Battery health automatic correction method, device, system and vehicle

Abstract

The embodiment of the present application relates to the technical field of battery management system, and discloses a battery health degree automatic correction method, device, system and vehicle, comprising: reading a preset hardware state identification flag bit when the vehicle is powered on, judging whether the storage value of the flag bit is a preset valid identification, if the value of the flag bit is not the preset valid identification, it is determined that the vehicle is in an after-sales scene, comparing the current cumulative charging capacity of the battery with the rated capacity of the battery, and determining whether there is a battery health degree distortion problem caused by hardware replacement or storage data invalidation based on the comparison result; if it is identified that there is a battery health degree distortion problem, vehicle mileage data is obtained, and a battery health degree correction value is obtained by combining a preset driving mileage and a corresponding relationship table of the battery health degree, and the battery health degree is corrected based on the battery health degree correction value. The present application can solve the technical problems of high cost, low efficiency, poor battery operation safety and poor user experience in the prior art.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, and specifically to a method, device, system, and vehicle for automatically correcting battery health. Background Technology

[0002] With the rapid development of new energy technologies, batteries, as the core energy storage unit of new energy vehicles, directly affect the safety, operating efficiency, and economic efficiency of the entire vehicle system. Battery State of Health (SOH) is a key indicator for measuring the degree of battery performance degradation, typically defined as the ratio of the battery's current maximum usable capacity to its initial rated capacity. Accurate SOH assessment is of paramount importance for the full lifecycle management of batteries.

[0003] When a battery maintains a high State of Health (SOH), its capacity decay rate is low and its internal resistance parameters are stable, ensuring that the vehicle and related systems meet design range standards and rated output power. Simultaneously, a battery with high SOH operates within its efficient range, exhibiting excellent energy conversion efficiency and an extremely low risk of thermal runaway, effectively maintaining the operational stability of the entire vehicle system and extending the battery pack equalization maintenance cycle. Conversely, a decrease in battery SOH can trigger a series of negative issues, severely impacting the performance and safety of the battery and the entire vehicle. Therefore, establishing a precise battery SOH monitoring and early warning correction mechanism is a core requirement for ensuring the safe, efficient, and economical operation of new energy vehicles.

[0004] Currently, in vehicle after-sales maintenance scenarios involving battery pack replacement and Battery Management System (BMS) replacement, existing vehicle State of Health (SOH) correction schemes have significant shortcomings. Conventional vehicle SOH correction mechanisms primarily rely on the vehicle's cumulative charging capacity and battery life for real-time correction. However, after BMS replacement during after-sales service, the original stored data within the battery is lost, and the cumulative charging capacity data is reset to zero. The original SOH correction strategy cannot be triggered normally, and the vehicle system defaults to a battery SOH of 100%. This mechanism can cause the displayed State of Health (SOH) of the vehicle to be much higher than the actual SOH of the battery, leading to numerous potential problems: In terms of performance and safety, the actual discharge capacity of an aging battery has significantly decreased. If the system misjudges the battery as brand new, it will output power according to the standard for a high-healthy battery. For example, a battery that actually only supports 60kW discharge may be controlled by the system to discharge at 80kW. This can easily cause the battery's undervoltage protection strategy to be falsely triggered at the end of the discharge, further accelerating battery life degradation. In severe cases, it can lead to battery thermal runaway, and the system cannot promptly alert the user to avoid danger, posing a significant safety hazard. In terms of economy and user experience, currently, the only solution to the above-mentioned data anomalies is for professional repair personnel to manually test the battery's capacity and calibrate its SOH at the store. After-sales repair scenarios are numerous, cover a wide range, and have uncertain frequency, which not only significantly increases the cost of manual maintenance and time but also causes excessively long waiting times for user repairs, affecting the user's driving experience.

[0005] In summary, there is an urgent need to design an automatic battery health correction method, device, system, and vehicle that is suitable for after-sales BMS replacement and battery pack replacement scenarios. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the purpose of this application is to provide a method, device, system and vehicle for automatic correction of battery health, so as to solve the technical problems of high cost, low efficiency, poor battery operation safety and poor user experience of manual calibration in the prior art.

[0007] In a first aspect, embodiments of this application provide a method for automatically correcting battery health, comprising the following steps:

[0008] When the vehicle is powered on, the preset hardware status identification flag is read, and it is determined whether the stored value of the flag is a preset valid identifier. If the value of the flag is not a preset valid identifier, the vehicle is determined to be in an after-sales scenario. The current cumulative charging capacity of the battery is compared with the rated capacity of the battery, and based on the comparison result, it is determined whether there is a problem of battery health distortion caused by hardware replacement or storage data failure.

[0009] If a battery health distortion problem is detected, the vehicle mileage data is obtained, and a battery health correction value is obtained by combining it with a preset correspondence table between mileage and battery health. The battery health is then corrected based on the battery health correction value.

[0010] In the above technical solution, this solution defines the core logic for automatic identification and correction of battery health in after-sales scenarios. It identifies after-sales repair scenarios through hardware status flags and accurately determines the health distortion caused by battery hardware replacement and storage data failure by comparing the cumulative charging capacity with the rated capacity. This solves the industry pain points of existing technologies where data is cleared after replacing the battery management system or battery pack, the system defaults to 100% battery health, and the health assessment is inflated and distorted. It avoids the problem of battery over-power discharge caused by misjudgment of battery health from the root, effectively reduces the safety risks of battery undervoltage faults and thermal runaway, and can complete the anomaly identification without manual intervention, which greatly reduces the operation and maintenance costs of manual calibration in after-sales.

[0011] One possible implementation involves determining, based on the comparison results, whether there is a problem with battery health distortion caused by hardware replacement or storage data failure, specifically:

[0012] If the cumulative charging capacity is greater than the battery's rated capacity, the stored data is considered normal and the battery health is considered to match the actual battery status; otherwise, it is considered that there is a problem with the battery health status due to hardware replacement or storage data failure.

[0013] In the above technical solution, this solution specifically defines the judgment logic of battery health distortion, and clearly uses the comparison result of the cumulative charging capacity and the battery rated capacity as the judgment basis. It accurately distinguishes between normal battery aging and degradation and abnormal health scenarios caused by hardware replacement and data failure. It solves the defects of existing correction mechanisms that cannot accurately identify after-sales hardware replacement failure scenarios and are prone to wrong correction or no correction. It improves the accuracy of after-sales abnormal scenario identification and avoids the problem of normal batteries being wrongly corrected and abnormal batteries not being corrected.

[0014] One possible implementation includes, after obtaining the battery health correction value based on the vehicle mileage data and correcting the battery health, the following steps are also taken:

[0015] Update battery operating data and save it after power failure. After power is restored, check whether the current cumulative charging capacity is greater than the preset battery capacity. If so, complete the battery health correction; otherwise, automatically correct the battery health again.

[0016] In the above technical solution, this solution defines the closed-loop logic of data storage and cyclic verification after battery health correction. By saving the running data after power failure and verifying the correction results again after power failure, the iterative calibration of battery health correction is realized, which solves the problems of insufficient single correction accuracy, easy data loss and unstable correction results in the existing technology.

[0017] One possible implementation is that if the value of the flag bit is a preset valid identifier, it is determined that the vehicle is not in an after-sales scenario. The vehicle network time is obtained, the actual service life of the battery is determined based on the vehicle network time, and a battery health correction value is obtained by combining the preset correspondence table between service life and battery health. The battery health is then corrected based on the battery health correction value.

[0018] In the above technical solution, this solution limits the battery health correction method based on the service life in non-after-sales scenarios. In normal use scenarios where the battery hardware has not been replaced and the data is normal, the battery service life is counted based on the vehicle network connection time and the corresponding health is matched.

[0019] One possible implementation is to determine that the vehicle is not in an after-sales service scenario if the value of the flag bit is a preset valid identifier.

[0020] Obtain the vehicle network connection time, determine the actual service life of the battery based on the vehicle network connection time, and obtain the battery health correction value one by combining the preset correspondence table between service life and battery health.

[0021] Obtain the cumulative charging capacity and, in conjunction with the preset correspondence table between cumulative charging capacity and battery health, obtain the second battery health correction value.

[0022] Obtain vehicle mileage data and combine it with a preset table of correspondence between mileage and battery health to obtain battery health correction value three;

[0023] The minimum value among the battery health correction value one, battery health correction value two, and battery health correction value three is selected as the final battery health correction value, and the battery health is corrected based on the battery health correction value.

[0024] In the above technical solution, this solution defines a health correction strategy that integrates multiple parameters. By using three sets of parameters—years, cumulative charging capacity, and driving mileage—to obtain correction values ​​and taking the minimum value as the final result, a multi-dimensional and comprehensive battery degradation status assessment is achieved. This solves the problem that the single assessment dimension of the existing technology is easily affected by vehicle operating conditions and has large deviations in health assessment. It significantly improves the accuracy and reliability of battery health assessment under all operating conditions and effectively avoids misjudgment of health caused by abnormal local operating conditions.

[0025] One possible implementation is that after the battery is installed in the vehicle, when the vehicle data is acquired for the first time and the time information transmitted by the cockpit domain controller is received, the time information is recorded as the initial time and stored; each time the vehicle is powered on and running, the actual service life of the battery is calculated based on the acquired vehicle network time and the initial time.

[0026] In the above technical solution, this solution defines the method for calculating the initial time calibration and service life of the battery. The initial installation time is recorded by the first network connection data of the whole vehicle, and the service life of the battery is dynamically calculated based on the real-time network time difference.

[0027] In one possible implementation, the battery health value of the non-calibrated nodes in the correspondence table is calculated using a linear interpolation algorithm.

[0028] In the above technical solution, this solution limits the numerical calculation method of linear interpolation, and performs accurate calculations for non-calibrated working condition nodes in the corresponding relationship table. This solves the defects of existing calibration parameters being only discrete values, the inability to accurately match intermediate working condition health, and large evaluation errors. It achieves continuous and smooth correction of battery health throughout the entire battery life cycle and under all usage conditions, significantly reduces battery health evaluation errors, and accurately matches the real-time degradation state of the battery.

[0029] Secondly, the present invention provides an automatic battery health correction device, comprising a memory and a controller. The memory stores a computer-readable program, which, when invoked by the controller, can execute the steps of the automatic battery health correction method as described in the present invention.

[0030] In the above technical solution, this solution provides hardware support for fully automatic, human-intervention-free health correction logic by storing programs in memory and calling and executing correction methods in the controller. It solves the shortcomings of existing vehicle electronic control devices that lack autonomous correction functions and require external diagnostic equipment for manual calibration in fault scenarios. It enables the vehicle to complete after-sales anomaly identification and health correction locally, improves correction efficiency, reduces after-sales maintenance workload, and optimizes the user's vehicle experience.

[0031] Thirdly, the battery health automatic correction system of the present invention implements the battery health automatic correction method as described in the present invention, including an in-vehicle network terminal, a cockpit domain controller, a vehicle controller and a battery management system.

[0032] The vehicle-mounted connected terminal is used to obtain the real-time network connection time and output it to the cockpit domain controller.

[0033] The cockpit domain controller is used to forward the real-time network connection time;

[0034] The vehicle controller is used to collect and output vehicle mileage data;

[0035] The battery management system is used to store various battery health status correspondence tables, identify after-sales scenarios, determine data anomalies, and automatically correct battery health status.

[0036] In the above technical solution, this solution defines the vehicle system architecture that adapts to the correction method. Relying on the collaborative cooperation of the vehicle network terminal, cockpit domain controller, vehicle controller and battery management system, it realizes real-time interaction and processing of time data, mileage data and battery status data. It solves the problems of uncoordinated data interaction between various modules of the existing vehicle, delayed acquisition of correction data and inability to implement correction logic. It ensures that the automatic health correction process in after-sales scenarios runs efficiently, stably and in real time. It eliminates the safety hazards caused by falsely high battery health and power miscontrol at the system level, while reducing the after-sales operation and maintenance costs of the vehicle.

[0037] In one possible implementation, the cockpit domain controller is used to collect and store the initial time information after the vehicle's first power-on matching is completed, and can synchronize the real-time network time of the vehicle-mounted network terminal in real time. By comparing the initial time with the real-time network time, the actual service life of the battery is calculated and transmitted to the battery management system.

[0038] In the above technical solution, this solution defines the time acquisition, storage, and calculation functions of the cockpit domain controller, and establishes a precise traceability mechanism for battery life. By fixing the initial time of the battery's first power-on, it avoids the problem of loss or disorder of reference time caused by vehicle power failure, after-sales disassembly and installation, and system reset.

[0039] In one possible implementation, the battery management system has three sets of correspondence tables pre-stored, which correspond to battery health status for driving mileage, battery service life, and cumulative charging capacity, respectively. It also has a built-in linear interpolation module for interpolating the battery health status values ​​of non-calibrated nodes in each correspondence table to obtain accurate battery health correction parameters.

[0040] In the above technical solution, this solution incorporates multiple health status correspondence tables and a linear interpolation calculation module, improving the system's core correction calculation capabilities. The multi-dimensional data tables can cover multiple loss assessment scenarios, including mileage, service life, and charging capacity, overcoming the limitations of a single correction dimension. Through linear interpolation algorithms, it accurately fits the values ​​of non-calibrated nodes, solving the problems of discrete calibration data and coarse health status estimation under intermediate operating conditions in traditional methods. This achieves continuous and accurate correction across all operating conditions, significantly improving battery health status correction accuracy and vehicle compatibility.

[0041] Fourthly, the vehicle described in this invention employs the automatic battery health correction system as described in this invention. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application will be described below.

[0043] Figure 1 This is a block diagram of a vehicle disclosed in an embodiment of this application;

[0044] Figure 2 This is a schematic diagram of the structure of an automatic battery health correction system disclosed in an embodiment of this application;

[0045] Figure 3 This is a flowchart of the automatic battery health correction device disclosed in the embodiments of this application;

[0046] Figure 4 This is one of the flowcharts for the automatic battery health correction method in this embodiment of the invention;

[0047] Figure 5 This is the second flowchart of the automatic battery health correction method in this embodiment of the invention.

[0048] Explanation of reference numerals in the attached figures:

[0049] 1. Automatic battery health correction system; 2. Vehicle network terminal; 3. Vehicle controller; 4. Cockpit domain controller; 5. Battery management system; 6. Memory; 7. Controller. Detailed Implementation

[0050] The embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to illustrate the principles of this application by way of example, but should not be used to limit the scope of this application, that is, this application is not limited to the described embodiments.

[0051] Please see Figure 1 , Figure 1 This is a schematic diagram of the vehicle structure disclosed in an embodiment of this application. The vehicle can be, but is not limited to, a pure electric vehicle (PEV / BEV), a hybrid electric vehicle (HEV), a range-extended electric vehicle (REEV), a plug-in hybrid electric vehicle (PHEV), or a new energy vehicle. One vehicle employs the battery health automatic correction system 1 of this application.

[0052] Please see Figure 2 , Figure 2 This is a schematic diagram of the automatic battery health correction system disclosed in an embodiment of this application. An automatic battery health correction system, implementing the automatic battery health correction method of this application, includes an in-vehicle network terminal 2 (Telematics BOX, TBOX), a cockpit domain controller 4 (Telematics Head Unit, THU), a vehicle controller 3 (Vehicle Control Unit, VCU), and a battery management system 5 (Battery Management System, BMS). The in-vehicle network terminal 2 is used to acquire real-time network connection time and output it to the cockpit domain controller 4. The cockpit domain controller 4 is used to forward the real-time network connection time. The vehicle controller 3 is used to collect and output vehicle mileage data. The battery management system 5 is used to store various battery health correspondence tables, identify after-sales scenarios, determine data anomalies, and complete automatic battery health correction.

[0053] This system defines the vehicle system architecture that adapts to the correction method. Relying on the collaborative cooperation of the vehicle network terminal 2, cockpit domain controller 4, vehicle controller 3, and battery management system 5, it realizes real-time interaction and processing of time data, mileage data, and battery status data. It solves the problems of uncoordinated data interaction between various modules of the existing vehicle, delayed acquisition of correction data, and inability to implement correction logic. It ensures the efficient, stable, and real-time operation of the automatic health correction process in after-sales scenarios, eliminates safety hazards caused by inflated battery health and miscontrolled power at the system level, and reduces the after-sales maintenance cost of the vehicle.

[0054] In one possible embodiment, the cockpit domain controller 4 is used to collect and store the initial time information after the vehicle's first power-on matching is completed, and can synchronize the real-time network time of the vehicle-mounted network terminal 2 in real time. By comparing the initial time with the real-time network time, the actual service life of the battery is calculated and transmitted to the battery management system 5.

[0055] This application defines the time acquisition, storage, and calculation functions of the cockpit domain controller, establishing a precise traceability mechanism for battery lifespan. By fixing the initial time of the battery's first power-on, it avoids issues of lost or incorrect reference time caused by vehicle power failure, after-sales disassembly and reassembly, or system reset.

[0056] In one possible embodiment, the battery management system 5 has three sets of correspondence tables pre-stored, which correspond to battery health status for driving mileage, battery service life, and cumulative charging capacity, respectively. It also has a built-in linear interpolation module for interpolating the battery health status values ​​of non-calibrated nodes in each correspondence table to obtain accurate battery health correction parameters.

[0057] This application incorporates multiple health status correspondence tables and a linear interpolation module, enhancing the system's core correction calculation capabilities. The multi-dimensional data tables cover various loss assessment scenarios, including mileage, service life, and charging capacity, overcoming the limitations of single correction dimensions. Through linear interpolation algorithms, it accurately fits non-calibrated node values, resolving the issues of discrete calibration data and coarse health status estimation under intermediate operating conditions. This achieves continuous and accurate correction across all operating conditions, significantly improving battery health status correction accuracy and vehicle compatibility.

[0058] Please see Figure 3 , Figure 3 This is a schematic diagram of the automatic battery health correction device disclosed in an embodiment of this application. An automatic battery health correction device includes a memory 6 and a controller 7. The memory 6 stores a computer-readable program. When the computer-readable program is invoked by the controller 7, it can execute the steps of the automatic battery health correction method as described in this application. This device, by storing the computer-readable program in the memory 6 and invoking the correction method in the controller 7, provides hardware support for fully automatic, unmanned health correction logic. It solves the shortcomings of existing vehicle electronic control devices that lack autonomous correction functions and require external diagnostic equipment for manual calibration in fault scenarios. It enables localized, autonomous completion of after-sales anomaly identification and health correction on the vehicle side, improving correction efficiency, reducing after-sales maintenance workload, and optimizing the user's driving experience.

[0059] Please see Figure 4 and Figure 5 , Figure 4 This is one of the flowcharts for the automatic battery health correction method disclosed in the embodiments of this application. Figure 5 This is a second flowchart of the automatic battery health correction method disclosed in this application. An automatic battery health correction method includes the following steps:

[0060] When the vehicle is powered on, a preset hardware status identification flag is read. The stored value of the flag is checked against a preset valid identifier. If the value is not a preset valid identifier, the vehicle is determined to be in an after-sales scenario. The current cumulative charging capacity of the battery is compared with its rated capacity. Based on the comparison result, it is determined whether there is a battery health distortion issue caused by hardware replacement or data corruption. If a battery health distortion issue is detected, the vehicle's mileage data is acquired, and a battery health correction value is obtained by combining it with a preset mileage-battery health correspondence table. The battery health is then corrected based on this correction value.

[0061] For example, the battery management system 5 has an internal storage unit, such as the battery flag identifier F277, which defaults to 0x0. An external device, through secure access (for information security considerations of the battery management system 5), enters the internal storage unit of the battery management system 5 and writes F277=0x1 upon power-off using a diagnostic device. This identifier is automatically stored as F277. Before powering on and implementing the battery health correction strategy, the software reads the F277 in the storage unit to determine if it is 0x0 or 0x1. If it is 0x1, it indicates that the battery management system 5 or battery pack has not been replaced, meaning the battery health matches the actual battery, and the accuracy of the battery health is normal. If it is not 0x1, it indicates a battery health distortion problem caused by hardware replacement or stored data failure.

[0062] For example, the vehicle controller 3 calculates the vehicle mileage in real time: vehicle mileage = wheel speed * tire circumference. After the battery management system 5 is powered on, it looks up a preset correspondence table between mileage and battery health using the vehicle mileage data to obtain a battery health correction value. The battery management system 5 then corrects the battery health based on this correction value.

[0063] In this application, the battery management system 5 identifies hardware replacement or battery data loss scenarios based on its own algorithm, corrects battery health based on vehicle mileage, and adjusts the cumulative charging capacity to ensure the authenticity and accuracy of battery health. Therefore, this application largely avoids the need for manual capacity testing to calibrate battery health over long periods, effectively reducing after-sales maintenance costs. Simultaneously, it significantly reduces user waiting time and improves user experience. Typically, certified electricians specializing in high-voltage electrical systems for new energy vehicles charge between 320 and 450 yuan per hour for labor, with after-sales calibration estimated at least 2 hours and an estimated cost of at least 640 yuan.

[0064] In one possible embodiment, in an after-sales scenario, the battery health distortion problem caused by hardware replacement or storage data failure is determined based on the comparison results. Specifically, if the cumulative charging capacity is greater than the battery's rated capacity, the storage data is considered normal, the battery health matches the actual battery normally, and no correction is needed; otherwise, the battery health distortion problem caused by hardware replacement or storage data failure is considered to exist, and the battery health is abnormal.

[0065] This application specifically defines the judgment logic for battery health distortion, clearly using the comparison result of the cumulative charging capacity and the battery's rated capacity as the judgment basis. It accurately distinguishes between normal battery aging and degradation and abnormal health scenarios caused by hardware replacement or data failure. It solves the defects of existing correction mechanisms that cannot accurately identify after-sales hardware replacement failure scenarios and are prone to wrong correction or no correction. It improves the accuracy of after-sales abnormal scenario identification and avoids the problem of normal batteries being wrongly corrected and abnormal batteries not being corrected.

[0066] In one possible embodiment, in an after-sales scenario, after obtaining the battery health correction value based on the vehicle mileage data and completing the correction of the battery health, the method further includes: updating the battery operation data and saving it after power failure; after power is restored, determining whether the current cumulative charging capacity is greater than the preset battery capacity (e.g., 1C battery capacity); if so, the battery health correction is completed; otherwise, the automatic correction of the battery health is performed again.

[0067] This application defines a closed-loop logic for data storage and cyclic verification after battery health correction. By saving the running data after power failure and verifying the correction results again after power-on, it achieves iterative calibration of battery health correction, solving the problems of insufficient single correction accuracy, easy data loss, and unstable correction results in the prior art.

[0068] In one possible embodiment, if the value of the flag bit is a preset valid identifier, it is determined that the vehicle is not in an after-sales scenario. The vehicle network time is obtained, the actual service life of the battery is determined based on the vehicle network time, and the battery health correction value is obtained by combining the preset correspondence table between service life and battery health. The battery health is then corrected based on the battery health correction value.

[0069] For example, the cockpit domain controller 4 receives the network connection time input in real time and sends it to the battery management system 5. The battery management system 5 calculates the battery health correction value based on the input value from the cockpit domain controller 4 (the in-vehicle network terminal 2 receives satellite signals from the built-in Global Navigation Satellite System Radio Frequency Module (GNSS) and integrates them into the network connection time (the current time) through software, which is then transmitted to the battery management system 5 and the cockpit domain controller 4 via the Controller Area Network (CAN). When the battery is installed in the vehicle and receives the vehicle's CAN data, the time transmitted by the cockpit domain controller 4 at this time is recorded as the initial time and stored in the storage unit of the battery management system 5. After each power-on, the difference between the current time transmitted by the cockpit domain controller 4 and the initial time is used as the service life. The battery management system 5 looks up the preset correspondence between the service life and the battery health value based on the service life to obtain the battery health correction value and stores it.

[0070] This application defines a battery health correction method based on the service life in non-after-sales scenarios. In normal use scenarios where the battery hardware has not been replaced and the data is normal, the battery service life is calculated based on the vehicle network connection time and the corresponding health is matched.

[0071] In one possible embodiment, if the value of the flag bit is a preset valid identifier, it is determined that the vehicle is not in an after-sales scenario. The vehicle network connection time is obtained, and the actual battery life is determined based on this time. A battery health correction value one is obtained by combining this with a preset mapping table of battery life and battery health. The cumulative charging capacity is obtained, and a battery health correction value two is obtained by combining this with a preset mapping table of battery health. The total vehicle mileage data is obtained, and a battery health correction value three is obtained by combining this with a preset mapping table of battery health. The minimum value among the battery health correction values ​​one, two, and three is selected as the final battery health correction value, and the battery health is corrected based on this value.

[0072] The battery will limit the charging and discharging power based on the battery health (maximum allowable charging and discharging power = maximum allowable charging and discharging power under rated capacity * SOH coefficient).

[0073] For example, a preset table showing the correspondence between service life and battery health:

[0074]

[0075] For example, a preset table showing the correspondence between cumulative charging capacity and battery health:

[0076]

[0077] For example, a preset table showing the correspondence between driving mileage and battery health:

[0078]

[0079] The preset correspondence tables between service life and battery health, the preset correspondence tables between cumulative charging capacity and battery health, and the preset correspondence tables between driving mileage and battery health in this application were all obtained through calibration.

[0080] The application specifies a health correction strategy that integrates multiple parameters. By using three sets of parameters—years of use, cumulative charging capacity, and mileage—to obtain correction values ​​and taking the minimum value as the final result, a multi-dimensional and comprehensive assessment of battery degradation status is achieved. This solves the problem that existing technologies with a single assessment dimension are easily affected by vehicle operating conditions and have large deviations in health assessment. It significantly improves the accuracy and reliability of battery health assessment under all operating conditions and effectively avoids misjudgments of health status caused by abnormal local operating conditions.

[0081] In one possible embodiment, after the battery is installed in the vehicle, when the vehicle data is first acquired and the time information transmitted by the cockpit domain controller 4 is received, the time information is recorded as the initial time and stored. Each time the vehicle is powered on and running, the actual service life of the battery is calculated based on the acquired vehicle network time and the initial time. This application defines the method for initial battery time calibration and service life calculation, recording the initial installation time through the vehicle's first network data, and dynamically calculating the battery service life based on the real-time network time difference.

[0082] In one possible embodiment, the battery health value of non-calibrated nodes in the correspondence table is calculated using a linear interpolation algorithm. This application specifies a linear interpolation method for numerical calculation, performing accurate calculations for non-calibrated operating condition nodes in the correspondence table. This solves the shortcomings of existing calibration parameters being only discrete values, inaccurate matching of intermediate operating condition health, and large evaluation errors. It achieves continuous and smooth correction of battery health throughout the entire battery lifecycle and under all usage conditions, significantly reducing battery health evaluation errors and accurately matching the real-time battery degradation state.

[0083] This application addresses industry pain points such as poor adaptability of traditional vehicle battery health correction solutions, delayed correction in after-sales scenarios, reliance on manual calibration, and inability to autonomously identify data distortion. It innovatively proposes an automatic battery health correction strategy based on multi-dimensional influencing factors. The core strategy selects three key lifespan influencing factors: vehicle mileage, cumulative battery charging capacity, and actual battery usage years, accurately covering the degradation characteristics of power batteries throughout their entire lifecycle. These three influencing factors correspond to three core aging mechanisms: battery wear and tear, charge-discharge cycle degradation, and natural aging, avoiding the shortcomings of traditional single-dimensional assessment methods that are one-sided and lack precision. Furthermore, based on extensive real-vehicle test data and battery degradation calibration experiments, this application establishes a corresponding relationship table for battery health degradation, quantitatively binding the three influencing factors to battery health. By replacing the traditional extensive estimation method with precise table lookup logic, it builds a standardized, implementable, and high-precision SOH correction technology logic, providing core data support for the autonomous correction of battery health.

[0084] This application focuses on specific application scenarios in vehicle after-sales maintenance, specifically addressing the industry-wide problem of distorted battery health data. During vehicle after-sales maintenance, common scenarios include battery pack replacement, battery management system (BMS) controller replacement, abnormal data failure in BMS storage, and program refresh / reset. These scenarios can lead to the loss or corruption of the vehicle's original battery historical operating data, aging calibration data, and health baseline data. This results in a significant discrepancy between the battery health identified by the BMS and the actual aging state of the battery, leading to problems such as falsely high or low State of Health (SOH) values, and data distortion due to zeroing. This greatly affects the accuracy of vehicle energy management, range estimation, and battery safety monitoring. Existing traditional correction solutions mostly rely on manual calibration and specialized equipment debugging during after-sales service, failing to achieve automated identification and autonomous correction, resulting in extremely low adaptability and intelligence.

[0085] In response, this patent features a unique SOH (Self-Operating Health) self-correction software strategy adapted to after-sales scenarios. Based on BMS (Battery Management System) underlying logic optimization, it achieves automatic identification and precise correction of fault scenarios. After the vehicle is powered on, the BMS can autonomously and accurately identify whether the vehicle is in an after-sales maintenance scenario through multiple logics, including hardware status identification flags and comparison of cumulative battery charging capacity with rated capacity. It can accurately determine abnormal conditions such as battery pack replacement, BMS controller replacement, and data storage failure, without requiring manual intervention. Upon identifying a health distortion issue, the system can link with the VCU (Vehicle Control Unit) to obtain real-time cumulative mileage data and, combined with a pre-stored lifespan degradation comparison table, quickly and instantly correct the battery health. This eliminates the need to wait for long-term vehicle driving data accumulation or for after-sales personnel to perform manual calibration using specialized equipment.

[0086] Compared to traditional technical solutions, this application offers significant technological advantages, completely breaking down the technical barriers to after-sales battery health correction. Through a purely software-based strategy, it achieves fully automated identification and correction without requiring additional hardware, significantly reducing equipment and labor costs for vehicle after-sales battery repair. Simultaneously, the real-time correction feature effectively solves the problems of delayed correction and persistent data deviations in traditional solutions, ensuring that battery health data can quickly recover to its true state after after-sales repair, accurately reflecting the actual battery degradation and effectively improving the accuracy of vehicle range estimation and battery safety management. This application is adaptable to various abnormal after-sales repair conditions, exhibiting strong compatibility and robustness. It can efficiently solve the problem of battery health distortion in after-sales scenarios, significantly improving the intelligence level of the power battery management system and the convenience of vehicle after-sales maintenance, possessing extremely high engineering and market application value.

[0087] The examples described above can be modified or altered by those skilled in the art based on the above description, and all such modifications and alterations should fall within the protection scope of the appended claims. Those skilled in the art can understand that implementing all or part of the processes of the above embodiments and making equivalent changes according to the claims of this application still fall within the scope of this application.

Claims

1. A battery health automatic correction method, characterized by, Includes the following steps: When the vehicle is powered on, the preset hardware status identification flag is read, and it is determined whether the stored value of the flag is a preset valid identifier. If the value of the flag is not a preset valid identifier, the vehicle is determined to be in an after-sales scenario. The current cumulative charging capacity of the battery is compared with the rated capacity of the battery, and based on the comparison result, it is determined whether there is a problem of battery health distortion caused by hardware replacement or storage data failure. If a battery health distortion problem is detected, the vehicle mileage data is obtained, and a battery health correction value is obtained by combining it with a preset correspondence table between mileage and battery health. The battery health is then corrected based on the battery health correction value.

2. The battery health automatic revision method of claim 1, wherein: Based on the comparison results, it is determined whether there is a problem with the battery health information due to hardware replacement or data storage failure. Specifically: If the cumulative charging capacity is greater than the battery's rated capacity, the stored data is considered normal and the battery health is considered to match the actual battery status; otherwise, it is considered that there is a problem with the battery health status due to hardware replacement or storage data failure.

3. The automatic battery health correction method according to claim 1, characterized in that: After obtaining the battery health correction value based on the vehicle mileage data and correcting the battery health, the process also includes: Update battery operating data and save it after power failure. After power is restored, check whether the current cumulative charging capacity is greater than the preset battery capacity. If so, complete the battery health correction; otherwise, automatically correct the battery health again.

4. The automatic battery health correction method according to claim 1, characterized in that: If the value of the flag bit is a preset valid identifier, it is determined that the vehicle is not in an after-sales scenario. The vehicle network time is obtained, and the actual service life of the battery is determined based on the vehicle network time. Combined with the preset correspondence table between service life and battery health, a battery health correction value is obtained, and the battery health is corrected based on the battery health correction value.

5. The automatic battery health correction method according to claim 1, characterized in that: If the value of the flag bit is a preset valid identifier, it is determined that the vehicle is not in an after-sales service scenario. Obtain the vehicle network connection time, determine the actual service life of the battery based on the vehicle network connection time, and obtain the battery health correction value one by combining the preset correspondence table between service life and battery health. Obtain the cumulative charging capacity and, in conjunction with the preset correspondence table between cumulative charging capacity and battery health, obtain the second battery health correction value. Obtain vehicle mileage data and combine it with a preset table of correspondence between mileage and battery health to obtain battery health correction value three; The minimum value among the battery health correction value one, battery health correction value two, and battery health correction value three is selected as the final battery health correction value, and the battery health is corrected based on the battery health correction value.

6. The automatic battery health correction method according to claim 4 or 5, characterized in that: After the battery is installed in the vehicle, when the vehicle data is acquired for the first time and the time information transmitted by the cockpit domain controller (4) is received, the time information is recorded as the initial time and stored; each time the vehicle is powered on, the actual service life of the battery is calculated based on the acquired vehicle network time and the initial time.

7. The automatic battery health correction method according to claim 1, 4, or 5, characterized in that: The battery health values ​​of non-calibrated nodes in the correspondence table are calculated using a linear interpolation algorithm.

8. A battery health automatic correction device, characterized in that: It includes a memory (6) and a controller (7), wherein the memory (6) stores a computer-readable program that, when invoked by the controller (7), can perform the steps of the automatic battery health correction method as described in any one of claims 1 to 7.

9. A battery health automatic correction system, characterized in that, The battery health automatic correction method as described in any one of claims 1 to 7 includes an in-vehicle network terminal (2), a cockpit domain controller (4), a vehicle controller (3), and a battery management system (5). The vehicle-mounted network terminal (2) is used to obtain the real-time network connection time and output it to the cockpit domain controller (4). The cockpit domain controller (4) is used to forward real-time network time; The vehicle controller (3) is used to collect and output vehicle mileage data; The battery management system (5) is used to store various battery health status correspondence tables, identify after-sales scenarios, determine data abnormalities, and complete automatic correction of battery health status.

10. The automatic battery health correction system according to claim 9, characterized in that: The cockpit domain controller (4) is used to collect and store the initial time information after the vehicle is first powered on and matched. It can also synchronize the real-time network time of the vehicle network terminal (2) in real time. By comparing the initial time with the real-time network time, it calculates the actual service life of the battery and transmits it to the battery management system (5).

11. The automatic battery health correction system according to claim 9, characterized in that: The battery management system (5) has three sets of corresponding relationship tables for driving mileage, battery service life and cumulative charging capacity respectively. It also has a built-in linear interpolation calculation module for interpolating the battery health values ​​of non-calibrated nodes in each corresponding relationship table to obtain accurate battery health correction parameters.

12. A vehicle, characterized in that, The battery health automatic correction system as described in any one of claims 9 to 11 is adopted.

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