A voltage prediction method and device based on residual polarization inversion and a medium

Abstract

This invention discloses a voltage prediction method, apparatus, and medium based on residual polarization inversion, relating to the field of automotive electronics. The method includes: acquiring the real-time operating state of a battery pack under test; inverting the real-time residual polarization voltage of the battery pack under test based on the real-time operating state; using the real-time residual polarization voltage as a non-zero initial state, and predicting the polarization voltage at each time point within the pulse duration based on the pulse current and its duration; determining the open-circuit voltage at each time point within the pulse duration; and predicting the terminal voltage of the battery pack under test at the end of the pulse duration based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage. This invention can fully consider the non-static state and residual polarization state of the current lithium iron phosphate battery before the pulse is applied, thereby more accurately predicting the battery voltage after a period of pulse load.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics, and more specifically to a voltage prediction method, apparatus, and dielectric based on residual polarization inversion. Background Technology

[0002] Lithium iron phosphate batteries have been widely used in electric vehicles, energy storage systems and low-voltage vehicle power supplies due to their advantages of high safety, long cycle life and low cost.

[0003] Lithium iron phosphate (LFP) batteries exhibit a prolonged plateau region. The inflection point at the end of this plateau is often used to determine whether battery voltage protection during the discharge period is necessary. Within the plateau region, the battery voltage of LFP batteries changes very little, but the state of charge (SOC) changes relatively significantly. At the end of the plateau region, the battery voltage decreases sharply as the SOC decreases.

[0004] When the vehicle is in operation, if the high voltage is abnormally disconnected, the low voltage battery needs to bear part of the critical safety load and generate a specific discharge pulse demand within a short period of time. Therefore, the Battery Management System (BMS) may need to determine whether the individual cell voltage or battery pack voltage is still higher than the preset protection threshold after the lithium iron phosphate battery in the vehicle has withstood a preset worst-case pulse current in the next few seconds.

[0005] However, this electrochemical characteristic of the plateau region makes it difficult for the BMS to predict the precise battery voltage of the lithium iron phosphate battery. In addition, it does not fully consider the residual polarization state of the battery before the pulse, which may lead to premature or delayed triggering of battery protection.

[0006] Therefore, accurately predicting the battery voltage of lithium iron phosphate batteries after a period of pulsed load is an important issue that the industry urgently needs to address. Summary of the Invention

[0007] In view of this, embodiments of the present invention provide a voltage prediction method, apparatus and medium based on residual polarization inversion, thereby solving the problem that existing battery voltage prediction methods do not fully consider the residual polarization state of the battery before the pulse, making it difficult to accurately predict the battery voltage of lithium iron phosphate batteries after a period of pulsed load.

[0008] According to a first aspect, embodiments of the present invention provide a voltage prediction method based on residual polarization inversion, the method comprising: Obtain the real-time operating status of the battery pack under test; the real-time operating status includes real-time battery voltage, battery current, battery temperature, state of charge, and battery health status; Based on the real-time operating status, the real-time residual polarization voltage of the battery pack under test is retrieved. Using the real-time residual polarization voltage as a non-zero initial state, and based on the pulse current and the pulse duration, the polarization voltage at each time point within the pulse duration is predicted. Determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, predict the terminal voltage of the battery pack under test at the end of the pulse duration.

[0009] In conjunction with the first aspect, in the first embodiment of the first aspect, the step of retrieving the real-time residual polarization voltage of the battery pack under test based on the real-time operating state specifically includes: The real-time open-circuit voltage of the battery pack under test is determined based on the state of charge and battery temperature. The battery voltage and battery current are processed based on the current step event to extract the real-time ohmic internal resistance of the battery pack under test. Construct a first-order resistor-capacitor equivalent circuit model, and invert the real-time residual polarization voltage based on the first-order resistor-capacitor equivalent circuit model, open-circuit voltage, battery current, and ohmic internal resistance.

[0010] In conjunction with the first aspect, in the second embodiment of the first aspect, the step of using the real-time residual polarization voltage as a non-zero initial state and predicting the polarization voltage at each time point within the pulse duration based on the pulse current and the pulse duration of the pulse current specifically includes: Offline parameter identification of polarization resistor and polarization capacitor is performed based on the first-order resistor-capacitor equivalent circuit model. The real-time residual polarization voltage is used as a non-zero initial state, and historical residual polarization decay information is obtained based on the polarization resistance and polarization capacitance. By determining the polarization voltage drop generated by the pulse current at each time point of the pulse duration, the newly added polarization information of the pulse can be obtained; Based on historical residual polarization decay information and pulse-added polarization information, the polarization voltage at each time point within the pulse duration is predicted.

[0011] In conjunction with the first aspect, in the third embodiment of the first aspect, determining the open-circuit voltage at each time point within the pulse duration, and predicting the terminal voltage of the battery pack under test at the end of the pulse duration based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, specifically includes: Based on the pulse current and the pulse duration, predict the state of charge at each time point within the pulse duration. Based on the state of charge at each time point and the real-time battery temperature, predict the open-circuit voltage at each time point during the pulse duration. Determine the ohmic voltage drop generated by the pulse current across the ohmic internal resistance; Based on the open-circuit voltage, ohmic voltage drop, and polarization voltage at each time point, the terminal voltage of the battery pack under test at the end of the pulse duration is predicted.

[0012] In conjunction with the third embodiment of the first aspect, in the fourth embodiment of the first aspect, the expression for the terminal voltage is:

[0013] in, Indicates pulse current continued Terminal voltage after seconds; Indicates the pulse duration; Indicates pulse current continued Polarization voltage after seconds; Indicates pulse current continued Open circuit voltage after seconds; This represents the internal resistance of the Ohm.

[0014] In conjunction with the fourth embodiment of the first aspect, in the fifth embodiment of the first aspect, the pulse current continued The expression for the polarization voltage after one second is:

[0015]

[0016] in, Indicates the real-time battery voltage; This indicates the real-time state of charge and open-circuit voltage under battery temperature conditions. Indicates the real-time battery current; Represents the real-time residual polarization voltage; Indicates polarization resistance; This indicates a polarized capacitor.

[0017] In conjunction with the first aspect, in the sixth embodiment of the first aspect, the method further includes: If the voltage at the end of the pulse duration is lower than a preset protection threshold, the voltage protection of the battery pack under test is triggered.

[0018] According to a second aspect, embodiments of the present invention also provide a voltage prediction device based on residual polarization inversion, the device comprising: The data acquisition module is used to acquire the real-time operating status of the battery pack under test; the real-time operating status includes real-time battery voltage, battery current, battery temperature, state of charge, and battery health status. The polarization inversion module is used to invert the real-time residual polarization voltage of the battery pack under test based on the real-time operating status. The polarization prediction module is used to predict the polarization voltage at each time point within the pulse duration, based on the real-time residual polarization voltage as a non-zero initial state and the pulse current and the pulse duration of the pulse current. The voltage prediction module is used to determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, it predicts the terminal voltage of the battery pack under test at the end of the pulse duration.

[0019] According to a third aspect, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the voltage prediction method based on residual polarization inversion as described above.

[0020] According to a fourth aspect, embodiments of the present invention also provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the voltage prediction method based on residual polarization inversion as described above.

[0021] The voltage prediction method, device, and medium based on residual polarization inversion of the present invention introduce the inversion of residual polarization voltage to instantaneously determine the unmeasurable internal polarization state of the battery pack under test using measurable external quantities, and use this as the non-zero initial condition for the subsequent first-order resistance-capacitance equivalent circuit model. In this way, before the pulse is applied, the non-static state and residual polarization state of the current lithium iron phosphate battery are fully considered, thereby more accurately predicting the battery voltage after the pulse load lasts for a period of time. It is more suitable for judging the risk of insufficient battery voltage in the plateau region, low SOC, low temperature, aging, and non-static parking scenarios of lithium iron phosphate batteries. Attached Figure Description

[0022] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings: Figure 1 A schematic flowchart of the voltage prediction method based on residual polarization inversion provided by the present invention is shown. Figure 2 A schematic diagram of the pulse test in the offline parameter identification stage of the voltage prediction method based on residual polarization inversion provided by the present invention is shown. Figure 3 A schematic diagram of the first-order resistance-capacitance equivalent circuit constructed in the voltage prediction method based on residual polarization inversion provided by the present invention is shown. Figure 4 The diagram shows the fitting of the equivalent circuit model and the voltage curve of the sample battery pack in the voltage prediction method based on residual polarization inversion provided by the present invention. Figure 5 This diagram illustrates the change in terminal voltage during the duration of a pulse current in the voltage prediction method based on residual polarization inversion provided by the present invention. Figure 6 A schematic diagram of the voltage prediction device based on residual polarization inversion provided by the present invention is shown. Figure 7 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] In the actual operation of electric vehicles, there are several scenarios where it is necessary to pre-determine whether the battery voltage may drop below the protection threshold under pulse load. For example, when the high-voltage battery is abnormally disconnected during vehicle operation, the low-voltage battery needs to bear some critical safety loads (such as power steering, parking brake, safety warning lights, etc.) and generate specific discharge pulse demands for a short period of time. The system must determine whether the battery voltage will fall below the minimum allowable operating voltage. If the prediction indicates that the battery voltage will drop below the preset protection threshold, it means that the battery may not be able to provide sufficient energy. The BMS will then take corresponding protective measures in advance, such as reducing the load power, to avoid the risk of load failure due to insufficient battery voltage.

[0025] During the self-test process before powering back on after a long period of parking, the BMS also needs to determine whether the current pulses in the startup sequence will cause a momentary undervoltage. In low-temperature cold start scenarios, the battery's internal resistance increases significantly, and the voltage drop caused by the pulse is more severe, requiring more accurate prediction of the battery voltage at the end of the pulse to assess startup feasibility. Furthermore, in the end-of-life (EOL) stage of the battery, capacity decay and increased internal resistance further increase the pulse voltage drop, making prediction of the battery voltage at the end of the pulse particularly important for ensuring the safe use of aging batteries. In these scenarios, accurately predicting the battery voltage of the lithium iron phosphate battery after a period of pulsed load is a key aspect of ensuring the functional safety and reliable operation of the vehicle battery system.

[0026] Currently, in the above application scenarios, the battery voltage of lithium iron phosphate batteries is mainly predicted using the following methods: The direct voltage threshold method is the simplest prediction method. This method directly determines whether the real-time battery voltage is higher than the protection threshold. If the current voltage is higher than the protection threshold, the corresponding action is allowed.

[0027] Meeting the threshold voltage condition at present does not guarantee that the voltage will still meet the condition after a pulsed load is applied for several seconds. This is because lithium iron phosphate batteries experience additional voltage drops under pulsed current due to ohmic voltage drop and polarization effects. The direct threshold method cannot predict this dynamic process at all, and therefore is prone to misjudgment under extreme conditions such as low SOC, low temperature, and aging. For example, in a scenario where the current SOC is 30% and the battery temperature is -10°C, the current terminal voltage may still be higher than the protection threshold, but after a fixed pulse of 100A lasts for 5 seconds, the terminal voltage may drop by tens or even hundreds of millivolts due to ohmic voltage drop and polarization effects, thus falling below the protection threshold. The direct voltage threshold method cannot identify this risk in advance.

[0028] Battery voltage prediction based on State of Charge (SOC) and Open Circuit Voltage (OCV) estimates the remaining capacity of the battery by using the current SOC and the corresponding OCV. This method utilizes a pre-calibrated OCV-SOC relationship curve, looking up the OCV from the current SOC in a table, and then superimposing the estimated voltage drop to predict the terminal voltage after the pulse. However, lithium iron phosphate (LFP) batteries exhibit a significant plateau characteristic between OCV and SOC, with a prolonged plateau region (approximately 20% to 80% SOC). Within this plateau region, the battery voltage changes very little, but the SOC changes relatively significantly. At the end of the plateau region (below approximately 20% SOC), the battery voltage decreases sharply with decreasing SOC. This electrochemical characteristic of the plateau region means that even with large changes in SOC, the change in OCV is very limited, resulting in a severe deficiency in the ability of SOC and OCV-based battery voltage prediction to characterize the true remaining voltage margin. Furthermore, SOC and OCV-based battery voltage prediction also fails to adequately consider the influence of the battery's internal polarization state before the pulse application on the prediction results. Within the plateau region, relying solely on OCV and SOC for voltage prediction often makes it difficult to distinguish whether the battery is in a safe operating state or is about to enter a risky state of insufficient battery voltage.

[0029] Equivalent Circuit Model (ECM) prediction is currently the most widely used method for predicting battery voltage. This method uses an equivalent circuit model to describe the external electrical characteristics of the battery and predicts future terminal voltage changes based on model parameters and the current state. The first-order resistive-capacitive (RC) equivalent circuit model is one of the most commonly used ECM structures, consisting of a voltage source (represented as OCV), an ohmic internal resistance... and a parallel polarization resistor. and polarization capacitor Composition. This model can describe the ohmic and polarization responses of a battery and predict the polarization voltage at a future moment through differential equations or time-domain analytical solutions. Although the equivalent circuit model prediction method is theoretically a relatively complete method for predicting battery voltage, there is a fundamental flaw in the existing technology: it assumes that the battery is in a fully quiescent state at the start of the prediction, with an initial polarization voltage of zero. However, in actual vehicle applications, the low-voltage battery is usually in an unbalanced state before and after vehicle startup. When the vehicle starts, the low-voltage load generates a discharge pulse; after the high-voltage system is established, the high-voltage battery charges and compensates the low-voltage battery through DC / DC, thereby forming a continuous charge and discharge pulse excitation. Due to the obvious dynamic and hysteresis characteristics of the internal polarization effect of the battery, the polarization voltage cannot be instantaneously established or disappeared with changes in current. Therefore, during the vehicle's operating cycle, there is usually a certain residual polarization voltage inside the battery, the value of which is related to factors such as the current history before and after startup, pulse amplitude, duration, and battery temperature, and is not zero under most operating conditions.

[0030] Correspondingly, if the zero initial polarization assumption is still adopted, the influence of this residual polarization voltage is completely ignored when predicting the polarization voltage after a fixed pulse lasting several seconds. When parking immediately after high-rate discharge, the polarization voltage inside the battery may reach tens or even hundreds of millivolts; when switching to parking immediately after fast charging, the polarity and amplitude of the polarization voltage are completely different from after discharge. Ignoring this magnitude of residual polarization voltage will directly lead to significant errors in the terminal voltage prediction. Secondly, the time constant τ of the first-order RC polarization network = Typically ranging from 1 to 100 seconds, while the pulse duration is from several seconds to tens of seconds (determined by functional safety analysis), when the pulse duration is on the same order of magnitude as the parameter τ, the residual polarization voltage only partially decays during the pulse, and its contribution to the terminal voltage is not negligible. Finally, also because the OCV changes gradually with SOC within the plateau region, the polarization voltage becomes the main factor distinguishing the true terminal voltage of the battery under different conditions. Within the plateau region, even a small deviation in polarization voltage can lead to misjudgment of the risk of insufficient battery voltage, such as misjudging a safe pre-parking state as a risk of insufficient battery voltage, or misjudging a true risk of insufficient battery voltage as a safe state.

[0031] It can be seen that, due to the failure to consider the residual polarization voltage inside the battery before the pulse is applied and the use of the zero initial polarization assumption, the prediction deviation is significant in the scenario where the vehicle is not stationary and cannot reliably determine the risk of insufficient battery voltage after the pulse lasts for several seconds.

[0032] In conclusion, accurately predicting the battery voltage of lithium iron phosphate batteries after a period of pulsed load is an important issue that the industry urgently needs to address.

[0033] To address the aforementioned issues, this specification provides a voltage prediction method based on residual polarization inversion. This method aims to fully consider the current non-static state and residual polarization state of the lithium iron phosphate battery before pulse application, thereby more accurately predicting the battery voltage after a period of pulse load. Figure 1 This is a schematic flowchart of a voltage prediction method based on residual polarization inversion according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method may include the following steps: S101. Obtain the real-time operating status of the battery pack to be tested. The real-time operating status includes real-time battery voltage, battery current, battery temperature, SOC, and battery state of health (SOH).

[0034] It is understandable that the battery pack under test is a lithium iron phosphate battery pack, which is composed of multiple lithium iron phosphate cells connected in series, and the battery voltage in real-time operation is the terminal voltage.

[0035] In this embodiment, the real-time State of Health (SOH) can be determined using the capacity decay method based on the real-time operating status. The capacity decay method calculates the capacity retention rate by comparing the current actual usable capacity of the battery pack with its nominal rated capacity, thereby obtaining the real-time SOH. By recording the accumulated ampere-hour data from each charge-discharge cycle, the current usable capacity is fitted to the data. Then, based on the nominal rated capacity of the battery pack at the time of manufacture, the current usable capacity is divided by the nominal rated capacity and multiplied by 100% to obtain the real-time SOH.

[0036] In the embodiments of this application, the real-time SOC can be determined by using the open-circuit voltage method, Kalman filtering based on the equivalent circuit model, or ampere-hour integration method, according to the real-time operating status.

[0037] In particular, as the real-time SOH estimate is continuously updated based on the real-time operating status, the capacity parameter in the ampere-hour integral is replaced with the updated SOH. That is, the capacity parameter in the ampere-hour integral uses the latest SOH. In this way, even if the battery pack ages, the percentage change in SOC corresponding to the same ampere-hour integral will be greater, ensuring that the determined real-time SOC is as consistent as possible with the actual remaining usable power.

[0038] It should be noted that other methods can also be used to determine the real-time SOC and SOH. The specific method depends on the BMS's operating strategy and the actual algorithm embedded in the BMS. Here, no restrictions are placed on the method for determining the real-time SOC and SOH, as long as the real-time SOC and SOH can be obtained based on the various parameters in the actual operating state.

[0039] S102. Based on the real-time operating status, invert the real-time residual polarization voltage of the battery pack under test.

[0040] During actual vehicle operation, the low-voltage load generates discharge pulses during the vehicle startup phase. After the high-voltage system returns to normal operation, the high-voltage battery charges the battery pack under test via a DC / DC converter, generating charging pulses. These charging and discharging pulses, acting on the battery pack under test, induce polarization within the battery. Because polarization voltage exhibits dynamic accumulation and gradual decay characteristics, a non-zero residual polarization voltage may exist within the battery pack under test at any given time. Ignoring this residual polarization will lead to significant deviations in the voltage prediction at the end of the subsequent pulse current duration.

[0041] In this embodiment, the real-time residual polarization voltage is obtained by constructing a first-order resistor-capacitor equivalent circuit model in the ECM, and by combining the real-time battery current and real-time battery voltage in the real-time operating state with the real-time ohmic internal resistance extracted from the real-time battery current and battery voltage.

[0042] S103. Using the real-time residual polarization voltage as a non-zero initial state, and based on the pulse current and the pulse duration of the pulse current, predict the polarization voltage at each time point within the pulse duration.

[0043] In this embodiment, the pulse current is the worst-case load current determined by functional safety analysis.

[0044] The real-time residual polarization voltage obtained by inversion is used as the non-zero initial state in the prediction stage of the first-order resistor-capacitor equivalent circuit model. The time-domain analytical solution of the first-order resistor-capacitor equivalent circuit model is used to further predict the polarization voltage at each time node during the pulse duration, which includes the polarization voltage at the end of the pulse duration.

[0045] S104. Determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, predict the terminal voltage of the battery pack under test at the end of the pulse duration.

[0046] The pulse current also consumes battery capacity. Predicting the terminal voltage of the battery pack under test at the end of the pulse duration can indirectly reflect the capacity change of the battery pack under test after the pulse duration, ensuring the accuracy of the calculation.

[0047] In this embodiment, the parameters used to predict the terminal voltage are derived from the real-time operating status of the battery pack under test and the residual polarization state at the current moment. This eliminates the need to rely on the complete current history since the system initialization moment, thereby avoiding the problem of calculation result distortion caused by the cumulative error of long-term integration of historical current and the problem of model inaccurate initialization due to missing historical current data.

[0048] During actual vehicle operation, a discharge pulse is generated when the low-voltage system load is connected during vehicle startup. After the high-voltage system is established, the high-voltage battery generates a charging pulse by charging the low-voltage battery through a DC / DC converter. Due to the dynamic accumulation and gradual decay characteristics of the internal polarization effect of the battery, the battery pack under test may have a non-zero residual polarization voltage at any time. Therefore, this application predicts subsequent polarization changes by inverting the residual polarization voltage at the current moment and combining it with a preset pulse current condition. This ensures that the polarization voltage at each moment within the predicted pulse duration can reflect both the decay process of historical residual polarization and the new polarization voltage changes caused by the pulse current.

[0049] The above method enables battery state safety assessment before high-voltage charging without relying on complete historical data. Within the lithium iron phosphate battery plateau region, the open-circuit voltage (OCV) changes relatively smoothly with SOC, and the terminal voltage difference is mainly affected by polarization voltage. Therefore, accurate prediction of polarization voltage is a crucial foundation for identifying battery voltage deficiency risks. By reducing the deviation between the initial and actual polarization voltage states, the accuracy of terminal voltage prediction is improved, enhancing the sensitivity and accuracy of identifying battery voltage deficiency risks within the plateau region. This method is particularly suitable for assessing battery voltage deficiency risks in lithium iron phosphate battery plateau regions, low SOC conditions, low-temperature conditions, aging conditions, and application scenarios where residual polarization exists, such as vehicle startup or non-stationary parking.

[0050] The voltage prediction method based on residual polarization inversion of the present invention introduces the inversion of residual polarization voltage to instantaneously determine the unmeasurable internal polarization state of the battery pack under test using measurable external quantities, and uses this as the non-zero initial condition for the subsequent first-order resistor-capacitor equivalent circuit model. In this way, before the pulse is applied, the non-static state and residual polarization state of the current lithium iron phosphate battery are fully considered, thereby more accurately predicting the battery voltage after the pulse load lasts for a period of time. It is more suitable for judging the insufficient battery voltage in the plateau region, low SOC, low temperature, aging, and non-static parking scenarios of lithium iron phosphate batteries.

[0051] In this embodiment of the application, step S102 specifically includes: S1021. Determine the real-time open-circuit voltage of the battery pack under test based on the state of charge and battery temperature.

[0052] In this embodiment of the application, the real-time open-circuit voltage is determined by a pre-constructed OCV-SOC-T curve and the obtained real-time SOC and battery temperature.

[0053] The OCV-SOC-T curve is constructed based on offline testing of sample batteries of the same model as the battery pack under test. During the offline testing phase, the OCV corresponding to each actual SOC of the sample battery during the discharge process is obtained, and the OCV-SOC curve is determined based on the SOC and its corresponding OCV.

[0054] Since the OCV of the battery pack is affected by the battery temperature, the sample battery is repeatedly subjected to offline testing at different test temperatures, thereby obtaining the OCV-SOC-T curves at different test temperatures.

[0055] For each test temperature, offline testing involves allowing the sample battery to rest for a preset time to ensure it reaches a stable internal state. Then, a preset constant charging current is used to charge the sample battery. When the sample battery is nearly fully charged (e.g., 90% SOC), a preset constant charging voltage is used to charge it until it reaches a preset current state. The preset current state is defined as the battery current dropping to a certain threshold to ensure the battery pack is fully charged. Next, a preset constant discharge current is used to discharge the sample battery at the preset current state, and the OCV corresponding to each actual SOC during the discharge process is obtained until the battery discharge is complete.

[0056] S1022. Process the battery voltage and battery current based on the current step event, and extract the real-time ohmic internal resistance of the battery pack under test. The current step event will occur when a vehicle start pulse, a high-voltage power-on / off load step, or a preset diagnostic pulse is applied.

[0057] During the online prediction phase, when a current step event occurs, the BMS can estimate the current ohmic internal resistance of the battery pack under test by utilizing the changes in battery voltage and current before and after the current step event. This ohmic internal resistance reflects the actual impedance level of the battery pack under test under the current SOC, temperature, and aging conditions. Specifically:

[0058] in, Indicates the internal resistance of the ohm; This represents the first pulse voltage value, which is also the battery voltage of the battery pack under test after the current step event occurs; This represents the second pulse voltage value, which is also the battery voltage of the battery pack under test before the current step event occurs. This represents the first pulse current value, which is also the average battery current of the battery pack under test after the current step event occurs; This represents the second pulse current value, which is also the pulse current value of the battery pack under test before the current step event occurs.

[0059] The ohmic internal resistance is estimated by utilizing the current steps that naturally occur during normal vehicle operation, without the need for a dedicated test pulse or additional hardware. When a start-up pulse or current step event is detected, the battery voltage and current data before and after the pulse are recorded. The ohmic internal resistance is determined based on the changes in battery voltage and current before and after the pulse.

[0060] The advantage of current step measurements lies in enabling online tracking of ohmic internal resistance using natural operating conditions, without interrupting normal battery operation or requiring additional specialized testing equipment. Since the battery pack under test is installed in the vehicle, multiple current step measurements form a time series of internal resistance changes over time, allowing tracking of the aging trend of the battery's internal resistance.

[0061] In this embodiment, the obtained ohmic internal resistance can also be compensated by combining battery temperature and SOC.

[0062] S1023. Construct a first-order resistor-capacitor equivalent circuit model, and invert the real-time residual polarization voltage based on the first-order resistor-capacitor equivalent circuit model, open-circuit voltage, battery current, and ohmic internal resistance.

[0063] According to the first-order resistor-capacitor equivalent circuit model, the formula for calculating the terminal voltage is:

[0064] in, This indicates the real-time terminal voltage, which is the real-time battery voltage during real-time operation. This represents the open-circuit voltage under current SOC and battery temperature conditions; Indicates the real-time battery current; Indicates the internal resistance of the ohm; This represents the real-time polarization voltage, also known as the real-time residual polarization voltage.

[0065] Based on the above formula for calculating the terminal voltage, the formula for calculating the real-time residual polarization voltage can be derived in reverse:

[0066] This allows for the real-time determination of the residual polarization voltage of the battery pack under test through inversion. It's important to note that this inversion method does not require the assumption that the battery pack is in a fully stationary state, making it applicable to non-stationary parking scenarios, including parking after drive discharge, parking after regenerative braking, and parking after fast charging. Regardless of the operating history before parking, the residual polarization voltage can be instantaneously determined using the currently measurable terminal voltage and battery current, thus eliminating the systematic error introduced by the zero initial polarization assumption.

[0067] In this embodiment of the application, step S103 specifically includes: S1031. Based on the first-order resistor-capacitor equivalent circuit model, perform offline parameter identification of the polarization resistor and polarization capacitor.

[0068] The goal of offline parameter identification is to obtain the variation patterns of various parameters in the first-order resistive-capacitive equivalent circuit model with SOC, temperature, and SOH by conducting systematic pulse testing experiments on sample batteries at different life stages, temperatures, and SOC conditions, and to establish corresponding offline lookup tables. More specifically, offline parameter identification can be: Each sample battery with different model information and different battery health status was subjected to pulse testing at different test temperatures.

[0069] Lithium iron phosphate battery packs of different models were selected as sample batteries, and pulse tests were performed at three stages: Beginning of Life (BOL), Middle of Life (MOL), and End of Life (EOL). The test temperatures covered the battery's operating temperature range, typically including -20°C, -10°C, 0°C, 10°C, 25°C, 35°C, 45°C, and 60°C. At each test temperature, the State of Charge (SOC) of the sample batteries was decreased from 100% to 0% in 10% increments, and a standard pulse test sequence was executed at each SOC.

[0070] like Figure 2 As shown, Figure 2 The upper half of the curve is the pulse current curve. Figure 2 The lower half of the chart shows the feedback curve of the sample battery pack voltage. The standard pulse test sequence includes: a discharge pulse of constant amplitude (e.g., 1C discharge for 10 seconds), a sufficiently long rest recovery time (e.g., 30 minutes to ensure complete polarization reduction), a charge pulse of constant amplitude (e.g., 0.5C charge for 10 seconds), followed by a rest recovery. During the pulse test, the terminal voltage and current of the battery pack are recorded at a sampling frequency of not less than 10Hz. The same test is performed on battery packs at the BOL, MOL, and EOL life stages to obtain offline test data covering the entire life cycle of the sample battery.

[0071] Strict control of the test environment and equipment is required when conducting pulse tests. The battery pack should be allowed to settle sufficiently before testing to ensure the initial polarization voltage is zero. For example, during testing, a constant temperature chamber or environmental chamber should be used to control the battery pack temperature within ±2°C of the target temperature. A programmable charge / discharge device should be used to apply precise current pulses, with a current accuracy of no less than ±0.1% of full scale, a voltage measurement accuracy of no less than ±5mV, a sampling frequency of no less than 10Hz, and a current measurement accuracy of no less than ±0.1% of full scale.

[0072] In particular, for large-capacity battery packs, if laboratory equipment cannot directly test the entire pack, representative modules or individual cells can be used for testing, and then the parameters of the entire pack can be calculated through series and parallel connections.

[0073] like Figure 3 As shown, the first-order resistor-capacitor (RC) equivalent circuit model (Thevenin model) used in this application consists of the following components: open-circuit voltage source (Its value varies with SOC and temperature), terminal voltage (Describes the actual output voltage that can be measured externally), internal resistance in ohms (Series resistance, describing the instantaneous ohmic response of the battery), and a parallel polarization internal resistance. and polarization capacitor (Describe the battery's polarization response, including electrochemical reaction kinetic limitations and concentration polarization effects). The offline parameter identification process can employ the least squares method, extracting voltage and current data during the discharge pulse for each temperature-SOC-SOH test point, as parameters. and For the parameters to be identified, the sum of squared errors between the predicted and measured terminal voltage values ​​is constructed as the objective function. A nonlinear least squares optimization algorithm (such as the Levenberg-Marquardt algorithm) is used to find the parameter combination that minimizes the objective function. The identified parameters should satisfy the physical constraints. >0, >0, >0.

[0074] like Figure 4 As shown, the predicted terminal voltage curve of the first-order resistor-capacitor equivalent circuit model under the identified parameters is compared with the voltage curve of the actual battery pack to verify the model's fitting accuracy. For example, under BOL and 25°C conditions, the root mean square error of the model's fitting of the terminal voltage should be less than 10mV, and the maximum absolute error should be less than 30mV. If the fitting accuracy does not meet the requirements, the quality of the test data can be checked, and the parameter identification algorithm can be optimized.

[0075] After parameter identification, a dataset of R0, R1, and C1 parameters covering different model information, test temperatures, SOC, and SOH conditions is obtained. This data is organized into a multidimensional lookup table and stored in the non-volatile memory of the BMS, thus yielding:

[0076]

[0077] in, It represents polarization resistance, used to characterize the steady-state voltage drop of battery electrochemical polarization and concentration polarization; It represents the polarization capacitance, used to characterize the inertia of the polarization process and determine the speed of voltage recovery and establishment; This represents the first mapping relationship between temperature, SOC, SOH, and polarization resistance. This represents the second mapping relationship between temperature, SOC, SOH, and polarization capacitance. This indicates the real-time temperature; Indicates the real-time SOH; This indicates the real-time SOC, which is also the real-time discharge capacity.

[0078] Subsequently, based on these two mapping relationships, parameters can be determined according to real-time battery temperature, SOC, and SOH. and parameters .

[0079] The parameter values ​​between adjacent lookup grid points can also be determined using linear interpolation. If the actual working point exceeds the lookup range, the nearest boundary value is used and marked as extrapolated.

[0080] S1032. Using the real-time residual polarization voltage as a non-zero initial state, and obtaining historical residual polarization decay information based on the polarization resistance and polarization capacitance.

[0081] S1033. Determine the polarization voltage drop generated by the pulse current at each time point of the pulse duration to obtain the pulse's newly added polarization information.

[0082] S1034. Based on historical residual polarization decay information and pulse-added polarization information, predict the polarization voltage at each time point within the pulse duration.

[0083] For a first-order resistor-capacitor equivalent circuit model, the polarization voltage satisfies the following calculation formula:

[0084] in, It represents the voltage across the polarization branch in the first-order resistor-capacitor equivalent circuit model, and can also be understood as the voltage offset generated inside the battery due to dynamic effects such as concentration polarization and electrochemical polarization. It represents the rate of change of polarization voltage with respect to time, that is, the rate at which polarization voltage increases or decreases with time; This indicates the battery current.

[0085] In pulse current Under the influence of [the current], the real-time polarization voltage obtained from the inversion is [obtained]. As the initial condition, the pulse duration is... The polarization voltage after 1 second can be expressed as:

[0086] in, Indicates pulse current continued Polarization voltage after seconds.

[0087] Combined pulse duration The formula for calculating the polarization voltage after one second shows that the pulse duration... The polarization voltage after a second is mainly contributed by two types of polarization. The first type of polarization is the historical residual polarization decay term, which is the historical residual polarization decay information. The second type of polarization is the pulse-added polarization term, that is, the pulse-added polarization information. .

[0088] Among them, the historical residual polarization attenuation term Used to characterize pre-pulse residual polarization Natural decay within seconds, fixed pulse with added polarization term Used to characterize pulse current continued The newly generated polarization voltage drop within seconds.

[0089] In the embodiments of this application, the prediction of polarization voltage takes into account both the natural decay contribution of historical residual polarization and the growth contribution of pulse-added polarization, which significantly improves the prediction accuracy of the fixed pulse end voltage in non-static parking scenarios. Furthermore, since the residual polarization voltage is obtained through inversion, it is not affected by the accumulation of historical current integration error, thus enhancing the reliability of the prediction results.

[0090] In this embodiment of the application, step S104 specifically includes: S1041. Based on the pulse current and the pulse duration of the pulse current, predict the state of charge at each time point within the pulse duration.

[0091] For pulse current pulse duration The SOC after 1 second can be expressed as:

[0092] in, Indicates pulse current continued SOC after seconds; This indicates the available capacity under the current SOC and battery temperature conditions.

[0093] Based on this formula, the state of charge at each time point during the pulse duration can be predicted.

[0094] S1042. Based on the state of charge at each time point and the real-time battery temperature, predict the open-circuit voltage at each time point during the pulse duration.

[0095] Furthermore, based on the predicted parameters Pulse current obtained from battery temperature continued The open-circuit voltage after seconds, specifically:

[0096] Based on this formula, the open-circuit voltage at each time point within the pulse duration can be predicted.

[0097] S1043. Determine the ohmic voltage drop generated by the pulse current across the ohmic internal resistance. The ohmic voltage drop can be expressed as: .

[0098] S1044. Based on the open-circuit voltage, ohmic voltage drop, and polarization voltage at each time point, predict the terminal voltage of the battery pack under test at the end of the pulse duration.

[0099] According to the formula for calculating terminal voltage, pulse current continued Polarization voltage after seconds It can also be expressed as:

[0100] in, Indicates pulse current continued The terminal voltage after seconds.

[0101] That is, pulse current continued Terminal voltage after seconds It can be represented as:

[0102] Will Substituting into the above equation, we can further expand to obtain:

[0103] Then After substituting, we get:

[0104] Please see Figure 5 The battery pack under test is a 12V lithium iron phosphate battery. It is particularly vulnerable to high-voltage disconnection during driving, especially in cases of abnormal operation. This battery pack supplies power to critical safety loads, and the BMS needs to be able to predict its operation in real time. The system monitors the terminal voltage status seconds later and provides early warnings to ensure vehicle safety. This will be illustrated using the example of a vehicle entering normal driving mode. Figure 5 The preceding operating conditions consist of the vehicle's starting discharge curve and the high-voltage charging current curve of the battery pack under test after starting (usually maintained at 85% SOC). The preceding operating conditions have a significant impact on the polarization of lithium iron phosphate batteries and will also have a huge impact on the subsequent terminal voltage prediction, resulting in poor early warning accuracy. Figure 5 Point A in the diagram represents the current terminal voltage of the battery pack under test, which is also the real-time terminal voltage of the battery pack under test. Figure 5 Point B is the pulse current. continued The terminal voltage after a few seconds, whether at point A or point B, is less than the open-circuit voltage. The pulse from point A to point B represents the worst-case scenario, typically requiring consideration of the pulse current needed to maintain critical safety loads (such as power steering, parking brake, and hazard lights). The curve, point B also needs to be reached earlier than point A. The predicted terminal voltage is calculated in seconds. If the predicted terminal voltage at point B at the current moment corresponding to point A is insufficient to maintain the minimum operating voltage for the critical load, it is activated in advance. Instant warning.

[0105] In this embodiment of the application, the method may further include the following steps: S201. Obtain the real-time operating status of the battery pack to be tested. See step S101 for details.

[0106] S202. Based on the real-time operating status, invert the real-time residual polarization voltage of the battery pack under test. The specific details are shown in step S102.

[0107] S203. Using the real-time residual polarization voltage as a non-zero initial state, and based on the pulse current and its duration, predict the polarization voltage at each time point within the pulse duration. The specific details are shown in step S103.

[0108] S204. Determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop across the internal resistance of the battery pack under test caused by the pulse current, and the polarization voltage, predict the terminal voltage of the battery pack under test at the end of the pulse duration. See step S104 for details.

[0109] S205. If the terminal voltage at the end of the pulse duration is lower than the preset protection threshold, the voltage protection of the battery pack under test is triggered.

[0110] The preset protection threshold can be set to 9V. Setting the protection threshold to 9V ensures that the battery pack under test can meet the minimum terminal voltage required for other automotive electronic control units to operate. The specific value of the protection threshold can also be set according to the minimum terminal voltage required by the vehicle's critical safety components. If the terminal voltage at the end of the pulse duration is not lower than the preset protection threshold, and it is determined that the battery pack under test will not experience insufficient battery voltage after a fixed pulse current duration, the BMS can allow high-voltage power-down or parking actions. Conversely, if the terminal voltage at the end of the pulse duration is lower than the preset protection threshold, and it is determined that the battery pack under test is at risk of insufficient battery voltage after a fixed pulse current duration, a battery voltage warning signal is output, high-voltage power-down actions are prohibited, and a series of corresponding battery voltage protection operations are triggered.

[0111] The voltage prediction device based on residual polarization inversion provided in the embodiments of the present invention will be described below. The voltage prediction device based on residual polarization inversion described below can be referred to in correspondence with the voltage prediction method based on residual polarization inversion described above.

[0112] To address the aforementioned issues, this specification provides a voltage prediction device based on residual polarization inversion, which aims to fully consider the current non-static state and residual polarization state of the lithium iron phosphate battery before pulse application, thereby more accurately predicting the battery voltage after a period of pulse load. Figure 6 This is a schematic diagram of a voltage prediction device based on residual polarization inversion according to an embodiment of the present invention, as shown below. Figure 6 As shown, the device may include: The data acquisition module 10 is used to acquire the real-time operating status of the battery pack under test. The real-time operating status includes real-time battery voltage, battery current, battery temperature, SOC, and SOH.

[0113] It is understandable that the battery pack under test is a lithium iron phosphate battery pack, which is composed of multiple lithium iron phosphate cells connected in series, and the battery voltage in real-time operation is the terminal voltage.

[0114] In this embodiment, the real-time State of Health (SOH) can be determined using the capacity decay method based on the real-time operating status. The capacity decay method calculates the capacity retention rate by comparing the current actual usable capacity of the battery pack with its nominal rated capacity, thereby obtaining the real-time SOH. By recording the accumulated ampere-hour data from each charge-discharge cycle, the current usable capacity is fitted to the data. Then, based on the nominal rated capacity of the battery pack at the time of manufacture, the current usable capacity is divided by the nominal rated capacity and multiplied by 100% to obtain the real-time SOH.

[0115] In the embodiments of this application, the real-time SOC can be determined by using the open-circuit voltage method, Kalman filtering based on the equivalent circuit model, or ampere-hour integration method, according to the real-time operating status.

[0116] In particular, as the real-time SOH estimate is continuously updated based on the real-time operating status, the capacity parameter in the ampere-hour integral is replaced with the updated SOH. That is, the capacity parameter in the ampere-hour integral uses the latest SOH. In this way, even if the battery pack ages, the percentage change in SOC corresponding to the same ampere-hour integral will be greater, ensuring that the determined real-time SOC is as consistent as possible with the actual remaining usable power.

[0117] It should be noted that other methods can also be used to determine the real-time SOC and SOH. The specific method depends on the BMS's operating strategy and the actual algorithm embedded in the BMS. Here, no restrictions are placed on the method for determining the real-time SOC and SOH, as long as the real-time SOC and SOH can be obtained based on the various parameters in the actual operating state.

[0118] The polarization inversion module 20 is used to invert the real-time residual polarization voltage of the battery pack under test based on the real-time operating status.

[0119] Before a vehicle equipped with a battery pack under test enters parking mode or is powered down, the battery pack may experience dynamic operating conditions such as drive discharge (continuous high-current discharge during acceleration), regenerative braking (high-current charging during deceleration), fast or slow charging, and DC / DC load power supply. The terminal voltage of the battery pack under test may not have fully recovered to its initial voltage value (OCV), and residual polarization exists internally. Ignoring this residual polarization will lead to a significant deviation in the prediction of the voltage at the end of the subsequent pulse current duration.

[0120] In this embodiment, the real-time residual polarization voltage is obtained by constructing a first-order resistor-capacitor equivalent circuit model in the ECM, and by combining the real-time battery current and real-time battery voltage in the real-time operating state with the real-time ohmic internal resistance extracted from the real-time battery current and battery voltage.

[0121] The polarization prediction module 30 is used to predict the polarization voltage at each time point within the pulse duration, based on the real-time residual polarization voltage as a non-zero initial state and the pulse current and the pulse duration of the pulse current.

[0122] In this embodiment, the pulse current is the worst-case load current determined by functional safety analysis.

[0123] The real-time residual polarization voltage obtained by inversion is used as the non-zero initial state in the prediction stage of the first-order resistor-capacitor equivalent circuit model. The time-domain analytical solution of the first-order resistor-capacitor equivalent circuit model is used to further predict the polarization voltage at each time node during the pulse duration, which includes the polarization voltage at the end of the pulse duration.

[0124] The voltage prediction module 40 is used to determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, it predicts the terminal voltage of the battery pack under test at the end of the pulse duration.

[0125] In this way, the predicted polarization voltage at each time point within the pulse duration can simultaneously take into account historical residual polarization decay and fixed pulse-added polarization voltage drop, making it more suitable for assessing the risk of insufficient battery voltage in the plateau region, low SOC, low temperature, aging, and non-stationary parking scenarios of lithium iron phosphate batteries.

[0126] The voltage prediction device based on residual polarization inversion of the present invention introduces the inversion of residual polarization voltage to instantaneously determine the unmeasurable internal polarization state of the battery pack under test using measurable external quantities, and uses this as the non-zero initial condition for the subsequent first-order resistance-capacitance equivalent circuit model. In this way, before the pulse is applied, the non-static state and residual polarization state of the current lithium iron phosphate battery are fully considered, thereby more accurately predicting the battery voltage after the pulse load lasts for a period of time. It is more suitable for judging the risk of insufficient battery voltage in the plateau region, low SOC, low temperature, aging, and non-static parking scenarios of lithium iron phosphate batteries.

[0127] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 can call logical commands in the memory 730 to execute a voltage prediction method based on residual polarization inversion, the method including: Obtain the real-time operating status of the battery pack under test; the real-time operating status includes real-time battery voltage, battery current, battery temperature, state of charge, and battery health status; Based on the real-time operating status, the real-time residual polarization voltage of the battery pack under test is retrieved. Using the real-time residual polarization voltage as a non-zero initial state, and based on the pulse current and the pulse duration, the polarization voltage at each time point within the pulse duration is predicted. Determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, predict the terminal voltage of the battery pack under test at the end of the pulse duration.

[0128] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0129] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the voltage prediction method based on residual polarization inversion provided by the above methods, the method comprising: Obtain the real-time operating status of the battery pack under test; the real-time operating status includes real-time battery voltage, battery current, battery temperature, state of charge, and battery health status; Based on the real-time operating status, the real-time residual polarization voltage of the battery pack under test is retrieved. Using the real-time residual polarization voltage as a non-zero initial state, and based on the pulse current and the pulse duration, the polarization voltage at each time point within the pulse duration is predicted. Determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, predict the terminal voltage of the battery pack under test at the end of the pulse duration.

[0130] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the voltage prediction methods provided above for performing residual polarization inversion, the method comprising: Obtain the real-time operating status of the battery pack under test; the real-time operating status includes real-time battery voltage, battery current, battery temperature, state of charge, and battery health status; Based on the real-time operating status, the real-time residual polarization voltage of the battery pack under test is retrieved. Using the real-time residual polarization voltage as a non-zero initial state, and based on the pulse current and the pulse duration, the polarization voltage at each time point within the pulse duration is predicted. Determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, predict the terminal voltage of the battery pack under test at the end of the pulse duration.

[0131] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0132] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A voltage prediction method based on residual polarization inversion, characterized in that, The method includes: Obtain the real-time operating status of the battery pack under test; the real-time operating status includes real-time battery voltage, battery current, battery temperature, state of charge, and battery health status; Based on the real-time operating status, the real-time residual polarization voltage of the battery pack under test is retrieved. Using the real-time residual polarization voltage as a non-zero initial state, and based on the pulse current and the pulse duration, the polarization voltage at each time point within the pulse duration is predicted. Determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, predict the terminal voltage of the battery pack under test at the end of the pulse duration.

2. The voltage prediction method based on residual polarization inversion according to claim 1, characterized in that, The step of retrieving the real-time residual polarization voltage of the battery pack under test based on its real-time operating status specifically includes: The real-time open-circuit voltage of the battery pack under test is determined based on the state of charge and battery temperature. The battery voltage and battery current are processed based on the current step event to extract the real-time ohmic internal resistance of the battery pack under test. Construct a first-order resistor-capacitor equivalent circuit model, and invert the real-time residual polarization voltage based on the first-order resistor-capacitor equivalent circuit model, open-circuit voltage, battery current, and ohmic internal resistance.

3. The voltage prediction method based on residual polarization inversion according to claim 1, characterized in that, The method of using the real-time residual polarization voltage as a non-zero initial state and predicting the polarization voltage at each time point within the pulse duration based on the pulse current and the pulse duration specifically includes: Offline parameter identification of polarization resistor and polarization capacitor is performed based on the first-order resistor-capacitor equivalent circuit model. The real-time residual polarization voltage is used as a non-zero initial state, and historical residual polarization decay information is obtained based on the polarization resistance and polarization capacitance. By determining the polarization voltage drop generated by the pulse current at each time point of the pulse duration, the newly added polarization information of the pulse can be obtained; Based on historical residual polarization decay information and pulse-added polarization information, the polarization voltage at each time point within the pulse duration is predicted.

4. The voltage prediction method based on residual polarization inversion according to claim 1, characterized in that, The process of determining the open-circuit voltage at each time point within the pulse duration, and predicting the terminal voltage of the battery pack at the end of the pulse duration based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, specifically includes: Based on the pulse current and the pulse duration, predict the state of charge at each time point within the pulse duration. Based on the state of charge at each time point and the real-time battery temperature, predict the open-circuit voltage at each time point during the pulse duration. Determine the ohmic voltage drop generated by the pulse current across the ohmic internal resistance; Based on the open-circuit voltage, ohmic voltage drop, and polarization voltage at each time point, the terminal voltage of the battery pack under test at the end of the pulse duration is predicted.

5. The voltage prediction method based on residual polarization inversion according to claim 4, characterized in that, The expression for the terminal voltage is: in, Indicates pulse current continued Terminal voltage after seconds; Indicates the pulse duration; Indicates pulse current continued Polarization voltage after seconds; Indicates pulse current continued Open circuit voltage after seconds; This represents the internal resistance of the Ohm.

6. The voltage prediction method based on residual polarization inversion according to claim 5, characterized in that, The pulse current continued The expression for the polarization voltage after one second is: in, Indicates the real-time battery voltage; This indicates the real-time state of charge and open-circuit voltage under battery temperature conditions. Indicates the real-time battery current; Represents the real-time residual polarization voltage; Indicates polarization resistance; This indicates a polarized capacitor.

7. The voltage prediction method based on residual polarization inversion according to claim 1, characterized in that, The method further includes: If the voltage at the end of the pulse duration is lower than a preset protection threshold, the voltage protection of the battery pack under test is triggered.

8. A voltage prediction device based on residual polarization inversion, characterized in that, The device includes: The data acquisition module is used to acquire the real-time operating status of the battery pack under test; the real-time operating status includes real-time battery voltage, battery current, battery temperature, state of charge, and battery health status. The polarization inversion module is used to invert the real-time residual polarization voltage of the battery pack under test based on the real-time operating status. The polarization prediction module is used to predict the polarization voltage at each time point within the pulse duration, based on the real-time residual polarization voltage as a non-zero initial state and the pulse current and the pulse duration of the pulse current. The voltage prediction module is used to determine the open-circuit voltage at each time point within the pulse duration. Based on the open-circuit voltage, the ohmic voltage drop generated by the pulse current across the ohmic internal resistance of the battery pack under test, and the polarization voltage, it predicts the terminal voltage of the battery pack under test at the end of the pulse duration.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the voltage prediction method based on residual polarization inversion as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the voltage prediction method based on residual polarization inversion as described in any one of claims 1 to 7.

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