Adaptive adjustment method and device for remaining battery power of vehicle and vehicle

Abstract

The present disclosure relates to the technical field of automobile battery storage, and relates to a vehicle battery residual capacity adaptive adjustment method and device and a vehicle. The method comprises the following steps: in response to a storage task of a vehicle battery, obtaining battery working condition parameters corresponding to the storage task and candidate battery residual capacities; based on an aging characteristic map and the battery working condition parameters, performing aging prediction on each candidate battery residual capacity to obtain an optimal battery residual capacity of each candidate battery residual capacity under the battery working condition parameters; and performing self-discharge correction on the optimal battery residual capacity to obtain a target battery residual capacity of the vehicle battery. The present disclosure distinguishes the difference in aging rates under different temperatures and different battery residual capacities, and explicitly takes into account the capacity drift caused by self-discharge, so as to reduce the capacity attenuation of the whole storage process to a lower level under the premise of ensuring the available capacity at the end of storage.

Description

Technical Field

[0001] This disclosure relates to the field of automotive battery storage technology, specifically to an adaptive adjustment method, device, and vehicle for the remaining charge of a vehicle battery. Background Technology

[0002] With the rapid development of new energy vehicles and the energy storage industry, the large-scale production and application of power batteries has brought about a significant demand for battery storage. Batteries often need to be stored in warehouses or containers for months or even years after leaving the factory, before being installed in vehicles, or during standby periods at energy storage power stations. During this period, although the batteries are not charged or discharged externally, they still undergo calendar aging, leading to capacity decay and increased internal resistance. Capacity loss due to storage aging can account for 15%-30% of the battery's total lifespan, significantly impacting its economic viability and safety.

[0003] For actual vehicle users, battery aging during periods of vehicle inactivity is also a significant concern. Users may leave their vehicles parked for weeks or months due to business trips, travel, or seasonal use. During this time, the battery remains in a static storage state, and calendar aging continues to occur.

[0004] Most battery manufacturers recommend maintaining the State of Charge (SOC) of the battery within the range of 40%-60%. This strategy does not consider the differences in optimal battery SOC at different temperatures, nor does it differentiate between different battery chemistry systems, making a fixed battery SOC unsuitable for adapting to dynamic changes in ambient temperature. For real-world vehicle users, this fixed strategy ignores the impact of self-discharge on battery SOC: when a user sets the vehicle to the recommended battery SOC and parks it, self-discharge will cause the battery SOC to gradually deviate from the optimal value. In high-temperature environments, the aging rate after deviating from the optimal battery SOC increases dramatically. Summary of the Invention

[0005] In view of this, the present disclosure provides an adaptive adjustment method, apparatus and vehicle for the remaining battery power of a vehicle, in order to solve the problems that the remaining battery power obtained by the relevant fixed strategy cannot adapt to the dynamic changes of ambient temperature, and ignores the impact of self-discharge on the remaining battery power, thus failing to obtain the optimal remaining battery power.

[0006] In a first aspect, this disclosure provides an adaptive adjustment method for the remaining charge of a vehicle battery, the method comprising: In response to the vehicle battery storage task, obtain the battery operating parameters and the remaining power of the candidate batteries corresponding to the storage task; Based on the aging feature map and battery operating parameters, the remaining capacity of each candidate battery is predicted by aging, and the optimal remaining capacity of each candidate battery under the battery operating parameters is obtained. Self-discharge correction is applied to the optimal remaining battery capacity to obtain the target remaining battery capacity of the vehicle battery.

[0007] In one alternative implementation, the method further includes: Before using aging feature maps and battery operating parameters, obtain multiple preset temperatures and multiple preset remaining battery power. A calendar aging test was conducted on the vehicle battery based on a preset temperature and a preset remaining battery charge to obtain the aging rate constant and time index under each operating condition combination. The operating condition combination is composed of a combination of each preset temperature and each preset remaining battery charge. Temperature-dependent regression is performed on the aging rate constants at different temperatures under the same battery remaining capacity in each working condition combination to obtain the reference activation energy corresponding to different temperatures under the same battery remaining capacity, and the activation energy of each preset battery remaining capacity at each preset temperature is obtained based on the reference activation energy. An aging characteristic map is generated based on the aging rate constant, time exponent, and activation energy.

[0008] In one optional implementation, a calendar aging test is conducted on the vehicle battery based on a preset temperature and a preset remaining battery charge to obtain the aging rate constant and time exponent for each operating condition combination, including: A calendar aging test was conducted on the vehicle battery based on a preset temperature and a preset remaining battery charge, and experimental data on the battery capacity retention rate as a function of storage time were collected under each operating condition combination. Curve fitting was performed on the experimental data to obtain the aging rate constant and time exponent for each operating condition combination.

[0009] In one alternative implementation, battery operating parameters include target storage duration, current temperature, and minimum capacity retention. Based on aging feature maps and battery operating parameters, aging prediction is performed on the remaining capacity of each candidate battery to obtain the optimal remaining capacity of each candidate battery under the battery operating parameters, including: The aging rate constant, time exponent, and activation energy corresponding to the remaining charge of each candidate battery are retrieved from the aging feature map. Based on the activation energy, the aging rate constant obtained from the query is corrected for temperature. The value at the reference temperature is converted to the value at the current temperature to obtain the aging rate constant at the current temperature. Based on the current temperature aging rate constant, time exponent, target storage duration and current temperature, calculate the predicted capacity retention rate of each candidate battery after the target storage duration; The optimal remaining battery capacity is determined based on the predicted capacity retention rate and the minimum capacity retention rate.

[0010] In one optional implementation, determining the optimal remaining battery capacity based on the predicted capacity retention rate and the minimum capacity retention rate includes: Determine if there is a reference battery with a predicted capacity retention rate greater than or equal to the minimum capacity retention rate; When a reference battery has a remaining charge, the largest remaining charge of the reference battery is selected as the optimal remaining charge of the battery. When no reference battery remaining capacity exists, an alarm and prompt message are generated, and the battery remaining capacity with the highest predicted capacity retention rate is selected as the optimal battery remaining capacity. In an optional implementation, self-discharge correction is applied to the optimal battery remaining capacity to obtain the target battery remaining capacity of the vehicle battery, including: The self-discharge rate is determined based on the self-discharge model. Based on the self-discharge rate, the decay of the remaining capacity of the optimal battery with storage time is modeled to obtain the aging accumulation of the remaining capacity of the optimal battery. The optimal remaining battery capacity is corrected based on the accumulated aging amount to obtain the target remaining battery capacity of the vehicle battery.

[0011] In one optional implementation, the optimal remaining battery capacity is corrected based on the accumulated aging to obtain the target remaining battery capacity of the vehicle battery, including: The optimal battery remaining capacity is modified by taking the minimum cumulative aging amount during the entire storage process as the optimization objective and the remaining battery capacity at the end of storage as a constraint condition that it is greater than or equal to the preset safe lower limit of the remaining battery capacity, so as to obtain the target battery remaining capacity.

[0012] Secondly, this disclosure provides an adaptive adjustment device for the remaining charge of a vehicle battery, the device comprising: The first acquisition module is used to acquire the battery operating parameters and the remaining power of the candidate batteries in response to the storage task of the vehicle battery. The prediction module is used to predict the remaining capacity of each candidate battery based on the aging feature map and battery condition parameters, and to obtain the optimal remaining capacity of each candidate battery under the battery condition parameters. The correction module is used to correct the self-discharge of the optimal remaining battery capacity to obtain the target remaining battery capacity of the vehicle battery.

[0013] Thirdly, this disclosure provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform the adaptive adjustment method for the remaining battery power of the vehicle according to the first aspect or any corresponding embodiment thereof.

[0014] Fourthly, this disclosure provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the adaptive adjustment method for the remaining power of a vehicle battery as described in the first aspect or any corresponding embodiment.

[0015] Fifthly, this disclosure provides a computer-readable storage medium storing computer instructions for causing a computer to execute the adaptive adjustment method for the remaining battery power of a vehicle according to the first aspect or any corresponding embodiment described above.

[0016] In a sixth aspect, this disclosure provides a computer program product, including computer instructions for causing a computer to execute the adaptive adjustment method for the remaining battery power of a vehicle as described in the first aspect or any corresponding embodiment.

[0017] In this embodiment, by responding to a storage task, the battery operating parameters and remaining capacity of candidate batteries corresponding to the storage task are obtained. Then, based on the aging feature map, aging prediction is performed on each candidate value to select the optimal remaining capacity. This is then corrected using a self-discharge model, ultimately selecting the target remaining capacity. This ensures that the setpoint for the remaining capacity during storage is no longer a fixed empirical value, but an adaptive value that can be dynamically adjusted according to storage conditions. Compared to the traditional approach of mechanically fixing the remaining capacity of the stored battery to a single value, this embodiment distinguishes the differences in aging rates under different temperatures and different remaining battery capacities, and explicitly includes the capacity drift caused by self-discharge. This reduces capacity decay throughout the storage process to a lower level while ensuring usability at the end of storage, solving the technical problems of fixed storage strategies being unable to adapt to dynamic temperature changes and ignoring the impact of self-discharge. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the specific embodiments of this disclosure or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an adaptive adjustment method for the remaining battery power of a vehicle according to an embodiment of this disclosure. Figure 2 This is a flowchart illustrating an adaptive adjustment method for the remaining battery power of a vehicle according to another embodiment of the present disclosure. Figure 3This is a flowchart illustrating an adaptive adjustment method for the remaining battery power of a vehicle according to yet another embodiment of the present disclosure. Figure 4 This is a structural block diagram of an adaptive adjustment device for the remaining battery power of a vehicle according to an embodiment of the present disclosure; Figure 5 This is a structural block diagram of a vehicle according to an embodiment of the present disclosure. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0021] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0022] The term "first" is used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" may explicitly or implicitly include one or more of that feature.

[0023] With the rapid development of new energy vehicles and the energy storage industry, the large-scale production and application of power batteries has brought about a significant demand for battery storage. For actual vehicle users, battery aging during vehicle downtime is also a concern. Users may leave their vehicles parked for weeks or months due to business trips, travel, or seasonal use. During this period, the battery is in a static storage state, and calendar aging continues to occur.

[0024] Most battery manufacturers recommend maintaining the storage state of charge (SOC) within the range of 40%-60%. This strategy has the following problems: The difference in optimal SOC at different temperatures was not taken into account. The different battery chemistry systems were not distinguished. A fixed SOC cannot adapt to dynamic changes in the current temperature; For real-world vehicle users, the fixed strategy ignores the impact of self-discharge on SOC: when a user sets the vehicle to the recommended SOC and parks it, self-discharge will cause the SOC to gradually deviate from the optimal value. In high-temperature environments, the aging rate after deviating from the optimal SOC increases sharply.

[0025] Question 1: Self-discharge causes SOC drift: Batteries experience self-discharge during storage. LFP batteries have a self-discharge rate of approximately 1%-3% per month, while ternary lithium batteries have a self-discharge rate of approximately 2%-5% per month. The initial SOC set by the user will naturally decrease over time. If the initial SOC is set too low, the SOC may enter an unsafe region (e.g., <5%) after self-discharge. If the initial SOC is set too high, although self-discharge will cause it to decrease, the SOC will pass through a high aging rate region during the decrease process.

[0026] Question 2: Idle SOC due to daily vehicle use is not conducive to extending vehicle life: After daily use, the battery SOC is typically in the following state: Fully charged and parked: Users should charge the battery to 100% and then park it. The aging rate is fastest at high SOC. Low battery parking: The user parks the vehicle after the battery is low. Although the SOC is low, it may be below the safety threshold. The optimal storage SOC (around 20%-30%) is rarely reached naturally in daily use.

[0027] Question 3: The optimal SOC differs between short-term and long-term storage: Short-term storage (1-7 days): Users are more concerned about convenience, so a higher SOC makes it easier to use the vehicle next time; Mid-term storage (1 week - 3 months): requires balancing self-discharge compensation and aging rate; Long-term storage (>3 months): Minimizing aging should be a priority.

[0028] To address the aforementioned problems, according to an embodiment of this disclosure, an adaptive adjustment method for the remaining charge of a vehicle battery 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.

[0029] Before proceeding with each step, several terms used in this disclosure are defined to avoid ambiguity in subsequent descriptions. State of Charge (SOC) refers to the percentage of usable charge currently stored in the battery relative to its nominal capacity, answering the question "How much charge is left in the battery?". State of Health (SOH) refers to the ratio of the maximum capacity the battery can currently discharge to its initial factory capacity, answering the question "How much charge can the battery still hold?" Therefore, SOH also reflects the battery capacity retention rate. These two terms belong to different dimensions: State of Charge fluctuates significantly over a short period with charging and discharging, while the capacity retention rate decreases slowly and monotonically with aging. Calendar aging refers to the capacity decay and internal resistance increase that occur in a battery during static storage without external charging or discharging, solely due to the passage of time and internal side reactions.

[0030] This embodiment provides an adaptive adjustment method for the remaining battery power of a vehicle, which can be used in a vehicle battery management system (BMS). Hereinafter referred to as the system, Figure 1 This is a flowchart of an adaptive adjustment method for the remaining battery power of a vehicle according to an embodiment of this disclosure, such as... Figure 1 As shown, the process includes the following steps: Step S101: In response to the storage task of the vehicle battery, obtain the battery operating parameters and the remaining power of the candidate batteries corresponding to the storage task.

[0031] Optionally, in this embodiment, the power battery often needs to be stored in warehouses or containers for months or even years between the production line and vehicle assembly, or during standby at the energy storage power station. For actual vehicle users, the vehicle may also be parked for weeks or months due to business trips, travel, seasonal use, etc. All of the above situations can be regarded as a "storage task".

[0032] When a vehicle is detected to be entering storage mode, or when the system receives the user's input of the estimated parking duration, it triggers a storage task and collects battery condition parameters related to the task. Currently, a candidate set of remaining battery charge levels can also be obtained. The value range of this candidate set of remaining battery charge levels [0%, 100%] can be discretized with a fixed step size (e.g., 1% or 5%) to obtain a series of discrete candidate remaining battery charge levels. Alternatively, users or maintenance personnel can directly specify several candidate points (e.g., 20%, 30%, 40%, 50%) as candidate remaining battery charge levels based on experience.

[0033] Step S102: Based on the aging feature map and battery operating parameters, aging prediction is performed on the remaining capacity of each candidate battery to obtain the optimal remaining capacity of each candidate battery under the battery operating parameters.

[0034] Optionally, the aging feature map is a parameterized dataset that characterizes the rate of battery aging under different temperatures and remaining battery capacity conditions. The system iterates through the set of candidate battery remaining capacities. For each candidate battery remaining capacity, it uses the aging feature map to estimate its capacity retention rate after storage time under given battery operating parameters. Then, it selects the battery with the lightest aging and best meeting storage requirements from all candidate battery remaining capacities as the optimal battery remaining capacity. Here, "optimal battery remaining capacity" is a single value selected from multiple candidate battery remaining capacities, representing the battery remaining capacity setpoint that best maintains the capacity retention rate during storage, without considering the effect of self-discharge.

[0035] Furthermore, the core of aging prediction lies in pre-establishing the quantitative relationship between "temperature, remaining battery capacity, and aging rate" in the graph, so that during actual storage, there is no need to repeat aging experiments that take several months; the prediction results can be obtained quickly simply by looking up the table and making conversions.

[0036] Step S103: Perform self-discharge correction on the optimal remaining battery capacity to obtain the target remaining battery capacity of the vehicle battery.

[0037] Optionally, batteries exhibit self-discharge during static storage, meaning that without any external load, the battery slowly loses charge due to internal side reactions. The self-discharge rate of lithium iron phosphate batteries is approximately 1%–3% per month, while that of ternary lithium batteries is approximately 2%–5% per month. This means that even if the battery's remaining charge is initially set precisely to the optimal level, the actual remaining charge will naturally decline over time, gradually deviating from the optimal setting. Furthermore, under high-temperature conditions, the aging rate after deviating from the optimal value will increase dramatically.

[0038] Therefore, this step uses a self-discharge model to describe the decay of the battery's remaining capacity over time, and performs self-discharge correction on the aforementioned optimal remaining battery capacity. After self-discharge correction, the system obtains the corrected target remaining battery capacity, which is the battery capacity setpoint that should be adopted and maintained during actual storage. This target remaining battery capacity should minimize the cumulative aging during the entire storage process, while ensuring that the remaining battery capacity does not fall below the safety lower limit at the end of storage, thus balancing the requirements of extending battery life and maintaining usability.

[0039] In this embodiment, by responding to a storage task, the battery operating parameters and remaining capacity of candidate batteries corresponding to the storage task are obtained. Then, based on the aging feature map, aging prediction is performed on each candidate value to select the optimal remaining capacity. This is then corrected using a self-discharge model, ultimately selecting the target remaining capacity. This ensures that the setpoint for the remaining capacity during storage is no longer a fixed empirical value, but an adaptive value that can be dynamically adjusted according to storage conditions. Compared to the traditional approach of mechanically fixing the remaining capacity of the stored battery to a single value, this embodiment distinguishes the differences in aging rates under different temperatures and different remaining battery capacities, and explicitly includes the capacity drift caused by self-discharge. This reduces capacity decay throughout the storage process to a lower level while ensuring usability at the end of storage, solving the technical problems of fixed storage strategies being unable to adapt to dynamic temperature changes and ignoring the impact of self-discharge.

[0040] As an optional embodiment, before step S102 above, the method further includes: Step a1: Obtain multiple preset temperatures and multiple preset remaining battery power.

[0041] Optionally, to cover the storage environments the battery may encounter, experimental points need to be set up within a wide temperature range and remaining battery charge range. Regarding temperature, several preset temperatures can be selected within the range of -40℃ to 60℃, such as -40℃, -30℃, -20℃, -10℃, 0℃, 10℃, 20℃, 30℃, 40℃, 50℃, and 60℃, for a total of 11 temperature points. Regarding remaining battery charge, several preset remaining battery charge values ​​can be selected within the range of 0% to 100%, such as 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%, for a total of 11 charge points. Each preset temperature and each preset remaining battery charge is paired to form a "condition combination," and all combinations form an 11×11 experimental matrix. To ensure the statistical reliability of the results, at least two parallel samples should be set up under each condition combination.

[0042] Step a2: Conduct a calendar aging test on the vehicle battery based on the preset temperature and preset remaining battery power to obtain the aging rate constant and time index under each operating condition combination. The operating condition combination is composed of the combination of each preset temperature and each preset remaining battery power.

[0043] Optionally, calendar aging tests involve placing the battery under set temperature and remaining charge conditions and periodically measuring its capacity retention over storage time to quantify the rate of aging under those conditions. The test can continue until the capacity retention drops below 80% or reaches a preset duration (e.g., over 700 days). By fitting the "capacity retention - storage time" data collected for each operating condition combination, the aging rate constant k, characterizing the aging rate under that condition, and the time exponent n, characterizing the trend of aging rate evolution over time, can be obtained. A larger aging rate constant indicates faster capacity decay per unit time; the time exponent reflects whether aging accelerates or decelerates over time.

[0044] Specifically, step a2 above includes: Step a21: Conduct a calendar aging test on the vehicle battery based on a preset temperature and a preset remaining battery charge, and collect experimental data on the battery capacity retention rate as a function of storage time under each operating condition combination. Step a22: Perform curve fitting on the experimental data to obtain the aging rate constant and time exponent for each working condition combination.

[0045] Optionally, under each operating condition combination, the battery is placed in a constant temperature environment and maintained at a set remaining battery charge. Its usable capacity is measured periodically (e.g., weekly), and the capacity retention rate (SOH), i.e., the ratio of current capacity to initial capacity, is calculated, with a value between 0 and 1. By plotting the storage time t on the horizontal axis and the capacity retention rate SOH on the vertical axis, and recording the values ​​sequentially, an experimental curve reflecting the aging process under that operating condition is obtained. The measurement should continue for a sufficiently long time to capture the evolution trend of aging in the middle and late stages.

[0046] For example, for the SOH-t data under each temperature-SOC condition, the least squares method is used to fit equation (1) to obtain the values ​​of k and n.

[0047] A power-law aging model is used to describe the change in battery capacity retention (SOH) over storage time: SOH(t) = 1 - k × t n (1) in: SOH(t) is the capacity retention rate at storage time t, defined as the ratio of the current capacity to the initial capacity, with a value ranging from 0 to 1; k is the aging rate constant, which is related to temperature and SOC; n is the time index, which is mainly related to SOC; t represents the storage time in days.

[0048] As can be seen from the above formula (1), SOH(t) is collected in real time, and t is also known, representing the current storage time. Then, k and n can be obtained by fitting using the least squares method.

[0049] Step a3: Perform temperature-dependent regression on the aging rate constants at different temperatures under the same battery remaining charge in each working condition combination to obtain the reference activation energy corresponding to different temperatures under the same battery remaining charge, and obtain the activation energy of each preset battery remaining charge at each preset temperature based on the reference activation energy.

[0050] Optionally, for a set of aging rate constants k corresponding to different temperatures under the same remaining battery charge, a linear regression is performed with ln(k) as the vertical axis and the reciprocal of thermodynamic temperature 1 / T as the horizontal axis. The slope of the obtained straight line multiplied by the negative value of the gas constant R is the reference activation energy under the remaining battery charge.

[0051] Based on the reference activation energy obtained at the remaining charge point of each battery, the activation energy distribution of each preset battery at each preset temperature can be further extrapolated or interpolated. The activation energy is a physical quantity that characterizes the temperature sensitivity of the aging reaction; a higher value indicates a more drastic change in the aging rate with temperature.

[0052] Step a4: Based on the aging rate constant, time exponent, and activation energy, generate an aging characteristic map.

[0053] Optionally, the aging rate constant k, time exponent n, and activation energy Ea obtained above can be arranged in a two-dimensional "temperature-remaining battery capacity" relationship to form an aging characteristic map covering the entire temperature and energy range. For temperature points where experiments were not directly conducted, the activation energy can be extrapolated using the Arrhenius relation to fill in the gaps; for remaining battery capacity points where experiments were not directly conducted, linear interpolation or spline interpolation can be used to fill in the gaps. Thus, the discrete experimental points are expanded into a continuous three-dimensional aging characteristic map of temperature-remaining battery capacity-aging parameters, which can be used for table lookup in subsequent steps.

[0054] Based on the measured data of two lithium iron phosphate batteries, the key parameters are shown in the table below:

[0055] It is of great significance to retain both the aging rate constant and the time exponent in the aging characteristic spectrum.

[0056] In this embodiment, the aging rate constant and time exponent are obtained through calendar aging experiments with multiple temperatures and remaining battery power. The activation energy is extracted by temperature-dependent regression, thereby generating an aging feature map covering the entire temperature and power range. This allows the aging behavior of the battery under any storage conditions to be found or extrapolated from the map, providing a parameter basis with clear physical meaning and sufficient data support for subsequent aging prediction.

[0057] As an optional embodiment, battery operating parameters include target storage duration, current temperature, and minimum capacity retention; such as Figure 2 The above step S102 includes: Step S1021: Query the aging rate constant, time index, and activation energy corresponding to the remaining charge of each candidate battery from the aging feature map.

[0058] Optionally, the target storage duration T target This refers to the expected duration of this storage operation; the current temperature T refers to the actual temperature of the storage location; and the minimum capacity retention rate SOH... min This refers to the minimum capacity retention rate required to be achieved when storage ends.

[0059] The system iterates through the set of remaining battery capacity for each candidate value. For each candidate value, it retrieves the corresponding aging rate constant, time exponent, and activation energy from the aging feature map, using these parameters as inputs for aging prediction. Since the aging feature map has already been constructed in the aforementioned embodiments, this is merely a table lookup operation and does not require repetition of the experiment.

[0060] Step S1022: Based on the activation energy, the aging rate constant obtained by the query is corrected for temperature by converting the value at the reference temperature to the value at the current temperature to obtain the aging rate constant at the current temperature.

[0061] Optionally, the aging rate constant obtained directly from experiments in the aging knowledge graph often corresponds to a certain reference temperature (e.g., 60°C), while the actual storage temperature is usually different from that, so temperature correction is required using activation energy.

[0062] The aging rate constant k varies with temperature according to the Arrhenius equation: k(T) = k ref × exp[-Ea / R × (1 / T+273.15 -1 / T ref )](2) in: k ref Reference temperature T ref The aging rate constant at a given temperature; in this embodiment, a reference temperature T is used. ref It is 60℃; Ea is the activation energy, which is related to SOC; R is the ideal gas constant, 8.314 J / (mol·K); T represents the current temperature, in degrees Celsius (°C).

[0063] After conversion using equation (2), the current temperature aging rate constant k(T) of the remaining charge of the candidate battery at the actual current temperature is obtained.

[0064] Step S1023: Based on the current temperature aging rate constant, time exponent, target storage duration and current temperature, calculate the predicted capacity retention rate of each candidate battery after the target storage duration.

[0065] Optionally, the current temperature aging rate constant k(T), the time exponent n, and the target storage duration T are used to... target Substituting the current temperature T into the power-law aging model yields the predicted capacity retention rate of each candidate battery after the target storage time.

[0066] Specifically, for each SOC value in the set of remaining battery capacity candidates, taking a current temperature of 60°C as an example: Calculate k = k 60(SOC) × exp[-Ea(SOC) / R × (1 / (T+273.15) - 1 / 333.15)]; Calculate the predicted capacity retention rate (SOH) pred = 1 - k × (365 × T target ) n ; Record (SOC, SOH) pred ).

[0067] Step S1024: Determine the optimal remaining battery capacity based on the predicted capacity retention rate and the minimum capacity retention rate.

[0068] Optionally, after obtaining the predicted capacity retention rate of each candidate value, the lowest capacity retention rate is used as a threshold for selection to determine the optimal remaining battery capacity SOC. opt .

[0069] Specifically, step S1024 above includes: Step b1: Determine whether there is a reference battery with a predicted capacity retention rate greater than or equal to the minimum capacity retention rate; Step b2: When a reference battery has a remaining charge, select the reference battery with the largest remaining charge as the optimal battery remaining charge. Step b3: When there is no reference battery remaining power, generate alarm and prompt information, and select the battery remaining power with the highest predicted capacity retention rate as the optimal battery remaining power.

[0070] Optionally, the predicted capacity retention rate of each candidate battery's remaining capacity is compared with the minimum capacity retention rate (SOH) one by one. min In comparison, all predicted capacity retention rates are not lower than SOH. min All remaining battery capacity values ​​are included in the reference battery capacity range. The remaining battery capacity of the reference battery constitutes a feasible subset that meets the storage life requirements, and the subsequent optimal value is generated from this subset.

[0071] If the feasible subset is not empty, the selection criterion is to use the largest value of the remaining reference battery capacity, and the maximum SOC is taken as the optimal remaining battery capacity.

[0072] If, under the current temperature and target storage duration, no value in the candidate set can make the predicted capacity retention rate reach SOH... min This indicates that the established storage conditions are too stringent and cannot maintain the required capacity retention rate within the target duration. In this case, the system generates an alarm and prompt message, suggesting lowering the current storage temperature or shortening the storage duration. Simultaneously, as a fallback, it selects the SOC with the highest predicted capacity retention rate as the optimal remaining battery power. This provides a suboptimal setpoint with the lightest aging even in infeasible scenarios, preventing the storage task from being interrupted due to a lack of solutions.

[0073] To further illustrate the above prediction process, a calculation example is provided. Assume the current temperature is 40℃, the target storage duration is 3 years, and the required minimum capacity retention rate (SOH) is... min =90%, and the predicted SOH after 3 years of storage at 40°C for each SOC is shown in the table below:

[0074] As shown in the table above, there are 8 values ​​that satisfy SOH ≥ 90%: 0.962, 0.950, 0.931, 0.923, 0.918, 0.914, 0.909, and 0.902. Among them, the SOC is the highest when SOH = 0.902, with a value of 35%. Therefore, the optimal solution is: SOC opt = 35% (the highest SOC that meets the constraints, while taking into account self-discharge loss).

[0075] Based on the above algorithm, the optimal SOC recommendation for different current temperatures can be obtained:

[0076] Pattern: For every 10°C increase in current temperature, the optimal SOC needs to decrease by approximately 10-15 percentage points.

[0077] As an alternative embodiment, such as Figure 3 The above step S103 includes: Step S1031: Determine the self-discharge rate based on the self-discharge model.

[0078] Optionally, during long-term storage, the battery will experience self-discharge. The system needs to periodically (e.g., monthly) check the battery's SOC. When the SOC deviates from the target value by more than the set range (e.g., ±5%), it should perform recharging or discharging adjustments to bring the SOC back to near the optimal set point.

[0079] The variation of LFP battery self-discharge rate with SOC and temperature: r sd (SOC, T) = r0× exp[α × (SOC - SOC ref )] × exp[-Ea sd / R × (1 / T - 1 / T ref )] (3) in: r sd Self-discharge rate (% / day); r0 is the reference self-discharge rate, approximately 0.05%-0.15% / day (LFP). α is the SOC dependence coefficient, approximately 0.005-0.01; Ea sd The activation energy for self-discharge is approximately 20-30 kJ / mol. SOC ref This represents the remaining battery power, with a value ranging from 10% to 90%, and currently 60% can be used. T ref This represents the reference temperature, with a value range of [25℃-60℃]. Currently, 60℃ can be selected.

[0080] r can be calculated using the above formula (3). sd .

[0081] Step S1032: Based on the self-discharge rate, model the decay of the remaining capacity of the optimal battery with storage time to obtain the aging accumulation of the remaining capacity of the optimal battery.

[0082] Let the initial SOC be the optimal remaining battery capacity SOC. opt The remaining battery charge after storage for t days can be expressed as: SOC(t) = SOC opt r sd ×t (4) Since the remaining battery capacity decreases over time, and the aging rate is a function of the remaining battery capacity, the aging process over the entire storage period needs to be accumulated integrally. A = (5) Equation (5) describes the entire storage duration interval [0, T] target Integrate over time to calculate the total cumulative aging over the entire storage cycle.

[0083] Step S1033: Correct the optimal remaining battery capacity based on the accumulated aging amount to obtain the target remaining battery capacity of the vehicle battery.

[0084] Optionally, the optimal remaining battery capacity can be adjusted to minimize the cumulative aging amount, thus obtaining the corrected target remaining battery capacity.

[0085] This embodiment models the decay of the remaining battery capacity over time and calculates the aging accumulation by integration. Based on this, the optimal remaining battery capacity is corrected, so that the storage strategy explicitly takes into account the capacity drift caused by self-discharge.

[0086] As an optional embodiment, step S1033 above includes: Step c1 involves adjusting the optimal remaining battery capacity to obtain the target remaining battery capacity, with the goal of minimizing the cumulative aging amount throughout the storage process and the constraint that the remaining battery capacity at the end of storage is greater than or equal to the preset safe lower limit of the remaining battery capacity.

[0087] Optionally, this step applies a dual screening approach of objective and constraint based on the optimal remaining battery capacity. The optimization objective is to minimize the cumulative aging amount throughout the storage process, i.e., to minimize the cumulative aging amount A defined in equation (5): The constraint is that the remaining battery charge at the end of storage is not lower than the preset safe minimum remaining battery charge (SOC). safemin That is, it satisfies: SOC opt r sd ×T target ≥ SOC safemin (6) Equation (6) ensures that even if the battery experiences a decline in self-discharge throughout the entire storage period, it will still have sufficient charge at the end of the storage period and will not fall into the unsafe area of ​​over-discharge.

[0088] Furthermore, the constraints define the feasible lower boundary of the remaining battery capacity to prevent setting the capacity too low in pursuit of minimal aging, which could lead to over-discharge at the end of storage; the optimization objective selects the setpoint within the feasible region that minimizes aging throughout the entire process. Together, these factors ensure that the target battery's remaining capacity is both extended and usable.

[0089] In practical deployment, periodic monitoring can also be incorporated: the system periodically (e.g., monthly) checks the remaining battery power. Once it deviates from the target value by more than a set range (e.g., ±5%), it triggers a recharge or discharge adjustment. The recharge target can be set as the target remaining battery power plus approximately a 5% safety margin. For example, the recharge target SOC: SOC opt + ΔSOC margin (ΔSOC) margin For a safety margin of approximately 5%, the timing for recharging is when SOC ≤ SOC. opt - ΔSOC margin hour.

[0090] When the remaining battery charge drops to the target value minus this margin, a recharge is triggered. The recharge frequency is determined by both the self-discharge rate and the safe charge range.

[0091] This embodiment takes minimizing the cumulative aging throughout the storage process as the optimization objective and ensuring that the remaining battery power at the end of storage is not lower than the safety limit as the constraint. The modified remaining battery power is then filtered to obtain the final target remaining battery power for execution, thus unifying the life extension target and availability requirements in the same decision-making process.

[0092] As an optional application embodiment, after obtaining the remaining power of the target battery, the battery operating parameters used in this method can be configured in a scenario-based manner according to the scenario in which the user expects to park for a certain period of time, so as to adapt to the different driving habits of real vehicle users.

[0093] The system reads the user's input of the expected parking time, maps it to a preset scene range, and automatically selects the aging loss constraint, battery remaining power orientation, and whether to enable self-discharge compensation corresponding to the scene, without requiring manual settings by the user.

[0094] Scenario A: This scenario corresponds to short-term storage, with an expected parking period of one to fourteen days. Users typically park their vehicles for business trips or short vacations, focusing on convenience for future use. In this scenario, capacity retention constraints are relatively lenient; aging loss during storage should be controlled within 0.5%. The battery remaining charge setting is biased towards convenience, allowing for a slightly higher value. Due to the short parking time, self-discharge-induced charge reduction is negligible. Considering all these factors, a battery remaining charge range of 30% to 50% is recommended.

[0095] Scenario B: Corresponding to medium-term storage, with an expected parking period of 15 days to three months. Users typically park the vehicle due to seasonal use or long business trips, requiring a balance between aging and convenience. In this scenario, capacity retention constraints are moderate, and aging loss during storage should be controlled within 2%. The battery's remaining charge setpoint needs precise optimization and should not be based on rough estimates from experience. Self-discharge compensation must also be factored in to offset the natural decline in charge during months of parking. The recommended remaining battery charge range is 20% to 35%, and this needs to be precisely calculated based on the current temperature.

[0096] Scenario C: For long-term storage, with an expected parking period exceeding three months, where users typically use idle or spare vehicles, prioritizing battery health. In this scenario, capacity retention is the most stringent, with aging loss during storage controlled to within 3%. The battery's remaining charge setpoint is the theoretically optimal value, determined by the aforementioned aging prediction and self-discharge correction. Self-discharge compensation must be strictly implemented, and regular capacity testing is recommended to monitor changes in battery health during long-term storage. The recommended remaining battery charge range is 10% to 25%.

[0097] The adaptive judgment of the above three scenarios is completed automatically by the system: the system selects the corresponding scenario based on the expected parking time input by the user, and incorporates the aging loss constraint, battery remaining capacity orientation and self-discharge compensation requirements of the scenario as battery operating condition parameters into the aforementioned method process, so that the same life extension method can provide differentiated storage strategies for different parking times.

[0098] This embodiment also provides an adaptive adjustment device for the remaining battery power of a vehicle. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be 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.

[0099] This embodiment provides an adaptive adjustment device for the remaining battery power of a vehicle, such as... Figure 4 As shown, it includes: The first acquisition module 401 is used to acquire the battery operating parameters and the remaining power of the candidate batteries in response to the storage task of the vehicle battery. Prediction module 402 is used to predict the remaining capacity of each candidate battery based on aging feature map and battery condition parameters, and to obtain the optimal remaining capacity of each candidate battery under the battery condition parameters. The correction module 403 is used to perform self-discharge correction on the optimal remaining battery power to obtain the target remaining battery power of the vehicle battery.

[0100] In this embodiment, by responding to storage tasks, battery operating parameters and the remaining capacity of candidate batteries are obtained. Then, based on aging feature maps, aging prediction is performed on each candidate value to select the optimal remaining battery capacity. This is then corrected using a self-discharge model, ultimately selecting the target battery capacity. This ensures that the battery capacity setpoint during storage is no longer a fixed empirical value, but an adaptive value that can be dynamically adjusted according to storage conditions. Compared to the traditional approach of mechanically fixing the remaining battery capacity to a single value, this embodiment distinguishes the differences in aging rates under different temperatures and battery capacities, and explicitly accounts for the capacity drift caused by self-discharge. This reduces capacity decay throughout the storage process to a lower level while ensuring usability at the end of storage, solving the technical problems of fixed storage strategies being unable to adapt to dynamic temperature changes and ignoring the impact of self-discharge.

[0101] In some alternative embodiments, the device includes: The second acquisition module is used to acquire multiple preset temperatures and multiple preset battery remaining power before the aging feature map and battery operating parameters are obtained. The first module is used to conduct a calendar aging test on the vehicle battery based on a preset temperature and a preset remaining battery charge, and to obtain the aging rate constant and time index under each operating condition combination. The operating condition combination is composed of a combination of each preset temperature and each preset remaining battery charge. The second module is used to perform temperature-dependent regression on the aging rate constants at different temperatures under the same battery remaining charge in each working condition combination, to obtain the reference activation energy corresponding to different temperatures under the same battery remaining charge, and to obtain the activation energy of each preset battery remaining charge at each preset temperature based on the reference activation energy. The generation module is used to generate aging characteristic maps based on the aging rate constant, time exponent, and activation energy.

[0102] In some optional implementations, the first obtaining module is used to conduct a calendar aging test on the vehicle battery based on a preset temperature and a preset remaining battery charge, and to collect experimental data on the battery capacity retention rate as a function of storage time under each operating condition combination. Curve fitting was performed on the experimental data to obtain the aging rate constant and time exponent for each operating condition combination.

[0103] In some alternative implementations, battery operating parameters include target storage duration, current temperature, and minimum capacity retention. In some optional implementations, the prediction module 402 is used to query the aging rate constant, time exponent, and activation energy corresponding to the remaining capacity of each candidate battery from the aging feature map; perform temperature correction on the aging rate constant obtained from the query based on the activation energy, converting the value at the reference temperature to the value at the current temperature to obtain the aging rate constant at the current temperature; calculate the predicted capacity retention rate of the remaining capacity of each candidate battery after the target storage time based on the aging rate constant at the current temperature, the time exponent, the target storage time, and the current temperature; and determine the optimal remaining capacity of the battery based on the predicted capacity retention rate and the minimum capacity retention rate.

[0104] In some optional implementations, the prediction module 402 is used to filter out reference battery remaining power with a predicted capacity retention rate greater than or equal to the minimum capacity retention rate; when reference battery remaining power exists, the reference battery remaining power with the highest predicted capacity retention rate is selected as the optimal battery remaining power; when the predicted capacity retention rates of multiple reference battery remaining power are similar, the reference battery remaining power with the lowest battery remaining power is selected as the optimal battery remaining power; when no reference battery remaining power exists, an alarm and prompt message are generated, and the reference battery remaining power with the highest predicted capacity retention rate is selected as the optimal battery remaining power.

[0105] In some optional implementations, the correction module 403 is used to determine the self-discharge rate based on the self-discharge model; to model the decay of the optimal battery remaining capacity with storage time based on the self-discharge rate, and to obtain the aging accumulation of the optimal battery remaining capacity; and to correct the optimal battery remaining capacity based on the aging accumulation, to obtain the target battery remaining capacity of the vehicle battery.

[0106] In some optional implementations, the correction module 403 is used to correct the optimal remaining battery power with the goal of minimizing the cumulative aging amount throughout the storage process and with the constraint that the remaining battery power at the end of storage is greater than or equal to a preset safe lower limit for the remaining battery power, so as to obtain the target remaining battery power.

[0107] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0108] In this embodiment, the adaptive adjustment device for the remaining battery power of the vehicle is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0109] This disclosure also provides a vehicle having the above-described features. Figure 4The device shown is an adaptive adjustment device for the remaining battery power of the vehicle.

[0110] Please see Figure 5 , Figure 5 This is a structural block diagram of a vehicle provided in an optional embodiment of this disclosure, such as... Figure 5 As shown, the vehicle includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0111] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0112] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0113] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0114] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0115] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0116] This disclosure also provides a computer-readable storage medium in which the methods described in this disclosure can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium after being downloaded over a network. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium may be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium may also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0117] Although embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An adaptive adjustment method for the remaining charge of a vehicle battery, characterized in that, The method includes: In response to a storage task of the vehicle battery, obtain the battery operating parameters and remaining charge of the candidate battery corresponding to the storage task; Based on the aging feature map and the battery operating parameters, the remaining capacity of each candidate battery is predicted by aging, and the optimal remaining capacity of each candidate battery under the battery operating parameters is obtained. The optimal remaining battery capacity is corrected for self-discharge to obtain the target remaining battery capacity of the vehicle battery.

2. The method according to claim 1, characterized in that, Prior to the aging feature map and the battery operating parameters, the method further includes: Get multiple preset temperatures and multiple preset remaining battery power; A calendar aging test is conducted on the vehicle battery based on the preset temperature and the preset remaining battery power to obtain the aging rate constant and time index under each operating condition combination, wherein the operating condition combination is composed of each preset temperature and each preset remaining battery power combination. Temperature-dependent regression is performed on the aging rate constant at different temperatures under the same battery remaining charge in each working condition combination to obtain the reference activation energy corresponding to different temperatures under the same battery remaining charge, and the activation energy of each preset battery remaining charge at each preset temperature is obtained based on the reference activation energy. The aging characteristic map is generated based on the aging rate constant, the time exponent, and the activation energy.

3. The method according to claim 2, characterized in that, The calendar aging test is conducted on the vehicle battery based on the preset temperature and the preset remaining battery capacity to obtain the aging rate constant and time index under each operating condition combination, including: A calendar aging test was conducted on the vehicle battery based on the preset temperature and the preset remaining battery power, and experimental data on the battery capacity retention rate as a function of storage time were collected under each operating condition combination. Curve fitting was performed on the experimental data to obtain the aging rate constant and time exponent for each operating condition combination.

4. The method according to claim 2, characterized in that, The battery operating parameters include target storage duration, current temperature, and minimum capacity retention. The step of predicting the remaining capacity of each candidate battery based on the aging feature map and the battery operating parameters, to obtain the optimal remaining capacity of each candidate battery under the battery operating parameters, includes: From the aging feature map, query the aging rate constant, the time exponent, and the activation energy corresponding to the remaining charge of each candidate battery; Based on the activation energy, the aging rate constant obtained by the query is corrected for temperature, and the value at the reference temperature is converted to the value at the current temperature to obtain the aging rate constant at the current temperature. Based on the current temperature aging rate constant, the time exponent, the target storage duration, and the current temperature, calculate the predicted capacity retention rate of each candidate battery after the target storage duration; Based on the predicted capacity retention rate and the minimum capacity retention rate, the optimal remaining battery capacity is determined.

5. The method according to claim 4, characterized in that, Determining the optimal remaining battery capacity based on the predicted capacity retention rate and the minimum capacity retention rate includes: Determine whether there is a reference battery with remaining charge that is greater than or equal to the predicted capacity retention rate; When the reference battery has a remaining charge, the largest remaining charge of the reference battery is selected as the optimal remaining charge of the battery. If the reference battery has no remaining power, an alarm and a prompt message are generated, and the remaining power of the battery with the highest predicted capacity retention rate is selected as the optimal remaining power of the battery.

6. The method according to claim 1, characterized in that, The step of performing self-discharge correction on the optimal remaining battery capacity to obtain the target remaining battery capacity of the vehicle battery includes: The self-discharge rate is determined based on the self-discharge model. Based on the self-discharge rate, the decay of the remaining capacity of the optimal battery with storage time is modeled to obtain the aging accumulation of the remaining capacity of the optimal battery. The optimal remaining battery capacity is corrected based on the accumulated aging amount to obtain the target remaining battery capacity of the vehicle battery.

7. The method according to claim 6, characterized in that, The step of correcting the optimal remaining battery capacity based on the accumulated aging amount to obtain the target remaining battery capacity of the vehicle battery includes: The optimal battery remaining capacity is modified by taking the minimum cumulative aging amount during the entire storage process as the optimization objective and the remaining battery capacity at the end of storage as a constraint condition that it is greater than or equal to the preset safe lower limit of the remaining battery capacity, so as to obtain the target battery remaining capacity.

8. An adaptive adjustment device for the remaining charge of a vehicle battery, characterized in that, The device includes: The first acquisition module is used to acquire battery operating parameters and remaining power of candidate batteries in response to the storage task of the vehicle battery. The prediction module is used to predict the remaining capacity of each candidate battery based on the aging feature map and the battery operating parameters, so as to obtain the optimal remaining capacity of each candidate battery under the battery operating parameters. The correction module is used to perform self-discharge correction on the optimal remaining battery power to obtain the target remaining battery power of the vehicle battery.

9. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the adaptive adjustment method for the remaining battery power of a vehicle as described in any one of claims 1 to 7.

10. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the adaptive adjustment method for the remaining charge of a vehicle battery as described in any one of claims 1 to 7.

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