Battery health state correction method and device, and electronic device

Abstract

The application discloses a battery health state correction method and device and electronic equipment. The method comprises the following steps: determining a current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile for the current charging process of the electric vehicle and the current charging pile; determining the identification result corresponding to the current state of charge of the electric vehicle based on the current ratio and the historical ratio of the electric vehicle and the current charging pile, wherein the historical ratio is obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle; in the case that the identification result is accurate, correcting the initial health state of the electric vehicle based on the uncertainty corresponding to the plurality of charging piles having charging interaction with the electric vehicle and the historical ratio of the electric vehicle and the plurality of charging piles, to obtain the target health state of the electric vehicle. The application solves the technical problem of the inaccurate estimation result of the battery health state of the electric vehicle in the related art.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and more specifically, to a battery health state correction method, apparatus, and electronic device. Background Technology

[0002] Battery health management is the cornerstone of the safe and reliable operation of electric vehicles. Accurate estimation of battery state, especially battery health state estimation, is the prerequisite and foundation for electric vehicle battery management. In the early stages of battery degradation, capacity decline is often not obvious, but in the later stages, capacity decline becomes more severe, significantly impacting the driving range and usability of electric vehicles. Therefore, accurately estimating the health state of electric vehicle batteries is crucial for extending the lifespan and operating distance of electric vehicles. Current battery health state estimation technologies primarily rely on the operating characteristics of the battery pack itself. However, since battery state cannot be measured directly, and battery degradation is a time-varying, dynamic, and highly nonlinear process, the estimation results often have significant errors, leading to unsatisfactory accuracy in electric vehicle battery health state estimation.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a battery health status correction method, apparatus, and electronic device to at least solve the technical problem of unsatisfactory accuracy of electric vehicle battery health status estimation results in related technologies.

[0005] According to one aspect of the embodiments of this application, a battery health state correction method is provided, comprising: determining a current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile for the current charging process of the current charging pile and the electric vehicle; determining an identification result corresponding to the current state of charge of the electric vehicle based on the current ratio and historical ratios between the electric vehicle and the current charging pile, wherein the historical ratios are obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle; and, if the identification result indicates that it is accurate, correcting the initial health state of the electric vehicle based on the uncertainties corresponding to multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, to obtain a target health state of the electric vehicle, wherein the multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile.

[0006] Optionally, for the current charging process of the current charging pile and the electric vehicle, a current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile is determined, including: using the current charging pile to perform energy detection on the current charging process to obtain an energy metering value; determining the change in the state of charge of the electric vehicle during the current charging process; and determining the current ratio based on the energy metering value and the change in the state of charge.

[0007] Optionally, based on the current ratio and the historical ratio between the electric vehicle and the current charging pile, the identification result corresponding to the current state of charge of the electric vehicle is determined, including: determining the fluctuation between the current ratio and the historical ratio; if the fluctuation is greater than or equal to a predetermined fluctuation value, the identification result is determined to be that the current state of charge is inaccurate; if the fluctuation is less than the predetermined fluctuation value, the identification result is determined to be that the current state of charge is accurate.

[0008] Optionally, based on the uncertainties corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios of the electric vehicle with each of the multiple charging piles, the initial health state of the electric vehicle is corrected to obtain the target health state of the electric vehicle. This includes: determining a first capacity characteristic parameter representing the initial health state of the electric vehicle and a second capacity characteristic parameter representing the target health state of the electric vehicle, based on the uncertainties corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios of the electric vehicle with each of the multiple charging piles; and correcting the initial health state based on the ratio between the second capacity characteristic parameter and the first capacity characteristic parameter to obtain the target health state.

[0009] Optionally, based on the uncertainties corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios of the electric vehicle with the multiple charging piles, a first capacity characteristic parameter representing the electric vehicle's initial healthy state and a second capacity characteristic parameter representing the electric vehicle's current healthy state are determined. This includes: identifying multiple initial charging piles among the multiple charging piles where the electric vehicle is in its initial healthy state; obtaining weighting coefficients corresponding to the multiple initial charging piles based on their respective uncertainties; determining the first capacity characteristic parameter based on the weighting coefficients and historical ratios of the multiple initial charging piles; obtaining weighting coefficients corresponding to the multiple charging piles based on their respective uncertainties; and determining the second capacity characteristic parameter based on the weighting coefficients and historical ratios of the multiple charging piles.

[0010] Optionally, based on the uncertainties corresponding to the multiple charging piles that interact with the electric vehicle, and the historical ratios of the electric vehicle with each of the multiple charging piles, the initial health state of the electric vehicle is corrected to obtain the target health state of the electric vehicle. This includes: correcting the initial health state of the electric vehicle based on the uncertainties corresponding to the multiple charging piles that interact with the electric vehicle, and the historical ratios of the electric vehicle with each of the multiple charging piles, to obtain an intermediate health state; acquiring historical behavior data of the electric vehicle, wherein the historical behavior data represents the behavioral pattern of the electric vehicle's charging and discharging depth; determining a first aging coefficient based on the historical behavior data; and correcting the intermediate health state based on the first aging coefficient to obtain the target health state.

[0011] Optionally, based on a first aging coefficient, the intermediate health state is corrected to obtain a target health state, including: acquiring temperature change data of the area where the electric vehicle is located; determining a second aging coefficient based on the temperature change data; and correcting the intermediate health state based on the first and second aging coefficients to obtain the target health state.

[0012] According to another aspect of the embodiments of this application, a battery health state correction device is provided, comprising: a charging conversion efficiency determination module, configured to determine a current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile for the current charging process of the current charging pile and the electric vehicle; an identification result determination module, configured to determine an identification result corresponding to the current state of charge of the electric vehicle based on the current ratio and a historical ratio between the electric vehicle and the current charging pile, wherein the historical ratio is obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle; and a target health state determination module, configured to, when the identification result indicates accuracy, correct the initial health state of the electric vehicle based on the uncertainties corresponding to multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, to obtain a target health state of the electric vehicle, wherein the multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile.

[0013] According to another aspect of the embodiments of this application, a non-volatile storage medium is provided, which stores a plurality of instructions adapted for a battery health state correction method, any one of which is loaded by a processor.

[0014] According to another aspect of the embodiments of this application, an electronic device is provided, including: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the battery health state correction methods.

[0015] In this embodiment, by considering the current charging process between the current charging pile and the electric vehicle, a current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile is determined. Based on the current ratio and the historical ratios between the electric vehicle and the current charging pile, the identification result corresponding to the current state of charge of the electric vehicle is determined, where the historical ratios are obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle. If the identification result indicates accuracy, the initial health state of the electric vehicle is corrected based on the uncertainties corresponding to multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and multiple charging piles, to obtain the target health state of the electric vehicle. Here, the multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile. This achieves the goal of correcting the current health state of the electric vehicle using the uncertainty of the charging pile and the initial health state of the electric vehicle, realizing the technical effect of improving the accuracy of the electric vehicle battery health state estimation result, and thus solving the technical problem of unsatisfactory accuracy of electric vehicle battery health state estimation results in related technologies. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This is a flowchart of an optional battery health status correction method provided according to an embodiment of this application;

[0018] Figure 2 This is a flowchart of an optional battery health status correction method provided according to an embodiment of this application;

[0019] Figure 3 This is a schematic diagram of an optional battery health status correction method provided according to an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of an optional battery health status correction device provided according to an embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0024] SoC (State of Charge) refers to the ratio of the battery's current remaining charge to its charge level when fully charged.

[0025] SoH (State of Health) refers to the ratio of the battery's current maximum capacity to its rated capacity. It reflects the degree of battery aging and performance degradation and is an indicator for quantifying the aging of vehicle battery packs.

[0026] According to an embodiment of this application, a method embodiment for correcting battery health status is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 This is a flowchart of a battery health status correction method according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0028] Step S102: For the current charging process of the current charging pile and the electric vehicle, determine the current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile.

[0029] It is understandable that during the current charging process of an electric vehicle, the current ratio of the charging conversion efficiency between the electric vehicle and the charging station is determined. This charging conversion efficiency reflects the efficiency of converting electrical energy from the charging station to the electric vehicle's battery. By determining the charging conversion efficiency between the electric vehicle and the charging station, the battery's health status can be effectively detected, and corresponding battery management strategies can be formulated based on the battery's health status to optimize battery energy management and reduce the rate of battery aging.

[0030] In one optional embodiment, determining a current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile for the current charging process of the current charging pile includes: using the current charging pile to perform energy detection on the current charging process to obtain an energy metering value; determining the change in the state of charge of the electric vehicle during the current charging process; and determining the current ratio based on the energy metering value and the change in the state of charge.

[0031] It is understandable that during the current charging process of an electric vehicle, energy detection is performed on the charging process at the current charging station. Utilizing the energy metering function of the current charging station, the energy metering value of the energy being charged into the electric vehicle's battery is accurately recorded. Simultaneously, the state of charge (SOC) of the electric vehicle is recorded before and after charging, and the change in SOC is obtained based on these changes. Based on the energy metering values ​​from the charging station and the change in SOC of the electric vehicle before and after charging, the current ratio of charging conversion efficiency between the electric vehicle and the charging station is obtained. By utilizing the energy metering function of the charging station, the accuracy of the energy metering values ​​is ensured, thereby improving the accuracy of the battery charging conversion efficiency results and providing data support for optimizing battery energy management.

[0032] Optionally, during the current charging process of the electric vehicle, the current energy metering value of the charging station is recorded as E, and the change in the state of charge of the electric vehicle before and after charging is recorded as ΔSoC. Then, the current ratio k of the charging conversion efficiency between the electric vehicle and the charging station during the i-th charging is calculated. i The formula is:

[0033]

[0034] Where i represents the i-th charge, E i △SoC represents the energy metering value of the charging station during the i-th charging session. i This represents the change in the state of charge of the electric vehicle before and after charging for the i-th time.

[0035] Step S104: Based on the current ratio and the historical ratio between the electric vehicle and the current charging pile, determine the identification result corresponding to the current state of charge of the electric vehicle. The historical ratio is obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle.

[0036] It is understandable that during the current charging process of an electric vehicle, the current ratio of charging conversion efficiency between the electric vehicle and the charging station is determined. This current ratio is then compared to historical ratios to determine the current state of charge (SOC) of the electric vehicle, i.e., the accuracy of the estimated SOC. The historical ratio of charging conversion efficiency between the electric vehicle and the charging station is obtained from historical ratios of charging conversion efficiency during the electric vehicle's charging history. By determining the changes in charging conversion efficiency between the electric vehicle and the charging station, the trend of SOC changes in the electric vehicle's battery can be effectively detected, and changes in battery health can be identified. This allows for optimization of battery charging strategies, reducing further battery damage and extending battery life.

[0037] In one optional embodiment, based on the current ratio and the historical ratio between the electric vehicle and the current charging pile, the identification result corresponding to the current state of charge of the electric vehicle is determined, including: determining the fluctuation amount between the current ratio and the historical ratio; if the fluctuation amount is greater than or equal to a predetermined fluctuation value, determining that the identification result is that the current state of charge is inaccurate; if the fluctuation amount is less than the predetermined fluctuation value, determining that the identification result is that the current state of charge is accurate.

[0038] It can be understood that the current ratio of charging conversion efficiency between the electric vehicle and the current charging station is compared with the historical ratio, and the difference is taken to obtain the fluctuation between the current ratio and the historical ratio. If the fluctuation is greater than or equal to a predetermined fluctuation value, the identification result of the current electric vehicle's state of charge is inaccurate, that is, the estimation result of the current electric vehicle's battery state of charge is inaccurate; if the fluctuation is less than the predetermined fluctuation value, the identification result of the current electric vehicle's state of charge is accurate, that is, the estimation result of the current electric vehicle's battery state of charge is accurate. By determining the fluctuation between the current ratio and the historical ratio of the electric vehicle, changes in battery health can be determined, and the battery charging strategy can be adjusted, such as optimizing the charging current and adjusting the charging termination conditions, to reduce battery aging and improve charging efficiency.

[0039] Step S106: If the identification result indicates that the electric vehicle is accurate, the initial health state of the electric vehicle is corrected based on the uncertainty corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, to obtain the target health state of the electric vehicle. Here, the multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile.

[0040] It can be understood that if the current identification result of the electric vehicle's state of charge (SOC) is accurate, meaning the current SOC estimation result of the electric vehicle's battery is accurate, then the initial health state of the electric vehicle is corrected based on the uncertainties corresponding to the multiple charging piles interacting with the electric vehicle, and the historical ratios corresponding to the multiple charging piles interacting with the electric vehicle, to obtain the target health state of the electric vehicle. The aforementioned uncertainty of the charging piles refers to a measure of the measurement error that may occur during the energy metering process, reflecting the degree of deviation between the metered energy value and the actual value. By introducing the uncertainty of the charging piles to correct the health state of the electric vehicle's battery, the accuracy of the battery health state estimation can be effectively improved, battery utilization efficiency can be increased, and unnecessary energy waste can be reduced.

[0041] In one optional embodiment, the initial health state of the electric vehicle is corrected based on the uncertainties corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, to obtain the target health state of the electric vehicle. This includes: determining a first capacity characteristic parameter representing the initial health state of the electric vehicle and a second capacity characteristic parameter representing the target health state of the electric vehicle, based on the uncertainties corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles; and correcting the initial health state based on the ratio between the second capacity characteristic parameter and the first capacity characteristic parameter to obtain the target health state.

[0042] It is understandable that, based on the uncertainties corresponding to multiple charging piles interacting with the electric vehicle, and the historical ratios of these charging piles, a first capacity characteristic parameter of the battery in its initial healthy state and a second capacity characteristic parameter of the battery in its target healthy state are determined. These capacity characteristic parameters represent the relationship between the battery's usable capacity and its state of charge (SOC) under different SOC conditions. Based on the ratio between the second and first capacity characteristic parameters, the initial healthy state of the electric vehicle is corrected to obtain its target healthy state. By utilizing these capacity characteristic parameters, the remaining battery capacity can be accurately predicted, aiding in battery fault diagnosis and prevention, and improving battery safety and lifespan.

[0043] In one optional embodiment, based on the uncertainties corresponding to multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios of the electric vehicle with each of the multiple charging piles, a first capacity characteristic parameter representing the electric vehicle's initial healthy state and a second capacity characteristic parameter representing the electric vehicle's current healthy state are determined. This includes: identifying multiple initial charging piles among the multiple charging piles where the electric vehicle is in its initial healthy state; obtaining weighting coefficients corresponding to each of the multiple initial charging piles based on their respective uncertainties; determining the first capacity characteristic parameter based on the weighting coefficients and historical ratios of the multiple initial charging piles; obtaining weighting coefficients corresponding to each of the multiple charging piles based on their respective uncertainties; and determining the second capacity characteristic parameter based on the weighting coefficients and historical ratios of the multiple charging piles.

[0044] It is understood that multiple charging stations that interact with the electric vehicle when it is in its initial healthy state are taken as initial charging stations, and the uncertainty corresponding to each initial charging station is determined. Based on the uncertainties corresponding to each initial charging station, weighting coefficients are obtained for each initial charging station. Based on the weighting coefficients and historical ratios of these initial charging stations, the first capacity characteristic parameter of the battery when the electric vehicle is in its initial healthy state is obtained. The uncertainty corresponding to each charging station that interacts with the electric vehicle when it is in its target healthy state is determined, and based on these uncertainties, weighting coefficients are obtained for each charging station when the electric vehicle is in its target healthy state. Based on the weighting coefficients and historical ratios of these charging stations, the second capacity characteristic parameter of the battery when the electric vehicle is in its target healthy state is obtained. By collecting and analyzing the historical charging data of the electric vehicle at multiple charging stations, combined with the energy metering uncertainty of the charging stations, the health state of the battery can be estimated more accurately, reducing estimation errors caused by data deviations from a single charging station or changes in the electric vehicle's operating environment.

[0045] Optionally, based on the uncertainty u corresponding to each of the multiple initial piles, the weighting coefficients corresponding to each of the multiple initial piles are obtained. Based on the weighting coefficients and historical ratios corresponding to the aforementioned initial stakes, the first capacity characteristic parameter P0 of the battery in the initial healthy state of the electric vehicle is obtained. The formula for P0 is:

[0046]

[0047] Among them, u n This represents the uncertainty of the initial stake during the nth charge. This represents the weight coefficient of the initial stake during the nth charging cycle.

[0048] In one optional embodiment, the initial health state of the electric vehicle is corrected based on the uncertainties corresponding to the multiple charging piles that interact with the electric vehicle and the historical ratios between the electric vehicle and the multiple charging piles to obtain the target health state of the electric vehicle. This includes: correcting the initial health state of the electric vehicle based on the uncertainties corresponding to the multiple charging piles that interact with the electric vehicle and the historical ratios between the electric vehicle and the multiple charging piles to obtain an intermediate health state; acquiring historical behavior data of the electric vehicle, wherein the historical behavior data represents the behavioral pattern of the electric vehicle's charging and discharging depth; determining a first aging coefficient based on the historical behavior data; and correcting the intermediate health state based on the first aging coefficient to obtain the target health state.

[0049] It is understandable that, based on the uncertainties corresponding to multiple charging piles that interact with the electric vehicle, and the historical ratios of the electric vehicle with each charging pile, the initial health state of the electric vehicle is corrected to obtain an intermediate health state. Historical behavior data of the electric vehicle is acquired, and based on this data, the first aging factor of the battery is determined. This historical behavior data represents the behavioral patterns of the electric vehicle's charge-discharge depth, which can be used to analyze the impact of different charging states on battery life and understand the battery's performance under different load conditions. Based on this first aging factor, the intermediate health state of the battery is corrected to obtain the target health state. By introducing historical behavior data representing the charge-discharge depth of the electric vehicle, estimation errors caused by differences in charging habits are reduced, making the assessment of the electric vehicle battery health state more comprehensive and accurate, providing strong technical support for the performance improvement and enhancement of electric vehicles.

[0050] Optionally, based on the ratio between the second capacity characteristic parameter P1 and the first capacity characteristic parameter P0, the initial health state SoH0 is corrected to obtain the intermediate health state SoH'1, and the formula for SoH'1 is:

[0051]

[0052] In one optional embodiment, the intermediate health state is corrected based on a first aging coefficient to obtain a target health state, including: acquiring temperature change data of the area where the electric vehicle is located; determining a second aging coefficient based on the temperature change data; and correcting the intermediate health state based on the first aging coefficient and the second aging coefficient to obtain the target health state.

[0053] It is understandable that after obtaining the intermediate health state of the electric vehicle battery based on the first aging coefficient, the intermediate health state is further corrected according to the environment in which the electric vehicle is located. Temperature change data of the area where the electric vehicle is located is obtained, and a second aging coefficient of the battery is derived based on this data. The intermediate health state of the battery is then corrected based on the first and second aging coefficients to obtain the target health state. Since the ambient temperature of the electric vehicle has a significant impact on the battery's health state, the influence of ambient temperature on the battery's health state is considered when correcting the electric vehicle's health state. This reduces the adverse effects of temperature on battery life, improves the accuracy of the electric vehicle battery health state estimation results, and enhances the overall performance and usage efficiency of the electric vehicle.

[0054] Through step S102, for the current charging process between the current charging pile and the electric vehicle, the current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile is determined; in step S104, based on the current ratio and the historical ratio between the electric vehicle and the current charging pile, the identification result corresponding to the current state of charge of the electric vehicle is determined, wherein the historical ratio is obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle; in step S106, if the identification result indicates accuracy, based on the uncertainties corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, the initial health state of the electric vehicle is corrected to obtain the target health state of the electric vehicle, wherein the multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile. This achieves the goal of correcting the current health state of the electric vehicle using the uncertainty of the charging pile and the initial health state of the electric vehicle, realizing the technical effect of improving the accuracy of the electric vehicle battery health state estimation result, and thus solving the technical problem of unsatisfactory accuracy of the electric vehicle battery health state estimation result in related technologies.

[0055] Based on the above embodiments and optional embodiments, this application proposes an optional implementation method to provide a method for detecting the accuracy of onboard SoC (i.e., battery state of charge) / SoH (i.e., battery state of health) measurement, as well as a method for estimating SoC / SoH, which solves the practical problem that related technical estimation methods are difficult to meet the needs of electric vehicle users.

[0056] Firstly, the accuracy of onboard SoC / SoH measurement is detected and estimated using the charging pile's energy metering value. Related estimation methods generally establish SoC / SoH estimation models based on the characteristics of the onboard battery itself; however, due to the high nonlinearity of the onboard battery system, the accuracy of the estimation model is difficult to guarantee. Secondly, data-driven methods use artificial intelligence algorithms to predict SoC / SoH. These methods heavily rely on real-world data, and their generalization ability is affected by the algorithm itself and data dispersion, making it difficult to guarantee prediction accuracy. Since the charging pile's energy metering value and the onboard SoC / SoH value are strongly correlated, and their relationship is roughly linear, the method of estimating SoC / SoH based on the charging pile's energy metering can effectively overcome the low prediction accuracy of related estimation methods and improve the accuracy of onboard BMS (Battery Management System) system detection data.

[0057] Figure 2 This is a flowchart of an optional battery health status correction method provided according to an embodiment of this application, such as... Figure 2 As shown, the battery health status correction method consists of two steps: Step S1: Obtain the energy-SoC characteristic curve of the vehicle battery in the initial state (i.e., obtain the first capacity characteristic parameter P0); Step S2: Detect the accuracy of SoC / SoH measurement and compensate for changes in SoH value.

[0058] Step S1: Obtain the energy-SoC characteristic curve of the vehicle battery in its initial state;

[0059] Step S101: Select a charging pile and determine the uncertainty u of the charging pile's power metering;

[0060] Step S102: Charge the electric vehicle sequentially using charging piles, and record the ratio of the energy metering value E of each charging pile to the change in the vehicle's SoC value before and after charging (i.e., the charging conversion efficiency between the electric vehicle and the charging pile). Then, the current ratio k of the charging conversion efficiency between the electric vehicle and the charging pile during the i-th charging is obtained. i The formula is:

[0061]

[0062] Where i represents the i-th charge, E i △SoC represents the energy metering value of the charging station during the i-th charging session. i This represents the change in the state of charge of the electric vehicle before and after charging for the i-th time.

[0063] Step S103: Use the reciprocal of the uncertainty of the charging pile's power measurement as the weight coefficient to calculate the power-SoC characteristic curve of the electric vehicle in its initial state. The slope of this curve is P0, and the formula for P0 is:

[0064]

[0065] where u n represents the uncertainty of the corresponding initial charging pile during the nth charging, and represents the weight coefficient of the corresponding initial charging pile during the nth charging.

[0066] Figure 3 is a schematic diagram of an optional method for correcting the state of health of the battery provided by an embodiment of the present application. As Figure 3 shown, when obtaining the power-SoC characteristic curve of the battery, its slope P0 is the first capacity characteristic parameter of the battery. The weight coefficient is the reciprocal of the uncertainty of the charging pile's power measurement. Based on the charging conversion efficiency and the weight coefficient between the electric vehicle and the charging pile, the first capacity characteristic parameter P0 of the battery is determined, and then the power-SoC characteristic curve of the battery is obtained.

[0067] Step S2: Detect the accuracy of SoC / SoH measurement and compensate for changes in the SoH value.

[0068] Step S201: After the electric vehicle is put into use, record the interaction information with the charging pile during each charging process;

[0069] Step S202: During multiple charging processes of the electric vehicle at the same charging pile, if the ratio k i of the power measurement value of the charging pile to the change in SoC fluctuates, and the fluctuation amount is △k i , and △k i ≥u, that is, the fluctuation amount is greater than or equal to the predetermined fluctuation value (i.e., the uncertainty u of this charging pile), it indicates that the estimated value of the on-vehicle SoC is inaccurate;

[0070] Step S203: If in Step S202, the maximum change amount of k i does not exceed the uncertainty of the charging pile, that is, △k i <u, then use the interaction information during the charging process of the electric vehicle with multiple charging piles to obtain the power-SoC characteristic curve of the electric vehicle in its current state. The slope of this curve is P1, and when P1 < P0, it means that the capacity of the on-vehicle battery has changed, and the ratio of the current capacity to the initial capacity is P1 / P0;

[0071] Step S204: Correct the initial state of health SoH0 of the battery according to the first capacity characteristic parameter P0 and the second capacity characteristic parameter P1;

[0072] Optionally, based on the ratio between the second capacity characteristic parameter P1 and the first capacity characteristic parameter P0, the initial health state SoH0 is corrected to obtain the intermediate health state SoH'1, and the formula for SoH'1 is:

[0073]

[0074] Step S205: Based on the intermediate health state SoH'1, the first aging coefficient, and the second aging coefficient, the intermediate health state is corrected to obtain the target health state SoH1.

[0075] This completed the detection and estimation correction of the accuracy of automotive SoC / SoH measurement, providing a new technical solution for the detection of automotive SoC / SoH and improving the accuracy of automotive BMS system detection data.

[0076] The above optional implementation methods achieve at least the following effects: by introducing uncertainty information of charging piles, the accuracy of electric vehicle battery capacity characteristic parameters is improved, thereby improving the accuracy of battery health status correction results; by introducing a first battery aging coefficient based on historical behavior data of electric vehicles and a second battery aging coefficient based on temperature change data, the influence of human factors and environmental factors on battery health status correction results is reduced, further improving the accuracy of battery health status correction results.

[0077] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0078] This embodiment also provides a battery health state correction device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0079] According to an embodiment of this application, an apparatus embodiment for implementing a battery health state correction method is also provided. Figure 4 This is a schematic diagram of a battery health status correction device according to an embodiment of this application, as shown below. Figure 4 As shown, the battery health status correction device includes a charging conversion efficiency determination module 402, an identification result determination module 404, and a target health status determination module 406. The device will be described below.

[0080] The charging conversion efficiency determination module 402 is used to determine the current ratio of the charging conversion efficiency between the electric vehicle and the current charging pile for the current charging process of the electric vehicle.

[0081] The identification result determination module 404 is connected to the charging conversion efficiency determination module 402. It is used to determine the identification result corresponding to the current state of charge of the electric vehicle based on the current ratio and the historical ratio between the electric vehicle and the current charging pile. The historical ratio is obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle.

[0082] The target health status determination module 406, connected to the identification result determination module 404, is used to correct the initial health status of the electric vehicle based on the uncertainty corresponding to each of the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios of the electric vehicle with each of the multiple charging piles, when the identification result indicates that it is accurate, so as to obtain the target health status of the electric vehicle. Here, the multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile.

[0083] In a battery health state correction device provided in this application embodiment, a charging conversion efficiency determination module 402 is set up. The charging conversion efficiency determination module is used to determine the current ratio of the charging conversion efficiency between the electric vehicle and the current charging pile for the current charging process of the electric vehicle. The identification result determination module 404 is connected to the charging conversion efficiency determination module 402 and is used to determine the identification result corresponding to the current state of charge of the electric vehicle based on the current ratio and the historical ratio between the electric vehicle and the current charging pile. The historical ratio is obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle. The target health state determination module 406 is connected to the identification result determination module 404 and is used to correct the initial health state of the electric vehicle based on the uncertainty corresponding to each of the multiple charging piles that have charging interaction with the electric vehicle and the historical ratio between the electric vehicle and the multiple charging piles when the identification result indicates that it is accurate, so as to obtain the target health state of the electric vehicle. The multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation of the corresponding charging pile in energy metering. This invention achieves the goal of correcting the current health status of electric vehicles by utilizing the uncertainty of charging piles and the initial health status of electric vehicles, thereby improving the accuracy of electric vehicle battery health status estimation results and solving the technical problem of unsatisfactory accuracy of electric vehicle battery health status estimation results in related technologies.

[0084] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0085] It should be noted that the charging conversion efficiency determination module 402, the identification result determination module 404, and the target health status determination module 406 correspond to steps S102 to S106 in the embodiments. The instances and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0086] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0087] The aforementioned battery health status correction device may also include a processor and a memory. The charging conversion efficiency determination module 402, the identification result determination module 404, and the target health status determination module 406 are all stored as program units in the memory, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0088] The processor contains a core that retrieves the corresponding program unit from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0089] This application provides a non-volatile storage medium storing a program that, when executed by a processor, implements a battery health state correction method.

[0090] This application provides an electronic device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: For the current charging process between the current charging pile and the electric vehicle, it determines a current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile; based on the current ratio and historical ratios between the electric vehicle and the current charging pile, it determines an identification result corresponding to the current state of charge of the electric vehicle, wherein the historical ratios are obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle; if the identification result indicates accuracy, it corrects the initial health state of the electric vehicle based on the uncertainties corresponding to multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, to obtain a target health state of the electric vehicle, wherein the multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile. The device in this document can be a server, PC, etc.

[0091] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: For the current charging process between the current charging pile and the electric vehicle, determine a current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile; based on the current ratio and historical ratios between the electric vehicle and the current charging pile, determine an identification result corresponding to the current state of charge of the electric vehicle, wherein the historical ratios are obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle; if the identification result indicates accuracy, correct the initial health state of the electric vehicle based on the uncertainties corresponding to multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, to obtain the target health state of the electric vehicle, wherein the multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile.

[0092] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0097] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0098] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0099] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0100] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for correcting battery health status, characterized in that, include: For the current charging process of the current charging pile and the electric vehicle, determine the current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile; Based on the current ratio and the historical ratio between the electric vehicle and the current charging station, the identification result corresponding to the current state of charge of the electric vehicle is determined, wherein the historical ratio is obtained based on the charging conversion efficiency between the corresponding current charging station and the electric vehicle. If the identification result indicates that it is accurate, the initial health state of the electric vehicle is corrected based on the uncertainty corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, to obtain the target health state of the electric vehicle. The multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile. The step of correcting the initial health state of the electric vehicle based on the uncertainties corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, to obtain the target health state of the electric vehicle, includes: identifying multiple initial charging piles among the multiple charging piles where the electric vehicle is in the initial health state; obtaining weight coefficients corresponding to the multiple initial charging piles based on the uncertainties corresponding to the multiple initial charging piles; determining a first capacity characteristic parameter based on the weight coefficients and historical ratios corresponding to the multiple initial charging piles; obtaining weight coefficients corresponding to the multiple charging piles based on the uncertainties corresponding to the multiple charging piles; determining a second capacity characteristic parameter based on the weight coefficients and historical ratios corresponding to the multiple charging piles; and correcting the initial health state based on the ratio between the second capacity characteristic parameter and the first capacity characteristic parameter to obtain the target health state.

2. The method according to claim 1, characterized in that, The determination of the current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile, for the current charging process of the current charging pile and the electric vehicle, includes: Using the current charging pile, the current charging process is monitored for electrical energy to obtain the electrical energy metering value; Determine the change in state of charge of the electric vehicle during the current charging process; The current ratio is determined based on the measured electrical energy value and the change in state of charge.

3. The method according to claim 1, characterized in that, The step of determining the identification result corresponding to the current state of charge of the electric vehicle based on the current ratio and the historical ratio of the electric vehicle to the current charging pile includes: Determine the fluctuation between the current ratio and the historical ratio; If the fluctuation amount is greater than or equal to a predetermined fluctuation value, the identification result is determined to be that the current state of charge is inaccurate; If the fluctuation amount is less than the predetermined fluctuation value, the identification result is determined to be accurate for the current state of charge.

4. The method according to any one of claims 1 to 3, characterized in that, The initial health state of the electric vehicle is corrected based on the uncertainties corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios of the electric vehicle with the multiple charging piles, to obtain the target health state of the electric vehicle, including: Based on the uncertainties corresponding to the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios between the electric vehicle and the multiple charging piles, the initial health state of the electric vehicle is corrected to obtain an intermediate health state. Acquire historical behavior data of the electric vehicle, wherein the historical behavior data represents the behavioral pattern of the electric vehicle's charging and discharging depth; Based on the aforementioned historical behavior data, a first aging coefficient is determined; Based on the first aging coefficient, the intermediate health state is corrected to obtain the target health state.

5. The method according to claim 4, characterized in that, The step of correcting the intermediate health state based on the first aging coefficient to obtain the target health state includes: Acquire temperature change data for the area where the electric vehicle is located; Based on the temperature change data, a second aging coefficient is determined; Based on the first aging coefficient and the second aging coefficient, the intermediate health state is corrected to obtain the target health state.

6. A battery health status correction device, characterized in that, include: The charging conversion efficiency determination module is used to determine the current ratio representing the charging conversion efficiency between the electric vehicle and the current charging pile for the current charging process of the current charging pile and the electric vehicle. The identification result determination module is used to determine the identification result corresponding to the current state of charge of the electric vehicle based on the current ratio and the historical ratio between the electric vehicle and the current charging pile, wherein the historical ratio is obtained based on the charging conversion efficiency between the corresponding current charging pile and the electric vehicle. The target health status determination module is used to correct the initial health status of the electric vehicle based on the uncertainty corresponding to each of the multiple charging piles that have charging interactions with the electric vehicle, and the historical ratios of the electric vehicle with each of the multiple charging piles, when the identification result indicates that it is accurate, so as to obtain the target health status of the electric vehicle. The multiple charging piles include the current charging pile, and the uncertainty represents the degree of deviation in the energy metering of the corresponding charging pile. The target health state determination module is further configured to: identify multiple initial charging piles in which the electric vehicle is in the initial health state; obtain weighting coefficients corresponding to the multiple initial charging piles based on the uncertainties corresponding to each initial charging pile; determine a first capacity characteristic parameter based on the weighting coefficients and historical ratios corresponding to the multiple initial charging piles; obtain weighting coefficients corresponding to the multiple charging piles based on the uncertainties corresponding to each initial charging pile; determine a second capacity characteristic parameter based on the weighting coefficients and historical ratios corresponding to the multiple charging piles; and correct the initial health state based on the ratio between the second capacity characteristic parameter and the first capacity characteristic parameter to obtain the target health state.

7. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions adapted for loading and execution by a processor of the battery health state correction method according to any one of claims 1 to 5.

8. An electronic device, characterized in that, include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the battery health state correction method according to any one of claims 1 to 5.

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