Battery life prediction method, system, device, storage medium, and program product

Abstract

The application discloses a battery life prediction method, system, device, storage medium and program product. The method comprises the following steps: loading simulation configuration parameters and user use conditions for prediction; distributing corresponding thermal zones for each battery cell based on battery structure parameters in the simulation configuration parameters and thermal zone mapping rules; generating SOC and battery current of the battery based on the user use conditions; obtaining a heat source term of each thermal zone by aggregating thermal influences on each battery cell based on the SOC and the battery current; switching a cooling mode used by the battery based on the heat source term of each thermal zone; calculating a battery cell capacity of each battery cell according to an aging model selected according to a working condition state of the battery current; aggregating the battery cell capacities to obtain a battery capacity of the battery; and calculating an SOH for life evaluation by using the battery capacity. Through the combination of topology management, thermal zone mapping, user use behavior driving, multi-thermal zone temperature adaptive thermal management and a joint aging model, high-precision calculable simulation of battery life is realized.

Description

Technical Field

[0001] This application relates to the field of battery technology, specifically to a method, system, device, storage medium, and program product for predicting battery life. Background Technology

[0002] As a key energy unit in new energy vehicles, energy storage systems, and mobile terminals, the lifespan degradation of batteries directly affects system safety, economy, and availability. Battery life prediction typically needs to consider the combined effects of calendar aging and cycle aging: under static or low-current conditions, batteries undergo calendar aging related to time, temperature, and state of charge; under charge-discharge conditions, batteries are also affected by cycle aging related to current, temperature, and charge levels. Due to the significant time-varying and uncertain nature of operating conditions in actual use, battery life evolution exhibits complex characteristics of electrical, thermal, and aging coupling, thus placing higher demands on the engineering applicability of life prediction methods.

[0003] In existing technologies, battery life estimation methods mostly employ empirical or semi-empirical models at the individual cell level, and then amplify these models at the battery pack level through simplified assumptions. For example, some schemes use constant current, fixed rate, or typical operating cycle as inputs, which makes it difficult to characterize the characteristics of current changes over time under real user behavior. Other schemes treat the battery pack as a thermally homogeneous body or use a single-zone temperature approximation, ignoring the uneven temperature distribution in the thermal zones caused by differences in the spatial location and heat dissipation conditions of the battery pack. Consequently, they fail to reflect the localized accelerated aging caused by localized high temperatures and its limiting effect on overall lifespan.

[0004] Therefore, the prediction accuracy of existing battery life prediction schemes is not high. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, device, storage medium, and program product for predicting battery life, which addresses the shortcomings of the prior art. This purpose is achieved through the following technical solutions.

[0006] The first aspect of this application proposes a method for predicting battery life, the method comprising: Load the simulation configuration parameters and user operating conditions used for prediction; the simulation configuration parameters include thermal mapping rules and battery structure parameters; the user operating conditions include the correspondence between time and charge / discharge rate; Based on the battery structure parameters and the thermal zone mapping rules, a corresponding thermal zone is assigned to each cell of the battery. Based on the user's operating conditions, the state of charge (SOC) and battery current of the battery are generated. Based on the thermal effects of the SOC and the battery current on each cell, the heat source item for each thermal zone is obtained by aggregating thermal zones. Based on the heat source items of each hot zone, switch the cooling mode used by the battery; The aging model is selected based on the operating condition of the battery current to calculate the cell capacity of each cell; The battery capacity is obtained by aggregating the cell capacity of each cell, and the state of health (SOH) for life assessment is calculated using the battery capacity.

[0007] A second aspect of this application proposes a battery life prediction system, the system comprising: The parameter management module is used to load simulation configuration parameters and user operating conditions for prediction; the simulation configuration parameters include thermal mapping rules and battery structure parameters; the user operating conditions include the correspondence between time and charge / discharge rate. The topology management module is used to assign a corresponding hot zone to each cell of the battery based on the battery structure parameters and the hot zone mapping rules. The heat source item calculation module is used to generate the state of charge (SOC) and battery current of the battery based on the user's operating conditions, and to obtain the heat source item of each hot zone by aggregating the heat sources of each hot zone based on the thermal impact of the SOC and battery current on each cell. The thermal management module is used to switch the cooling mode used by the battery based on the heat source items in each hot zone; The life prediction module is used to select an aging model based on the operating conditions of the battery current to calculate the cell capacity of each cell. The topology management module is also used to obtain the battery capacity of the battery based on the aggregation of the cell capacity of each cell; The lifespan prediction module is also used to calculate the state of health (SOH) for lifespan assessment using the battery capacity of the battery.

[0008] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the method as described in the first aspect.

[0009] The fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method as described in the first aspect.

[0010] The fifth aspect of this application provides a computer program product comprising a computer program that is executed by a processor to implement the method as described in the first aspect.

[0011] Based on the battery life prediction method, system, device, storage medium, and program product described above, this application has the following beneficial effects or advantages: By introducing a hot zone mapping rule to assign a hot zone to each cell and aggregating the thermal impact of user behavior on the cells into heat source items based on hot zones, the temperature evolution of different hot zone locations can be calculated independently. Compared to the simplified method of treating the battery pack as a temperature-uniform body, this scheme can explicitly reflect the accelerated aging phenomenon caused by local high-temperature areas, thereby more accurately predicting the cell lifespan dispersion and overall pack lifespan degradation caused by temperature unevenness.

[0012] By actively switching the cooling mode used by the battery based on the heat source items of each hot zone, the system updates the corresponding heat dissipation parameters, forming a closed loop of heat generation, temperature rise, cooling decision, and temperature drop, providing a basis for thermal management. Compared with conventional simulations that use fixed heat dissipation boundaries or constant cooling intensity, which cannot assess the impact of different thermal management methods on lifespan.

[0013] By loading user operating conditions that correspond to time and charge / discharge rates, and generating time-varying SOC (State of Charge) and battery current accordingly, the simulation process can realistically reflect load changes under typical user scenarios. Compared to existing methods that use constant current or fixed operating condition cycles, this approach significantly improves the simulation accuracy of the aging accumulation process under actual dynamic usage conditions.

[0014] Different aging models (calendar aging or cycle aging) are selected based on the battery's operating condition (such as resting or charging / discharging). This adaptive selection mechanism avoids errors caused by a single aging model under unsuitable conditions, making the calculation of cell capacity more consistent with the actual cell degradation mechanism. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This application illustrates a flowchart of a method for predicting battery life according to an exemplary embodiment. Figure 2 This application illustrates a mapping relationship between SOC and DC internal resistance according to an exemplary embodiment. Figure 3 This application illustrates a comparison chart of experimental data and predicted data during a calendar aging process according to an exemplary embodiment. Figure 4 This application illustrates a schematic diagram of temperature changes in different thermal zones over a simulated time series according to an exemplary embodiment. Figure 5 This application illustrates a schematic diagram of battery SOH variation over a simulated time series according to an exemplary embodiment. Figure 6 This application illustrates a schematic diagram of battery capacity variation over a simulated time series according to an exemplary embodiment. Figure 7 This application illustrates a schematic diagram of current and SOC changes over a simulated time series according to an exemplary embodiment. Figure 8 This is a structural diagram illustrating a battery life prediction system according to an exemplary embodiment of this application; Figure 9 This application illustrates a battery life simulation architecture diagram according to an exemplary embodiment. Figure 10 This is a schematic diagram of the hardware structure of an electronic device according to an exemplary embodiment of this application. Detailed Implementation

[0016] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of systems and methods consistent with some aspects of this application as detailed in the appended claims.

[0017] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0018] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0019] Batteries commonly employ various series-parallel structures and topologies in engineering applications. The current distribution, equivalent internal resistance, and heat source terms of cells within a series-parallel system dynamically change with varying states of charge. Without structured topology management and thermal mapping mechanisms, it is difficult to establish a multi-level relationship of "cell-parallel group-series string-battery pack" across the entire battery structure and to consistently transfer the thermal zone assignment and thermal stress input of cells to the aging model. This makes it difficult to accurately describe application problems where localized thermal stress and degradation limit the overall lifespan of the weakest cell.

[0020] In existing thermal management modeling methods, some schemes only consider fixed heat dissipation boundaries or fixed cooling intensities, making it difficult to achieve adaptive switching of cooling modes in combination with temperature conditions. This makes it difficult to use simulation results to evaluate the impact of different thermal management strategies on lifespan.

[0021] Therefore, existing technologies still have shortcomings in simultaneously taking into account topological differences, current input under real-world operating conditions, dynamic heating caused by SOC-related internal resistance, uneven temperature distribution in multiple hot zones and thermal management switching, as well as the coupled evolution of capacity decay due to calendar and cycle aging. As a result, the accuracy, traceability, and engineering applicability of battery pack life simulation prediction need to be improved.

[0022] There is an urgent need for a method that can achieve high-precision, computable simulation of battery life by combining topology management, thermal zone mapping, user behavior-driven approaches, multi-thermal zone temperature adaptive thermal management, and joint aging models.

[0023] The technical solution of this application and how it solves the aforementioned technical problems are described in detail below with specific embodiments. The listed specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0024] Figure 1 This is a flowchart illustrating an embodiment of a battery life prediction method according to an exemplary embodiment of this application. Figure 1 As shown, the method may include the following steps: Step 101: Load the simulation configuration parameters and user operating conditions used for prediction. The simulation configuration parameters include thermal mapping rules and battery structure parameters. The user operating conditions include the correspondence between time and charge / discharge rate.

[0025] In this step, simulation configuration parameters are used for configuration during the simulation process and are inputs to a predefined simulation model. These parameters describe prior information about the battery pack structure and thermal zone division, allowing the simulation model to be quickly reused for different battery pack structures and thermal management strategies without additional manual programming modifications. Thermal zone mapping rules define the correspondence between cells or parallel groups and physical thermal zones, for example, mapping the spatial location of a cell in the battery pack (series index, parallel index) to a specific thermal zone number. A thermal zone refers to an area within the battery with a similar temperature distribution. Battery structure parameters describe the series and parallel topology of the battery, such as the number of series connections. Parallel number And whether to string first and then merge, or merge first and then string, etc.

[0026] In addition, simulation configuration parameters can also include simulation duration and time step, the relationship between the battery's state of charge and DC internal resistance, cooling mode parameters and thermal management strategy parameters, calendar aging model parameters, and cycle aging model parameters. Furthermore, the aging model parameters can be set to prioritize the loading strategy of fitting parameters (i.e., parameters obtained through training) over directly setting parameters. When a parameter set obtained by fitting experimental data exists, that parameter set is used first, thereby improving the consistency and adaptability of the aging model for specific cells or batches.

[0027] For example, the simulation configuration parameters mentioned above can be organized and managed using a unified configuration file, which can be in XML format. Different types of parameters can be read using a path-based access method, and default values ​​and error handling can be provided.

[0028] Users use operating condition characterization to represent the changes in battery charge and discharge intensity over different times, presented in the form of a "time-charge / discharge rate" relationship. For example, an operating condition file records the rate value per second. This operating condition is used to simulate load changes during real driving or charging / discharging processes, rather than a simple constant current or constant power assumption. During the simulation, the rate value corresponding to the current moment can be obtained at any given time through table lookup, interpolation, or piecewise hold.

[0029] Step 102: Based on the battery structure parameters and thermal mapping rules, assign a corresponding thermal zone to each cell of the battery.

[0030] In this step, the battery structure parameters describe the series and parallel arrangement of the batteries, such as the number of series connections. Parallel number Topology type, etc. The total number of battery cells is... The topology involves various configurations, including series-to-parallel, parallel-to-serial, and other complex hybrid structures. The hot zone mapping rule can be understood as a predefined function or lookup table. This rule, combined with the battery structure, automatically associates cells with hot zones, eliminating the need for individual specification and thus achieving automatic cell-to-hot zone allocation. Cells within the same hot zone share the same temperature evolution model in subsequent calculations.

[0031] By refining the macroscopic battery structure into cell-level thermal zone assignments, cells in different spatial locations can have different temperatures, thus reflecting the temperature differences in actual batteries caused by uneven heat dissipation and varying proximity to heat sources.

[0032] Step 103: Based on the user's operating conditions, generate the battery's state of charge (SOC) and battery current.

[0033] In this step, the user provides the charge / discharge rate over time based on the operating conditions. The SOC and current at any given simulation moment can be generated as follows: SOC:

[0034] in, Indicates the SOC at the next moment. Indicates time The multiplier value, Represents the SOC at the current moment. Indicates the time step. For Coulomb efficiency.

[0035] Battery current:

[0036] in, Indicates time Battery current, This refers to the battery's rated capacity (or preset baseline capacity).

[0037] Step 103 can transform the abstract user operating conditions into physical quantities that can be directly used for electrothermal calculations, and the generated SOC and battery current are time-varying parameters.

[0038] Step 104: Based on the thermal impact of the generated SOC and battery current on each cell, obtain the heat source item for each thermal zone by aggregating according to the thermal zone.

[0039] In this step, since the State of Charge (SOC) affects the internal resistance of the battery cell, both the SOC and the current will cause the battery cell to heat up. Therefore, the thermal effect can be understood as the heat generated by the battery cell.

[0040] Step 104 summarizes the heat generated at the cell level to the thermal zone level, which serves as the heat source input for the subsequent temperature model. This model characterizes the heat distribution characteristics under the combined effects of changes in user operating conditions, changes in State of Charge (SOC), and differences in spatial thermal zones. This approach preserves the dynamic influence of SOC on the cell's internal resistance while avoiding the computational burden of solving thermal equations individually for each cell, achieving a balance between accuracy and efficiency.

[0041] Step 105: Based on the heat source items of each hot zone, switch the cooling mode used by the battery.

[0042] In this step, different cooling modes correspond to different heat dissipation parameters. The simulation configuration parameters include relevant parameters for different cooling modes. Therefore, when the cooling mode is switched to a different mode than the one previously used, the heat dissipation parameters need to be updated to ensure that the thermal management strategy can be reflected in a closed loop in the simulation, which facilitates the evaluation of the lifespan effect of different cooling strategies.

[0043] This adaptive switching of cooling modes allows the simulation to reflect the closed-loop control behavior of a real battery management system, which involves "temperature rise → stronger cooling activated → temperature drop". It can be used to evaluate the impact of different cooling strategies on battery life.

[0044] Step 106: Select an aging model based on the operating conditions of the battery current to calculate the cell capacity of each cell.

[0045] In this step, the operating condition refers to the battery's operating mode, including the idle operating condition and the charge / discharge operating condition. The operating condition can be determined based on the relationship between the current battery current amplitude (absolute value) and a preset threshold.

[0046] The operating conditions determine which type of aging model to use to calculate cell capacity loss. An aging model is a mathematical expression used to quantify the decay of battery capacity over time (or cycle count). In this embodiment, two types of models are used depending on the operating conditions: a calendar aging model and a cycle aging model. The calendar aging model describes the irreversible capacity loss caused by heat, state of charge (SOC), and other chemical potentials under no-current or very low-current conditions. The cycle aging model describes the capacity loss caused by factors such as current, temperature, charge window, and cumulative flux during charge-discharge cycles.

[0047] Therefore, the choice of aging model is based on the actual battery aging mechanism. The two aging models correspond to different physical degradation mechanisms: when the battery is at rest, even without current, temperature and SOC will still drive chemical decay (calendar aging); while the stress caused by current during charging and discharging (such as volume change, lithium plating, and regeneration) is the dominant factor in cycle aging.

[0048] Step 107: Based on the cell capacity of each cell, aggregate to obtain the battery capacity, and use the battery capacity to calculate the SOH for life assessment.

[0049] In this step, the battery capacity of the complete battery is obtained by aggregating the cell capacities of each cell according to specific rules.

[0050] The state of health (SOH) of a battery can be expressed as:

[0051] in, Indicates time health status, Indicates time Battery capacity, This indicates the initial battery capacity.

[0052] Based on the above embodiments, the entire process is covered, including configuration loading, topology and thermal zone modeling, operating condition driving, dynamic heat generation and thermal zone aggregation, adaptive thermal management, joint aging calculation, capacity aggregation, and SOH output. This integrates real-world user behavior, spatial thermal non-uniformity, adaptive thermal management strategies, and calendar / cycle joint aging mechanisms into a computable simulation framework, improving the accuracy of battery life prediction.

[0053] In some embodiments of this application, step 102, based on battery structure parameters and thermal mapping rules, assigning a corresponding thermal zone to each cell of the battery, may include: The topological relationship of the cells in the battery is constructed based on the battery structure parameters. The hierarchical structure of the topological relationship includes cells, parallel groups, series strings and battery packs. A hot zone identifier is assigned to each cell in the topological relationship according to the hot zone mapping rules.

[0054] Based on battery structure parameters, a four-level topological relationship can be established from a single cell to the entire battery pack: Cell: The smallest unit of energy storage, and the basic object of aging calculation.

[0055] Parallel Group: A group of cells connected in parallel. Each cell in the group has the same terminal voltage, and the current is distributed according to the internal resistance.

[0056] A series string is composed of several parallel groups connected in series. The current in each string is the same, and the voltages are added together.

[0057] A battery pack is a package consisting of several series-connected batteries connected in parallel (or directly output).

[0058] Based on the battery topology constructed above, the position coordinates of all cells are obtained by traversing all cells: series position index. ( Values ​​range from 1 to ) and parallel position index ( Values ​​range from 1 to Then call the hotspot mapping rules. The hot zone number is obtained. .

[0059] In an exemplary application scenario, if the number of series connections Parallel number If the topology type is first parallel then serial, the program will create it automatically. Each of the following concatenation strings contains a series of strings. If there are multiple battery cells, then each cell can be assigned a unique location coordinate (string index). and index ).

[0060] This process transforms abstract battery structural parameters into a computable object hierarchy, providing a structured foundation for subsequent current distribution, thermal mapping, and capacity aggregation. Therefore, this hierarchical structure forms the skeleton for all subsequent calculations (current distribution, thermal aggregation, aging calculations, and capacity aggregation).

[0061] Thus, by using spatial differentiation modeling, cells in different locations can have different temperature trajectories, reflecting the thermal non-uniformity within a real battery pack.

[0062] In some embodiments of this application, in step 104, based on the thermal impact of the generated SOC and battery current on each cell, the heat source item for each hot zone is obtained by aggregating hot zones, which may include: The equivalent internal resistance and cell current of each cell are determined based on the generated SOC and battery current. The thermal power loss of each cell is obtained using the equivalent internal resistance and cell current of each cell. The thermal power loss of each cell is then aggregated according to the thermal zones to obtain the heat source term of each thermal zone.

[0063] The thermal impact of the battery cell is expressed in terms of Joule thermal power, which is the thermal power loss of the battery cell.

[0064] In practical implementation, a pre-loaded state-of-charge (SOC) related DC internal resistance relationship is used, i.e., the SOC-DC internal resistance relationship. This relationship can be a lookup table (discrete points) or a fitting function (such as a polynomial or exponential form). See also Figure 2 The mapping relationship between SOC and DC internal resistance is shown. Since SOC is a continuously changing quantity, the equivalent internal resistance value corresponding to the current SOC is usually obtained from the table through linear interpolation or spline interpolation, as shown below:

[0065] in, Let be the equivalent internal resistance of cell c. This indicates the relationship between DC internal resistance and SOC. Indicates time The state of charge.

[0066] Furthermore, considering the current distribution in parallel branches, the cell current can be defined as:

[0067] in, This represents the current in cell c at time t. This represents the battery current at time t. This represents the current distribution coefficient. It is typically determined by the battery pack topology and parallel branch characteristics, distributing the total current to each cell. Under the ideal equal distribution assumption (with identical parameters for all cells within the same parallel group), this is the current distribution coefficient for each cell within the same parallel group. , Indicates the number of parallel circuits, in general. It can also be determined by a combination of topological constraints and differences in internal resistance.

[0068] Based on the cell current and equivalent internal resistance, the heat power loss of the cell is calculated using Joule's law, as follows:

[0069] in, This represents the heat power loss of cell c at time t.

[0070] Based on the above calculations of cell heat generation, it can be seen that when current passes through the internal resistance, electrical energy is converted into heat energy, which is the main internal heat source for the rise in battery temperature. Since the internal resistance changes with the state of charge (SOC) and the current changes with time, the heat power loss is time-varying and non-uniform (different cells may have the same SOC but different current distributions, or cells in the same hot zone may have different internal resistances due to differences in SOC).

[0071] Aggregation by thermal zone sums up the heat power loss of all cells within the same thermal zone to obtain the total heat source term for that thermal zone.

[0072] in, The heat source term (i.e., the heat region) represents the heat source term of the heat region z at time t. (unit volume heat source term) This represents the set of battery cells belonging to the hot zone z.

[0073] It is understandable that the heat sources in different hot zones may differ greatly (for example, the heat dissipation of cells in the middle area is poor and the temperature is high, but the heat generation may not be the greatest; or some cells generate more heat due to their high internal resistance). After aggregation, each hot zone obtains its own heat input, thus reflecting spatial non-uniformity in temperature updates.

[0074] In this embodiment, starting from the total current and SOC at the battery pack level, the process is gradually refined to the heat generation at the cell level, and then aggregated back to the heat source at the thermal zone level. This ensures both physical accuracy (SOC-related internal resistance, current distribution) and computational efficiency (thermal zone aggregation), eliminating the need to solve the thermal equations separately for each cell.

[0075] In some embodiments of this application, in step 105, switching the cooling mode used by the battery based on the heat source items of each hot zone may include: For each hot zone, the temperature of the hot zone is calculated based on the heat source items of that hot zone and the cooling mode used by the battery. Based on the highest temperature among the hot zone temperatures of each hot zone, a target cooling mode is selected, and the cooling mode used by the battery is switched using the target cooling mode.

[0076] The calculation of the hot zone temperature can be achieved based on various thermal modeling methods, which can be selected according to the simulation accuracy requirements and computational complexity limitations. For example, a circuit thermal model, a lumped parameter thermal model, or a thermal simulation model based on three-dimensional computational fluid dynamics can be used to describe the evolution of the hot zone temperature.

[0077] The following description uses a first-order thermal circuit model as an example. Those skilled in the art will understand that this application is not limited to this implementation.

[0078] For any hot zone z, its temperature state can be discretely updated based on the heat source input and the heat dissipation conditions corresponding to the cooling mode of hot zone z, as follows:

[0079] in, This indicates the temperature of the heat region z at the next moment. The value represents the temperature of the heat region z at the current moment, and Δt represents the time step. This represents the heat capacity of the hot region z. The term representing the heat source in the hot zone z at time t is... Let z be the heat dissipation specific surface area of ​​the hot zone. This represents the equivalent thermal resistance of the hot region z. This indicates that the hot zone z is in cooling mode. The equivalent cooling temperature is denoted as , and the set of cooling modes is denoted as . Different cooling modes This corresponds to different heat dissipation parameters. It can be seen that the increase in hot zone temperature is driven by internal heat sources, while the decrease in hot zone temperature is determined by the heat dissipation system (related to the current cooling mode) removing heat. Different cooling modes will change... and This affects the rate of temperature rise and the equilibrium temperature.

[0080] Furthermore, to enhance the flexibility of heat dissipation modeling, a temperature correction term is introduced to characterize the heat transfer enhancement or control compensation effect. The corrected hot zone temperature can be expressed as:

[0081] in, Indicates hot zone The temperature correction factor is used to describe the intensity of additional heat dissipation correction or control compensation. For hot zones... In cooling mode The equivalent cooling temperature under these conditions can be expressed as:

[0082] in, Indicates hot zone In cooling mode The equivalent cooling temperature below Indicates ambient temperature. Indicates cooling mode Lower heating zone Cooling offset relative to ambient temperature.

[0083] After calculating the temperature of each hot zone, the highest hot zone temperature can be extracted, and the target cooling mode can be selected based on the thermal management strategy. The highest hot zone temperature can be expressed as...

[0084]

[0085] Based on the highest hot zone temperature, the target cooling mode selection function can be expressed as:

[0086] in, Indicates time The selected target cooling mode, Represents a set of hot zones. Indicates time The highest temperature in the hot zone, This represents the mode selection function defined based on the temperature threshold range of the thermal management strategy. The thermal management strategy is usually defined as the mapping relationship between the temperature threshold range and the cooling mode.

[0087] In this way, the temperature of the hot zone can be dynamically updated with the input of the heat source, and the cooling mode can be adaptively switched according to the highest temperature state, so that the thermal management strategy and the temperature evolution process form a closed loop coupling, which can be used to characterize the temperature rise difference under different operating conditions and different heat dissipation conditions.

[0088] In some embodiments of this application, step 106, which involves selecting an aging model to calculate the cell capacity of each cell based on the operating condition of the battery current, may include the following two cases: (1) When the absolute value of the battery current is greater than or equal to the preset threshold, the cycle aging model is selected to calculate the cell capacity of each cell; the cycle aging model calculates the cell capacity based on the hot zone temperature of the cell's hot zone, the battery current and the cycle aging rate obtained from the SOC.

[0089] The preset threshold can be a pre-configured small current threshold, such as 0.01C. The current value corresponding to 0.05C.

[0090] The cyclic aging rate in the cyclic aging model is expressed as a comprehensive factor combining the effects of temperature, current, state of charge or depth of discharge, and cumulative flux. The cyclic aging rate is expressed as follows:

[0091] in, Indicates the cyclic aging rate. Indicates the cyclic aging coefficient. Indicates the current index, Indicates the cyclic aging activation energy. This represents the relevant function terms of SOC. The relevant function terms representing cumulative flux, This represents the cumulative flux.

[0092] Regarding the representation of cyclic aging rate, Specifically, the temperature of the thermal zone of the cell. Different thermal zones have different temperatures, resulting in different cycle aging rates for cells in different locations within the same battery. Specifically, battery current The magnitude of the current affects the stress level of the electrode material and the rate of side reactions; Specifically, SOC, that is The state of charge affects the electrochemical potential and side reaction tendency of the electrode; for example, cyclic aging is often accelerated under high SOC. Cumulative flux... Based on battery current The moment of gain Cumulative ampere-hours .

[0093] Based on the cycle aging rate, the cell is in time step The capacity loss due to cyclic aging can be expressed as:

[0094] in, Indicates time Cyclic aging capacity loss, Indicates time Battery current, Indicates time The cumulative ampere-hours, Indicates time battery cells The capacity.

[0095] After calculating the capacity loss due to cycle aging, the cell capacity update can be expressed as:

[0096] in, Indicates the next moment of the battery cell The capacity.

[0097] (2) When the absolute value of the battery current is less than the preset threshold, the calendar aging model is selected to calculate the cell capacity of each cell. The calendar aging model calculates the cell capacity based on the thermal zone temperature of the cell and the calendar aging rate obtained by SOC.

[0098] The calendar aging rate in the calendar aging model, expressed as a combined factor of temperature and state of charge, can be expressed in the Arrhenius form:

[0099] in, Indicates the calendar aging rate. Indicates the calendar aging frequency factor. Indicates calendar aging activation energy. Represents the gas constant. Specifically, the temperature of the thermal zone of the cell. , Indicates the state of charge (SOC). This indicates the SOC impact item.

[0100] In some embodiments, the SOC impact term can be expressed in power function form as follows:

[0101] in, The SOC index parameter represents calendar aging.

[0102] Based on calendar aging rates, the battery cell is in time step The calendar aging capacity loss within the time frame can be expressed as:

[0103] in, Indicates time Calendar aging capacity loss, Indicates battery cell Temperature of the corresponding hot zone Indicates time battery cells The capacity.

[0104] After calculating the calendar aging capacity loss, the cell capacity update can be expressed as:

[0105] in, Indicates the next moment of the battery cell The capacity.

[0106] like Figure 3 As shown in the figure, the fitting results of experimental and predicted data on the capacity loss relationship during battery calendar aging under different temperature conditions are presented. The results show a high degree of consistency between the predicted and experimental data, validating the effectiveness of the aging model parameter identification and lifetime prediction methods.

[0107] In this embodiment, a combined aging simulation with adaptive operating conditions was implemented, realistically reflecting the dual aging mechanism of the battery during alternating charge / discharge and rest periods. By incorporating hot zone temperature into both models, cells at different locations will exhibit differentiated capacity decay due to temperature variations, even when subjected to the same current and SOC curve.

[0108] In some embodiments of this application, step 107, which aggregates the cell capacities of each cell to obtain the battery capacity, may include: Based on the weakest constraint rule, the cell capacity of each cell is topologically aggregated to obtain the battery capacity of the battery.

[0109] Among them, the weakest constraint rule is used to characterize that in the series or parallel structure of a battery, the overall capacity is limited by the worst-performing cell.

[0110] In practical implementation, the weakest constraint rule is that the capacity of a parallel group is the minimum value of the capacity of each cell in the group, the capacity of a series string is the minimum value of the capacity of each parallel group in the string, and the capacity of a battery with a series-to-parallel structure is the minimum value of the capacity of each series string and the capacity of a battery with a parallel-to-series structure is the sum of the capacities of each parallel group.

[0111] The entire aggregation process starts from the bottom layer (cells) and aggregates upwards level by level until the total capacity of the battery pack is obtained. Let g be the set of parallel groups to which cell c belongs, and b be the set of series groups. Taking the example of series first and then parallel, the capacity of all cells in the battery is denoted as... The capacity of the serial string can be expressed as:

[0112] Furthermore, multiple series strings are connected in parallel to form a parallel group, which is the final battery pack. Its capacity can be expressed as:

[0113] in, This indicates the number of batteries connected in series.

[0114] This aggregation method can naturally reflect the engineering principle that the weakest cell in the battery limits the overall performance, making the simulation output more sensitive to capacity decay caused by local high temperature and local high loss, thereby improving the reliability of lifetime prediction.

[0115] like Figures 4-7 As shown, during the simulation, the temperature of each thermal zone in the time series can be recorded. State of Health (SOH(t)) and Battery Capacity The simulation can generate current I(t) and SOC(t), and output lifetime curves and statistical results for evaluation after the simulation, thus providing a basis for thermal management strategy design, assessment of the impact of operating conditions on lifetime, and battery structure optimization. Figure 5 The green line represents the simulated SOH(t), and the red dashed line represents the SOH threshold. This threshold represents the cutoff value for battery health; if the battery's SOH reaches this threshold, it indicates that the battery's health is not good. Figure 8 This is a structural diagram illustrating a battery life prediction system according to an exemplary embodiment of this application. Figure 8 As shown, the system includes an input layer, a core module layer, and an output layer.

[0116] The input layer includes configuration files and user operating condition data, primarily used to define model parameters and set simulation conditions. The core module layer contains functional modules such as: parameter management, topology management, heat source calculation, thermal management, and lifetime prediction. This layer is used to predict various battery parameters and calculate and couple related physical fields. The output layer includes both visualization and data output. Visualization provides users with an intuitive view of battery performance trends, while data output facilitates further extraction and analysis of simulation results.

[0117] In practical implementation, the roles of each functional module in the core module layer are as follows: The parameter management module is used to load simulation configuration parameters and user operating conditions for prediction; the simulation configuration parameters include thermal mapping rules and battery structure parameters; the user operating conditions include the correspondence between time and charge / discharge rate. The topology management module is used to assign a corresponding hot zone to each cell of the battery based on the battery structure parameters and the hot zone mapping rules. The heat source item calculation module is used to generate the state of charge (SOC) and battery current of the battery based on the user's operating conditions, and to obtain the heat source item of each hot zone by aggregating the heat sources of each hot zone based on the thermal impact of the SOC and battery current on each cell. The thermal management module is used to switch the cooling mode used by the battery based on the heat source items in each hot zone; The life prediction module is used to select an aging model based on the operating conditions of the battery current to calculate the cell capacity of each cell. The topology management module is also used to obtain the battery capacity of the battery based on the aggregation of the cell capacity of each cell; The lifespan prediction module is also used to calculate the state of health (SOH) for lifespan assessment using the battery capacity of the battery.

[0118] For details on the implementation methods and technical effects of the above functional modules, please refer to the section on Figure 1 The relevant introduction to the method shown will not be repeated here.

[0119] Figure 9 This is a diagram illustrating a battery life simulation architecture according to an exemplary embodiment of this application. Figure 9 As shown, under the constraints of a given thermal model and combined with user-input dynamic operating condition data, the simulation framework distributes current in the series-parallel structure through battery topology management, and calculates the heat generation and dissipation processes under the action of thermal management strategies to obtain the battery temperature field evolution. On this basis, the calendar aging model and the cycle aging model are coupled to jointly evaluate the capacity decay of individual cells and the system, ultimately forming a complete battery life prediction simulation closed loop covering "operating condition-thermal-aging-performance degradation".

[0120] Figure 10This application illustrates a hardware structure diagram of an electronic device according to an exemplary embodiment. The electronic device includes a communication interface 401, a processor 402, a computer-readable storage medium 403, and a bus 404. This electronic device can be understood as a device employing an ultrasonic transducer array, such as an ultrasonic ranging device. The communication interface 401, processor 402, and computer-readable storage medium 403 communicate with each other via the bus 404. The processor 402 can execute the battery life prediction method described above by reading and executing machine-executable instructions corresponding to the control logic of the battery life prediction method in the computer-readable storage medium 403. The specific content of this method is described in the above embodiment and will not be repeated here.

[0121] The computer-readable storage medium 403 mentioned in this application can be any electronic, magnetic, optical, or other physical storage system that can contain or store information, such as executable instructions, data, etc. For example, a machine-readable storage medium can be volatile memory, non-volatile memory, or similar storage media. Specifically, the computer-readable storage medium 403 can be RAM (Random Access Memory), flash memory, a storage drive (such as a hard disk drive), any type of storage disk (such as an optical disc, DVD, etc.), or similar storage media, or combinations thereof.

[0122] This application also provides a computer-readable storage medium corresponding to the battery life prediction method provided in the foregoing embodiments, wherein a computer program (i.e., a program product) is stored thereon, and the computer program, when run by a processor, executes the battery life prediction method provided in any of the foregoing embodiments.

[0123] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0124] The computer-readable storage medium provided in the above embodiments of this application and the battery life prediction method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0125] This application also provides a computer program product corresponding to the battery life prediction method provided in the foregoing embodiments. The computer program product includes a computer program that is executed by a processor to implement the battery life prediction method provided in the foregoing embodiments.

[0126] The computer program products provided in the above embodiments of this application and the battery life prediction method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0127] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0128] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0129] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for predicting battery life, characterized in that, The method includes: Load the simulation configuration parameters and user operating conditions used for prediction; the simulation configuration parameters include thermal zone mapping rules and battery structure parameters; the user operating conditions include the correspondence between time and charge / discharge rate, and the thermal zone mapping rules are used to define the mapping relationship between the spatial position of the cell in the battery and the thermal zone, where the thermal zone refers to the area with similar temperature distribution inside the battery; Based on the battery structure parameters and the thermal zone mapping rules, a corresponding thermal zone is assigned to each cell of the battery. Based on the user's operating conditions, the state of charge (SOC) and battery current of the battery are generated. Based on the thermal effects of the SOC and the battery current on each cell, the heat source item for each thermal zone is obtained by aggregating thermal zones. Based on the heat source items of each hot zone, switch the cooling mode used by the battery; The aging model is selected based on the operating condition of the battery current to calculate the cell capacity of each cell; The battery capacity of the battery is obtained by aggregating the cell capacity of each cell, and the state of health (SOH) for life assessment is calculated using the battery capacity of the battery. The step of assigning a corresponding hot zone to each cell of the battery based on the battery structure parameters and the hot zone mapping rules includes: Based on the battery structure parameters, a topological relationship of the battery cells is constructed. The hierarchical structure of the topological relationship includes cells, parallel groups, series strings, and battery packs. A hot zone identifier is assigned to each cell in the topological relationship according to the hot zone mapping rules.

2. The method according to claim 1, characterized in that, The thermal impact of the SOC and battery current on each cell, aggregated according to thermal zones to obtain the heat source item for each thermal zone, includes: The equivalent internal resistance and cell current of each cell are determined based on the SOC and the battery current. The thermal power loss of each cell is obtained by using the equivalent internal resistance and cell current of each cell. By utilizing the heat power loss of each battery cell, the heat source item of each heat zone is obtained by aggregating according to the heat zone.

3. The method according to claim 1, characterized in that, The method of switching the cooling mode used by the battery based on the heat source items of each hot zone includes: For each hot zone, the temperature of the hot zone is calculated based on the heat source item of the hot zone and the cooling mode used by the battery; The target cooling mode is selected based on the highest temperature in each hot zone. The target cooling mode is used to switch the cooling mode used by the battery.

4. The method according to claim 1, characterized in that, The step of selecting an aging model to calculate the cell capacity of each cell based on the operating condition of the battery current includes: When the absolute value of the battery current is greater than or equal to a preset threshold, a cycle aging model is selected to calculate the cell capacity of each cell; the cycle aging model calculates the cell capacity based on the thermal zone temperature of the cell's thermal zone, the battery current, and the cycle aging rate obtained from the SOC. When the absolute value of the battery current is less than the preset threshold, a calendar aging model is selected to calculate the cell capacity of each cell; the calendar aging model calculates the cell capacity based on the thermal zone temperature of the cell's thermal zone and the calendar aging rate obtained from the SOC.

5. The method according to claim 1, characterized in that, The process of aggregating the cell capacities of each cell to obtain the battery capacity includes: Based on the weakest constraint rule, the cell capacity of each cell is topologically aggregated to obtain the battery capacity of the battery. The weakest constraint rule is that the capacity of a parallel group is the minimum value of the capacity of each cell in the group, the capacity of a series string is the minimum value of the capacity of each parallel group in the string, the battery capacity of a battery with a series-to-parallel structure is the minimum value of the capacity of each series string, and the battery capacity of a battery with a parallel-to-series structure is the sum of the capacities of each parallel group.

6. A battery life prediction system, characterized in that, The system includes: The parameter management module is used to load simulation configuration parameters and user operating conditions for prediction. The simulation configuration parameters include thermal zone mapping rules and battery structure parameters. The user operating conditions include the correspondence between time and charge / discharge rate. The thermal zone mapping rules are used to define the mapping relationship between the spatial position of the battery cell and the thermal zone. The thermal zone refers to the area with similar temperature distribution inside the battery. The topology management module is used to assign a corresponding hot zone to each cell of the battery based on the battery structure parameters and the hot zone mapping rules. The heat source item calculation module is used to generate the state of charge (SOC) and battery current of the battery based on the user's operating conditions, and to obtain the heat source item of each hot zone by aggregating the heat sources of each hot zone based on the thermal impact of the SOC and battery current on each cell. The thermal management module is used to switch the cooling mode used by the battery based on the heat source items in each hot zone; The life prediction module is used to select an aging model based on the operating conditions of the battery current to calculate the cell capacity of each cell. The topology management module is also used to obtain the battery capacity of the battery based on the aggregation of the cell capacity of each cell; The lifespan prediction module is also used to calculate the state of health (SOH) for lifespan assessment using the battery capacity of the battery. The topology management module is specifically used to construct the topology relationship of the battery cells based on the battery structure parameters. The hierarchical structure of the topology relationship includes cells, parallel groups, series strings, and battery packs. It assigns a hot zone identifier to each cell in the topology relationship according to the hot zone mapping rules.

7. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as claimed in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores a computer program thereon, which is executed by a processor to implement the method as described in any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program, which is executed by a processor to implement the method of any one of claims 1-5.

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