (such as those under different temperature-rate conditions) will be used to identify the results of each online identification (such as those under different temperature-rate conditions). , , The data is resampled at fixed SOC intervals (e.g., 0.1%, i.e., SOC=0%, 0.1%, 0.2%, ..., 100%) to ensure that each SOC point has a corresponding parameter value.

[0164] 2. Differentiated interpolation based on sub-parameters to preserve physical properties.

[0165] for , By employing the piecewise cubic Hermitian interpolation method, the discrete values ​​obtained online can be... , The data was segmented according to SOC intervals, and each segment was fitted with a PCHIP curve to ensure smooth connections between adjacent segments and no extreme value abrupt changes; for A linear interpolation + extrapolation processing mechanism is used to process the data obtained from online identification. The data is filled with blank points at fixed SOC intervals using linear interpolation, while for regions outside the SOC boundary (e.g., SOC < 0% or > 100%), extended values ​​are generated using linear extrapolation (the range needs to be limited by a physical constraint engine). In some embodiments, the high-density parameters obtained by the electronic device can support multi-platform compatible output such as .mat (Matlab), .csv (Python / Excel), and .dll (C++), facilitating development and application in different scenarios.

[0166] The high-density parameter table can include battery parameters corresponding to different operating conditions, such as ohmic internal resistance. Polarization internal resistance Polarized capacitors The high-density parameter table records the curves of battery parameters as a function of state of charge (SOC) and battery temperature (T), such as... , , The high-density parameter table features extremely small SOC intervals (e.g., 0.1%), a wide temperature coverage (e.g., -20℃ to 55℃), and dense data points (e.g., 1000+ SOC-T combinations). The high-density parameter table is generated through hybrid interpolation (partial parameter PCHIP / linear interpolation), ensuring the rationality of trends. A physical constraint engine filters out outliers, and battery parameters conform to battery electrochemical laws, such as internal resistance decreasing with increasing temperature and initially decreasing then increasing with increasing SOC.

[0167] In this exemplary embodiment, a hybrid interpolation strategy is fused with physical constraints. By combining the advantages of different interpolation algorithms (such as spline, linear, and polynomial), the optimal local interpolation method is selected based on data distribution and parameter characteristics, thereby improving the overall interpolation smoothness and accuracy. Furthermore, the inherent physical constraints of the battery are forcibly embedded during the interpolation process, effectively filtering and correcting potential outlier data points. This ensures that the final high-density parameter table is not only data-intensive but also strictly conforms to the basic physicochemical laws of the battery, significantly improving the physical rationality and accuracy of the interpolation results and providing a reliable data foundation for subsequent applications.

[0168] In one exemplary embodiment, such as Figure 5 As shown, a method for determining battery parameters is provided, including:

[0169] Multi-condition characteristic test: Perform multi-condition HPPC test to obtain the time-series data stream of the battery during charging and discharging, including the terminal voltage, current, battery temperature measured at each moment, and the state of charge quantity estimated by the algorithm.

[0170] Data preprocessing: Preprocessing can be performed on time-series data streams, including signal filtering and open-circuit voltage mapping relationship construction. The open-circuit voltage mapping relationship can include multiple sets of correspondences between battery temperature, state of charge and open-circuit voltage.

[0171] Battery parameter identification: Battery parameters are identified based on the terminal voltage, current, battery temperature measured at various times and the state of charge estimated by the algorithm. For example, battery parameters can be identified based on the temperature-compensated AFRLS algorithm to obtain the battery parameters.

[0172] Hybrid interpolation: Cubic Hermitian interpolation is used to interpolate the polarization resistance / ohmic resistance, and linear interpolation is used to interpolate the polarization capacitance.

[0173] Physical characteristic constraints: Physical characteristic constraints are used to verify the battery parameters obtained through hybrid interpolation, including time constant correction (e.g., for polarization time constant). (Make corrections) and parameter truncation (such as non-negativity constraints on battery parameters) to generate a high-density parameter table;

[0174] Multiple output formats: The high-density parameter table supports multiple output formats, including .mat lookup tables, .csv standardized tables, and .dll dynamic libraries.

[0175] The battery parameter determination method provided in this application covers a complete process of "online identification - hybrid interpolation - dynamic constraints - compatible output," determining battery parameters based on a temperature-compensated online identification algorithm and a hybrid constraint interpolation strategy. The hybrid interpolation strategy effectively solves the problem that a single interpolation method cannot adequately consider the characteristics of all parameters, improving the interpolation accuracy and physical consistency of high-density parameter tables, and providing more reliable underlying data for subsequent high-precision simulations and strategy optimization. By setting a fixed SOC interval (e.g., 0.1%), PCHIP interpolation is used for internal resistance parameters (ohmic internal resistance, polarization internal resistance), which strictly maintains the inherent "relatively monotonic characteristics" of internal resistance parameters. For example, the trend of resistance change with temperature or SOC within a specific SOC range ensures the physical rationality and smoothness of the internal resistance interpolation curve. For polarization capacitors, a linear interpolation combined with an extrapolation mechanism is used, fully considering the characteristic that polarization capacitors typically change relatively smoothly over a wide SOC range. While ensuring computational efficiency and stability, the intelligent extrapolation mechanism effectively handles data boundary regions, avoiding accuracy loss caused by boundary effects.

[0176] For the physical constraint engine, a fundamental shift has been achieved from "data-driven interpolation" to "data filtering and interpolation dominated by physical laws". This effectively shields potential outliers in online identification and ensures that the data in the final high-density parameter table strictly conforms to the basic physical and chemical principles of the battery. This greatly improves the reliability and engineering practicality of the parameter table and avoids model failure or strategy errors caused by abnormal parameters.

[0177] The battery parameter determination method provided in this application, through temperature-considered 3D modeling and the AFFRLS algorithm, ensures higher accuracy and temperature robustness of the original parameter points identified online, especially under extreme conditions such as low temperatures. This provides a more accurate and reliable input data source for subsequent hybrid interpolation and physical constraint processing, forming the foundation for the high accuracy of the entire process. The high-precision, high-density parameter table, optimized by the hybrid interpolation strategy and guaranteed by the physical constraint engine, supports multi-platform compatible output and can seamlessly integrate with mainstream development platforms (Matlab, Python, C++, etc.). This significantly reduces the integration threshold and time cost of BMS development, battery model construction, thermal management simulation, and strategy optimization, rapidly transforming core innovative value into practical engineering benefits.

[0178] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0179] Based on the same inventive concept, this application also provides a battery parameter determining device for implementing the battery parameter determining method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more battery parameter determining device embodiments provided below can be found in the limitations of the battery parameter determining method described above, and will not be repeated here.

[0180] In one exemplary embodiment, such as Figure 6As shown, a battery parameter determining device 600 is provided, including: an operating parameter determining module 601, an open-circuit voltage determining module 602, a correction processing module 603, and a parameter mapping module 604, wherein:

[0181] The working parameter determination module 601 is used to determine the battery's terminal voltage, current, battery temperature, and state of charge at the current moment during the charging and discharging process of the battery.

[0182] The open-circuit voltage determination module 602 is used to determine the open-circuit voltage of the battery at the current moment based on the battery temperature and state of charge.

[0183] The correction processing module 603 is used to obtain the actual voltage difference at the current moment based on the terminal voltage and the open circuit voltage, and to correct the intermediate parameters of the previous moment based on the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters of the current moment; the predicted voltage difference is predicted for the current moment based on the intermediate parameters of the previous moment.

[0184] The parameter mapping module 604 is used to obtain the battery parameters at the current time based on the intermediate parameters and parameter mapping relationship at the current time; the parameter mapping relationship is used to describe multiple sets of correspondences between the intermediate parameters and the battery parameters.

[0185] In some embodiments, the correction processing module 603 is further configured to: determine the voltage difference error at the current moment based on the actual voltage difference and the predicted voltage difference; determine the gain at the current moment based on the forgetting factor at the previous moment and the regression vector at the current moment; the forgetting factor at the previous moment is determined based on the voltage difference error at the previous moment; the regression vector at the current moment is obtained based on the current, the actual voltage difference at the previous moment, and the current at the previous moment; and correct the intermediate parameters at the previous moment based on the gain at the current moment and the voltage difference error at the current moment to obtain the intermediate parameters at the current moment.

[0186] In some embodiments, the correction processing module 603 is further configured to determine the intermediate parameters of the previous time step, and obtain the predicted voltage difference based on the intermediate parameters of the previous time step and the regression vector of the current time step; and obtain the voltage difference error of the current time step based on the difference between the actual voltage difference and the predicted voltage difference.

[0187] In some embodiments, the open-circuit voltage determination module 602 is further configured to obtain an open-circuit voltage mapping relationship, which includes multiple sets of correspondences between battery temperature, state of charge and open-circuit voltage; and determine the open-circuit voltage of the battery at the current moment based on the mapping relationship between battery temperature, state of charge and open-circuit voltage.

[0188] In some embodiments, the battery parameter determination device 600 further includes a parameter mapping relationship determination module, used to determine the equivalent circuit of the battery, the equivalent circuit including ohmic internal resistance, polarization internal resistance and polarization capacitance; determine the transfer function relationship between the battery's open-circuit voltage and current and the equivalent circuit; obtain the parameter mapping relationship based on the transfer function relationship; the parameter mapping relationship includes multiple sets of correspondences between intermediate parameters and the values ​​of ohmic internal resistance, polarization internal resistance and polarization capacitance.

[0189] In some embodiments, the battery parameter determination device 600 further includes a parameter table generation module, which is used to obtain the battery parameters at each time point during the charging and discharging process; under the constraints of the battery's physical characteristics, interpolation processing is performed on the battery parameters at each time point to obtain a high-density parameter table of the battery; the high-density parameter table includes the battery parameters corresponding to the battery under different operating conditions.

[0190] In some embodiments, the parameter table generation module is further configured to: under the physical characteristic constraints of the battery, interpolate the first battery parameters at each time point according to a preset state of charge threshold to obtain a high-density parameter table of the battery; the first battery parameters include the values ​​of the ohmic internal resistance and / or polarization internal resistance in the equivalent circuit of the battery; under the physical characteristic constraints of the battery, interpolate the second battery parameters at each time point according to a preset state of charge threshold based on a linear interpolation and extrapolation mechanism to obtain a high-density parameter table of the battery; the second battery parameters include the values ​​of the polarization capacitance in the equivalent circuit of the battery.

[0191] Each module in the aforementioned battery parameter determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware form or independent of it, or stored in the memory of the electronic device in software form, so that the processor can call and execute the operations corresponding to each module.

[0192] In one exemplary embodiment, an electronic device is provided, which may be a computer device (terminal / server) or a controller in a vehicle, and its internal structure diagram may be as follows. Figure 7As shown, the electronic device includes a processor, memory, input / output interface, communication interface, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface and input device are connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a battery parameter determination method.

[0193] Those skilled in the art will understand that Figure 7 The structure shown is a block diagram of a partial structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0194] In one exemplary embodiment, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0195] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0196] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0197] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0198] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program mentioned can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0199] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0200] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining battery parameters, characterized in that, The method includes: During the charging and discharging process of the battery, determine the battery's terminal voltage, current, battery temperature, and state of charge at the current moment; Based on the battery temperature and the state of charge, determine the open-circuit voltage of the battery at the current moment; The actual voltage difference at the current moment is obtained based on the terminal voltage and the open-circuit voltage. The intermediate parameters at the previous moment are corrected based on the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters at the current moment. The predicted voltage difference is predicted for the current moment based on the intermediate parameters at the previous moment. Based on the intermediate parameters and parameter mapping relationship at the current moment, the battery parameters at the current moment are obtained; the parameter mapping relationship is used to describe multiple sets of correspondences between the intermediate parameters and the battery parameters.

2. The method according to claim 1, characterized in that, The step of correcting the intermediate parameters of the previous moment based on the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters of the current moment includes: The voltage difference error at the current moment is determined based on the actual voltage difference and the predicted voltage difference. The gain at the current moment is determined based on the forgetting factor at the previous moment and the regression vector at the current moment; the forgetting factor at the previous moment is determined based on the voltage difference error at the previous moment; the regression vector at the current moment is obtained based on the current, the actual voltage difference at the previous moment, and the current at the previous moment. Based on the gain and voltage difference error at the current moment, the intermediate parameters from the previous moment are corrected to obtain the intermediate parameters at the current moment.

3. The method according to claim 2, characterized in that, The step of determining the voltage difference error at the current moment based on the actual voltage difference and the predicted voltage difference includes: Determine the intermediate parameters of the previous time step, and obtain the predicted voltage difference based on the intermediate parameters of the previous time step and the regression vector of the current time step; The voltage difference error at the current moment is obtained based on the difference between the actual voltage difference and the predicted voltage difference.

4. The method according to claim 1, characterized in that, Determining the open-circuit voltage of the battery at the current moment based on the battery temperature and the state of charge includes: Obtain the open-circuit voltage mapping relationship, which includes multiple sets of correspondences between battery temperature, state of charge, and open-circuit voltage. Based on the mapping relationship between the battery temperature, the state of charge, and the open-circuit voltage, the open-circuit voltage of the battery at the current moment is determined.

5. The method according to claim 1, characterized in that, The method further includes: Determine the equivalent circuit of the battery, wherein the equivalent circuit includes ohmic internal resistance, polarization internal resistance and polarization capacitance; Determine the transfer function relationship between the open-circuit voltage and current of the battery and the equivalent circuit; The parameter mapping relationship is obtained based on the transfer function relationship; the parameter mapping relationship includes multiple sets of correspondences between intermediate parameters and the values ​​of the ohmic internal resistance, the polarization internal resistance, and the polarization capacitance.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Obtain the battery parameters of the battery at each moment during the charging and discharging process; Under the constraints of the battery's physical characteristics, interpolation is performed on the battery parameters at each time point to obtain a high-density parameter table for the battery; the high-density parameter table includes the battery parameters corresponding to the battery under different operating conditions.

7. The method according to claim 6, characterized in that, Under the constraints of the battery's physical characteristics, interpolation processing is performed on the battery parameters at each time point to obtain a high-density parameter table for the battery, including at least one of the following: Under the physical characteristic constraints of the battery, according to the preset state of charge threshold, the first battery parameters at each time moment are interpolated to obtain the high-density parameter table of the battery; the first battery parameters include the values ​​of ohmic internal resistance and / or polarization internal resistance in the equivalent circuit of the battery. Under the physical characteristic constraints of the battery, according to the preset state of charge threshold, based on the linear interpolation and extrapolation processing mechanism, the second battery parameters at each time point are interpolated to obtain the high-density parameter table of the battery; the second battery parameters include the value of the polarization capacitance in the equivalent circuit of the battery.

8. A battery parameter determining device, characterized in that, The device includes: The working parameter determination module is used to determine the battery's terminal voltage, current, battery temperature, and state of charge at the current moment during the battery's charging and discharging process. An open-circuit voltage determination module is used to determine the open-circuit voltage of the battery at the current moment based on the battery temperature and the state of charge quantity; The correction processing module is used to obtain the actual voltage difference at the current moment based on the terminal voltage and the open circuit voltage, and to correct the intermediate parameters of the previous moment based on the actual voltage difference and the predicted voltage difference to obtain the intermediate parameters of the current moment; the predicted voltage difference is predicted for the current moment based on the intermediate parameters of the previous moment. The parameter mapping module is used to obtain the battery parameters of the battery at the current time based on the intermediate parameters and the parameter mapping relationship at the current time; the parameter mapping relationship is used to describe multiple sets of correspondences between the intermediate parameters and the battery parameters.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images