Battery thermal management method and device, electronic equipment, storage medium and terminal

Abstract

The application discloses a battery thermal management method, a battery thermal management device, an electronic device, a storage medium and a terminal, and belongs to the field of battery thermal management. Temperature information and life information of a battery are obtained, a thermal management control target is determined according to the temperature information and the life information, and then the battery is subjected to thermal management control according to the thermal management control target. In the scheme, the thermal management decision can simultaneously respond to the temperature and life requirements of the battery, overcomes the limitation of control according to a single temperature threshold, and realizes intelligent thermal management considering the safety, life and energy efficiency of the battery.

Description

Technical Field

[0001] This application belongs to the field of battery thermal management, specifically relating to a battery thermal management method, a battery thermal management device, an electronic device, a readable storage medium, and a terminal. Background Technology

[0002] With the increasing popularity of electric vehicles, the long-term lifespan of power batteries has become a core concern for both users and manufacturers. The battery thermal management system is a key component for ensuring battery safety and performance.

[0003] Existing battery thermal management solutions primarily focus on real-time or short-term temperature control, such as controlling the start and stop of cooling or heating systems through preset temperature thresholds to meet current safety and performance requirements. However, such methods have limitations in optimizing the long-term lifespan of batteries. They typically struggle to effectively assess the long-term degradation trend of batteries and lack corresponding mechanisms to proactively adjust thermal management strategies accordingly. Summary of the Invention

[0004] The purpose of this application is to provide a battery thermal management method, a battery thermal management device, an electronic device, a readable storage medium, and a terminal, which can solve the problem of limitations in optimizing the long-term life of batteries and the lack of corresponding mechanisms to adjust the thermal management strategy accordingly.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a thermal management method for a battery, the method comprising: Obtain battery temperature and lifespan information; Based on the temperature information and the lifespan information, determine the thermal management control objectives; Thermal management control is performed on the battery according to the stated thermal management control objectives.

[0006] Secondly, embodiments of this application provide a thermal management method for a battery, the method comprising: Obtain the selected target control mode; wherein, the target control mode is a mode for thermal management control of the battery by combining the battery's temperature information and lifespan information; According to the target control mode, thermal management control is performed on the battery.

[0007] Thirdly, embodiments of this application provide a thermal management device for a battery, the device comprising: The information acquisition module is used to acquire battery temperature and lifespan information. The target determination module is used to determine the thermal management control target based on the temperature information and the lifetime information; A thermal management control module is used to perform thermal management control on the battery according to the thermal management control objectives.

[0008] Fourthly, embodiments of this application provide a thermal management device for a battery, the device comprising: A mode acquisition module is used to acquire a selected target control mode; wherein, the target control mode is a control mode that combines the temperature information and lifespan information of the battery; A thermal management control module is used to perform thermal management control on the battery according to the target control mode.

[0009] Fifthly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first or second aspect.

[0010] In a sixth aspect, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first or second aspect.

[0011] In a seventh aspect, embodiments of this application provide a terminal, including the electronic device described above or the readable storage medium described above.

[0012] According to the embodiments of this application, by obtaining the battery's temperature and lifespan information, a thermal management control objective is determined based on the temperature and lifespan information, and then thermal management control is performed on the battery according to the thermal management control objective. In this solution, the thermal management decision can simultaneously respond to the battery's temperature and lifespan requirements, overcoming the limitations of controlling based solely on a single temperature threshold, and achieving intelligent thermal management that balances battery safety, lifespan, and energy efficiency. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the steps of a battery thermal management method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the thermal management architecture; Figure 3 This is a schematic diagram of the system deployment in the cloud and on the vehicle. Figure 4 This is a flowchart of the steps of a battery thermal management method provided in another embodiment of this application; Figure 5 This is a structural block diagram of an embodiment of the battery thermal management device provided in this application. Figure 6 This is a structural block diagram of another embodiment of the thermal management device for a battery provided in this application; Figure 7 This is a structural block diagram of an electronic device for thermal management of a battery, according to an exemplary embodiment. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0016] The thermal management method for batteries provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0017] Offline calibration strategies based on fixed parameters or simple thresholds are difficult to adjust once written into the vehicle software, and cannot adapt to changes in operating conditions caused by user driving style, long-term geographical and climatic changes, and battery aging during actual use.

[0018] Solutions that rely on cloud-based data analysis and push policy upgrades suffer from slow response times and an inability to address sudden changes in vehicle status. Their analysis based on historical data also struggles to accurately predict future user behavior, and in regions with strict privacy regulations, the inability to upload vehicle data renders the policy optimization function ineffective.

[0019] Traditional long-term life simulations are mostly based on idealized standard operating conditions (such as NEDC (New European Driving Cycle) and WLTP (Worldwide Harmonized Light Vehicles Test Procedure), which deviate significantly from the actual, dynamic usage loads of vehicles. Simply extrapolating historical data cannot reflect complex factors such as seasonal changes and evolving driving habits, leading to unreliable predictions and limited effectiveness of thermal management strategies developed based on them.

[0020] Existing solutions mostly focus on battery activity periods such as driving and charging, neglecting temperature management during the resting period (which accounts for the majority of a vehicle's lifespan). After the vehicle goes into hibernation, the battery temperature is often passively controlled by the environment, or intervened with only fixed and blind strategies, missing the critical window for delaying battery aging through intelligent temperature control.

[0021] The battery thermal management method described in this application can be executed by a local battery management system (BMS) or other suitable control unit. Its computational tasks can be deployed on a local device, a cloud server, or an edge-cloud collaborative architecture; this application does not impose any limitations on this. This thermal management method can be widely applied to various scenarios using power batteries, including but not limited to electric vehicles, robots, androids, electric aircraft, energy storage power stations, and portable energy storage devices, and is particularly suitable for fields requiring proactive optimization management of battery long-term lifespan.

[0022] Example 1 Reference Figure 1 The diagram illustrates a flowchart of a battery thermal management method according to an embodiment of this application, which may specifically include the following steps: Step 101: Obtain battery temperature and lifespan information.

[0023] In the embodiments of this application, temperature information is a parameter reflecting the thermal state of the battery. It can be in the form of real-time collected battery temperature value, future temperature value predicted based on historical and current data (predicted temperature information), or other data that can characterize the thermal state of the battery. The embodiments of this application do not limit this.

[0024] In the embodiments of this application, lifetime information is a parameter reflecting the battery's health degradation state. Its form can be the current state of health (SOH) estimated based on the model, the predicted future capacity degradation trend (predicted lifetime information), or other data that can characterize the battery's lifetime state. The embodiments of this application do not limit this.

[0025] In the embodiments of this application, the above information can be obtained by directly reading the internal state quantities of the sensor or the battery management system (BMS), or by calculating it through a predictive model.

[0026] In this embodiment of the application, the system obtains temperature information and lifetime information synchronously or asynchronously through a data interface or prediction module, which serves as the input basis for thermal management decisions.

[0027] In one implementation, the Battery Management System (BMS) periodically collects the battery's real-time temperature as temperature information and estimates the current State of Health (SOH) as lifespan information using the ampere-hour integration method. In another implementation, the BMS calls a prediction model, inputs the ambient temperature forecast and the current battery state, obtains the battery temperature prediction curve for the next 24 hours as temperature information, and inputs historical operating data to obtain the capacity degradation prediction value for the next year as lifespan information.

[0028] For example, after an electric vehicle is parked and stationary, its BMS obtains two sets of information: temperature information: the current battery temperature measured by the sensor is 35°C, and according to the weather forecast, the ambient temperature will reach 38°C tomorrow afternoon; lifespan information: based on the battery's usage history, the model evaluates the current SOH to be 92%, and the prediction shows that if the current thermal management strategy is maintained, the SOH will decay to 89% after one year.

[0029] Step 102: Determine the thermal management control target based on the temperature information and the lifespan information.

[0030] In the embodiments of this application, the thermal management control objective is a control command or set value that can be directly executed by the thermal management system to achieve specific battery management objectives (such as extending battery life and ensuring safety). Its core lies in making a balanced management decision by comprehensively considering the battery's immediate thermal state and long-term health state.

[0031] In this embodiment, the system performs comprehensive analysis on the obtained temperature and lifespan information to assess the current and future battery status, thereby deriving the thermal management intervention measures to be applied.

[0032] In one specific implementation, the BMS decision module receives temperature and lifespan information. If the information indicates that the current battery temperature is high (e.g., 35°C) and the long-term lifespan prediction shows rapid degradation (e.g., SOH below 90% after one year), the decision module determines that enhanced cooling is needed to address both short-term temperature rise and long-term degradation risks. Based on this, it calculates a specific thermal management control target, for example, setting the average battery temperature management target to 30°C during the upcoming resting period.

[0033] For example, based on the information obtained above (current temperature 35℃, high temperature tomorrow, lifespan prediction not met), the decision module determines a thermal management control target as: "Initiate enhanced cooling to reduce the battery temperature to 30℃ and maintain it before 12:00 noon tomorrow." Step 103: Perform thermal management control on the battery according to the thermal management control objective.

[0034] In the embodiments of this application, thermal management control of the battery refers to driving the actuators of the thermal management system, such as fans, water pumps, air conditioning compressors, etc., to work in order to achieve the thermal management control objectives.

[0035] In this embodiment, the execution layer receives the thermal management control target, parses it into control instructions for specific execution components (such as PWM duty cycle, switching signals), and drives its operation.

[0036] In one specific implementation, after the BMS control output module receives the target of "reducing the battery temperature to 30°C and maintaining it", it calculates the required heat dissipation power based on the difference between the real-time battery temperature and the target temperature using a closed-loop control algorithm (such as PID control), and then adjusts the speed of the coolant circulation pump and the speed of the cooling fan until the battery temperature reaches and stabilizes near the target value.

[0037] For example, to achieve the above goals, the BMS controls the cooling system to operate after determining that cooling needs to be activated. If the battery temperature is detected to be 35°C in real time, the fan is controlled to run at high speed and the water pump to run at full speed to accelerate heat dissipation; when the temperature drops to 30°C, the fan and water pump are adjusted to low speed to maintain a stable temperature with lower energy consumption.

[0038] According to the embodiments of this application, battery temperature information and lifespan information are obtained. Then, based on the temperature information and lifespan information, a thermal management control objective is determined. Finally, thermal management control is performed on the battery according to the thermal management control objective. In this solution, the thermal management decision can simultaneously respond to the battery's temperature and lifespan requirements, overcoming the limitations of controlling based solely on a single temperature threshold, and achieving intelligent thermal management that balances battery safety, lifespan, and energy efficiency.

[0039] The battery thermal management approach boasts significant architectural advantages. Its complete logical closed loop of prediction, decision-making, and execution can be completed independently within the vehicle's local battery management system, without relying on real-time data interaction with cloud servers. This design brings multiple benefits: First, it enhances user privacy and data security, avoiding the risk of uploading and leaking sensitive data such as battery operating status and user habits. Second, it improves the system's global adaptability and reliability, ensuring continuous availability of thermal management functions even in scenarios with poor network signals or no network at all (such as underground parking garages or remote areas). Third, it meets broader market compliance requirements, especially suitable for regions with strict restrictions on cross-border data transfer, facilitating global deployment.

[0040] In particular, this method can intelligently adjust based on the assessment results of battery life information. When the assessment indicates that the battery life margin is sufficient and far exceeds design expectations, the decision-making system will automatically tend to reduce or delay unnecessary heat dissipation operations while ensuring that life targets are met, and will only actively intervene in special situations such as extreme temperatures. This "on-demand management" mode can effectively avoid ineffective energy consumption, thereby significantly reducing the average energy consumption of the thermal management system and indirectly helping to extend the driving range of electric vehicles.

[0041] Example 2 In one optional embodiment of this application, a specific implementation of determining the thermal management control target based on the temperature information and the lifetime information may include: determining the thermal management control target based on the lifetime information, the temperature information, and constraint information; wherein the constraint information includes at least one of the following: lifetime constraint information, energy consumption constraint information, disturbance constraint information, and safety constraint information. Constraint information refers to the boundary conditions or cost parameters that need to be considered, including at least one of the following: lifetime constraint information, safety constraint information, energy consumption constraint information, and disturbance constraint information.

[0042] Among them, lifespan constraint information includes quantitative targets or limits that the battery's long-term health status must meet. For example, the "margin" requirement that the battery's capacity degradation should reach at a specific future time point (such as 1 year, 3 years, or 8 years). These requirements may include "hard constraints" that must be met first (such as an annual capacity degradation margin ≥ -1%) and "optimization targets" that are expected to be achieved (such as a 3-year margin ≥ -2%).

[0043] Among them, energy consumption constraint information includes the upper limit or budget of energy consumption allowed to be consumed when performing thermal management operations, such as daily, monthly, and annual energy consumption quotas, or relative restrictions (such as the increase in heat dissipation energy consumption not exceeding a percentage of the vehicle's average daily energy consumption).

[0044] The interference constraint information includes restrictions on the user's perceived experience (mainly noise) of thermal management operations, such as the maximum duration of a single operation, the maximum frequency per day, and prohibitions on high-noise operations during specific sensitive periods (such as nighttime).

[0045] Among these, safety constraint information includes rigid physical boundaries that must be adhered to to ensure the safe operation of the battery system; these are red lines that cannot be crossed in any decision-making process. For example, the battery's maximum allowable operating temperature, minimum allowable operating temperature, and maximum allowable temperature rise rate are used to prevent safety risks such as thermal runaway and low-temperature lithium plating.

[0046] For example, the constraint information considered in the decision-making process may include the following exemplary quantitative indicators: Lifetime decay constraint: The system must meet phased lifetime decay targets at specific points in time. As an example, the system is configured as follows: Next year's margin: The capacity decay must be no less than 1% of the annual target value (i.e., margin ≥ -1%), which is a constraint that must be met first.

[0047] Three-year and eight-year margins: respectively, capacity decay should not be less than 2% of the target value (margin ≥ -2%) and not less than the target value (margin ≥ 0%). These two can be used as optimization targets.

[0048] Energy consumption constraints: Limiting the additional energy consumption caused by thermal management. For example, it can be set as follows: Absolute upper limit constraints: such as daily energy consumption limit ≤ 3 kWh, monthly energy consumption limit ≤ 30 kWh, and annual energy consumption limit ≤ 90 kWh, to prevent excessive consumption of electricity in the short or long term.

[0049] Relative incremental constraints: such as the additional energy consumption increase caused by heat dissipation not exceeding 10% of the vehicle's average daily energy consumption, in order to maintain a relative balance in energy consumption.

[0050] Interference mitigation constraints: These are set to reduce perceptible interference from thermal management operations to the user. For example, they can be limited to: Duration of a single operation: such as a single continuous heat dissipation time of ≤ 1 hour.

[0051] Operation frequency: such as ≤ 2 times of starting active cooling per day.

[0052] Prohibitions during noise-sensitive periods: Avoid operating high-speed fans or other high-noise operations during periods such as 10:00 PM to 7:00 AM the next morning.

[0053] In one specific implementation, the decision-making module of the battery management system (BMS) receives temperature information, lifetime information, and the aforementioned constraint information. Its decision-making logic follows a hierarchical priority principle: First, any candidate thermal management control objective must ensure that it does not violate safety constraints (e.g., the predicted battery temperature must not exceed the safety limit at any time). Second, under the premise of safety, the decision must strive to satisfy the hard requirements in the lifetime constraint information. Then, among the feasible solutions that simultaneously satisfy both safety and lifetime hard constraints, the system further optimizes, striving to make the solution conform to energy consumption constraints and disturbance constraints, or achieve an optimal balance among these constraints.

[0054] For example, the system's temperature information predicts that the battery's highest temperature will reach 38°C tomorrow, and the lifespan information indicates that the current annual capacity degradation is approaching the hard constraint threshold. The system initially generates a candidate control target for intensive cooling (e.g., immediately initiating maximum power cooling to 25°C). However, when evaluating with constraint information, it is found that: 1) the estimated energy consumption of this scheme will exceed the daily energy consumption budget, failing to meet the energy consumption requirements; 2) this scheme requires continuous high-speed fan operation at night, violating the anti-interference requirement of "prohibiting high-speed fans at night." Therefore, this intensive cooling scheme is excluded. The system then adjusts its strategy, generating a new candidate target: initiating moderate-intensity cooling the following morning (avoiding interference periods), with a target temperature of 30°C. Verification shows that the predicted lifespan degradation under this scheme still meets the hard constraint, and the estimated energy consumption and interference level both meet their respective requirements. Therefore, the system ultimately determines this as the target.

[0055] By incorporating multi-dimensional constraints such as energy consumption and disturbances, along with corresponding conditions, when determining thermal management control objectives, the decision-making process is upgraded from a single-dimensional consideration of lifetime or temperature to an optimization process that systematically weighs multiple objectives (lifetime, energy consumption, disturbances, and safety). This ensures that the resulting thermal management strategy is not only effective but also economical and user-friendly, enhancing its overall practical value and level of intelligence.

[0056] Example 3 In one optional embodiment of this application, the constraint information includes the lifetime constraint information, the energy consumption constraint information, and the disturbance constraint information. A specific implementation of determining the thermal management control objective based on the lifetime information, the temperature information, and the constraint information may include: determining the lifetime deviation cost based on the lifetime information and the lifetime constraint information; determining the energy consumption cost generated by thermal management based on the temperature information and the energy consumption constraint information; determining the degree of disturbance caused by thermal management based on the temperature information and the disturbance constraint information; constructing a comprehensive cost based on the lifetime deviation cost, the energy consumption cost, and the degree of disturbance; and determining the thermal management control objective based on the comprehensive cost and the cost target.

[0057] Lifetime deviation cost is an indicator used to quantify the degree of deviation between the predicted lifespan and the target lifespan. The greater the deviation, the higher the cost. For example, the system sets lifespan cost constraints as follows: it must meet phased lifespan degradation targets of 1 year, 3 years, and 8 years, where the margin for the next year is ≥-1% (hard constraint); the margin for 3 years is ≥-2% (soft constraint); and the margin for 8 years is ≥0% (soft constraint). "Margin" refers to the difference between the predicted capacity degradation value and the target degradation value.

[0058] Energy cost is a metric used to quantify the cost of electrical energy consumed in performing thermal management operations. Its calculation requires combining temperature information (the predicted temperature profile determines heat dissipation requirements) with energy consumption constraints (such as budget limits). Higher energy consumption, or consumption closer to the budget limit, results in higher costs.

[0059] Interference level is an indicator used to quantify the negative impact of thermal management operations on users' perceived disturbances (such as operating noise). Its assessment requires combining the temperature information (which determines the timing and duration of operation) with the interference constraints (such as noisy period restrictions). Operating during sensitive periods or exceeding the permitted time limits results in high interference costs.

[0060] The overall cost is the weighted sum of the above, namely: Overall Cost = α·Lifetime Deviation Cost + β·Energy Consumption Cost + γ·Interference Level, where α, β, and γ are weighting coefficients reflecting the degree of importance attached to different objectives. The cost objective usually refers to minimizing the overall cost.

[0061] In one specific implementation, the planning layer of the battery management system (BMS) performs the following optimization calculations: For a candidate thermal management control objective (e.g., a specific combination of a "target temperature adjustment amount ΔT" and its "start-up timing"), the system performs the following steps to evaluate its overall cost: First, based on the candidate target, the system combines obtained lifetime information (such as predicted capacity decay values) and lifetime constraint information (such as annual margin ≥ -1%) to calculate future lifetime deviations and map them into lifetime deviation costs. Simultaneously, based on temperature information (such as predicted high-temperature periods) and energy consumption constraints, the system simulates the thermal management actions (such as fan operating time and power) required to execute this target, estimates its total energy consumption, and converts it into energy consumption costs. Similarly, based on temperature information and disturbance constraints, the system assesses the noise periods and durations of this action and quantifies them as disturbance levels.

[0062] Then, the system calculates the comprehensive cost of the candidate solution based on the preset weighting coefficients (α, β, γ). The system will traverse the decision space consisting of multiple discrete combinations of "ΔT" and "starting time" and calculate the comprehensive cost of each combination.

[0063] Ultimately, the system selects the candidate control objective that minimizes the overall cost as the final thermal management control objective to be determined and implemented.

[0064] For example, the system performs a comprehensive cost evaluation of two candidate solutions when making a decision: Candidate Option A (Strong Intervention): Set ΔT = -3℃ and immediately start cooling. Its lifespan deviation cost is very low (effectively improves margin), but the energy consumption cost is very high (strong cooling consumes a lot of power), and the interference cost is also very high (running high-speed fans at night).

[0065] Candidate Option B (weak intervention): Set ΔT = -2℃ and adjust the start-up time to the early morning of the following day. Its lifetime deviation cost is slightly higher than Option A (weaker improvement effect), but its energy consumption cost and interference cost are significantly lower than Option A.

[0066] After weighted cost calculation, the system found that although Option A performed better in the single dimension of "lifespan," its higher energy consumption and interference costs resulted in a higher overall cost than Option B. Therefore, with minimizing the overall cost as the ultimate goal, the system selected Option B as the thermal management control objective. This means that the system does not simply pursue maximizing lifespan benefits, but automatically finds a globally optimal balance point among lifespan extension, energy consumption costs, and user experience.

[0067] For example, to achieve efficient optimization, the system discretizes continuous decision variables. Specifically, the target temperature adjustment (ΔT) is defined as a finite number of candidate values, each corresponding to a clear thermal management intensity, for example: ΔT=-5°C: This represents an enhanced cooling strategy designed to minimize battery temperature and slow down battery life degradation.

[0068] ΔT=-3°C: This represents a slightly enhanced cooling strategy that achieves a moderate balance between cooling and energy consumption.

[0069] ΔT=0°C: This represents the baseline strategy, where the battery temperature mainly follows natural variations.

[0070] ΔT = +3°C or +5°C: This represents a reduced cooling strategy, which tolerates higher temperatures to significantly save energy while ensuring safety.

[0071] Based on this, the steps to achieve the overall cost target and determine the thermal management control target, in a specific implementation, include the following process: First, based on the current data, the model is run to obtain the future temperature and lifetime prediction curves under the baseline (ΔT=0).

[0072] Assessing the current constraints involves quantifying the lifetime margin (deviation from the target) and combining it with real-time energy consumption budget and experience costs.

[0073] The system iterates through all the discrete ΔT candidate values, and for each value, quickly simulates its corresponding future temperature curve, re-evaluates lifespan, energy consumption, and experience cost, and calculates its overall cost.

[0074] The system prioritizes ensuring that hard constraints (such as lifetime margin crisis) are met, and then selects the ΔT value with the optimal overall cost from all feasible solutions as the control objective. Its decision-making logic is intuitively reflected as follows: When the lifespan margin is sufficient, it is preferable to have ΔT = 0 or +5°C. The effect is to minimize energy consumption while meeting the lifespan target.

[0075] When the lifespan margin is insufficient but the energy consumption budget is sufficient, ΔT = -3°C or -5°C is preferred. The effect is to actively restore the lifespan margin using the sufficient budget.

[0076] When lifespan margin is insufficient and energy consumption budget is tight, ΔT = -3°C is preferred. The effect is to balance the need for extended lifespan with energy consumption control within budget constraints.

[0077] When a lifespan margin crisis occurs, a mandatory selection of ΔT = -5°C is made. The effect is to prioritize ensuring core lifespan targets while temporarily relaxing energy consumption constraints.

[0078] By unifying and quantifying constraints and objectives across multiple dimensions such as lifespan, energy consumption, and user experience into cost indicators, and constructing a comprehensive cost function for minimization, a clear mathematical framework is provided for scientific and automated decision-making in complex scenarios with conflicting objectives. This method upgrades the process of determining thermal management control objectives from rule-based, either-or constraint checks to global optimization based on quantitative models, enabling precise trade-offs and thus intelligently generating thermal management strategies with optimal overall performance.

[0079] Example 4 In one optional embodiment of this application, the thermal management control objective includes at least one of the following: the amount of adjustment to the battery temperature during battery resting, and the timing of initiation of thermal management operation.

[0080] The adjustment to battery temperature during resting periods specifically refers to the adjustment value set to optimize long-term lifespan, representing the expected average battery temperature during resting relative to its natural temperature range (e.g., target temperature adjustment value ΔT). This adjustment directly controls the workload of the cooling or heating system. The timing of thermal management operation initiation refers to the specific point in time when a particular thermal management action (such as activating fan cooling) begins. Choosing this initiation timing is crucial for optimizing energy consumption and avoiding disruption to the user experience.

[0081] In one specific implementation, the thermal management control objective ultimately output by the battery management system (BMS) is a combination of these specific control instructions. For example, a complete control objective might be stated as: "At 06:00 the next morning (start-up time), begin thermal management operations to set the average temperature of the battery during the subsequent resting period to be 2 degrees Celsius lower than the ambient temperature (adjustment amount, ΔT = -2℃)."

[0082] In determining these specific objectives, the system relies on the prediction and optimization processes described in the aforementioned embodiments. For example, the system predicts that the capacity margin for the following year will be -1.8%, violating hard constraints, and that temperatures will be high in the coming days. Through comprehensive cost optimization calculations, the system evaluates the comprehensive costs of different combinations (adjustment amount, activation timing): Option A: ΔT = -3℃, start immediately. This option offers high lifespan benefits, but nighttime operation incurs significant fan interference costs and high energy consumption.

[0083] Option B: ΔT = -2℃, start-up at 06:00 tomorrow morning. This option offers acceptable lifespan benefits, avoids nighttime interference, and has lower energy consumption.

[0084] Calculations showed that combination B had a lower overall cost, and therefore it was selected as the final thermal management control target.

[0085] By defining the thermal management control objectives as the temperature adjustment during battery rest and the timing of operation initiation, the results of the aforementioned prediction and optimization process can be transformed into clear and executable control commands. This not only achieves precise management of battery temperature to optimize lifespan but also effectively balances energy consumption and user experience through intelligent selection of start-up timing, demonstrating the refinement and intelligence of the method presented in this application.

[0086] Example 5 In one optional embodiment of this application, the temperature information includes predicted temperature information. One specific implementation of obtaining the battery's temperature information may include: predicting the battery's predicted temperature information based on the battery's environmental data and its state data.

[0087] Predicted temperature information refers to data obtained by extrapolating the future thermal state of a battery. It can take the form of a battery temperature change curve over a future period of time, or characteristic values ​​such as average temperature and maximum temperature extracted from the curve.

[0088] Environmental data are external variables that affect battery temperature changes. They mainly include ambient temperature, but may also include humidity, solar irradiance, geographic location information, etc.

[0089] State data are internal variables that reflect the battery's own thermal characteristics, mainly including parameters such as the battery's real-time temperature, current, voltage, internal resistance, and heat capacity.

[0090] By combining real-time environmental data, status data, and predictive models, the system can overcome the limitations of relying solely on current information and generate predictions of future battery temperature changes, thereby enabling more forward-looking thermal management planning.

[0091] In one specific implementation, the prediction module of the Battery Management System (BMS) performs the following operations: It collects or receives real-time forecasts of future ambient temperature data (e.g., a 24-hour weather forecast) and simultaneously obtains the battery's current state data (such as real-time temperature and current load). This data is then input into a pre-deployed temperature prediction model. This model can be a mechanistic model built based on physical thermodynamic principles or a machine learning model trained on a large amount of historical data. The model simulates the heat exchange process between the battery and the environment based on the input data, calculates, and outputs predicted temperature information for a future period.

[0092] For example, after an electric vehicle is parked in a garage at night, its BMS (Battery Management System) begins to make thermal management decisions. To obtain temperature information, the system collects the current ambient temperature (25°C) and the current battery temperature (30°C) in the garage, and obtains the weather forecast data for the entire next day (showing that the afternoon high temperature will reach 35°C). The system inputs this environmental data and battery status data into a thermal network model. After calculation, the model outputs a battery temperature prediction curve for the next 24 hours. This curve shows that, without any intervention, the battery temperature will roughly follow the changes in ambient temperature, reaching a peak of approximately 33°C around 13:00 the following afternoon. This prediction curve, or the feature of "highest temperature 33°C" extracted from it, is provided as predicted temperature information to subsequent decision-making modules.

[0093] In one specific implementation, environmental data and state data within the recent period are input into a temperature prediction model; the temperature prediction model predicts the future state of the battery to obtain a battery temperature curve within the recent period, or a temperature feature value characterizing temperature changes.

[0094] Temperature prediction models are computational models used to simulate and predict battery temperature changes. These models can be mechanistic models based on thermodynamic and electrochemical principles, data-driven models trained on large amounts of historical data (such as machine learning models), or hybrid models combining both. Their core function is to receive environmental and state data sequences and output predictions of future temperature changes.

[0095] A battery temperature profile is a predicted trajectory describing the continuous change of battery temperature over a recent period. Temperature feature values ​​are key indicators extracted from the predicted trajectory that characterize the overall temperature state, such as average temperature and maximum temperature. These feature values ​​facilitate subsequent rapid decision-making logic processing.

[0096] In one specific implementation, the prediction module of the battery management system (BMS) periodically performs the following operations: It first obtains the forecast ambient temperature data for a recent period (e.g., the next 24 hours), as well as the battery's current state data (such as real-time temperature and current load) and physical parameters (such as heat capacity and internal resistance). Then, it passes this data as input to a temperature prediction model pre-deployed within the system. This model, through its internal computational logic (such as solving thermal balance equations or performing time-series inference), outputs a direct prediction of the battery's future temperature.

[0097] For example, the BMS receives 24-hour ambient temperature forecast data (showing a rise from 20°C at night to 35°C at midday) and the current battery temperature of 30°C. The system inputs this data into a temperature prediction model based on thermal network principles. This model calculates and outputs a 24-hour battery temperature prediction curve. The curve shows that, without intervention, the battery temperature will initially decrease and then rise following the ambient temperature, reaching a peak of 33°C around 13:00 the following afternoon. Alternatively, the model can directly output temperature characteristic values ​​such as an average temperature of 28°C and a maximum temperature of 33°C for rapid evaluation by the planning layer.

[0098] By utilizing environmental and state data to predict future battery temperatures, the thermal management system can "anticipate" temperature trends. This provides crucial future scenario inputs for determining thermal management control objectives, shifting decision-making from reactive response to proactive planning. This allows for the early deployment of management strategies to address adverse temperature changes, effectively improving the timeliness, accuracy, and overall efficiency of thermal management.

[0099] The thermal management method exhibits significant environmental adaptability. Because environmental data contains information reflecting long-term geographical and climatic characteristics (such as typical temperatures, temperature differences, and annual humidity cycles in different regions), the prediction model and decision-making algorithm can inherently learn and adapt to climate patterns in different regions. Therefore, the same method, when applied to regions with vastly different climates (such as the frigid winters of Northeast China and the year-round hot and humid climate of Hainan), will automatically generate distinctly different but optimal thermal management action characteristics for each region. For example, in Hainan, the system may more frequently predict high-temperature risks, thus tending to plan enhanced heat dissipation strategies in advance; while in Northeast China, the system may focus more on low-temperature protection or utilize nighttime natural cooling for pre-regulation during seasons with large temperature differences, demonstrating a location-specific intelligent approach. This ensures the universality and effectiveness of the method globally.

[0100] Example 6 In one optional embodiment of this application, the lifespan information includes predicted lifespan information. A specific implementation of obtaining the battery lifespan information may include: predicting the predicted lifespan information of the battery based on the battery's state data, the battery's environmental data, and / or various thermal management control strategies.

[0101] Predicted lifespan information refers to a quantitative estimate of the battery's health status (such as State of Health, SOH) or capacity degradation over a future period, based on the battery's current state, historical and future environmental loads. State data includes parameters reflecting the battery's current health and operating status, such as current state of health (SOH), real-time temperature, internal resistance, and cumulative cycle count.

[0102] Environmental data includes external conditions that affect battery aging, such as historical and predicted ambient temperatures.

[0103] Multiple thermal management control strategies define different temperature management intensities or modes that may be adopted in the future, such as a baseline natural cooling strategy, an enhanced active cooling strategy, and a reduced cooling strategy. By introducing strategy variables, simulations can be performed to compare how the lifespan will change if different management methods are adopted.

[0104] In this embodiment of the application, by combining the individual state of the battery, its environmental conditions, and possible management actions, the system can make personalized and contextual predictions of future lifespan degradation trends, providing a direct basis for selecting the optimal management strategy.

[0105] In one specific implementation, the battery management system (BMS) lifetime prediction module performs the following operations: The module collects current battery status data (e.g., current SOH is 92%), historical environmental and usage data, and future environmental prediction data (e.g., monthly average temperature prediction for the next year).

[0106] Based on this data, time series analysis or models are used to predict the average load conditions that the battery will experience in the future (such as the next year), mainly including average temperature load and average current load.

[0107] The predicted future load data is input into a calibrated battery life degradation model (such as an electrochemical-empirical hybrid model) to calculate the predicted lifespan information at future time points (e.g., the predicted SOH is 89% after one year).

[0108] Optionally, for decision optimization, the system can further define multiple candidate thermal management control strategies (e.g., Strategy A: enhanced cooling; Strategy B: baseline cooling). The system will simulate the impact of each strategy on future temperature loads (e.g., Strategy A reduces the average temperature load by 3°C), thereby generating multiple sets of different future load condition data, which are then input into the lifetime model to obtain the predicted lifetime information for each strategy, for subsequent comparison.

[0109] For example, for an electric vehicle that frequently operates in a hot summer region, the BMS obtains its current State of Harshness (SOH) of 92%, and combines this with climate forecasts for the region over the next year. The system predicts that under the current baseline strategy (i.e., natural cooling only), the average battery temperature load will be high over the next year. Based on the lifetime model calculations, the predicted lifetime information is that the SOH will drop to 88% after one year. Simultaneously, the system simulates an enhanced cooling strategy, which is expected to reduce the average battery temperature by 2°C over the next year. Based on this adjusted temperature load, the lifetime model is run again, yielding new predicted lifetime information: the SOH can be maintained at 89.5% after one year. These two (or more) sets of different predicted lifetime information clearly quantify the potential impact of different thermal management strategies on long-term lifespan.

[0110] By utilizing state and environmental data to predict battery lifespan information and optionally combining multiple strategies for simulation comparison, the system can quantitatively assess the future degradation risk of batteries and the long-term benefits of different management measures. This provides crucial long-term trend data and decision-making basis for determining thermal management control objectives, achieving a leap from "diagnosis" based on current state to "planning" based on future predictions, greatly enhancing the scientific rigor and foresight of thermal management decisions.

[0111] For example, such as Figure 2 The diagram shows a thermal management architecture that includes a cloud computing module and a vehicle-side data acquisition / computing module.

[0112] On the cloud side, the communication gateway module receives a comprehensive data packet uploaded from the vehicle terminal. This data packet contains historical battery operating condition data for a portion of the vehicles (not the full amount). The cloud computing module uses this historical data to train the operating condition prediction model, resulting in a trained operating condition prediction model. Subsequently, the software upgrade service module delivers the updated calculation model to the vehicle terminal via online / online upgrades.

[0113] On the vehicle side, the vehicle-side data acquisition / computing module receives real-time location / weather data (as part of the environmental data) from the navigation / positioning / weather service module and combines it with the vehicle's real-time battery status data. This module runs a trained operating condition prediction model downloaded from the cloud, executes the prediction and planning decision-making process as described in the previous embodiments, and finally generates a thermal management strategy for planning decisions.

[0114] Finally, the vehicle-side data acquisition / computing module sends the determined thermal management strategy to the thermal management control module. This control module generates specific control commands based on the strategy, driving actuators (such as fans and water pumps) to perform thermal management operations on the power battery, thus forming a complete intelligent control closed loop from data perception, cloud-based model iteration, real-time vehicle-side decision-making to final execution.

[0115] like Figure 3 The diagram shows the system deployment in the cloud and on the vehicle. The system clarifies the collaborative model of "cloud training and vehicle application" and the hierarchical processing logic of "perception-prediction-planning-execution" within the vehicle.

[0116] In the cloud, the system uses extensively collected historical vehicle data to train a driving condition prediction model, generating a more accurate future driving condition generator model. Simultaneously, the cloud integrates environmental geographic information (such as navigation traffic conditions and weather forecasts) to provide rich environmental data input to the vehicle.

[0117] On the vehicle side, the processing flow is divided into four layers: Perception layer (data acquisition and feature processing): responsible for real-time acquisition of the vehicle's battery status information (such as voltage, current, and temperature) and receiving environmental geographic information from the cloud, and completing data preprocessing and fusion.

[0118] Prediction Layer (Prediction Estimation): Receives data from the perception layer, runs the operating condition generation model downloaded from the cloud to predict future load conditions, and performs lifetime prediction based on this, thereby obtaining prediction results of near-term temperature conditions and long-term lifetime degradation.

[0119] Planning layer (thermal management planning): This is the core decision-making stage of this method. It receives the results from the prediction layer, performs multi-objective optimization calculations, and completes the thermal management target planning after balancing multiple constraints and objectives such as lifetime and energy consumption (as shown by the balance symbol in the figure), and outputs specific control objectives (such as target temperature adjustment amount ΔT, start-up timing).

[0120] Execution layer (thermal management control): Receives instructions from the planning layer and drives the thermal management system execution components through specific operations such as working mode control and cooling / heating control, ultimately achieving precise management of the power battery.

[0121] This architecture diagram intuitively reveals the entire process from data to decision-making and then to execution. In particular, the planning layer, as the core "brain" that connects prediction and execution and undertakes multi-objective optimization tasks, perfectly reflects the intelligence and systematic nature of the method in this application.

[0122] By integrating all steps of prediction, planning, and execution within the vehicle's local battery management system, a high degree of operational autonomy is achieved without relying on continuous data interaction with cloud servers. This architecture gives the thermal management method two key advantages: First, strong privacy and geographical adaptability. Even in regions with poor network conditions or restrictions on data uploads (such as some overseas markets), the system can still independently perform personalized lifespan prediction and thermal management decisions, ensuring user data privacy and meeting global market compliance requirements. Second, extremely high reliability, avoiding the potential impact of network latency or interruptions on real-time thermal management decisions.

[0123] Example 7 In one optional embodiment of this application, a specific implementation of predicting the predicted lifespan information of the battery based on the battery's state data, the battery's environmental data, and various thermal management control strategies may include: determining the battery's load condition prediction data for at least one time period based on the environmental data and the state data; determining the load condition simulation data for each of the at least one time period corresponding to various thermal management control strategies based on the load condition prediction data for the at least one time period; and determining the predicted lifespan information corresponding to the various thermal management control strategies based on the load condition simulation data for the at least one time period.

[0124] Load condition prediction data is a holistic description of the electrical and environmental stresses that a battery will experience over one or more future periods (e.g., the next year) based on historical and future environmental data, battery status, and usage habits. The core of the data includes the predicted average current load and base temperature load.

[0125] The load condition simulation data is generated by superimposing the effects of a specific thermal management control strategy (mainly the adjustment of the temperature load component) on the above-mentioned basic prediction data, resulting in a more refined description of the future load corresponding to that strategy.

[0126] After inputting the load condition simulation data corresponding to various strategies into the life decay model, the quantitative results that reflect the long-term health status of the battery under the strategy are calculated, which are the predicted life information.

[0127] In one specific implementation, the prediction module of the battery management system (BMS) performs the following operations: The module integrates historical environmental data, future climate forecasts, and typical user charging and discharging patterns (derived from state data) to determine the battery's load condition prediction data for a target long-term period (such as the next 12 months) using a prediction algorithm. For example, it predicts that the average monthly ambient temperature for the next year will be 28°C, and the average monthly discharge rate will be 0.25C.

[0128] The system predefines several candidate thermal management control strategies, such as the baseline strategy (L_0), enhanced cooling strategy (L_A), and reduced cooling strategy (L_B). For each strategy, the system quantitatively adjusts the temperature load component in the baseline load condition prediction data according to its defined temperature management intensity, generating load condition simulation data that corresponds one-to-one with the strategy. For example, for strategy L_A, the monthly average temperature prediction value is lowered by 2℃; for strategy L_B, it is increased by 1℃; and strategy L_0 remains unchanged.

[0129] The system generates multiple sets of simulated load conditions (corresponding to L_0, L_A, and L_B respectively) as different input scenarios, and feeds them in parallel into the same battery life degradation model for simulation calculation. The model outputs a corresponding long-term life prediction value for each set of input data.

[0130] In one specific implementation, the system performs the following operations: Generate simulation data for the generated strategy: The system first obtains the predicted baseline load conditions (e.g., the predicted average ambient temperature for the next year). Then, the system defines several candidate thermal management operating strategies, such as: Baseline strategy (L_0): It is assumed that the thermal management system only maintains basic safe operation and the battery temperature is mainly affected by the ambient temperature.

[0131] Enhanced cooling strategy (L_A): This assumes that aggressive cooling is implemented during the resting period, i.e., the average temperature of the battery is controlled to be lower than the baseline strategy by a set value (e.g., ≥1°C).

[0132] Reduced cooling strategy (L_B): This assumes that the cooling intensity is reduced during the resting period, i.e., the average temperature of the battery is controlled to be higher than the baseline strategy by a set value (e.g., ≥1℃).

[0133] The system quantifies the impact of each strategy on temperature and superimposes it onto the baseline load condition prediction data, thereby generating three different load condition simulation data corresponding to L_0, L_A, and L_B. The main difference lies in the different average temperature loads that the battery will experience in the future.

[0134] Parallel lifetime prediction: The system uses the simulation data of the three different load conditions mentioned above as inputs and feeds them into the same battery lifetime degradation model for parallel simulation calculations. The model will output three different predicted lifetime information, corresponding to the expected lifetime status of the battery at the end of the long-term period under the L_0, L_A, and L_B strategies, respectively.

[0135] For example, the system predicts the average ambient temperature for the coming year to be 25°C based on historical data. A multi-strategy simulation is then performed. For the L_0 strategy, the average battery temperature in the load condition simulation data is approximately 26°C. After inputting into the lifetime model, it is predicted that the capacity will decay to 90% after one year.

[0136] For the L_A strategy, the average battery temperature in the load condition simulation data is corrected to 24°C due to active cooling. After inputting into the lifetime model, the predicted capacity degradation is 91.5% after one year.

[0137] For the L_B strategy, the average battery temperature in the load condition simulation data is corrected to 28°C due to reduced cooling. When input into the lifetime model, the predicted capacity degradation to 88% after one year is calculated.

[0138] By employing basic load prediction, strategy impact simulation, and parallel lifetime calculation, the system can efficiently and quantitatively assess the potential impact of different thermal management control strategies on the long-term lifespan of batteries. This provides a direct and reliable data comparison basis for subsequent steps to intelligently select the optimal solution from multiple strategies, ensuring that thermal management decisions are based on comprehensive forward-looking analysis rather than a single assumption, significantly improving the scientific rigor and accuracy of the decisions.

[0139] Example 8 In one optional embodiment of this application, the thermal management control strategy includes at least two of the following: a baseline strategy, an enhanced cooling strategy, and a reduced cooling strategy; wherein, the baseline strategy is a strategy to control the temperature of the battery to follow the natural temperature change; the enhanced cooling strategy is a strategy to control the average temperature of the battery during the resting period to be lower than the average temperature of the battery corresponding to the baseline strategy; and the reduced cooling strategy is a strategy to control the average temperature of the battery during the resting period to be higher than the average temperature of the battery corresponding to the baseline strategy.

[0140] The baseline strategy is a neutral reference strategy that simulates a scenario where the thermal management system performs only the most basic safety interventions, and the battery temperature primarily follows the natural changes in ambient temperature. Its corresponding future temperature load prediction provides a baseline for evaluating the effectiveness of other strategies.

[0141] The enhanced cooling strategy simulates a scenario where active cooling measures are taken, and its core feature is that it actively reduces the average temperature of the battery during the resting period.

[0142] The reduced cooling strategy simulates a scenario where cooling intensity is reduced to save energy. Its core feature is that it tolerates the battery being at a higher average temperature during the resting period. By comparing the lifetime prediction results corresponding to these three strategies (or at least two of them), the impact of different thermal management intensities on long-term lifetime can be clearly quantified.

[0143] In one specific implementation, the system performs the above-mentioned multi-strategy parallel lifetime prediction as follows: Baseline Strategy: This strategy assumes that thermal management actions remain at their current calibration, without any additional lifetime optimization interventions. Therefore, the corresponding long-term temperature load is determined entirely based on predictions of future ambient temperatures (i.e., natural temperature variations). For example, if the predicted average monthly ambient temperature for the next year is 25°C, then the average monthly battery temperature under the baseline strategy will also be approximately 25°C.

[0144] Enhanced cooling strategy: This assumes that the system will implement aggressive cooling during the future resting period. The effect is quantified as reducing the average temperature during the resting period to below the average temperature corresponding to the baseline strategy. For example, if the baseline strategy has a monthly average temperature of 25°C, the enhanced cooling strategy aims to reduce it to 23°C or lower.

[0145] Reduced cooling strategy: This assumes that the system will reduce its cooling intensity during future rest periods. The effect is quantified as causing the average temperature during the rest period to be higher than the average temperature corresponding to the baseline strategy. For example, based on a baseline monthly average temperature of 25°C, the reduced cooling strategy might tolerate a rise to 27°C or higher.

[0146] By clearly defining three strategies with explicit temperature correlations—basic assessment, enhanced cooling, and reduced cooling—a clear and reasonable comparative benchmark is provided for multi-scenario lifetime prediction analysis. This makes the assessment of the lifetime benefits or costs brought by different thermal management strategies intuitive and quantifiable, providing a clear, data-driven decision-making basis for subsequent selection of the optimal strategy, highlighting the scientific and systematic nature of the proposed method in weighing the advantages and disadvantages of different management intensities.

[0147] Example 9 In one optional embodiment of this application, before determining the thermal management control target based on the temperature information and the lifespan information, the method may further include: performing the step of obtaining the battery's temperature information and lifespan information when the battery is in a resting period, or performing the step of determining the thermal management control target based on the temperature information and the lifespan information.

[0148] The resting period refers to the time when the battery is not in an active operating state such as driving or external charging, such as after the vehicle is parked (engine off and locked). During this period, the battery generates little heat, but its temperature is still significantly affected by the ambient temperature. Determining that the battery requires thermal management control is a decision point, meaning that the system assessment believes that in the current resting state, it is necessary to initiate an active thermal management process that includes predictive and optimization decisions, rather than simply maintaining minimal safety monitoring.

[0149] In one specific implementation, the battery management system (BMS) continuously monitors the vehicle's status. When it detects that the vehicle has entered a static state (such as when the vehicle is powered off), it is determined that it has entered a static period.

[0150] In the specific scenario of a battery being in a resting period, this intelligent management resource can be focused on critical stages that have a significant impact on battery life and where traditional solutions are insufficient. This avoids unnecessary operating overhead (such as when the vehicle controller is responsible for thermal management during driving, or when the environment is mild), achieving a balance between management precision and system efficiency, and ensuring the practicality and economy of this method in real-world applications.

[0151] Example 10 In one optional embodiment of this application, the predicted lifespan information of the battery is predicted based on the battery's state data, the battery's environmental data, and / or various thermal management control strategies. The method further includes: obtaining health status assessment data of the battery; and correcting the predicted lifespan information based on the battery's health status assessment data to obtain corrected predicted lifespan information.

[0152] Health status assessment data typically refers to quantitative indicators estimated by the battery management system (BMS) that reflect the current degree of performance degradation of the battery. The most commonly used is the state of health (SOH), which is usually expressed as the percentage of the current maximum usable capacity to the initial rated capacity. Correcting predicted lifespan information involves using the battery's current actual health status as prior knowledge or a calibration benchmark to adjust the initial predicted value calculated by a general model, making the predicted results more consistent with the actual aging trajectory of that individual battery.

[0153] In one specific implementation, after obtaining the initial predicted lifetime information through the aforementioned step-by-step cascaded prediction, the system performs the following calibration steps: Obtaining SOH data: The BMS estimates and records the current SOH value of the battery in real time or periodically using the ampere-hour integration method, open-circuit voltage method, or model-based algorithms. For example, the system obtains that the current SOH of the battery is 92%.

[0154] Prediction correction: The system compares the measured SOH value with the theoretical prediction of the lifespan model at the same point in time (i.e., "current"). Suppose the model predicts a current SOH of 94% based on standard operating conditions, while the measured value is 92%, indicating that the battery's actual aging rate is slightly faster than the model baseline. The system then calculates a correction factor (or uses a calibration function) based on this deviation (92% - 94% = -2%) and applies it to predictions for future time points. For example, the initial prediction of an SOH of 89% after one year is corrected to a more conservative 88.5%, thus obtaining the corrected predicted lifespan information.

[0155] By incorporating real-time health status assessment data of individual batteries to correct the lifetime prediction based on a general model, the prediction bias caused by individual battery differences and varying usage histories is effectively overcome. This calibration step combines macroscopic model predictions with microscopic individual conditions, making the final predicted lifetime information more targeted and accurate. This provides a more reliable and personalized input basis for subsequent thermal management strategy optimization decisions, and is another key guarantee for this method to achieve precise lifetime management.

[0156] Example 11 Reference Figure 4 The diagram illustrates a flowchart of a battery thermal management method according to an embodiment of this application, which may specifically include the following steps: Step 201: Obtain the selected target control mode; wherein, the target control mode is a mode for thermal management control of the battery by combining the battery's temperature information and lifespan information.

[0157] In this embodiment, the target control mode refers to a pattern or strategy that defines the behavioral logic, control parameters, or decision rules of the thermal management system. The generation, selection, or parameterization of this mode is based on a comprehensive analysis and trade-off of two types of information: battery temperature state and battery lifespan state. The meaning, form, and acquisition method of the temperature and lifespan information can be consistent with those described in the preceding embodiments, for example, including real-time measured values ​​and future values ​​obtained based on prediction models.

[0158] In this embodiment, the target control mode can be selected automatically by the system based on preset rules, or it can be selected based on user input or external commands. This embodiment does not limit the selection in this way. The core is that the final applied control mode is the result of fully considering the battery's thermal state and long-term health state.

[0159] In one specific implementation, the Battery Management System (BMS) or associated domain controller, based on real-time acquired or predicted battery temperature information (such as "sustained high temperatures will occur in the next 24 hours") and battery life information (such as "the current capacity degradation trend is approaching the annual allowable threshold"), calculates and generates an optimal control strategy under current and expected conditions using an internal optimization algorithm. This generated strategy is selected as the "target control mode." For example, after calculation, the system determines that a mode called "active equalization temperature control" (which defines a specific temperature target curve, cooling system start-stop conditions, and intensity) is optimal in the current scenario, and selects it as the target control mode.

[0160] Step 202: Perform thermal management control on the battery according to the target control mode.

[0161] In the embodiments of this application, thermal management control of the battery refers to driving the actuators of the thermal management system (such as fans, water pumps, air conditioning compressors, PTC heaters, etc.) to work according to the rules, parameters or instructions defined by the selected target control mode, so as to achieve the battery state management target expected by the mode.

[0162] In the embodiments of this application, the execution layer parses and executes the target control mode. This typically involves converting the high-level strategy description in the mode into specific control signals for the underlying execution components (such as PWM duty cycle, switching state, target temperature setpoint, etc.), and forming closed-loop or open-loop control.

[0163] According to the embodiments of this application, a selected target control mode is obtained; wherein, the target control mode is a control mode that combines the temperature information and lifespan information of the battery; then, thermal management control is performed on the battery according to the target control mode. This solution's thermal management control can systematically respond to the multi-dimensional state requirements of the battery, encapsulate the complex multi-objective decision-making process into a specific control mode, simplify the logic of the execution layer, and ensure a high degree of consistency between the control behavior and the battery's temperature and lifespan, providing a flexible and intelligent thermal management implementation framework.

[0164] In one optional embodiment of this application, obtaining the selected target control mode further includes: obtaining at least one of lifetime constraint information, energy consumption constraint information, interference constraint information, and safety constraint information set for the target control mode.

[0165] In a specific implementation, there are multiple ways to associate constraint information with the target control mode: For example, built-in modes: In the system's predefined or cloud-updated control mode library, each mode (such as "Extreme Battery Life Mode", "Silent Guardian Mode", "Battery Long Life Mode") has a built-in set of matching constraint parameters. When the user or system selects the mode, this set of constraints is simultaneously enabled.

[0166] For example, user customization: After selecting a basic mode, users or maintenance personnel can personalize some of its constraint parameters. For instance, in the "intelligent temperature control mode," users can manually adjust the "annual decay margin" in the "lifespan constraint" from -1% to -0.5% to pursue more extreme lifespan protection.

[0167] For example, system recommendation: The system dynamically recommends a set of optimized constraint parameters that match a certain target control mode based on the vehicle's current state (such as battery SOH, regional climate) and computing resources.

[0168] For example, a user selects the Summer Long-Distance Escort Mode as the target control mode on the in-vehicle screen. This mode not only associates with a specific temperature management algorithm in the background but also binds to a set of preset constraint information: Safety constraints: Battery temperature must always be ≤48℃ (to prevent thermal runaway under fast charging and direct sunlight).

[0169] Lifetime constraint: The estimated battery degradation increment for this trip is ≤0.01% (to ensure battery health after long-distance driving).

[0170] Energy consumption constraints: Thermal management energy consumption shall not exceed 8% of the total energy consumption of this trip (to avoid excessive impact on range).

[0171] Disturbance constraints: When there are people in the passenger cabin, avoid noticeable noise from the sudden start of the compressor.

[0172] When the system executes this mode subsequently, all thermal management decisions (such as cooling intensity during charging, temperature maintenance target during driving, and pre-cooling strategy when stationary) will be optimized based on the premise of simultaneously satisfying this set of constraints.

[0173] By setting or associating specific lifespan, energy consumption, disturbance, and safety constraints with the target control mode, the abstract concept of a control mode is concretized into an executable scheme with clearly quantifiable goals and boundaries. This greatly enhances the differentiation and specificity between different control modes, enabling users or systems to precisely achieve their desired trade-offs between battery life, vehicle energy consumption, ride experience, and system safety by selecting different modes, thereby improving the customization capabilities and user predictability of the thermal management system.

[0174] It should be noted that the battery thermal management method provided in this application embodiment can be executed by a battery thermal management device, or a control module in the battery thermal management device for executing the method of applying battery thermal management. This application embodiment uses the battery thermal management device executing the method of applying battery thermal management as an example to illustrate the battery thermal management method provided in this application embodiment.

[0175] Reference Figure 5 This diagram illustrates a structural block diagram of a battery thermal management device embodiment provided in another embodiment of this application, which may specifically include: The information acquisition module 301 is used to acquire battery temperature information and lifespan information; The target determination module 302 is used to determine the thermal management control target based on the temperature information and the lifespan information; The thermal management control module 303 is used to perform thermal management control on the battery according to the thermal management control target.

[0176] Optionally, the target determination module includes: The target determination submodule is used to determine the thermal management control target based on the lifetime information, the temperature information, and the constraint information; wherein the constraint information includes at least one of the following: lifetime constraint information, energy consumption constraint information, disturbance constraint information, and safety constraint information.

[0177] Optionally, the constraint information includes the lifetime constraint information, the energy consumption constraint information, and the disturbance constraint information; the target determination submodule includes: A lifetime cost determination unit is used to determine lifetime deviation cost based on the lifetime information and the lifetime constraint information; An energy cost determination unit is used to determine the energy cost generated by thermal management based on the temperature information and the energy constraint information. An interference level determination unit is used to determine the level of interference caused by thermal management based on the temperature information and the interference constraint information. A cost construction unit is used to construct a comprehensive cost based on the lifetime deviation cost, the energy consumption cost, and the degree of interference. The target determination unit is used to determine the thermal management control target based on the comprehensive cost and cost target.

[0178] Optionally, the thermal management control objective includes at least one of the following: the amount of adjustment to the battery temperature during battery resting, and the timing of initiating thermal management operations.

[0179] Optionally, the temperature information includes predicted temperature information, and the information acquisition module includes: The temperature prediction submodule is used to predict the predicted temperature information of the battery based on the environmental data and the state data of the battery.

[0180] Optionally, the lifetime information includes predicted lifetime information, and the information acquisition module includes: The lifespan prediction submodule is used to predict the predicted lifespan information of the battery based on the battery's state data, the battery's environmental data, and / or various thermal management control strategies.

[0181] Optionally, the lifetime prediction submodule includes: A load prediction unit is used to determine the load condition prediction data of the battery during the at least one time period based on the environmental data and the status data. The load simulation unit is used to determine the load simulation data for each of the at least one time period corresponding to various thermal management control strategies, based on the load condition prediction data for the at least one time period. The lifetime determination unit is used to determine the predicted lifetime information corresponding to the multiple thermal management control strategies based on the load condition simulation data within the at least one time period.

[0182] Optionally, the thermal management control strategy includes at least two of the following: a baseline strategy, an enhanced cooling strategy, and a reduced cooling strategy; The baseline strategy is a strategy that controls the temperature of the battery to follow the natural temperature change. The enhanced cooling strategy is a strategy that controls the average temperature of the battery during the resting period to be lower than the average temperature of the battery corresponding to the baseline strategy. The cooling reduction strategy is a strategy that controls the average temperature of the battery during the resting period to be higher than the average temperature of the battery corresponding to the benchmark strategy.

[0183] Optionally, the device further includes: The control determination module is used to perform the step of obtaining the battery's temperature information and lifespan information, or the step of determining the thermal management control target based on the temperature information and lifespan information, when the battery is in a resting period, before determining the thermal management control target based on the temperature information and lifespan information.

[0184] Reference Figure 6 This diagram illustrates a structural block diagram of a battery thermal management device embodiment provided in another embodiment of this application, which may specifically include: The mode acquisition module 401 is used to acquire the selected target control mode; wherein, the target control mode is a mode for thermal management control of the battery by combining the battery's temperature information and lifespan information; The thermal management control module 402 is used to perform thermal management control on the battery according to the target control mode.

[0185] Optionally, the pattern acquisition module further includes: The requirement is to obtain a submodule for obtaining at least one of the following: lifetime constraint information, energy consumption constraint information, interference constraint information, and safety constraint information set for the target control mode.

[0186] Furthermore, in conjunction with the battery thermal management method in the above embodiments, this application embodiment can provide a terminal for implementation. This terminal may include the aforementioned electronic device or readable storage medium.

[0187] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0188] Figure 7 This is a structural block diagram illustrating an electronic device 700 for thermal management of a battery according to an exemplary embodiment. For example, the electronic device 700 may be a computer.

[0189] Reference Figure 7 The electronic device 700 may include one or more of the following components: a processing component 702, a memory 704, a power supply component 706, a multimedia component 708, an audio component 710, an input / output (I / O) interface 712, a sensor component 714, and a communication component 716.

[0190] Processing component 702 typically controls the overall operation of electronic device 700, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 702 may include one or more modules to facilitate interaction between processing component 702 and other components. For example, processing component 702 may include a multimedia module to facilitate interaction between multimedia component 708 and processing component 702.

[0191] Memory 704 is configured to store various types of data to support the operation of device 700. Examples of this data include instruction messages, pictures, videos, etc., for any application or method operating on electronic device 700. Memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0192] Power supply component 706 provides power to various components of electronic device 700. Power supply component 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 700.

[0193] Multimedia component 708 includes a screen that provides an output interface between the electronic device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation.

[0194] Audio component 710 is configured to output and / or input audio signals. For example, audio component 710 includes a microphone (MIC) configured to receive external audio signals when electronic device 700 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 704 or transmitted via communication component 716. In some embodiments, audio component 710 also includes a speaker for outputting audio signals.

[0195] I / O interface 712 provides an interface between processing component 702 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0196] Sensor assembly 714 includes one or more sensors for providing state assessments of various aspects of electronic device 700. For example, sensor assembly 714 may detect the on / off state of device 700, the relative positioning of components such as the display and keypad of electronic device 700, changes in position of electronic device 700 or a component of electronic device 700, the presence or absence of user contact with electronic device 700, orientation or acceleration / deceleration of electronic device 700, and temperature changes of electronic device 700. Sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 714 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 714 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0197] Communication component 716 is configured to facilitate wired or wireless communication between electronic device 700 and other devices. Electronic device 700 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 716 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 716 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0198] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0199] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 704 including instructions, which can be executed by a processor 720 of an electronic device 700 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0200] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a terminal's processor, enables the terminal to perform a battery thermal management method, the method comprising: Obtain battery temperature and lifespan information; Based on the temperature information and the lifespan information, determine the thermal management control objectives; Thermal management control is performed on the battery according to the stated thermal management control objectives.

[0201] Optionally, determining the thermal management control objective based on the temperature information and the lifetime information includes: Based on the lifetime information, the temperature information, and the constraint information, the thermal management control objective is determined; wherein the constraint information includes at least one of the following: lifetime constraint information, energy consumption constraint information, disturbance constraint information, and safety constraint information.

[0202] Optionally, the constraint information includes the lifetime constraint information, the energy consumption constraint information, and the disturbance constraint information. Determining the thermal management control objective based on the lifetime information, the temperature information, and the constraint information includes: Determine the lifetime deviation cost based on the lifetime information and the lifetime constraint information; The energy cost generated by thermal management is determined based on the temperature information and the energy consumption constraint information; and the degree of interference caused by thermal management is determined based on the temperature information and the interference constraint information. A comprehensive cost is constructed based on the lifetime deviation cost, the energy consumption cost, and the degree of interference. The thermal management control objectives are determined based on the overall cost and cost targets.

[0203] Optionally, the thermal management control objective includes at least one of the following: the amount of adjustment to the battery temperature during battery resting, and the timing of initiating thermal management operations.

[0204] Optionally, the temperature information includes predicted temperature information, and obtaining the battery temperature information includes at least one of the following: Based on the environmental and state data of the battery, the predicted temperature information of the battery is obtained.

[0205] Optionally, the lifespan information includes predicted lifespan information, and obtaining the battery lifespan information includes: Based on the battery's state data, the battery's environmental data, and / or various thermal management control strategies, the predicted lifespan information of the battery is obtained.

[0206] Optionally, the step of predicting the predicted lifespan information of the battery based on the battery's state data, the battery's environmental data, and various thermal management control strategies includes: Based on the environmental data and the status data, predict the load condition of the battery for at least one time period. Based on the load condition prediction data within the at least one time period, determine the load condition simulation data within the at least one time period corresponding to each thermal management control strategy. Based on the load condition simulation data within the at least one time period, the predicted lifetime information corresponding to the various thermal management control strategies is determined.

[0207] Optionally, the thermal management control strategy includes at least two of the following: a baseline strategy, an enhanced cooling strategy, and a reduced cooling strategy; The baseline strategy is a strategy that controls the temperature of the battery to follow the natural temperature change. The enhanced cooling strategy is a strategy that controls the average temperature of the battery during the resting period to be lower than the average temperature of the battery corresponding to the baseline strategy. The cooling reduction strategy is a strategy that controls the average temperature of the battery during the resting period to be higher than the average temperature of the battery corresponding to the benchmark strategy.

[0208] Optionally, before determining the thermal management control target based on the temperature information and the lifetime information, the method further includes: When the battery is in a resting period, the steps of obtaining battery temperature information and lifespan information are performed, or the steps of determining thermal management control targets based on the temperature information and lifespan information are performed. A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a terminal's processor, enables the terminal to execute a battery thermal management method, the method comprising: Obtain the selected target control mode; wherein, the target control mode is a mode for thermal management control of the battery by combining the battery's temperature information and lifespan information; According to the target control mode, thermal management control is performed on the battery.

[0209] Optionally, obtaining the selected target control mode further includes: Obtain at least one of the following: lifetime constraint information, energy consumption constraint information, interference constraint information, and safety constraint information set for the target control mode.

[0210] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts between the various embodiments, please refer to each other.

[0211] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0212] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0213] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0214] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0215] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments, as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0216] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0217] The above provides a detailed description of a battery thermal management method, a battery thermal management device, an electronic device, a readable storage medium, and a terminal provided by this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A thermal management method for a battery, characterized in that, The method includes: Obtain battery temperature and lifespan information; Based on the temperature information and the lifespan information, determine the thermal management control objectives; Thermal management control is performed on the battery according to the stated thermal management control objectives.

2. The method according to claim 1, characterized in that, The step of determining the thermal management control objective based on the temperature information and the lifespan information includes: Based on the lifetime information, the temperature information, and the constraint information, the thermal management control objective is determined; wherein the constraint information includes at least one of the following: lifetime constraint information, energy consumption constraint information, disturbance constraint information, and safety constraint information.

3. The method according to claim 2, characterized in that, The constraint information includes the lifetime constraint information, the energy consumption constraint information, and the disturbance constraint information. Determining the thermal management control objective based on the lifetime information, the temperature information, and the constraint information includes: Determine the lifetime deviation cost based on the lifetime information and the lifetime constraint information; The energy cost generated by thermal management is determined based on the temperature information and the energy consumption constraint information; and the degree of interference caused by thermal management is determined based on the temperature information and the interference constraint information. A comprehensive cost is constructed based on the lifetime deviation cost, the energy consumption cost, and the degree of interference. The thermal management control objectives are determined based on the overall cost and cost targets.

4. The method according to any one of claims 1-3, characterized in that, The thermal management control objectives include at least one of the following: the amount of adjustment to the battery temperature during battery resting, and the timing of initiating thermal management operations.

5. The method according to any one of claims 1-4, characterized in that, The temperature information includes predicted temperature information, and the obtained battery temperature information includes at least one of the following: Based on the environmental and state data of the battery, the predicted temperature information of the battery is obtained.

6. The method according to any one of claims 1-5, characterized in that, The lifespan information includes predicted lifespan information, and obtaining the battery lifespan information includes: Based on the battery's state data, the battery's environmental data, and / or various thermal management control strategies, the predicted lifespan information of the battery is obtained.

7. The method according to claim 6, characterized in that, The process of predicting the battery's lifespan based on the battery's state data, environmental data, and various thermal management control strategies includes: Based on the environmental data and the status data, predict the load condition of the battery for at least one time period. Based on the load condition prediction data within the at least one time period, determine the load condition simulation data within the at least one time period corresponding to each thermal management control strategy. Based on the load condition simulation data within the at least one time period, the predicted lifetime information corresponding to the various thermal management control strategies is determined.

8. The method according to claim 6 or 7, characterized in that, The thermal management control strategy includes at least two of the following: a baseline strategy, an enhanced cooling strategy, and a reduced cooling strategy; The baseline strategy is a strategy that controls the temperature of the battery to follow the natural temperature change. The enhanced cooling strategy is a strategy that controls the average temperature of the battery during the resting period to be lower than the average temperature of the battery corresponding to the baseline strategy. The cooling reduction strategy is a strategy that controls the average temperature of the battery during the resting period to be higher than the average temperature of the battery corresponding to the benchmark strategy.

9. The method according to any one of claims 1-8, characterized in that, Before determining the thermal management control target based on the temperature information and the lifetime information, the method further includes: When the battery is in a resting period, the steps of obtaining the battery's temperature information and lifespan information are performed, or the steps of determining the thermal management control target based on the temperature information and the lifespan information are performed.

10. A thermal management method for a battery, characterized in that, The method includes: Obtain the selected target control mode; wherein, the target control mode is a mode for thermal management control of the battery by combining the battery's temperature information and lifespan information; According to the target control mode, thermal management control is performed on the battery.

11. The method according to claim 10, characterized in that, The selected target control mode further includes: Obtain at least one of the following: lifetime constraint information, energy consumption constraint information, interference constraint information, and safety constraint information set for the target control mode.

12. A thermal management device for a battery, characterized in that, The device includes: The information acquisition module is used to acquire battery temperature and lifespan information. The target determination module is used to determine the thermal management control target based on the temperature information and the lifetime information; A thermal management control module is used to perform thermal management control on the battery according to the thermal management control objectives.

13. A thermal management device for a battery, characterized in that, The device includes: A mode acquisition module is used to acquire a selected target control mode; wherein, the target control mode is a mode for thermal management control of the battery by combining the battery's temperature information and lifespan information; A thermal management control module is used to perform thermal management control on the battery according to the target control mode.

14. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the thermal management method for a battery as described in any one of claims 1-11.

15. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the thermal management method for a battery as described in any one of claims 1-11.

16. A terminal, characterized in that, This includes the electronic device as described in claim 14 or the readable storage medium as described in claim 15.

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