Battery health estimation method, device, vehicle, and computer storage medium

Abstract

The application discloses a battery health estimation method and device, a vehicle and a computer storage medium, and relates to the technical field of batteries. The method comprises the following steps: acquiring a battery temperature and battery historical use data of a vehicle battery in a vehicle, and acquiring a capacity health change trend and an internal resistance health change trend according to the battery temperature; estimating a capacity health degree of the vehicle battery according to the capacity health change trend and the battery historical use data, and estimating an internal resistance health degree according to the internal resistance health change trend and the battery historical use data; and determining the minimum health degree in the capacity health degree and the internal resistance health degree as a target health degree of the vehicle battery. The application solves the technical problem of low accuracy of the health degree estimation of the vehicle battery.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to battery health estimation methods, devices, vehicles, and computer storage media. Background Technology

[0002] With the rapid development of new energy vehicles, the accuracy of State of Health (SOH) estimation for power batteries directly affects battery safety and lifespan. Currently, filtering algorithms are commonly used to estimate SOH, but these algorithms are typically based on linear or near-linear assumptions. However, battery health does not change linearly, leading to low estimation accuracy. Consequently, when the actual battery health has fallen below the safety threshold, the estimated battery health may still be above it, resulting in the continued use of batteries with poor health, which increases the risk of battery fire. Therefore, there is currently a technical problem of poor vehicle battery safety due to low accuracy in vehicle battery health estimation.

[0003] The above content is only used to help understand the technical solutions of the embodiments of this application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a battery health estimation method, apparatus, vehicle, and computer storage medium, aiming to solve the technical problem of poor vehicle battery safety caused by low accuracy in estimating vehicle battery health.

[0005] To achieve the above objectives, this application provides a battery health estimation method, which includes: acquiring the battery temperature and historical battery usage data of the vehicle battery in the vehicle, and acquiring the capacity health change trend and internal resistance health change trend based on the battery temperature;

[0006] Based on the capacity health change trend and the battery historical usage data, the capacity health of the vehicle battery is estimated, and based on the internal resistance health change trend and the battery historical usage data, the internal resistance health is estimated.

[0007] The minimum health value between the capacity health value and the internal resistance health value is determined as the target health value of the vehicle battery.

[0008] In one embodiment, the steps of obtaining the capacity health change trend and the internal resistance health change trend based on the battery temperature include:

[0009] If a target preset temperature corresponding to the battery temperature exists in the preset temperature trend database, the target capacity health change trend and target internal resistance health change trend of the target preset temperature obtained from the preset temperature trend database will be used as the capacity health change trend and internal resistance health change trend of the battery temperature.

[0010] If the target preset temperature does not exist in the preset temperature trend database, a first preset temperature and a second preset temperature adjacent to the battery temperature are searched in the preset temperature trend database, wherein the first preset temperature is greater than the battery temperature and the second preset temperature is less than the battery temperature;

[0011] Based on the first preset capacity health change trend at the first preset temperature and the second preset capacity health change trend at the second preset temperature, the capacity health change trend at the battery temperature is determined between the first preset capacity health change trend and the second preset capacity health change trend.

[0012] Based on the first preset internal resistance health change trend at the first preset temperature and the second preset internal resistance health change trend at the second preset temperature, the internal resistance health change trend of the battery temperature is determined between the first preset internal resistance health change trend and the second preset internal resistance health change trend.

[0013] In one embodiment, the preset temperature trend database includes preset capacity health change trends and preset internal resistance health change trends corresponding to preset temperatures, wherein the preset capacity health change trends include preset cycle curves and preset storage curves; the method further includes:

[0014] The preset battery cells were subjected to cyclic charge-discharge tests at multiple preset temperatures to obtain preset cycle curves between preset cycle number and preset cycle health at each preset temperature.

[0015] Storage tests were conducted on preset battery cells at each preset temperature to obtain preset storage curves between preset storage duration and preset storage health at each preset temperature.

[0016] The internal resistance of the preset battery cell is tested at each preset temperature to obtain the trend of preset internal resistance health change between the preset internal resistance and the preset internal resistance health at each preset temperature.

[0017] In one embodiment, the step of estimating the capacity health of the vehicle battery based on the capacity health change trend and the battery's historical usage data includes:

[0018] The cumulative storage time and number of battery cycles are obtained from the battery's historical usage data, and the cycle curve and storage curve are obtained from the capacity health change trend.

[0019] Obtain the cycle health corresponding to the number of battery cycles from the cycle curve, and obtain the storage health corresponding to the cumulative storage time of the battery from the storage curve;

[0020] Obtain the cycle decay coefficient and the storage decay coefficient, and perform a weighted summation of the cycle health and the storage health based on the cycle decay coefficient and the storage decay coefficient to obtain the first capacity health.

[0021] The battery's on-time capacity, battery runtime, and battery operating current when the vehicle is powered on are obtained, and the battery's off-time capacity when the vehicle is powered off are obtained. Based on the battery runtime and the battery operating current, the charge and discharge amount of the vehicle battery during the battery runtime is calculated.

[0022] The capacity difference is obtained by calculating the difference between the battery's current capacity and its current capacity. The ratio of the charge / discharge amount to the capacity difference is taken as the ampere-hour integrated capacity, and the ratio of the ampere-hour integrated capacity to the vehicle battery's preset nominal capacity is taken as the second capacity health.

[0023] The minimum health value between the first capacity health value and the second capacity health value is determined as the capacity health value.

[0024] In one embodiment, the steps of obtaining the cyclic attenuation coefficient and storing the attenuation coefficient include:

[0025] The cumulative battery cycle time and the total usage time of the vehicle battery are determined from the battery history usage data.

[0026] The ratio of the cumulative battery cycle time to the total usage time is used as the cycle attenuation coefficient, and the ratio of the cumulative battery storage time to the total usage time is used as the storage attenuation coefficient.

[0027] In one embodiment, the internal resistance health change trend includes a mapping relationship between a preset internal resistance and a preset health level, and also includes a mapping relationship between a preset usage time and a preset internal resistance; the step of estimating the internal resistance health level based on the internal resistance health change trend and the battery's historical usage data includes:

[0028] The total usage time of the vehicle battery is determined from the battery's historical usage data;

[0029] The target internal resistance for the total usage time is determined from the internal resistance health change trend, and the internal resistance health level corresponding to the target internal resistance is determined from the internal resistance health change trend.

[0030] In one embodiment, the battery health estimation method further includes:

[0031] When the target battery health is greater than a preset health and safety threshold, and the target battery health is less than or equal to a preset warning health threshold, a battery maintenance prompt is output.

[0032] If the target battery health is less than or equal to a preset health and safety threshold, a battery life termination warning will be output.

[0033] Wherein, the preset early warning health threshold is greater than the preset health and safety threshold.

[0034] Furthermore, to achieve the above objectives, embodiments of this application provide a battery health estimation device, the device comprising:

[0035] The acquisition module is used to acquire the battery temperature and historical battery usage data of the vehicle battery in the vehicle, and to acquire the capacity health change trend and internal resistance health change trend based on the battery temperature.

[0036] The estimation module is used to estimate the capacity health of the vehicle battery based on the capacity health change trend and the battery historical usage data, and to estimate the internal resistance health based on the internal resistance health change trend and the battery historical usage data.

[0037] A determination module is used to determine the minimum health value among the capacity health value and the internal resistance health value as the target health value of the vehicle battery.

[0038] Furthermore, to achieve the above objectives, this application also provides a vehicle, the vehicle including: a memory, a processor, and a program of the battery health estimation method stored in the memory and executable on the processor, wherein when the program of the battery health estimation method is executed by the processor, the steps of the battery health estimation method as described above can be implemented.

[0039] In addition, to achieve the above objectives, embodiments of this application also provide a computer-readable storage medium storing a program for implementing a battery health estimation method. When the program for the battery health estimation method is executed by a processor, it implements the steps of the battery health estimation method as described above.

[0040] In addition, to achieve the above objectives, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the battery health estimation method described above.

[0041] One or more technical solutions proposed in this application have at least the following technical effects: In this embodiment, by acquiring battery temperature and historical battery usage data, and by acquiring the capacity health change trend and internal resistance health change trend through battery temperature, it is possible to dynamically acquire the capacity health change trend and internal resistance health change trend through battery temperature, rather than estimating battery health based on a fixed trend. Since different battery temperatures have different effects on the health of vehicle batteries, this embodiment acquires the capacity health change trend and internal resistance health change trend through battery temperature, so as to improve the accuracy of subsequent battery health estimation.

[0042] Furthermore, in this embodiment, the capacity health is estimated based on the capacity health change trend and historical battery usage data, and the internal resistance health is estimated based on the internal resistance health change trend and historical battery usage data. Since historical battery usage data reflects the battery's usage status, and different usage statuses have different impacts on battery health, combining historical battery usage data and capacity health change trends can determine the capacity health corresponding to the historical battery usage data. Similarly, combining internal resistance health change trends can determine the internal resistance health corresponding to the historical battery usage data. The minimum of the internal resistance health and capacity health is determined as the target health status of the vehicle battery. This method does not use simple linear or near-linear filtering algorithms to estimate battery health, but rather uses a combination of capacity health change trends, internal resistance health change trends, and historical battery usage data to determine the vehicle battery's health status. This improves the accuracy of battery health estimation and reduces the possibility of batteries with poor health continuing to be used while the estimated battery health remains above the safety threshold, thus improving vehicle battery safety and solving the technical problem of poor vehicle battery safety caused by low battery health estimation accuracy. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with those described herein and, together with the specification, serve to explain the principles of those embodiments.

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating one embodiment of the battery health estimation method of this application.

[0046] Figure 2This is a flowchart illustrating another embodiment of the battery health estimation method in this application.

[0047] Figure 3 This is a flowchart illustrating an example of the battery health estimation method in this application.

[0048] Figure 4 This is a schematic diagram of the module structure of the battery health estimation device according to an embodiment of this application;

[0049] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the battery health estimation method in this application embodiment.

[0050] The objectives, features, and advantages of the embodiments described in this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0051] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of the embodiments of this application and are not intended to limit the embodiments of this application.

[0052] To better understand the technical solutions of the embodiments of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0053] With the rapid development of new energy vehicles, the accuracy of state-of-health (SOH) estimation for power batteries directly affects battery safety and lifespan. Improving SOH estimation accuracy while ensuring safety has become a constant challenge for researchers. Batteries age during use. Generally, when the SOH drops to 70%, the battery is considered no longer suitable for electric vehicles. Traditional SOH estimation mainly relies on filtering algorithms, which, in practice, have an accuracy of around 5%. Therefore, the poor vehicle battery safety caused by low accuracy in battery health estimation is a pressing technical problem that needs to be solved.

[0054] In this embodiment, battery temperature and historical battery usage data are acquired, and the battery temperature is used to obtain the capacity health change trend and internal resistance health change trend. This allows for the dynamic acquisition of capacity health change trends and internal resistance health change trends based on battery temperature, rather than estimating battery health based on fixed trends. Since different battery temperatures have different effects on the health of vehicle batteries, this embodiment uses battery temperature to obtain capacity health change trends and internal resistance health change trends in order to improve the accuracy of subsequent battery health estimation.

[0055] Furthermore, in this embodiment, the capacity health is estimated based on the capacity health change trend and historical battery usage data, and the internal resistance health is estimated based on the internal resistance health change trend and historical battery usage data. Since historical battery usage data reflects the battery's usage status, and different usage statuses have different impacts on battery health, combining historical battery usage data and capacity health change trends can determine the capacity health corresponding to the historical battery usage data. Similarly, combining internal resistance health change trends can determine the internal resistance health corresponding to the historical battery usage data. The minimum of the internal resistance health and capacity health is determined as the target health status of the vehicle battery. This method does not use simple linear or near-linear filtering algorithms to estimate battery health, but rather uses a combination of capacity health change trends, internal resistance health change trends, and historical battery usage data to determine the vehicle battery's health status. This improves the accuracy of battery health estimation and reduces the possibility of batteries with poor health continuing to be used while the estimated battery health remains above the safety threshold, thus improving vehicle battery safety and solving the technical problem of poor vehicle battery safety caused by low battery health estimation accuracy.

[0056] Based on this, embodiments of this application provide a battery health estimation method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the battery health estimation method according to this application. The battery health estimation method includes steps S10 to S30:

[0057] Step S10: Obtain the battery temperature and historical battery usage data of the vehicle battery in the vehicle, and obtain the capacity health change trend and internal resistance health change trend based on the battery temperature.

[0058] It should be noted that the battery temperature can be obtained from the temperature sensor set in the vehicle battery. Historical battery usage data reflects the usage status of the vehicle battery and may include the cumulative storage time, battery cycle count, cumulative cycle time, and total usage time. This embodiment does not impose specific limitations on these. Cumulative storage time refers to the time the vehicle battery is in a static state without charging or discharging. Battery cycle count refers to the number of times the vehicle battery has been charged and discharged. Cumulative cycle time refers to the duration of charging and discharging. Total usage time refers to the time from when the vehicle battery was first used after leaving the factory to the current time. Total usage time can be the sum of cycle time and storage time.

[0059] Different battery temperatures may lead to different trends in capacity health and internal resistance health. Capacity health trends reflect the relationship between health and capacity, while internal resistance health trends reflect the relationship between health and internal resistance. Internal resistance health trends can be represented as curves. The horizontal axis of these curves can be time, and the coordinate system can have two vertical axes: internal resistance and health. These two axes can share a single horizontal axis, and both axes are on the same straight line but do not overlap. Capacity health trends can include cycle time curves and storage curves. Cycle time curves reflect the relationship between the number of cycles and health, while storage curves reflect the relationship between storage time and health. The horizontal axis of the cycle time curve coordinate system can be the number of cycles, and the vertical axis can be cycle health. The horizontal axis of the storage curve coordinate system can be storage time, and the vertical axis can be internal resistance health.

[0060] Step S20: Estimate the capacity health of the vehicle battery based on the capacity health change trend and battery historical usage data, and estimate the internal resistance health based on the internal resistance health change trend and battery historical usage data.

[0061] It should be noted that both capacity health and internal resistance health can reflect the aging degree of a vehicle battery. Capacity health and internal resistance health reflect the aging degree of a vehicle battery from different dimensions. Capacity health reflects the energy storage capacity of the vehicle battery; a higher capacity health indicates stronger energy storage capacity and a longer driving range, while a lower capacity health indicates weaker energy storage capacity and a shorter driving range. Internal resistance health reflects the internal resistance state of the vehicle battery; a higher internal resistance health indicates a lower relative increase rate of internal resistance and higher power output efficiency, and vice versa. The trends in capacity health and internal resistance health corresponding to battery temperature can be predetermined.

[0062] For example, the capacity health corresponding to historical battery usage data can be determined from the capacity health change trend. Similarly, the internal resistance health corresponding to historical battery usage data can be determined from the internal resistance health change trend.

[0063] Step S30: Determine the minimum health value between capacity health value and internal resistance health value as the target health value of the vehicle battery.

[0064] It's important to note that internal resistance and capacity are both key indicators for measuring battery health. Capacity degradation directly impacts a vehicle's driving range, while increased internal resistance leads to reduced power output efficiency. For vehicle batteries, performance is limited by even worse indicators; for example, even with high capacity, excessive internal resistance will restrict usable energy, and conversely, low capacity, even with low internal resistance, will result in insufficient driving range. Therefore, this embodiment determines the minimum health level between capacity and internal resistance as the target health level for the vehicle battery, thus reflecting the actual health of the battery. The target health level reflects the actual health of the vehicle battery.

[0065] For example, the minimum health level between capacity health and internal resistance health can be determined as the target health level for the vehicle. In this embodiment, by acquiring battery temperature and historical battery usage data, and obtaining the capacity health change trend and internal resistance health change trend through battery temperature, it is possible to dynamically obtain the capacity health change trend and internal resistance health change trend through battery temperature, rather than estimating battery health based on a fixed trend. Since different battery temperatures have different effects on the health of the vehicle battery, this embodiment obtains the capacity health change trend and internal resistance health change trend through battery temperature in order to improve the accuracy of subsequent battery health estimation.

[0066] Furthermore, in this embodiment, the capacity health is estimated based on the capacity health change trend and historical battery usage data, and the internal resistance health is estimated based on the internal resistance health change trend and historical battery usage data. Since historical battery usage data reflects the battery's usage status, and different usage statuses have different impacts on battery health, combining historical battery usage data and capacity health change trends can determine the capacity health corresponding to the historical battery usage data. Similarly, combining internal resistance health change trends can determine the internal resistance health corresponding to the historical battery usage data. The minimum of the internal resistance health and capacity health is determined as the target health status of the vehicle battery. This method does not use simple linear or near-linear filtering algorithms to estimate battery health, but rather uses a combination of capacity health change trends, internal resistance health change trends, and historical battery usage data to determine the vehicle battery's health status. This improves the accuracy of battery health estimation and reduces the possibility of batteries with poor health continuing to be used while the estimated battery health remains above the safety threshold, thus improving vehicle battery safety and solving the technical problem of poor vehicle battery safety caused by low battery health estimation accuracy.

[0067] In a feasible embodiment, step S10 further includes steps S11 to S14:

[0068] Step S11: If a target preset temperature corresponding to the battery temperature exists in the preset temperature trend database, the target capacity health change trend and target internal resistance health change trend of the target preset temperature obtained from the preset temperature trend database shall be used as the capacity health change trend and internal resistance health change trend of the battery temperature.

[0069] It should be noted that the preset temperature trend database stores preset capacity health change trends and preset internal resistance health change trends corresponding to multiple preset temperatures. The preset temperature trend database can be predetermined. When the preset temperature trend database contains a target preset temperature corresponding to the battery temperature, it means that the preset temperature trend database contains corresponding capacity health change trends and internal resistance health change trends, which can then be directly obtained from the preset temperature trend database.

[0070] In other embodiments, in the preset temperature trend database, each preset temperature may have one or more preset capacity health change trends and preset internal resistance health change trends corresponding to preset operating conditions. That is, each preset temperature has a preset capacity health change trend and a preset internal resistance health change trend under each preset operating condition, and each preset temperature may have multiple preset capacity health change trends and preset internal resistance health change trends. The preset operating conditions can characterize the vehicle's operating conditions. Multiple preset operating conditions may include CLTC (China Light-duty Vehicle Test Cycle), high-temperature fast charging, high-speed driving, etc. This embodiment does not specifically limit these conditions.

[0071] If a target preset temperature for the battery temperature exists in the preset temperature trend database, and multiple preset operating conditions exist under this target preset temperature, corresponding to preset capacity health trends and preset internal resistance health trends, then the operating conditions of the vehicle containing the battery can be obtained. The target operating condition corresponding to this operating condition can be found among the multiple preset operating conditions, and its target capacity health trend and target internal resistance health trend can be used as the battery temperature's capacity health trend and internal resistance health trend. If there is only one preset operating condition with preset capacity health trend and preset internal resistance health trend under the target preset temperature, then that preset operating condition's preset capacity health trend and preset internal resistance health trend can be directly used as the battery temperature's corresponding capacity health trend and internal resistance health trend.

[0072] In the preset temperature trend database, each preset temperature has at least one preset capacity health change trend and preset internal resistance health change trend under commonly used preset operating conditions. This makes it easier to match the actual capacity health change trend and internal resistance health change trend corresponding to the battery temperature with the current operating conditions of the vehicle, thereby making it easier to obtain more accurate capacity change trend and internal resistance change trend, so as to improve the accuracy of subsequent battery health estimation.

[0073] For example, if a target preset temperature corresponding to the battery temperature exists in the preset temperature trend database, and the number of preset operating conditions corresponding to the target preset temperature is 1, then the preset internal resistance health change trend and preset capacity health change trend of the preset operating condition under the target preset temperature are used as the capacity health change trend and internal resistance health change trend of the battery temperature. If multiple preset operating conditions exist under the target preset temperature, then the current operating condition of the vehicle is obtained, and the target operating condition is determined from among the multiple preset operating conditions under the target preset temperature. The target capacity health change trend and target internal resistance health change trend of the target operating condition are used as the capacity health change trend and internal resistance health change trend of the battery temperature. If no operating condition exists among the multiple preset operating conditions under the target preset temperature, then a target commonly used operating condition is determined from among the multiple preset operating conditions. The target capacity health change trend and target internal resistance health change trend of the target commonly used operating condition are used as the capacity health change trend and internal resistance health change trend of the battery temperature. Alternatively, any target operating condition can be determined from among the multiple preset operating conditions; this embodiment does not specifically limit this. The target common operating condition can be determined based on the actual situation. This embodiment does not make specific limitations on this. Common operating conditions can be labeled with common tags so that the target common operating condition can be determined from each preset operating condition.

[0074] Step S12: If the target preset temperature does not exist in the preset temperature trend database, search for the first preset temperature and the second preset temperature adjacent to the battery temperature in the preset temperature trend database, wherein the first preset temperature is greater than the battery temperature and the second preset temperature is less than the battery temperature.

[0075] Step S13: Based on the first preset capacity health change trend at the first preset temperature and the second preset capacity health change trend at the second preset temperature, determine the capacity health change trend at the battery temperature between the first preset capacity health change trend and the second preset capacity health change trend.

[0076] Step S14: Based on the first preset internal resistance health change trend at the first preset temperature and the second preset internal resistance health change trend at the second preset temperature, determine the internal resistance health change trend of the battery temperature between the first preset internal resistance health change trend and the second preset internal resistance health change trend.

[0077] It should be noted that if the target preset temperature does not exist in the preset temperature trend database, it means that the database does not contain a corresponding capacity health trend or internal resistance health trend for the battery temperature. Both the first and second preset temperatures are adjacent to the battery temperature, and the battery temperature falls between the first and second preset temperatures. Among the preset temperatures above the battery temperature, the first preset temperature is the closest to the battery temperature; among the preset temperatures below the battery temperature, the second preset temperature is the closest to the battery temperature.

[0078] The absolute value of the difference between the first preset temperature and the second preset temperature is calculated to obtain the first temperature difference, and the absolute value of the difference between the first preset temperature and the battery temperature is calculated to obtain the second temperature difference. The ratio of the second temperature difference to the first temperature difference is used as the adjustment ratio. The capacity health trend can be determined based on the adjustment ratio, the first preset capacity health trend, and the second preset capacity health trend; similarly, the internal resistance health trend can be determined based on the adjustment ratio, the first preset internal resistance health trend, and the second preset internal resistance health trend. This is to improve the accuracy of subsequent battery health estimations.

[0079] The adjustment ratio can be the ratio of the second temperature difference to the first temperature difference. The first preset capacity health trend can be adjusted by adjusting the ratio and the second preset capacity health trend, or vice versa, to obtain the capacity health trend. Similarly, the first preset internal resistance health trend can be adjusted by adjusting the ratio and the second preset internal resistance health trend, or vice versa, to obtain the internal resistance health trend. Therefore, linear interpolation can be performed according to a certain gradient to obtain the capacity health trend and internal resistance health trend related to battery temperature.

[0080] For example, taking a first preset temperature as t1, a second preset temperature as t2, a battery temperature as tx, and the first preset internal resistance health change trend as C1(i) ​​and the second preset internal resistance health change trend as C2(i), then for each time i, the battery temperature internal resistance health change trend Cx(i) can be expressed as: Cx(i) = C2(i) + [(tx-t2) / (t1-t2)]*[C1(i)-C2(i)]. Where [(tx-t2) / (t1-t2)] can be the adjustment ratio. The first preset capacity health change trend can include a first preset cycle curve and a first preset storage curve; the second preset capacity health change trend can include a second preset cycle curve and a second preset storage curve; the capacity health change trend can include both a cycle curve and a storage curve. Taking the first preset cycle curve as Y1(n), the second preset cycle curve as Y2(n), and the first preset storage curve as K1(m) and the second preset storage curve as K2(m) as examples, the cycle curve Yx(n) of the battery temperature for each cycle number n can be expressed as: Yx(n) = Y2(n) + [(tx-t2) / (t1-t2)]*[Y1(n)-Y2(n)]. n can be a positive integer. Then, for each storage duration m, the storage curve Kx(m) of the battery temperature can be expressed as Kx(m) = K2(m) + [(tx-t2) / (t1-t2)]*[K1(m)-K2(m)].

[0081] In other embodiments, if multiple preset operating conditions exist at a first preset temperature and multiple preset operating conditions exist at a second preset temperature, the current operating condition of the vehicle can be obtained. A first target operating condition can be determined from the multiple preset operating conditions at the first preset temperature, and a second target operating condition can be determined from the multiple preset operating conditions at the second preset temperature. The preset capacity health change trend and preset internal resistance health change trend of the first target operating condition can be used as the first preset capacity health change trend and the first preset internal resistance health change trend. The preset capacity health change trend and preset internal resistance health change trend of the second target operating condition can be used as the second preset capacity health change trend and the second preset internal resistance health change trend.

[0082] When there are multiple preset operating conditions at the first preset temperature, and the number of preset operating conditions at the second preset temperature is 1, the first target operating condition can be determined from the multiple preset operating conditions at the first preset temperature. The preset capacity health change trend and the preset internal resistance health change trend of the first target operating condition can be used as the first preset capacity health change trend and the first preset internal resistance health change trend. The preset capacity health change trend and the preset internal resistance health change trend of the preset operating conditions at the second preset temperature can be used as the second preset capacity health change trend and the second preset internal resistance health change trend.

[0083] When there are multiple preset operating conditions at the second preset temperature, and the number of preset operating conditions at the first preset temperature is 1, the second target operating condition can be determined from the multiple preset operating conditions at the second preset temperature. The preset capacity health change trend and preset internal resistance health change trend of the second target operating condition can be used as the second preset capacity health change trend and the second preset internal resistance health change trend. The preset capacity health change trend and preset internal resistance health change trend of the preset operating conditions at the first preset temperature can be used as the first preset capacity health change trend and the first preset internal resistance health change trend.

[0084] When the number of preset operating conditions at the first preset temperature is 1, and the number of preset operating conditions at the second preset temperature is 1, the preset capacity health change trend and the preset internal resistance health change trend of the preset operating conditions at the first preset temperature are taken as the first preset capacity health change trend and the first preset internal resistance health change trend, and the preset capacity health change trend and the preset internal resistance health change trend of the preset operating conditions at the second preset temperature are taken as the second preset capacity health change trend and the second preset internal resistance health change trend.

[0085] This embodiment uses linear interpolation to determine the internal resistance health trend and capacity health trend of battery temperature, thereby achieving a more accurate acquisition of the trend corresponding to battery temperature, which facilitates the improvement of the accuracy of subsequent battery health estimation.

[0086] In one feasible embodiment, please refer to Figure 2 The battery health estimation method further includes: acquiring a preset temperature trend database, which includes preset capacity health change trends and preset internal resistance health change trends corresponding to preset temperatures, and the preset capacity health change trends include preset cycle curves and preset storage curves; the step of acquiring the preset temperature trend database may include steps A10 to A30:

[0087] Step A10: Perform cycle charge-discharge tests on preset battery cells at multiple preset temperatures to obtain preset cycle curves between preset cycle number and preset cycle health at each preset temperature;

[0088] Step A20: Perform storage tests on preset battery cells at each preset temperature to obtain a preset storage curve between preset storage duration and preset storage health at each preset temperature;

[0089] Step A30: Perform internal resistance testing on the preset battery cell at each preset temperature to obtain the preset internal resistance health change trend between the preset internal resistance and the preset internal resistance health degree at each preset temperature.

[0090] It should be noted that the model of the preset battery cell is the same as that of the vehicle battery, which makes the preset internal resistance health change trend, preset storage curve, and preset cycle curve obtained through the preset battery cell test more compatible with the vehicle battery. Cyclic charge-discharge testing refers to performing cyclic charge-discharge on the preset battery cell and detecting the preset cycle health after each cycle. For example, during each cyclic charge-discharge, the preset battery cell can be discharged to the cutoff voltage with a constant current, and the discharge time and current integral value can be recorded to calculate the actual discharge capacity. Alternatively, the actual discharge capacity of the preset battery cell can be detected using a battery capacity testing device. This embodiment does not specifically limit this; the ratio of the actual discharge capacity to the nominal capacity of the preset battery cell can be used as the preset cycle health.

[0091] Cyclic charge-discharge tests are performed at each preset temperature. In this embodiment, multiple preset battery cells can be used, with each preset temperature having its own corresponding preset battery cell, thus facilitating a more accurate determination of the preset cycle curve at each preset temperature. Cyclic charge-discharge tests, storage tests, and internal resistance tests are performed at each preset temperature. New preset battery cells are used for each new test at each preset temperature. All preset battery cells have the same battery model, thus ensuring the accuracy of the preset storage curve and the preset internal resistance health change trend.

[0092] For example, performing a storage test at a preset temperature means placing a preset battery cell at the preset temperature, allowing the battery cell to stand still without cyclic charging and discharging, and detecting the preset storage health of the battery cell at different preset storage durations. During the storage test, accelerated aging tests can be performed on the preset battery cell to obtain the preset storage health over a longer preset storage duration, thereby improving the comprehensiveness of the preset storage curve. For example, the preset storage health could be the ratio of the current capacity of the preset battery cell to its nominal capacity at a preset storage duration, etc., but this embodiment does not specifically limit this.

[0093] Internal resistance testing at each preset temperature refers to detecting the preset internal resistance health of a preset battery cell at different preset internal resistances. For example, when the preset internal resistance of a preset battery cell is Rnow, the preset internal resistance health can be (R_end - Rnow) / (R_end - R_initial), where R_end is the internal resistance at the end of the preset battery cell's lifespan, and R_initial is the internal resistance when the preset battery cell is first used. Internal resistance testing can also include testing the preset internal resistance of a preset battery cell at different usage durations. This can be achieved through accelerated aging tests, testing the preset internal resistance at different usage durations. Therefore, the trend of preset internal resistance health changes includes not only the mapping relationship between the preset internal resistance and the preset internal resistance health, but also the mapping relationship between the preset internal resistance and the preset usage duration.

[0094] In other embodiments, multiple preset operating conditions can be determined. Under each preset operating condition, a preset battery cell at each preset temperature is subjected to cyclic charge-discharge testing, a storage test, and an internal resistance test. This yields a preset cycle curve, a preset storage curve, and a preset internal resistance health trend for each preset temperature under each preset operating condition. That is, each preset temperature can have multiple preset cycle curves, preset storage curves, and preset internal resistance health trends. In other embodiments, preset battery cells at multiple preset temperatures can also be tested directly under a commonly used preset operating condition. The preset cycle curve, preset storage curve, and preset internal resistance health trend corresponding to each preset temperature can be 1. Commonly used preset operating conditions can be CLTC conditions, or common operating conditions of the vehicle model where the vehicle battery is located. This embodiment does not specifically limit these conditions.

[0095] In a feasible embodiment, step S20 further includes steps S21 to S26:

[0096] Step S21: Obtain the cumulative storage time and number of battery cycles from the battery's historical usage data, and obtain the cycle curve and storage curve from the capacity health change trend.

[0097] Step S22: Obtain the cycle health corresponding to the number of battery cycles from the cycle curve, and obtain the storage health corresponding to the cumulative storage time of the battery from the storage curve.

[0098] Step S23: Obtain the cycle decay coefficient and the storage decay coefficient. Based on the cycle decay coefficient and the storage decay coefficient, perform a weighted summation of the cycle health and the storage health to obtain the first capacity health.

[0099] It should be noted that the cumulative battery storage time represents the time the vehicle battery remains idle without undergoing charge-discharge cycles, while the battery cycle count represents the number of charge-discharge cycles performed on the vehicle battery. The capacity health trend includes both cycle curves and storage curves. The cycle curves include the mapping relationship between multiple preset cycle counts and their corresponding preset cycle health levels, while the storage curves include the mapping relationship between multiple preset storage times and their corresponding preset storage health levels.

[0100] The cycle health of a battery can be found in the cycle curve (number of cycles), and the storage health can be found in the storage curve (cumulative storage time). The cycle decay coefficient represents the ratio of battery cycle charging / discharging time to total usage time, while the storage decay coefficient represents the battery's idle time, i.e., the ratio of cumulative storage time to total usage time. Different cycle charging / discharging times and different cumulative storage times have different impacts on battery capacity. For example, electric vehicles like taxis generally have longer cycle charging / discharging times than battery idle time, while private electric vehicles generally have shorter cycle charging / discharging times than battery idle time. Therefore, the battery wear and tear differ, and so does the impact on battery health. Thus, it is necessary to perform a weighted sum of the cycle health and storage health based on the cycle decay coefficient and storage decay coefficient to obtain the first capacity health.

[0101] For example, the cycle health corresponding to the number of battery cycles is obtained from the cycle curve, and the storage health corresponding to the cumulative storage time of the battery is obtained from the storage curve; the product of the cycle decay coefficient and the cycle health is calculated to obtain a first value, and the product of the storage decay coefficient and the storage health is calculated to obtain a second value. The sum of the first value and the second value is used as the first capacity health. In other embodiments, the first capacity health can also be directly used as the capacity health. This embodiment improves the accuracy of the first capacity health by obtaining the capacity health change trend corresponding to the battery temperature, rather than by determining the capacity health through a fixed capacity health change trend. Because different battery temperatures have different effects on battery health, this embodiment improves the estimation accuracy of battery health by considering the influence of battery temperature on battery health.

[0102] In other embodiments, the first capacity health can also be determined based on historical capacity health. For example, historical cycle health, historical capacity health, and historical usage duration can be determined from historical battery usage data. Historical cycle health is the cycle health most recently calculated before the vehicle is powered on this time, historical capacity health is the cycle health most recently calculated before the vehicle is powered on this time, and historical usage duration is the duration between the initial use of the vehicle battery and the current power-on. After the vehicle is powered on, the current battery temperature can be obtained, and the first duration of this battery temperature can be recorded. The reduction in cycle health between the last moment of the historical usage duration and the end moment of the first duration can be obtained from the battery temperature cycle curve. The difference between the historical cycle health and the reduction in cycle health can be used as the cycle health. The reduction in storage health between the last moment of the historical usage duration and the end moment of the first duration can be obtained from the battery temperature storage curve. The difference between the historical storage health and the reduction in storage health can be used as the storage health. Then, the storage health and cycle health are weighted and summed by combining the cycle decay coefficient and the storage decay coefficient to obtain the first capacity health. For example, if the historical usage time of a battery is ta, there are time periods t1, t2, and t3 within that historical usage time ta. The sum of t1, t2, and t3 equals ta. The battery temperatures corresponding to t1, t2, and t3 are c1, c2, and c3, respectively. Since the battery temperatures are the same within the same time period, the cycle health at the end of t1 is the cycle health at the end of t1 in the cycle curve of c3. The cycle health at the end of t2 is the cycle health at t1 minus the decrease in cycle health between the end of t1 and the end of t2 in the c2 cycle curve. The battery health at the end of t3 is the cycle health at t2 minus the decrease in cycle health between the end of t2 and the end of t3 in the c3 cycle curve.

[0103] Step S24: Obtain the battery power-on capacity, battery running time, battery operating current when the vehicle is powered on, and the battery power-off capacity when the vehicle is powered off. Calculate the charge and discharge amount of the vehicle battery during the battery running time based on the battery running time and battery operating current.

[0104] Step S25: Calculate the difference between the battery's current capacity and its current capacity to obtain the capacity difference. Use the ratio of the charge / discharge amount to the capacity difference as the ampere-hour integrated capacity and the ratio of the ampere-hour integrated capacity to the vehicle battery's preset nominal capacity as the second capacity health.

[0105] Step S26: Determine the minimum health value between the first and second capacity health values ​​as the capacity health value.

[0106] It should be noted that vehicle power-on signifies the start of vehicle use, and battery power-on capacity represents the vehicle's capacity at the time of power-on. Battery power-on capacity can be the battery capacity at the time of the vehicle's last power-off, or it can be determined by looking up the capacity corresponding to the voltage at the time of power-on in a preset voltage-capacity mapping relationship between preset voltage and preset battery capacity. For example, if the time between the vehicle's last power-off and the current power-on moment is greater than a preset resting time, the battery power-on capacity corresponding to the voltage at the time of power-on is looked up in the preset voltage-capacity mapping relationship; if the time between the vehicle's last power-off and the current power-on moment is less than or equal to the preset resting time, the battery capacity at the time of the last power-off is used as the battery power-on capacity.

[0107] Battery runtime can be the duration from the vehicle's current power-on to power-off. Battery operating current can be the current after the vehicle's current power-on. Battery de-energization capacity can be found in a preset voltage-capacity mapping relationship, specifically the battery de-energization capacity corresponding to the vehicle's voltage at the time of power-off. The second capacity health is calculated, while the first capacity health is determined through capacity health change trends and historical battery usage data.

[0108] For example, the formula for calculating the second capacity health can be Formula 1:

[0109] SOHr=Q(now) / Qb=[∫Idt / (SOC1-SOC0)] / Qb (Formula 1);

[0110] Where SOHr is the second capacity health status, Q(now) is the current capacity of the vehicle battery, Qb is the nominal capacity of the vehicle battery, and ∫Idt is the charge / discharge amount, which can be obtained by integrating the battery operating current. SOC0 is the battery charge at startup, and SOC1 is the battery charge at shutdown. ∫Idt / (SOC1-SOC0) is the integrated charge in ampere-hours.

[0111] After determining the first and second capacity health levels, the lower one can be selected as the capacity health level. This avoids situations where a battery with poor health, due to an excessively high capacity health level, continues to be used, thus improving battery safety.

[0112] In a feasible embodiment, step S23 further includes steps S231 to S232:

[0113] Step S231: Determine the cumulative battery cycle time and the total usage time of the vehicle battery from the battery history usage data;

[0114] Step S232: The ratio of the cumulative battery cycle time to the total usage time is used as the cycle attenuation coefficient, and the ratio of the cumulative battery storage time to the total usage time is used as the storage attenuation coefficient.

[0115] It should be noted that the cumulative battery cycle time is the total time for the vehicle battery to undergo cycle charging and discharging, while the total usage time is the total usage time of the vehicle battery. The total usage time can be the time between when the vehicle battery starts to be used and the current time. The sum of the cumulative battery cycle time and the cumulative battery storage time equals the total usage time.

[0116] In other embodiments, the vehicle model of the vehicle containing the battery can be obtained, along with the cycle time and storage time of other vehicles with the same model. The cycle time of each vehicle is calculated to obtain the total cycle time, and the storage time of each vehicle is calculated to obtain the total storage time. The sum of the total cycle time and the total storage time is used as the total time limit. The ratio of the total cycle time to the total time limit can then be used as the cycle attenuation coefficient, or the ratio of the total storage time to the total time limit can be used as the storage attenuation coefficient. This eliminates the need for the vehicle itself to record the storage time and cycle time in real time, allowing for the determination of the storage attenuation coefficient and the cycle attenuation coefficient, thus reducing the operational burden on the vehicle.

[0117] In a feasible embodiment, the internal resistance health change trend includes a mapping relationship between a preset internal resistance and a preset health level, and also includes a mapping relationship between a preset usage time and a preset internal resistance. Step S20 further includes steps B10 to B20:

[0118] Step B10: Determine the total usage time of the vehicle battery from the battery history usage data;

[0119] Step B20: Determine the target internal resistance for total usage time from the internal resistance health change trend, and determine the internal resistance health level corresponding to the target internal resistance from the internal resistance health change trend.

[0120] It should be noted that the internal resistance health change trend includes the mapping relationship between the preset internal resistance and the preset health level, as well as the mapping relationship between the preset usage time and the preset internal resistance. Therefore, we can first obtain the target internal resistance corresponding to the total usage time from the internal resistance health change trend, and then obtain the internal resistance health level corresponding to the target internal resistance from the internal resistance health change trend. The total usage time is the time from the initial use of the vehicle battery to the current moment.

[0121] Since the trend of internal resistance health changes corresponds to the current battery temperature of the vehicle battery, the internal resistance health can be determined more accurately through the trend of internal resistance health changes, so as to improve the accuracy of battery health estimation.

[0122] In other embodiments, internal resistance health can also be determined based on historical internal resistance health. For example, historical internal resistance health and historical usage duration can be determined from historical battery usage data. The historical internal resistance health is the most recently calculated internal resistance health before the current power-on, and the historical usage duration is the time between the initial use of the vehicle battery and the current power-on. After the vehicle is powered on, the current battery temperature can be obtained, and the first duration of this temperature can be recorded. The decrease in internal resistance health between the last moment of the historical usage duration and the end moment of the first duration can be obtained from the internal resistance health change trend of the battery temperature. The difference between the historical internal resistance health and the decrease in internal resistance health can be used as the internal resistance health. Furthermore, the impact of different battery temperatures on health during historical usage can be taken into account, thereby improving the accuracy of health estimation.

[0123] In a feasible embodiment, the battery health estimation method further includes steps C10 to C20:

[0124] Step C10: When the target battery health is greater than the preset health and safety threshold and the target battery health is less than or equal to the preset warning health threshold, output a battery maintenance prompt.

[0125] Step C20: If the target battery health is less than or equal to a preset health and safety threshold, output a battery life termination prompt.

[0126] Among them, the preset early warning health threshold is greater than the preset health and safety threshold.

[0127] It should be noted that the preset health and safety threshold can be 70% or 80%, etc., and this embodiment does not specifically limit it. When the target battery health is lower than the preset health and safety threshold, it indicates that the battery health is too low, and continued use is not recommended. Continued use may lead to lithium plating and fire. The battery life end warning indicates that the vehicle battery's lifespan has ended, and continued use is not recommended. The battery maintenance warning is used to remind the user that the vehicle battery's lifespan is nearing its end and that maintenance is required.

[0128] If the preset warning health threshold is lower than the preset health and safety threshold, and the target battery health is less than or equal to the preset warning health threshold but greater than the preset health and safety threshold, the user is prompted to maintain the battery to extend its lifespan. For example, the preset warning health threshold could be a value such as 85% or 86%, but this embodiment does not specify a particular value.

[0129] In other embodiments, when the number of detected mutations in the target battery health exceeds a preset threshold, battery maintenance suggestions are output. The preset threshold can be determined based on actual conditions, for example, it could be a value such as 5 or 6; this embodiment does not specifically limit this. A mutation in target battery health refers to the difference between the target battery health and the historical target battery health exceeding a preset difference value. The preset difference value can be determined based on actual conditions; this embodiment does not specifically limit this. The historical target battery health can be the target battery health calculated at a specific time step above the target battery health.

[0130] When the number of mutations in the target battery health is found to exceed a preset threshold, battery usage data corresponding to each mutation is acquired. This battery usage data may include the user's vehicle battery usage habits. Target user behaviors that occur more frequently than a preset high-frequency threshold can be identified from the battery usage data. Battery maintenance recommendations may include suggestions to reduce these target user behaviors. For example, target user behaviors might include low battery levels or frequent fast charging; this embodiment does not specifically limit these behaviors.

[0131] To better understand this embodiment, please refer to Figure 3 The process of this embodiment is briefly described as follows: Step X10: Start; Step X20: Detect battery temperature; Step X30: Obtain the cycle curve, storage curve, and internal resistance health change trend corresponding to the battery temperature; Step X31a: Obtain the cycle health corresponding to the number of battery cycles in the cycle curve, and obtain the storage health corresponding to the cumulative storage time of the battery in the storage curve. Then, execute step X32: SOHa = Cycle Health * Cycle Attenuation Coefficient j1% + Storage Health * Storage Attenuation Coefficient j2%, where both the cycle attenuation coefficient and the storage attenuation coefficient are less than 1. SOHa is the capacity health, which can be directly taken as Cycle Health * Cycle Attenuation Coefficient j1% + Storage Health * Storage Attenuation Coefficient j2%. After step X30, step X31b is executed: obtain the internal resistance health level SOHb from the internal resistance health change trend; then step X40 can be executed: SOHm = min(SOHa, SOHb), where SOHm is the target battery health level, and min(SOHa, SOHb) represents the smaller of the capacity health level SOHa and the internal resistance health level SOHb. Step X50: SOHm is less than the preset health safety threshold; that is, determine whether the target battery health level is less than or equal to the preset health safety threshold. If so, execute step X60: output a battery life termination prompt. If the target battery health level is greater than the preset health safety threshold, return to step X20.

[0132] This application also provides a battery health estimation device; please refer to... Figure 4 The device includes:

[0133] The acquisition module 10 is used to acquire the battery temperature and historical battery usage data of the vehicle battery in the vehicle, and to acquire the capacity health change trend and internal resistance health change trend based on the battery temperature.

[0134] The estimation module 20 is used to estimate the capacity health of the vehicle battery based on the capacity health change trend and the battery's historical usage data, and to estimate the internal resistance health based on the internal resistance health change trend and the battery's historical usage data.

[0135] The determination module 30 is used to determine the minimum health value among capacity health value and internal resistance health value as the target health value of the vehicle battery.

[0136] The battery health estimation device provided in this application adopts the battery health estimation method in the above embodiments, aiming to solve the technical problem of low accuracy in estimating the health of vehicle batteries. Compared with the prior art, the beneficial effects of the battery health estimation method provided in this application are the same as those of the battery health estimation method provided in the above embodiments, and other technical features in this battery health estimation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0137] This application provides a vehicle, which includes a vehicle body, a vehicle battery, and a controller. The controller and the vehicle battery are both located in the vehicle body. The controller includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the battery health estimation method in Embodiment 1 above.

[0138] The following is for reference. Figure 5 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of this application. The electronic devices in these embodiments may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The vehicle shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0139] like Figure 5As shown, the vehicle may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for vehicle operation. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the vehicle to communicate wirelessly or wiredly with other devices to exchange data. Although the diagram shows vehicles with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0140] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0141] The vehicle provided in this application, employing the battery health estimation method described in the above embodiments, can solve the technical problem of low accuracy in estimating the health of vehicle batteries. Compared with the prior art, the beneficial effects of the vehicle provided in this application are the same as those of the battery health estimation method provided in the above embodiments, and other technical features of the vehicle are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0142] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0143] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0144] This embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon, which are used to execute the battery health estimation method in the first embodiment described above.

[0145] The computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, apparatuses, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable EPROM (Electrical Programmable Read Only Memory) or flash memory, optical fiber, portable compact disk CD-ROM (compact discread-only memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution device, apparatus, or apparatus. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0146] The aforementioned computer-readable storage medium may be included in the vehicle or may exist independently and not installed in the vehicle.

[0147] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a vehicle, cause the vehicle to: acquire battery temperature and historical battery usage data of the vehicle battery, and acquire capacity health change trends and internal resistance health change trends based on the battery temperature; estimate the capacity health of the vehicle battery based on the capacity health change trends and historical battery usage data, and estimate the internal resistance health based on the internal resistance health change trends and historical battery usage data; and determine the minimum health among the capacity health and internal resistance health as the target health of the vehicle battery.

[0148] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a LAN (local area network) or WAN (wide area network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based device that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0150] The modules described in the embodiments of this disclosure can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0151] The computer-readable storage medium provided in this application embodiment stores computer-readable program instructions for executing the above-described battery health estimation method, aiming to solve the technical problem of low accuracy in estimating the health of vehicle batteries. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application embodiment are the same as the beneficial effects of the battery health estimation method provided in the above embodiments, and will not be repeated here.

[0152] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the battery health estimation method described above.

[0153] The computer program product provided in this application aims to solve the technical problem of low accuracy in estimating the health of vehicle batteries. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the battery health estimation method provided in the above embodiments, and will not be repeated here.

[0154] The above are merely preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structural or procedural transformations made using the description and drawings of the present application, or direct or indirect applications in other related technical fields, are similarly included within the patent processing scope of the present application.

Claims

1. A battery health estimation method, characterized in that, The method includes: Obtain the battery temperature and historical usage data of the vehicle battery, and obtain the capacity health change trend and internal resistance health change trend based on the battery temperature. Based on the capacity health change trend and the battery historical usage data, the capacity health of the vehicle battery is estimated, and based on the internal resistance health change trend and the battery historical usage data, the internal resistance health is estimated. The internal resistance health change trend includes the mapping relationship between preset internal resistance and preset health, and also includes the mapping relationship between preset usage time and preset internal resistance. The minimum health value between the capacity health value and the internal resistance health value is determined as the target health value of the vehicle battery. The steps for obtaining the capacity health change trend and internal resistance health change trend based on the battery temperature include: If a target preset temperature corresponding to the battery temperature exists in the preset temperature trend database, the target capacity health change trend and target internal resistance health change trend of the target preset temperature obtained from the preset temperature trend database will be used as the capacity health change trend and internal resistance health change trend of the battery temperature. The step of estimating the capacity health of the vehicle battery based on the capacity health change trend and the battery's historical usage data includes: The cumulative storage time and number of battery cycles are obtained from the battery's historical usage data, and the cycle curve and storage curve are obtained from the capacity health change trend. Obtain the cycle health corresponding to the number of battery cycles from the cycle curve, and obtain the storage health corresponding to the cumulative storage time of the battery from the storage curve; Obtain the cycle decay coefficient and the storage decay coefficient, and perform a weighted summation of the cycle health and the storage health based on the cycle decay coefficient and the storage decay coefficient to obtain the first capacity health. The battery's on-time capacity, battery runtime, and battery operating current when the vehicle is powered on are obtained, and the battery's off-time capacity when the vehicle is powered off are obtained. Based on the battery runtime and the battery operating current, the charge and discharge amount of the vehicle battery during the battery runtime is calculated. The capacity difference is obtained by calculating the difference between the battery's current capacity and its current capacity. The ratio of the charge / discharge amount to the capacity difference is taken as the ampere-hour integrated capacity, and the ratio of the ampere-hour integrated capacity to the vehicle battery's preset nominal capacity is taken as the second capacity health. The minimum health value between the first capacity health value and the second capacity health value is determined as the capacity health value; The internal resistance health change trend includes a mapping relationship between a preset internal resistance and a preset health level, as well as a mapping relationship between a preset usage time and a preset internal resistance; the step of estimating the internal resistance health level based on the internal resistance health change trend and the battery's historical usage data includes: The total usage time of the vehicle battery is determined from the battery's historical usage data; The target internal resistance for the total usage time is determined from the internal resistance health change trend, and the internal resistance health level corresponding to the target internal resistance is determined from the internal resistance health change trend.

2. The battery health estimation method as described in claim 1, characterized in that, The steps of obtaining the capacity health change trend and internal resistance health change trend based on the battery temperature further include: If the target preset temperature does not exist in the preset temperature trend database, a first preset temperature and a second preset temperature adjacent to the battery temperature are searched in the preset temperature trend database, wherein the first preset temperature is greater than the battery temperature and the second preset temperature is less than the battery temperature; Based on the first preset capacity health change trend at the first preset temperature and the second preset capacity health change trend at the second preset temperature, the capacity health change trend at the battery temperature is determined between the first preset capacity health change trend and the second preset capacity health change trend. Based on the first preset internal resistance health change trend at the first preset temperature and the second preset internal resistance health change trend at the second preset temperature, the internal resistance health change trend of the battery temperature is determined between the first preset internal resistance health change trend and the second preset internal resistance health change trend.

3. The battery health estimation method as described in claim 1, characterized in that, The preset temperature trend database includes preset capacity health change trends and preset internal resistance health change trends corresponding to preset temperatures. The preset capacity health change trends include preset cycle curves and preset storage curves. The method further includes: The preset battery cells were subjected to cyclic charge-discharge tests at multiple preset temperatures to obtain preset cycle curves between preset cycle number and preset cycle health at each preset temperature. Storage tests were conducted on preset battery cells at each preset temperature to obtain preset storage curves between preset storage duration and preset storage health at each preset temperature. The internal resistance of the preset battery cell is tested at each preset temperature to obtain the trend of preset internal resistance health change between the preset internal resistance and the preset internal resistance health at each preset temperature.

4. The battery health estimation method as described in claim 1, characterized in that, The steps of obtaining the cyclic decay coefficient and storing the decay coefficient include: The cumulative battery cycle time and the total usage time of the vehicle battery are determined from the battery history usage data. The ratio of the cumulative battery cycle time to the total usage time is used as the cycle attenuation coefficient, and the ratio of the cumulative battery storage time to the total usage time is used as the storage attenuation coefficient.

5. The battery health estimation method according to any one of claims 1-4, characterized in that, The battery health estimation method also includes: When the target battery health is greater than a preset health and safety threshold, and the target battery health is less than or equal to a preset warning health threshold, a battery maintenance prompt is output. If the target battery health is less than or equal to a preset health and safety threshold, a battery life termination warning will be output. Wherein, the preset early warning health threshold is greater than the preset health and safety threshold.

6. A battery health estimation device, characterized in that, The device includes: The acquisition module is used to acquire the battery temperature and historical battery usage data of the vehicle battery in the vehicle, and to acquire the capacity health change trend and internal resistance health change trend based on the battery temperature. The estimation module is used to estimate the capacity health of the vehicle battery based on the capacity health change trend and the battery historical usage data, and to estimate the internal resistance health based on the internal resistance health change trend and the battery historical usage data. A determination module is used to determine the minimum health value among the capacity health value and the internal resistance health value as the target health value of the vehicle battery. The acquisition module is further configured to, when the target preset temperature corresponding to the battery temperature exists in the preset temperature trend database, use the target capacity health change trend and target internal resistance health change trend of the target preset temperature obtained from the preset temperature trend database as the capacity health change trend and internal resistance health change trend of the battery temperature. The estimation module is further configured to obtain the cumulative storage time and number of battery cycles from the battery's historical usage data; obtain the cycle curve and storage curve from the capacity health change trend; obtain the cycle health corresponding to the number of battery cycles from the cycle curve, and obtain the storage health corresponding to the cumulative storage time from the storage curve; obtain the cycle decay coefficient and storage decay coefficient; and perform a weighted summation of the cycle health and storage health based on the cycle decay coefficient and storage decay coefficient to obtain a first capacity health; obtain the battery's on-time capacity, battery running time, battery operating current when the vehicle is powered on, and battery's off-time capacity when the vehicle is powered off, and calculate the charge and discharge amount of the vehicle battery within the battery running time based on the battery running time and battery operating current; calculate the difference between the off-time capacity and the on-time capacity to obtain the capacity difference; use the ratio of the charge and discharge amount to the capacity difference as the ampere-hour integrated capacity, and use the ratio of the ampere-hour integrated capacity to the preset nominal capacity of the vehicle battery as a second capacity health; and determine the minimum health among the first capacity health and the second capacity health as the capacity health. The estimation module is also used to determine the total usage time of the vehicle battery from the battery's historical usage data; determine the target internal resistance of the total usage time from the internal resistance health change trend; and determine the internal resistance health level corresponding to the target internal resistance from the internal resistance health change trend.

7. A vehicle, characterized in that, The vehicle includes a vehicle body, a vehicle battery, and a controller, both of which are located in the vehicle body. The controller is used to perform the steps of implementing the battery health estimation method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and the computer-readable storage medium stores a program that implements the battery health estimation method, the program that implements the battery health estimation method being executed by a processor to implement the steps of the battery health estimation method as described in any one of claims 1 to 5.

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