High-precision intelligent battery remaining capacity detection method and device

Abstract

The present invention discloses a high-precision intelligent battery remaining capacity detection method and device, which relates to the field of battery capacity detection technology. The method includes: real-time acquisition of the terminal current and terminal voltage of the target battery module, judging whether the target battery module triggers a characteristic load scenario; recording the triggering moment when the characteristic load scenario meets any of a plurality of triggering conditions; analyzing the polarization voltage difference based on the triggering moment, introducing a polarization energy loss model to perform polarization loss analysis on the polarization voltage difference, and obtaining polarization loss data; obtaining the basic battery remaining capacity of the target battery module; using the polarization loss data as correction data to perform polarization loss correction on the basic battery remaining capacity, obtaining the corrected battery remaining capacity and outputting it. This method solves the technical problem that the influence of battery polarization on the remaining capacity is not fully considered in the prior art, resulting in low accuracy in battery remaining capacity detection, and achieves the technical effect of improving the accuracy of battery remaining capacity detection.

Description

Technical Field

[0001] The present invention relates to the technical field of battery capacity detection, and in particular to a high-precision intelligent battery remaining capacity detection method and device. Background Art

[0002] In battery management systems, accurate estimation of the remaining battery capacity is a key step in ensuring safe battery operation, extending battery life, and optimizing energy management strategies. Most existing detection technologies ignore the impact of battery polarization on remaining capacity detection under different load scenarios. When the battery is subjected to characteristic load scenarios such as high current charge and discharge, polarization reactions occur within the battery, generating polarization voltage and polarization energy loss. This polarization loss causes the battery's terminal voltage to change, thereby interfering with the accurate determination of the battery's remaining capacity. For example, when an electric vehicle accelerates or brakes suddenly, the battery undergoes a transient high current charge and discharge process, during which polarization is significant. If polarization loss is not taken into account, the remaining capacity detected based on traditional methods may differ significantly from the actual value. Summary of the Invention

[0003] The present application provides a high-precision intelligent battery remaining capacity detection method and device, which solves the technical problem that the existing technology does not fully consider the impact of battery polarization on the remaining capacity, resulting in low accuracy in battery remaining capacity detection.

[0004] In a first aspect of the present application, a high-precision intelligent battery remaining capacity detection method is provided, the method comprising:

[0005] The terminal current and terminal voltage of the target battery module are collected in real time to determine whether the target battery module triggers a characteristic load scenario; the triggering moment is recorded when the characteristic load scenario satisfies any one of multiple triggering conditions, wherein the multiple triggering conditions include that the instantaneous current value is greater than or equal to a preset instantaneous current threshold, the voltage drop rate is greater than or equal to a preset voltage drop rate, and the average power is greater than or equal to a preset average power; based on the triggering moment, the polarization voltage difference is analyzed, and a polarization energy loss model is introduced to perform polarization loss analysis on the polarization voltage difference to obtain polarization loss data; the basic battery remaining capacity of the target battery module is obtained according to the OCV open circuit voltage method; the polarization loss data is used as correction data to perform polarization loss correction on the basic battery remaining capacity to obtain and output the corrected battery remaining capacity.

[0006] A second aspect of the present application provides a high-precision intelligent battery remaining capacity detection device, the device comprising:

[0007] Data acquisition unit: collects the terminal current and terminal voltage of the target battery module in real time to determine whether the target battery module triggers a characteristic load scenario; trigger recording unit: records the trigger moment when the characteristic load scenario meets any of multiple trigger conditions, wherein the multiple trigger conditions include the instantaneous current value being greater than or equal to a preset instantaneous current threshold, the voltage drop rate being greater than or equal to a preset voltage drop rate, and the average power being greater than or equal to a preset average power; polarization loss analysis unit: analyzes the polarization voltage difference based on the trigger moment, introduces a polarization energy loss model to perform polarization loss analysis on the polarization voltage difference, and obtains polarization loss data; capacity acquisition unit: obtains the basic battery remaining capacity of the target battery module according to the OCV open circuit voltage method; correction unit: uses the polarization loss data as correction data to perform polarization loss correction on the basic battery remaining capacity, obtains the corrected battery remaining capacity and outputs it.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, the terminal current and terminal voltage of the target battery module are collected in real time to determine whether the target battery module triggers the characteristic load scenario; when the characteristic load scenario meets any of the multiple trigger conditions, the trigger moment is recorded, wherein the multiple trigger conditions include the instantaneous current value being greater than or equal to the preset instantaneous current threshold, the voltage drop rate being greater than or equal to the preset voltage drop rate, and the average power being greater than or equal to the preset average power. Next, the polarization voltage difference is analyzed based on the trigger moment, and a polarization energy loss model is introduced to perform polarization loss analysis on the polarization voltage difference to obtain polarization loss data. Then, the basic battery remaining capacity of the target battery module is obtained according to the OCV open circuit voltage method. Finally, the polarization loss data is used as correction data to perform polarization loss correction on the basic battery remaining capacity to obtain the corrected battery remaining capacity and output it. This solves the technical problem that the influence of battery polarization on the remaining capacity is not fully considered in the prior art, resulting in low accuracy in battery remaining capacity detection, and achieves the technical effect of improving the accuracy of battery remaining capacity detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a flow chart of a high-precision intelligent battery remaining capacity detection method provided in an embodiment of the present application;

[0012] Figure 2A graph showing the terminal voltage changing over time provided in an embodiment of the present application;

[0013] Figure 3 A diagram showing battery power deviation and energy loss area during the polarization phase provided in an embodiment of the present application;

[0014] Figure 4 This is a schematic diagram of the structure of a high-precision intelligent battery remaining capacity detection device provided in an embodiment of the present application.

[0015] Description of reference numerals: data acquisition unit 11 , trigger recording unit 12 , polarization loss analysis unit 13 , capacity acquisition unit 14 , correction unit 15 . DETAILED DESCRIPTION

[0016] The present application solves the technical problem that the prior art fails to fully consider the influence of battery polarization on the remaining capacity, resulting in low accuracy in battery remaining capacity detection, by providing a high-precision intelligent battery remaining capacity detection method and device.

[0017] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0018] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0019] Example 1, as Figure 1 As shown, the present application provides a high-precision intelligent battery remaining capacity detection method, wherein the method includes:

[0020] The terminal current and terminal voltage of the target battery module are collected in real time to determine whether the target battery module triggers a characteristic load scenario.

[0021] By configuring current sensors and voltage sensors at both ends of the target battery module, the terminal current (i.e., the current value at the positive and negative ends of the battery) and terminal voltage (i.e., the voltage value at both ends of the battery) of the target battery module during operation are continuously sampled. The sampling period can be set to milliseconds to ensure the real-time and integrity of the data.

[0022] Based on the collected current and voltage data and the preset conditions for triggering characteristic load scenarios, it is determined whether the target battery module triggers the characteristic load scenario. The characteristic load scenario refers to the polarization effect that the battery will produce under a specific battery operating state. Figure 2 As shown in the figure, in the initial stage (0~100s): the battery is in a stable working state and the terminal voltage remains stable; in the high load triggering stage (100s): the terminal voltage drops significantly and forms a polarization state; in the recovery stage (100~300s): the terminal voltage slowly recovers to a near steady state.

[0023] The triggering moment is recorded when the characteristic load scenario satisfies any one of multiple triggering conditions, wherein the multiple triggering conditions include the instantaneous current value being greater than or equal to a preset instantaneous current threshold, the voltage drop rate being greater than or equal to a preset voltage drop rate, and the average power being greater than or equal to a preset average power.

[0024] In an embodiment of the present application, the trigger judgment of the characteristic load scenario is based on multiple preset trigger conditions, specifically including: when the instantaneous current value at any moment is greater than or equal to the preset instantaneous current threshold (the threshold is set according to the normal working range of the battery), it is determined that the characteristic load scenario trigger condition is met; based on two consecutive sets of voltage sampling points, the voltage change rate per unit time (i.e., ΔU / Δt) is calculated, and when the voltage drop rate is greater than or equal to the preset voltage drop rate threshold (for example, set to 10mV / ms), it is determined that the characteristic load scenario trigger condition is met; within the set time sliding window (for example, 200ms), the average power value of the current interval is calculated, and the formula is When the average power is greater than or equal to a preset average power threshold (such as 80% of the rated power), it is determined that the characteristic load scenario trigger condition is met, where: Indicates the average power value, n indicates the number of sampling points in the sliding window, that is, the total number of sampling times within the set time window (such as 200ms), Represents the voltage value corresponding to the i-th sampling point, that is, the terminal voltage, Represents the current value corresponding to the i-th sampling point, that is, the terminal current.

[0025] When any trigger condition is met, the current moment is determined to be the characteristic load scenario triggering moment, and the triggering timestamp is recorded as the starting time point of the polarization behavior.

[0026] Based on the analysis of the polarization voltage difference at the triggering moment, a polarization energy loss model is introduced to perform polarization loss analysis on the polarization voltage difference to obtain polarization loss data.

[0027] In an embodiment of the present application, based on the recorded trigger moment, the system enters the polarization behavior analysis phase. Specifically, starting from the trigger moment, the terminal voltage and terminal current data of the target battery module within a specific time window are continuously collected to identify the dynamic response of the polarization interval. The system extracts the real-time voltage data within the polarization interval, and combines the operating status information of the target battery module (such as temperature, discharge rate, historical state of charge, etc.) and inputs it into the modeled internal resistance voltage theoretical model to calculate the theoretical voltage value at the corresponding moment. The theoretical voltage is not affected by polarization disturbances and can reflect the voltage response of the battery under ideal conditions. The system compares the real-time voltage with the theoretical voltage, calculates the difference, and records it as the polarization voltage difference. The difference reflects the transient voltage deviation caused by polarization and has time dynamics.

[0028] The system introduces a polarization energy loss model to quantitatively assess energy loss caused by polarization. The polarization energy loss model uses the polarization voltage difference and synchronously collected current data as inputs to calculate the actual output power and theoretical output power at the corresponding moment, and constructs a battery output power deviation curve based on this. The battery output power deviation curve accurately displays the battery's energy loss under characteristic load conditions, especially the loss caused by polarization. The system integrates the battery output power deviation curve within the polarization range to calculate the polarization energy loss.

[0029] Furthermore, the method of analyzing the polarization voltage difference based on the triggering moment includes:

[0030] Based on the trigger moment, the terminal current and terminal voltage of the target battery module are continuously collected to extract the real-time voltage in the polarization range; the temperature, discharge rate and historical charging state of the target battery module are obtained; the temperature, discharge rate and historical charging state of the target battery module are calculated using an internal resistance model to obtain a theoretical voltage; and the real-time voltage and the theoretical voltage are compared to output the polarization voltage difference.

[0031] From the moment of triggering, the system continuously samples the terminal current and terminal voltage of the target battery module at fixed intervals (e.g., every 10ms). This sampling period covers the entire polarization interval, from the moment the load changes until the battery output returns to a stable state. Within this polarization interval, the real-time voltage is extracted.

[0032] The system retrieves parameter information related to the current operating status of the target battery module, including but not limited to: current battery temperature (monitored in real time by the temperature sensor), current discharge rate (obtained by the ratio of current current to battery nominal capacity), and historical charging status information (such as the time of the most recent full charge, remaining SOC, etc.), to construct the battery current state vector.

[0033] Using an internal resistance model (such as a combined thermal-electric model or an extended Thevenin model), the above state vector is used as the input variable, and combined with battery manufacturing parameters and electrochemical characteristics, the theoretical voltage value that the target battery module should present in the current discharge state in the absence of polarization interference is calculated. This voltage value can be output through modeling simulation or data fitting.

[0034] The system compares the real-time voltage within the polarization range with the theoretical voltage at the corresponding moment, calculates the difference between the two, and obtains the polarization voltage difference that reflects the degree of battery polarization.

[0035] Furthermore, the polarization interval is the sum of the polarization triggering moment and the time required for polarization recovery. Calculating the time required for polarization recovery includes:

[0036] Based on the trigger moment, the terminal current and terminal voltage of the target battery module are continuously collected to calculate the current change rate and voltage change rate; when the absolute value of any one of the current change rate and voltage change rate is lower than the preset threshold and meets the preset duration, the time required for polarization recovery is output.

[0037] The polarization interval is the complete period from the moment the characteristic load is triggered to the moment the battery output parameters return to stability. This means the polarization interval consists of two key moments: the polarization trigger moment and the end moment, defined by the calculated time required for polarization recovery.

[0038] Specifically, after the trigger moment, the system continuously collects the terminal current and terminal voltage of the target battery module; for each set of continuous sampling data, the current change rate and voltage change rate are calculated to determine whether the battery output has stably recovered from the polarization disturbance.

[0039] The system presets a set of voltage and current rate-of-change thresholds, as well as a minimum stable duration parameter. When the absolute value of the current or voltage rate-of-change falls below the set thresholds, and this stable state persists for a certain period of time, such as several seconds, the system determines that the polarization process has ended, meaning the battery has recovered from its polarized state. At this point, the system outputs the time elapsed from the trigger moment to the current moment as the polarization recovery time.

[0040] Furthermore, a polarization energy loss model is introduced to perform polarization loss analysis on the polarization voltage difference to obtain polarization loss data, and the method includes:

[0041] The polarization voltage difference and the terminal current are input into the polarization energy loss model for power calculation, and real-time power and theoretical power are output; a battery output power deviation curve is constructed according to the real-time power and theoretical power; and the battery output power deviation curve is integrated in the polarization range to output polarization loss data.

[0042] The system uses the polarization voltage difference and the collected terminal current as input data and feeds it into the polarization energy loss model for processing. The polarization energy loss model analyzes the relationship between the voltage difference and current to calculate the battery's actual output power in the current polarization state and its theoretical output power in the absence of polarization. Based on the difference between actual and theoretical power, the system constructs a curve representing the power deviation trend—the battery output power deviation curve. This curve reflects the energy deviation generated by the battery at each moment under polarization.

[0043] After constructing the battery output power deviation curve, in order to further quantify the energy loss caused by polarization behavior, the system uses the polarization interval as the integration time range and integrates the power deviation curve to obtain the polarization energy loss value.

[0044] Specifically, the starting time of the polarization interval is set as the polarization trigger time t0, and the ending time is set as the polarization recovery time t1. During this time period, the real-time output power is recorded as , the theoretical output power is recorded as , then the polarization loss power is the difference between the two, that is, .

[0045] Furthermore, the polarization loss data, i.e. the polarization energy loss It can be calculated by the following formula: .

[0046] Exemplarily, as shown in Table 1, after integrating the battery output power deviation curve, the polarization energy loss value is obtained.

[0047] Table 1: Polarization loss data obtained based on the integration of the power deviation curve

[0048]

[0049] Furthermore, a polarization energy loss model is introduced to perform polarization loss analysis on the polarization voltage difference to obtain polarization loss data. The method further includes:

[0050] The temperature correction factor and the SOH correction factor of the target battery module are collected, and aging loss data is output according to the temperature correction factor and the SOH correction factor; and the polarization loss data is updated according to the aging loss data.

[0051] In order to improve the accuracy of polarization loss analysis, based on the introduction of the polarization energy loss model, the long-term impact of battery operating temperature and state of health (SOH) on polarization behavior is further considered.

[0052] Preferably, after detecting polarization behavior and outputting preliminary polarization loss data, the system collects the current operating temperature of the target battery module and generates a corresponding temperature correction factor based on an empirical mapping relationship between temperature and polarization effects. Simultaneously, the system also uses the state-of-health (SOH) parameters output by the battery life management module, combining historical usage records with aging models to generate an SOH (battery state of health) correction factor to describe degradation factors such as capacity fade and internal resistance increase. The temperature correction factor reflects the impact of the battery's current environment or operating temperature on polarization characteristics, while the SOH correction factor reflects the aging effects of the battery over long-term use due to factors such as capacity fade and internal resistance increase.

[0053] The system inputs the temperature correction factor and the SOH correction factor into the aging loss analysis module. Based on the nonlinear coupling relationship between polarization loss, temperature, and SOH, it calculates the additional energy loss caused by aging and temperature factors under the current operating conditions and outputs the aging loss data. The system updates the polarization loss data based on the aging loss data.

[0054] For example, the energy loss of a battery during the polarization phase is as follows: Figure 3 As shown, Figure 3 It is a graph of battery power deviation and energy loss area during the polarization stage. Figure 3 In the figure, the actual power curve decreases with time and gradually approaches a steady state. The gray shaded area represents the integral of the polarization loss power difference, that is, the polarization energy loss. The gray shaded area is converted into battery capacity deviation through an equivalent model for correction.

[0055] The remaining capacity of the target battery module is obtained according to the open circuit voltage (OCV) method.

[0056] Furthermore, the remaining basic battery capacity of the target battery module is obtained according to the open circuit voltage (OCV) method, and the method includes:

[0057] Detect whether the target battery module meets the open circuit state identification condition; when the target battery module meets the open circuit state identification condition, collect the open circuit voltage value; map the open circuit voltage value according to the open circuit voltage-remaining capacity mapping curve, and output the calculated basic battery remaining capacity.

[0058] Specifically, the system continuously collects terminal current and terminal voltage signals from the target battery module and identifies whether the battery is in an open circuit state based on pre-set judgment conditions. The open circuit identification conditions include the battery module's terminal current value being lower than the set quiescent current threshold for a continuous preset time period, and the terminal voltage change rate being lower than the set voltage fluctuation threshold. When both conditions are met, the battery is determined to be in a static state, i.e., an open circuit state.

[0059] When the system detects that the target battery module meets the open-circuit state identification criteria, it triggers a voltage acquisition operation, collecting the terminal voltage value at that time and using it as the open-circuit voltage input value. The system calls the OCV-SOC mapping curve stored locally or in a backend database. This mapping curve is calibrated under standard test conditions based on the target battery type. It has good monotonicity and mappability, and can reflect the static voltage values ​​corresponding to different SOC (remaining capacity percentage). The system uses the collected open-circuit voltage value as the input value and performs a search and interpolation operation on this OCV-SOC mapping curve to ultimately obtain the basic remaining capacity value corresponding to the current target battery module.

[0060] Furthermore, when the target battery module does not trigger a characteristic load scenario, the basic battery remaining capacity of the target battery module is obtained according to an OCV open circuit voltage method; and the basic battery remaining capacity is output as a battery remaining capacity detection result.

[0061] If the system determines that the target battery module has not triggered any characteristic load conditions—that is, there are no significant high-dynamic load behaviors such as current surges, voltage drops, or power surges—the system assumes that the battery is in a relatively stable operating state. At this point, the system directly uses the OCV (open-circuit voltage) method to perform a basic estimate of the target battery module's remaining capacity. Specifically, this involves first determining whether the target battery module meets the open-circuit state condition. If so, the current terminal voltage is collected as the open-circuit voltage. Then, based on the OCV-SOC mapping curve corresponding to the battery type, the open-circuit voltage is mapped to the current battery remaining capacity percentage, i.e., the basic battery remaining capacity. Finally, the system directly outputs this basic battery remaining capacity as the current battery remaining capacity test result.

[0062] The polarization loss data is used as correction data to perform polarization loss correction on the basic battery remaining capacity, and a corrected battery remaining capacity is obtained and output.

[0063] Based on the battery rated capacity and battery rated voltage of the target battery module, an equivalent conversion model is constructed to convert the polarization loss data into an equivalent capacity error value. This capacity error is used as a correction factor to correct the basic battery remaining capacity obtained by the OCV open circuit voltage method, thereby obtaining a more accurate corrected battery remaining capacity and outputting it.

[0064] Furthermore, the polarization loss data is used as correction data to perform polarization loss correction on the remaining capacity of the basic battery, and the method includes:

[0065] Obtain the battery rated capacity and battery rated voltage of the target battery module; define an equivalent conversion model based on the battery rated capacity and battery rated voltage, use the equivalent conversion model to perform an equivalent conversion of the polarization loss data to the battery remaining capacity error, and output the battery remaining capacity error; use the battery remaining capacity error as correction data to perform polarization loss correction on the basic battery remaining capacity.

[0066] Preferably, key calibration parameters of the target battery module are obtained, including the battery rated capacity and the battery rated voltage, wherein the battery rated capacity is usually expressed in ampere-hours (Ah), reflecting the maximum charge and discharge capacity of the battery under ideal working conditions; the battery rated voltage is usually expressed in volts (V), which is the basis for constructing the power conversion relationship.

[0067] Based on the battery's rated capacity and rated voltage, the system defines an equivalent conversion model to convert polarization loss data into a capacity error value. This model uses the product of the battery's rated capacity and rated voltage as the energy calibration benchmark (i.e., rated energy = rated capacity × rated voltage). It proportionally maps the energy loss during polarization to the capacity loss domain, ensuring that the converted capacity error has actual physical meaning. The unit is typically ampere-hours (Ah) or percentage of capacity (%). After the conversion is complete, the system uses the resulting battery remaining capacity error as correction data to perform deviation correction on the base battery remaining capacity. Based on the sign and magnitude of the capacity error, the base remaining capacity is deducted or compensated accordingly to generate the final corrected battery remaining capacity.

[0068] Furthermore, defining an equivalent conversion model according to the battery rated capacity and the battery rated voltage includes obtaining the product of the battery rated capacity and the battery rated voltage, and establishing a ratio of the polarization loss data to the product to obtain an equivalent conversion model.

[0069] The battery's rated capacity represents the total available charge under rated operating conditions (measured in Ah), while the battery's rated voltage represents the energy equivalent of this capacity in the voltage dimension (measured in V). The system calculates the rated total energy of the battery module by multiplying the rated capacity by the rated voltage, typically expressed in watt-hours (Wh). This rated energy serves as the battery's standard energy reference and is used in ratio calculations with actual polarization loss data.

[0070] The system performs a ratio calculation between the collected polarization loss data (expressed in energy form) and the above-mentioned rated total energy to construct an equivalent conversion model. This model can map any polarization loss energy to the corresponding capacity loss value, that is, capacity error (Ah) = polarization loss data (Wh) ÷ battery rated voltage (V), or capacity error percentage (%) = polarization loss data (Wh) ÷ (rated capacity × rated voltage) × 100%.

[0071] In summary, the embodiments of the present application have at least the following technical effects:

[0072] First, the terminal current and terminal voltage of the target battery module are collected in real time to determine whether the target battery module triggers the characteristic load scenario; when the characteristic load scenario meets any of the multiple trigger conditions, the trigger moment is recorded, wherein the multiple trigger conditions include the instantaneous current value being greater than or equal to the preset instantaneous current threshold, the voltage drop rate being greater than or equal to the preset voltage drop rate, and the average power being greater than or equal to the preset average power. Next, the polarization voltage difference is analyzed based on the trigger moment, and a polarization energy loss model is introduced to perform polarization loss analysis on the polarization voltage difference to obtain polarization loss data. Then, the basic battery remaining capacity of the target battery module is obtained according to the OCV open circuit voltage method. Finally, the polarization loss data is used as correction data to perform polarization loss correction on the basic battery remaining capacity to obtain the corrected battery remaining capacity and output it. This solves the technical problem that the influence of battery polarization on the remaining capacity is not fully considered in the prior art, resulting in low accuracy in battery remaining capacity detection, and achieves the technical effect of improving the accuracy of battery remaining capacity detection.

[0073] The second embodiment is based on the same inventive concept as the high-precision intelligent battery remaining capacity detection method in the above embodiment. Figure 4 As shown, the present application provides a high-precision intelligent battery remaining capacity detection device, wherein the device includes:

[0074] The data acquisition unit 11 collects the terminal current and terminal voltage of the target battery module in real time to determine whether the target battery module triggers a characteristic load scenario; the trigger recording unit 12 records the trigger moment when the characteristic load scenario meets any of the multiple trigger conditions, wherein the multiple trigger conditions include the instantaneous current value being greater than or equal to the preset instantaneous current threshold, the voltage drop rate being greater than or equal to the preset voltage drop rate, and the average power being greater than or equal to the preset average power; the polarization loss analysis unit 13 analyzes the polarization voltage difference based on the trigger moment, introduces a polarization energy loss model to perform polarization loss analysis on the polarization voltage difference, and obtains polarization loss data; the capacity acquisition unit 14 obtains the basic battery remaining capacity of the target battery module according to the OCV open circuit voltage method; the correction unit 15 uses the polarization loss data as correction data to perform polarization loss correction on the basic battery remaining capacity, obtains the corrected battery remaining capacity and outputs it.

[0075] Furthermore, the polarization loss analysis unit 13 is configured to perform the following method:

[0076] Based on the trigger moment, the terminal current and terminal voltage of the target battery module are continuously collected to extract the real-time voltage in the polarization range; the temperature, discharge rate and historical charging state of the target battery module are obtained; the temperature, discharge rate and historical charging state of the target battery module are calculated using an internal resistance model to obtain a theoretical voltage; and the real-time voltage and the theoretical voltage are compared to output the polarization voltage difference.

[0077] Furthermore, the polarization loss analysis unit 13 is configured to perform the following method:

[0078] The polarization voltage difference and the terminal current are input into the polarization energy loss model for power calculation, and real-time power and theoretical power are output; a battery output power deviation curve is constructed according to the real-time power and theoretical power; and the battery output power deviation curve is integrated in the polarization range to output polarization loss data.

[0079] Furthermore, the polarization loss analysis unit 13 is configured to perform the following method:

[0080] Based on the trigger moment, the terminal current and terminal voltage of the target battery module are continuously collected to calculate the current change rate and voltage change rate; when the absolute value of any one of the current change rate and voltage change rate is lower than the preset threshold and meets the preset duration, the time required for polarization recovery is output.

[0081] Furthermore, the correction unit 15 is configured to perform the following method:

[0082] Obtain the battery rated capacity and battery rated voltage of the target battery module; define an equivalent conversion model based on the battery rated capacity and battery rated voltage, use the equivalent conversion model to perform an equivalent conversion of the polarization loss data to the battery remaining capacity error, and output the battery remaining capacity error; use the battery remaining capacity error as correction data to perform polarization loss correction on the basic battery remaining capacity.

[0083] Furthermore, the correction unit 15 is configured to perform the following method:

[0084] Defining an equivalent conversion model according to the battery rated capacity and the battery rated voltage includes obtaining the product of the battery rated capacity and the battery rated voltage, and establishing a ratio of the polarization loss data to the product to obtain an equivalent conversion model.

[0085] Furthermore, the capacity acquisition unit 14 is configured to execute the following method:

[0086] Detect whether the target battery module meets the open circuit state identification condition; when the target battery module meets the open circuit state identification condition, collect the open circuit voltage value; map the open circuit voltage value according to the open circuit voltage-remaining capacity mapping curve, and output the calculated basic battery remaining capacity.

[0087] Furthermore, the capacity acquisition unit 14 is configured to execute the following method:

[0088] When the target battery module does not trigger the characteristic load scenario, the basic battery remaining capacity of the target battery module is obtained according to the OCV open circuit voltage method; and the basic battery remaining capacity is output as the battery remaining capacity detection result.

[0089] Furthermore, the polarization loss analysis unit 13 is configured to perform the following method:

[0090] The temperature correction factor and the SOH correction factor of the target battery module are collected, and aging loss data is output according to the temperature correction factor and the SOH correction factor; and the polarization loss data is updated according to the aging loss data.

[0091] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0093] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A high-precision intelligent battery remaining capacity detection method, characterized in that: The method comprises: Collect the terminal current and terminal voltage of the target battery module in real time to determine whether the target battery module triggers a characteristic load scenario; When the characteristic load scenario satisfies any one of a plurality of trigger conditions, a triggering time is recorded, wherein the plurality of trigger conditions include a current instantaneous value being greater than or equal to a preset current instantaneous threshold, a voltage drop rate being greater than or equal to a preset voltage drop rate, and an average power being greater than or equal to a preset average power; Analyzing the polarization voltage difference based on the triggering moment, introducing a polarization energy loss model to perform polarization loss analysis on the polarization voltage difference to obtain polarization loss data; Obtaining the remaining capacity of the target battery module based on the open circuit voltage (OCV) method; Using the polarization loss data as correction data to perform polarization loss correction on the basic battery remaining capacity, obtaining and outputting a corrected battery remaining capacity; Analyzing the polarization voltage difference based on the triggering moment, the method includes: Continuously collecting the terminal current and terminal voltage of the target battery module based on the triggering moment, and extracting the real-time voltage in the polarization interval; Obtaining the temperature, discharge rate, and historical charge state of the target battery module; Calculating the temperature, discharge rate, and historical state of charge of the target battery module using an internal resistance model to obtain a theoretical voltage; Comparing the real-time voltage with the theoretical voltage, outputting a polarization voltage difference; A polarization energy loss model is introduced to perform polarization loss analysis on the polarization voltage difference to obtain polarization loss data, and the method includes: Inputting the polarization voltage difference and the terminal current into the polarization energy loss model to perform power calculation, and outputting real-time power and theoretical power; Constructing a battery output power deviation curve according to the real-time power and theoretical power; The battery output power deviation curve is integrated in the polarization interval to output polarization loss data.

2. The high-precision intelligent battery remaining capacity detection method according to claim 1, characterized in that: The polarization interval is the sum of the polarization triggering moment and the time required for polarization recovery. Calculating the time required for polarization recovery includes: Continuously collecting the terminal current and terminal voltage of the target battery module based on the triggering moment, and calculating the current change rate and voltage change rate; When the absolute value of any one of the current change rate and the voltage change rate is lower than a preset threshold and meets a preset duration, the time required for polarization recovery is output.

3. The high-precision intelligent battery remaining capacity detection method according to claim 1, characterized in that: Using the polarization loss data as correction data to perform polarization loss correction on the remaining capacity of the basic battery, the method includes: Obtaining the rated battery capacity and rated battery voltage of the target battery module; defining an equivalent conversion model according to the battery rated capacity and the battery rated voltage, performing an equivalent conversion of the polarization loss data into a battery remaining capacity error using the equivalent conversion model, and outputting the battery remaining capacity error; The battery remaining capacity error is used as correction data to perform polarization loss correction on the basic battery remaining capacity.

4. The high-precision intelligent battery remaining capacity detection method according to claim 3, characterized in that: Defining an equivalent conversion model according to the battery rated capacity and the battery rated voltage includes obtaining the product of the battery rated capacity and the battery rated voltage, and establishing a ratio of the polarization loss data to the product to obtain an equivalent conversion model.

5. The high-precision intelligent battery remaining capacity detection method according to claim 1, characterized in that: Obtaining the remaining basic battery capacity of the target battery module according to the OCV open circuit voltage method, the method comprising: Detecting whether the target battery module meets the open circuit state identification condition; When the target battery module meets the open circuit state identification condition, collecting the open circuit voltage value; The open circuit voltage value is mapped according to an open circuit voltage-remaining capacity mapping curve, and the calculated basic battery remaining capacity is output.

6. The high-precision intelligent battery remaining capacity detection method according to claim 5, characterized in that: When the target battery module does not trigger the characteristic load scenario, obtaining the basic battery remaining capacity of the target battery module according to the OCV open circuit voltage method; The basic battery remaining capacity is output as a battery remaining capacity detection result.

7. The high-precision intelligent battery remaining capacity detection method according to claim 1, characterized in that: Introducing a polarization energy loss model to perform polarization loss analysis on the polarization voltage difference to obtain polarization loss data, the method further comprising: collecting a temperature correction factor and a SOH correction factor of the target battery module, and outputting aging loss data according to the temperature correction factor and the SOH correction factor; The polarization loss data is updated according to the aging loss data.

8. High-precision intelligent battery remaining capacity detection device, characterized in that: The device is used to implement the high-precision intelligent battery remaining capacity detection method according to any one of claims 1 to 7, comprising: Data acquisition unit: collects the terminal current and terminal voltage of the target battery module in real time to determine whether the target battery module triggers a characteristic load scenario; A trigger recording unit: recording a trigger moment when the characteristic load scenario satisfies any one of a plurality of trigger conditions, wherein the plurality of trigger conditions include a current instantaneous value being greater than or equal to a preset current instantaneous threshold, a voltage drop rate being greater than or equal to a preset voltage drop rate, and an average power being greater than or equal to a preset average power; Polarization loss analysis unit: analyzing the polarization voltage difference based on the triggering moment, introducing a polarization energy loss model to perform polarization loss analysis on the polarization voltage difference, and obtaining polarization loss data; The capacity acquisition unit acquires the basic battery remaining capacity of the target battery module according to the OCV open circuit voltage method; the correction unit uses the polarization loss data as correction data to perform polarization loss correction on the basic battery remaining capacity, obtains the corrected battery remaining capacity and outputs it.

Images