Method and device for predicting available discharge energy of battery, electronic equipment and medium

Abstract

The embodiment of the invention provides a method and device for predicting available discharge energy of a battery, electronic equipment and a medium, and relates to the technical field of batteries. Comprising the steps of determining a current temperature, a current state of charge and a plurality of historical discharge test data of a to-be-predicted battery; constructing a first data mapping table among the temperature, the state of charge and the SOE according to the plurality of historical discharge test data; determining the discharge cut-off temperature of the battery to be predicted according to the current temperature and the current charge state; and looking up the first data mapping table according to the current state of charge and the discharge cut-off temperature to obtain the predicted SOE of the to-be-predicted battery. According to the scheme, the discharge cut-off temperature close to the actual value of the battery is calculated, the predicted SOE is obtained by looking up the table according to the discharge predicted cut-off temperature and the current state of charge, and compared with prediction only according to the current temperature of the battery, the influence of the temperature rise of the battery on the SOE in the discharge process can be simulated, so that the predicted SOE is close to the actual working condition of the battery; therefore, the prediction accuracy of the SOE is improved.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, electronic device and medium for predicting the usable discharge energy of a battery. Background Technology

[0002] With the widespread adoption of electric vehicles and the rapid development of new energy technologies, batteries, such as lithium-ion batteries, have become a reliable energy source for electric vehicle power systems. During battery use, real-time monitoring of the battery's operating status is crucial to ensure battery safety and extend its lifespan.

[0003] In related technologies, during battery discharge, the available discharge energy (SOE) of the battery is predicted by static lookup table method based on the real-time status data of the battery, providing a quantitative basis for battery operation, scheduling, and safety management.

[0004] However, since the state of a battery changes dynamically, the SOE predicted by the static lookup table method has low accuracy. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and medium for predicting the available discharge energy of a battery, in order to improve the accuracy of the prediction of available discharge energy.

[0006] In a first aspect, embodiments of this application provide a method for predicting the available discharge energy (SOE) of a battery, comprising: in response to a request to predict the SOE of a battery, determining the current temperature, current state of charge (SBC), and multiple historical discharge test data of the battery to be predicted; constructing a first data mapping table between temperature, SBC, and SOE based on the multiple historical discharge test data; determining the discharge cutoff temperature of the battery to be predicted based on the current temperature and the current SBC; and obtaining the predicted SOE of the battery to be predicted by looking up the predicted SOE from the first data mapping table based on the current SBC and the discharge cutoff temperature.

[0007] Secondly, embodiments of this application provide a device for predicting the available discharge energy (SOE) of a battery, comprising: an acquisition module, configured to determine the current temperature, current state of charge (SBC), and multiple historical discharge test data of the battery to be predicted in response to a prediction request for the available discharge energy (SOE); a construction module, configured to construct a first data mapping table between temperature, SBC, and SOE based on the multiple historical discharge test data; a prediction module, configured to determine the discharge cutoff temperature of the battery to be predicted based on the current temperature and the current SBC; and a lookup module, configured to look up the predicted SOE of the battery to be predicted from the first data mapping table based on the current SBC and the discharge cutoff temperature.

[0008] Thirdly, embodiments of this application provide a device for predicting the available discharge energy of a battery, including: a memory and a processor;

[0009] The memory stores computer-executed instructions;

[0010] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0011] Fourthly, embodiments of this application provide a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0012] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0013] The present application provides a method, apparatus, electronic device, and medium for predicting the usable discharge energy (SOE) of a battery. The method includes: responding to a request to predict the SOE of a battery, determining the current temperature, current state of charge (SBC), and multiple historical discharge test data of the battery to be predicted; constructing a first data mapping table between temperature, SBC, and SOE based on the multiple historical discharge test data; determining the discharge cutoff temperature of the battery to be predicted based on the current temperature and the current SBC; and obtaining the predicted SOE of the battery to be predicted by looking up a value in the first data mapping table based on the current SBC and the discharge cutoff temperature. This scheme calculates the predicted SOE based on a discharge cutoff temperature close to the actual value of the battery, and obtains the predicted SOE by looking up a value in the table based on the predicted discharge cutoff temperature and the current SBC. Compared to predicting only based on the current battery temperature, this method can simulate the impact of battery temperature rise on SOE during discharge, making the predicted SOE closer to the actual operating conditions of the battery, thereby improving the accuracy of SOE prediction. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0015] Figure 1 A schematic diagram illustrating an application scenario of a method for predicting the usable discharge energy of a battery provided in this application embodiment;

[0016] Figure 2 A flowchart illustrating a method for predicting the usable discharge energy of a battery, provided in an embodiment of this application;

[0017] Figure 3 A flowchart illustrating another method for predicting the available discharge energy of a battery provided in an embodiment of this application;

[0018] Figure 4 A schematic diagram of the dynamic discharge process provided in the embodiments of this application;

[0019] Figure 5 A schematic diagram of iterative simulated discharge provided in an embodiment of this application;

[0020] Figure 6 A schematic diagram illustrating battery temperature changes provided in an embodiment of this application;

[0021] Figure 7 A schematic diagram illustrating the prediction error provided in an embodiment of this application;

[0022] Figure 8 A schematic diagram illustrating the dynamic updating of SOE as provided in an embodiment of this application;

[0023] Figure 9 A schematic diagram of a battery available discharge energy prediction device provided in an embodiment of this application;

[0024] Figure 10 A schematic diagram of another battery available discharge energy prediction device provided in an embodiment of this application;

[0025] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0026] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0027] 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 denote 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 apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0028] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0029] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the display interface provided in the embodiments of this application is merely an example, and the display interface may include more or less content.

[0030] It should be noted that the method, apparatus, electronic device and medium for predicting the usable discharge energy of batteries in this application can be used in the field of battery technology, or in any field other than batteries. The application field of the method, apparatus, electronic device and medium for predicting the usable discharge energy of batteries in this application is not limited.

[0031] Figure 1 This is a schematic diagram illustrating an application scenario of a method for predicting the available discharge energy of a battery, as provided in this application embodiment. The scenario illustrated is as follows: During battery operation, the battery's state parameters are collected in real time, displayed to the user, and the battery's operation is adjusted based on these parameters.

[0032] In practical applications, the Battery Management System (BMS) serves as the core control unit of the battery, enabling it to monitor the battery's operating status in real time and provide key parameters to ensure battery safety and extend its service life.

[0033] Among these, the State of Charge (SOC) and State of Energy (SOE) are the core functional indicators of the Battery Management System (BMS). The State of Charge reflects the current percentage of remaining battery capacity. SOE is defined as the total energy that the battery can release when discharged from its current state to the cutoff voltage under specific conditions; SOE is directly related to the battery's driving range. SOE provides users with accurate and reliable driving range information; provides a basis for vehicle powertrain scheduling; and enables the rational planning of charging and discharging strategies to ensure the efficient operation of the energy storage system.

[0034] However, battery performance is highly dependent on temperature. During discharge, the battery's internal resistance fluctuates with temperature changes, causing the actual discharge cutoff voltage to deviate from the theoretical value, resulting in some charge not being released. Furthermore, temperature changes at the end of discharge significantly affect the determination of the lower limit window (i.e., the state-of-charge threshold at which the battery prematurely cuts off discharge due to increased internal resistance), leading to errors in SOE estimation. Therefore, dynamically predicting SOE under complex operating conditions (such as high temperature, low temperature, or rapid discharge scenarios) has become a key technical challenge for improving battery management accuracy.

[0035] In related technologies, a data mapping table between battery temperature and SOE is pre-configured, and the real-time temperature of the battery is collected during battery operation. The corresponding SOE is then looked up from the data mapping table based on the real-time temperature to obtain the predicted SOE.

[0036] However, the state of a battery is dynamic, and there is a difference between the battery's real-time temperature and its discharge cutoff temperature. This difference also varies under different operating conditions. The discharge cutoff temperature directly affects the SOE (State of Energy). Predicting SOE directly based on real-time temperature results in significant deviations, impacting battery regulation and the user experience.

[0037] The method for predicting the usable discharge energy of a battery provided in this application aims to solve the above-mentioned technical problems in related technologies.

[0038] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0039] Figure 2 A flowchart illustrating a method for predicting the usable discharge energy of a battery, provided in an embodiment of this application, includes the following steps:

[0040] S201. In response to a request to predict the available discharge energy (SOE) of a battery, determine the current temperature, current state of charge, and multiple historical discharge test data of the battery to be predicted.

[0041] For example, the SOE prediction request is used to trigger the prediction of the SOE of the battery to be predicted at the current moment. The current temperature and the current state of charge are battery state parameters collected at the current moment.

[0042] Among these, the current temperature is the core characteristic of the thermal state of the battery to be predicted, directly affecting its energy release capacity and temperature rise pattern. The current state of charge (SBC) is the percentage of remaining charge in the battery to be predicted, and is a core quantitative indicator of the battery's state of charge.

[0043] For example, historical discharge test data can be obtained by conducting discharge tests on the same model or batch of batteries to be predicted under different test conditions. Historical discharge test data can reflect the performance variation pattern of the battery to be predicted under different operating conditions.

[0044] S202. Based on multiple historical discharge test data, construct the first data mapping table between temperature, state of charge, and SOE.

[0045] For example, based on multiple historical discharge test data, a first data mapping table is constructed through data processing methods such as data cleaning, outlier removal, fitting and completion, and standardization.

[0046] For example, the first data mapping table includes two indices: temperature and state of charge. That is, any set of temperatures and any set of states of charge uniquely corresponds to one SOE. For example, 25°C (temperature) and 80% (state of charge) correspond to an SOE of 10 kWh.

[0047] S203. Determine the discharge cutoff temperature of the battery to be predicted based on the current temperature and current state of charge.

[0048] For example, based on the current temperature and current state of charge, combined with the temperature rise pattern of the battery discharge to be predicted, the discharge cutoff temperature is calculated, which is the final temperature of the battery when it discharges from the current state of charge to the state of charge limit window.

[0049] To illustrate with a scenario example, taking a lithium-ion battery as an example, at low temperatures, lithium-ion transport is hindered. Lithium-ions that are not embedded in the negative electrode in time will deposit lithium on the surface of the negative electrode. Setting a high state-of-charge (SOC) threshold window allows the battery to terminate discharge early, ensuring battery safety. For example, if the SOC threshold window is set to 15%, when the battery's actual SOC drops to 15%, discharge will stop even if the battery has remaining usable capacity.

[0050] At high temperatures, the thermal stability of the electrodes and electrolyte decreases. To avoid accelerating battery aging, the state-of-charge limit window should be increased to allow the battery to terminate discharge earlier.

[0051] Based on the above implementation methods, the discharge cutoff temperature of the battery directly affects the state-of-charge (SOE) limit window, which in turn directly affects SOE. Therefore, the accuracy of SOE prediction can be improved by using the discharge cutoff temperature.

[0052] S204. Based on the current state of charge and discharge cutoff temperature, look up the table in the first data mapping table to obtain the predicted SOE of the battery to be predicted.

[0053] With the help of scenario examples, the principle of predicting SOE is as follows: the state of charge at the discharge cutoff temperature is determined by the discharge cutoff temperature, the difference between the current state of charge and the state of charge at the discharge cutoff temperature is calculated, and the remaining amount of the state of charge of the battery to be predicted can be reduced by how much. Then, the SOE is obtained by multiplying the difference by the total capacity of the battery to be predicted.

[0054] For example, the current state of charge and discharge cutoff temperature are used as indexes to query the first data mapping table to obtain the corresponding predicted SOE. Compared to real-time calculation, this table lookup method can provide a faster and more accurate prediction result.

[0055] The battery usable discharge energy prediction method provided in this application, in response to a battery usable discharge energy (SOE) prediction request, determines the current temperature, current state of charge (SBC), and multiple historical discharge test data of the battery to be predicted; constructs a first data mapping table between temperature, SBC, and SOE based on the multiple historical discharge test data; determines the discharge cutoff temperature of the battery to be predicted based on the current temperature and current SBC; and obtains the predicted SOE of the battery to be predicted by looking up the table from the first data mapping table based on the current SBC and discharge cutoff temperature. This scheme calculates the predicted SOE based on a discharge cutoff temperature close to the actual battery value, and obtains the predicted SOE by looking up the table from the predicted discharge cutoff temperature and current SBC. Compared to predicting only based on the current battery temperature, this method can simulate the impact of battery temperature rise on SOE during discharge, making the predicted SOE closer to the actual operating conditions of the battery, thereby improving the accuracy of SOE prediction.

[0056] Based on any of the above embodiments, the following, in conjunction with Figure 3 The detailed process of predicting the available discharge energy of a battery is explained.

[0057] Figure 3 This is a flowchart illustrating another method for predicting the usable discharge energy of a battery, provided in an embodiment of this application. Figure 3 As shown, the method includes:

[0058] S301, in response to a request to predict the available discharge energy (SOE) of a battery, determine the current temperature, current state of charge, and multiple historical discharge test data of the battery to be predicted.

[0059] It should be noted that the execution process of S301 is the same as that of S201, and will not be repeated here.

[0060] S302. Based on multiple historical discharge test data, construct the first data mapping table between temperature, state of charge, and SOE.

[0061] It should be noted that the execution process of S302 is the same as that of S202, and will not be repeated here.

[0062] S303. Based on multiple current state information, determine the current power-on state of the battery to be predicted. The current power-on state is either the initial power-on state or a non-initial power-on state.

[0063] For example, determining the current power-on state is a preliminary step in the discharge cutoff temperature calculation. Its core function is to first determine, based on the real-time operating parameters of the battery to be predicted, whether it is currently in the initial power-on state or not. Subsequently, based on the determination result, differentiated calculation logic is used to calculate the discharge cutoff temperature to adapt to the temperature rise characteristics of the battery at different operating stages, thereby improving the accuracy of the discharge cutoff temperature calculation.

[0064] Optionally, real-time operating parameters can be collected through the BMS, and the current power-on status can be determined by the historical temperature rise parameters and / or discharge data in the real-time operating parameters.

[0065] The initial power-on state refers to the state where the battery to be predicted has just completed power-on and has not yet entered the continuous discharge stage (or the discharge time is extremely short). The core characteristics are the lack of effective historical temperature rise data, the lack of continuous discharge records, and the battery temperature being in a stable state after being left to stand.

[0066] Optionally, the current power-on state can be determined based on the current battery's running time after power-on. If the running time is greater than or equal to the duration threshold, the current power-on state is determined to be a non-initial power-on state; otherwise, the current power-on state is determined to be an initial power-on state.

[0067] The non-initial power-on state refers to the state where the battery to be predicted has completed power-on startup and is in a continuous discharge phase, such as when an electric vehicle is in motion, when an energy storage battery is continuously discharging into the grid, or when an electric device is in operation. The core characteristic is the existence of continuous historical temperature rise data and discharge data, indicating that the battery has experienced a significant temperature rise due to continuous discharge and is in a dynamic temperature change process.

[0068] As illustrated by the scenario example, the temperature rise patterns and data bases of the battery under different initial power-on and non-initial power-on states are completely different. Using a uniform method to calculate the discharge cutoff temperature will lead to significant deviations in the results. Determining the corresponding discharge cutoff temperature calculation method based on the specific current power-on state can improve the accuracy of the discharge cutoff temperature calculation.

[0069] S304. If the current power-on state is the initial power-on state, then determine the second data mapping table of the temperature and temperature rise rate of the battery to be predicted, and determine the discharge cutoff temperature based on the current temperature and the second data mapping table.

[0070] The second data table mapping table is constructed based on uncontrolled temperature discharge test data from multiple historical discharge test data, and the temperature rise rate is the rate at which the temperature of the battery to be predicted increases relative to the battery discharge capacity.

[0071] For example, in the initial power-on state, the BMS does not collect any historical temperature data during the current discharge process, therefore it cannot predict the discharge cutoff temperature by analyzing the real-time temperature rise trend. At this time, the temperature rise rate is obtained by looking up the second data mapping table, and the discharge cutoff temperature is calculated based on the temperature rise rate.

[0072] For example, uncontrolled discharge test data is obtained by simulating the actual heat generation of the battery under test during discharge testing without external interference with the battery temperature. This allows us to obtain the temperature rise pattern of the battery under actual operating conditions.

[0073] Optionally, uncontrolled discharge test data includes the discharge cutoff temperature for complete discharge tests conducted at different initial temperatures. For each uncontrolled discharge test data point, the temperature rise rate is calculated using the following formula:

[0074]

[0075] in, This indicates the rate of temperature rise.

[0076] For example, a second data mapping table is established by correlating the temperature rise rate calculated from uncontrolled discharge test data with the corresponding initial temperature. The temperature rise rate represents the temperature increase per unit of charge released by the battery to be predicted.

[0077] One feasible implementation method is to determine the discharge cutoff temperature in the initial power-on state by: determining the first temperature rise of the battery to be predicted from the current state of charge to the discharge cutoff based on the current temperature and the second data mapping table; and determining the sum of the current temperature and the first temperature rise as the discharge cutoff temperature.

[0078] For example, based on the current temperature, a lookup is performed in the second data mapping table to obtain the temperature rise rate corresponding to the battery to be predicted when the current temperature is used as the initial temperature. The product of the temperature rise rate and the remaining capacity of the battery to be predicted is determined as the first temperature rise, where the remaining capacity is the energy that the battery to be predicted can release from the current state of charge to the discharge cutoff.

[0079] For example, the sum of the current temperature and the first temperature rise is the discharge cutoff temperature at which the battery to be predicted discharges at the rate of temperature rise.

[0080] With the scenario example, the first temperature rise indicates that the battery to be predicted will continue to heat up at the rate of temperature rise obtained during the testing phase. Starting from the current temperature, this represents the release of the heat accumulated corresponding to the remaining capacity.

[0081] In this feasible implementation, trend analysis is impossible at the initial power-on state due to the lack of historical temperature rise data for the current discharge process. A second data mapping table, pre-stored and constructed based on extensive uncontrolled discharge test data, maps the current temperature to an experimentally verified, statistically significant temperature rise rate. This ensures that the prediction behavior is also based on data from the test conditions of the battery being predicted, rather than theoretical estimates or fixed empirical values, thereby improving the accuracy of the prediction.

[0082] S305. If the current power-on state is not the initial power-on state, then determine the third data mapping table of the temperature and charge state limit window of the battery to be predicted, and determine the discharge cutoff temperature based on the current temperature, the current charge state and the third data mapping table.

[0083] The third data table mapping table is constructed based on temperature-controlled discharge test data from multiple historical discharge test data.

[0084] For example, temperature-controlled discharge test data is test data obtained by controlling the battery to be predicted to discharge at different fixed temperatures (e.g., -10℃, 20℃, 40℃) under external conditions (e.g., a constant temperature chamber).

[0085] For example, the state-of-charge limit window corresponding to each fixed temperature is obtained from the temperature-controlled discharge test data, and a third data mapping table is constructed. The influence of temperature on the state-of-charge limit window can be accurately quantified using the temperature-controlled discharge test data.

[0086] For example, in the non-power-on initial state, historical data of the battery under actual operating conditions can be obtained, and the discharge cutoff temperature can be derived based on the historical data without using a second data mapping table. This makes the calculation process consistent with the actual operating conditions of the battery under test, thereby improving the accuracy of the prediction.

[0087] One feasible implementation method, in the non-power-on initial state, is to determine the discharge cutoff temperature by the following steps: determining multiple historical temperature rise data of the battery to be predicted; fitting the multiple historical temperature rise data to obtain the temperature rise curve of the battery to be predicted; determining the second temperature rise corresponding to the unit change in state of charge based on the temperature rise curve; and determining the discharge cutoff temperature based on the current temperature, the current state of charge, the second temperature rise, and a third data mapping table.

[0088] For example, when the battery to be predicted has completed power-on and is in a continuous discharge operation state, the BMS can collect continuous historical temperature rise data under the actual operating conditions of the battery. At this time, the discharge cutoff temperature can be predicted based on the temperature rise pattern in the historical temperature rise data, and the temperature change pattern of the battery to be predicted can be accurately obtained based on the data under the actual operating conditions.

[0089] For example, a temperature rise curve is obtained by fitting the state of charge as the x-axis and temperature as the y-axis.

[0090] Optionally, the fitting method includes, but is not limited to, at least one of the following: linear fitting, least squares fitting, or exponential fitting.

[0091] For example, the second temperature rise is the temperature change corresponding to a unit change in state of charge. Taking a unit change in state of charge of 5% as an example, the second temperature rise represents the temperature change corresponding to a 5% reduction in state of charge.

[0092] For example, the discharge cutoff temperature is determined by using the current temperature as the starting reference for temperature rise, the current state of charge as the starting reference for discharge, and the second temperature rise as the basis for the temperature rise of discharge corresponding to a unit state of charge, in conjunction with a third data mapping table, through simulation derivation and iterative judgment.

[0093] Below, in conjunction with Figure 4 The dynamic discharge process is explained.

[0094] Figure 4 This is a schematic diagram of the dynamic discharge process provided in an embodiment of this application. Figure 4 As shown, the current of the battery under test fluctuates dynamically during the discharge process. Correspondingly, the temperature of the battery also changes dynamically. Related technologies that directly predict the SOE based on real-time temperature have significant deviations. This paper calculates a second temperature rise to predict the discharge cutoff temperature that matches the operating conditions of the battery under test.

[0095] Based on the above implementation method, the temperature rise pattern of the battery under actual operating conditions can be quantified by calculating the second temperature rise, and the abstract temperature rise pattern can be converted into specific numerical parameters, so that the battery prediction conforms to the actual operating conditions, thereby improving the prediction accuracy.

[0096] One feasible implementation method for determining the discharge cutoff temperature includes: determining the predicted temperature after a unit change in state of charge (SOC); determining a predicted SOC lower limit window based on the predicted temperature and a third data mapping table; determining the predicted SOC as the difference between the current SOC and the unit change in SOC; if the predicted SOC is less than or equal to the predicted SOC lower limit window, determining the discharge cutoff temperature based on the predicted temperature; if the predicted SOC is greater than the predicted SOC lower limit window, iteratively updating the predicted temperature, predicted SOC, and predicted SOC lower limit window using the unit change in SOC until the updated predicted SOC is less than or equal to the updated predicted SOC lower limit window, and determining the discharge cutoff temperature based on the updated predicted temperature.

[0097] For example, a second temperature rise corresponding to a unit change in state of charge is superimposed on the current temperature to obtain the predicted temperature after undergoing a unit change in state of charge. Based on the predicted temperature, a third data mapping table is consulted to obtain the predicted state of charge limit window that matches the predicted temperature.

[0098] For example, using a unit change in state of charge as the step size for the simulated discharge process, the predicted temperature and predicted state of charge are recalculated after each step of simulated discharge. The predicted temperature is the temperature of the battery to be predicted after the next step.

[0099] For example, the predicted state of charge (SOC) is the predicted SOC of the battery after the next step. The predicted SOC limit window is the predicted SOC limit window of the battery corresponding to the predicted temperature in the third data mapping table.

[0100] With the example of the scenario, since the state of charge limit window will change after the battery temperature rises, the predicted state of charge limit window is updated iteratively instead of being a fixed value, so that the temperature matches the predicted state of charge limit window in real time.

[0101] Below, in conjunction with Figure 5 The iterative simulated discharge is explained.

[0102] Figure 5 This is a schematic diagram of iterative simulated discharge provided in an embodiment of this application. Figure 5 As shown, the current state of charge is 80%, the current temperature is 25℃, and the minimum window for the current state of charge is 5%. After one iteration of simulation, the predicted state of charge is 75%, the predicted temperature is 25.2℃, and the minimum window for the predicted state of charge is 5%. At this point, the predicted state of charge of 75% is greater than the minimum window for the predicted state of charge, so iteration continues. Iteration continues until after n+1 iterations, when the predicted state of charge is less than the minimum window for the predicted state of charge, at which point iteration stops.

[0103] Below, in conjunction with Figure 6 Explain the changes in battery temperature.

[0104] Figure 6 This is a schematic diagram illustrating battery temperature changes provided in an embodiment of this application. Figure 6 As shown, the actual discharge cutoff temperature cannot be directly obtained before the battery to be predicted reaches its discharge cutoff temperature, which directly affects the SOE (State of Charge). Related technologies predict SOE based on the current actual temperature of the battery. However, since the current actual temperature differs significantly from the actual discharge cutoff temperature, and the larger the state of charge of the battery, the greater the temperature deviation, the lower the accuracy of the predicted SOE. The predicted discharge cutoff temperature determined in this application is closer to the actual discharge cutoff temperature at different states of charge; therefore, predicting the discharge cutoff temperature can improve the accuracy of the predicted SOE.

[0105] Below, in conjunction with Figure 7 Explain the prediction error.

[0106] Figure 7 This is a schematic diagram illustrating the prediction error provided in an embodiment of this application. Figure 7 As shown, during the discharge process of the battery to be predicted, as the state of charge decreases, the actual temperature of the battery to be predicted gets closer to the discharge cutoff temperature, and the error of the predicted SOE relative to the true SOE decreases accordingly. Furthermore, the error of the predicted SOE determined by predicting the discharge cutoff temperature is smaller than the error of the predicted SOE determined by the current actual temperature of the battery to be predicted.

[0107] In this feasible implementation, compared with the static lookup table calculation logic, the predicted battery temperature rise is bound to the dynamic change of the state of charge limit window. Each iteration determines the corresponding predicted state of charge limit window based on the new predicted temperature, so that the state of charge constraint conditions are updated in real time with the battery thermal state. The calculation process conforms to the actual working conditions of the battery being predicted, such as continuous discharge, gradual temperature rise, and dynamic change of the state of charge limit window, thereby improving the accuracy of the prediction.

[0108] One feasible implementation method is to determine the discharge cutoff temperature after iteration by means of the following: if the predicted state of charge is equal to the predicted state of charge limit window, then the predicted temperature is determined as the discharge cutoff temperature; if the predicted state of charge is less than the predicted state of charge limit window, then the discharge cutoff temperature is determined by interpolation based on the third data mapping table and the predicted state of charge limit window.

[0109] For example, if the predicted state of charge is equal to the predicted state of charge lower limit window, it means that the iterative simulation has just simulated the scenario where the battery to be predicted is discharged to the safe lower limit, and no additional correction is needed. The predicted temperature is then determined as the discharge cutoff temperature.

[0110] For example, if the predicted state of charge is less than the lower limit window of the predicted state of charge, it means that the discharge of the battery to be predicted exceeds the safety lower limit after the iteration ends, which does not meet the actual discharge safety control requirements and needs to be further corrected.

[0111] For example, interpolation methods include, but are not limited to, at least one of the following: linear interpolation, quadratic polynomial interpolation, or cubic spline interpolation.

[0112] Please refer to the following examples and scenarios. Figure 5 After n+1 iterations, the predicted state of charge (SOC) is less than 11%, indicating over-simulation and an over-predicted temperature. The actual discharge cutoff temperature should be less than 30.2℃. Furthermore, the temperature of the battery under test increases during the n+1 iterations. Considering the mapping relationship in the third data mapping table where the SOC limit window is constant at 11% within the 30℃-30.2℃ range, the actual discharge cutoff temperature can be determined to be between 30℃ and 30.2℃. Based on this temperature range locked in by the third data mapping table and the corresponding SOC limit window of 11%, interpolation calculations within the 30℃-30.2℃ range yield a discharge cutoff temperature closer to the actual value.

[0113] In this feasible implementation, by distinguishing between scenarios where the predicted state of charge is equal to or less than the lower bound window of the predicted state of charge, the error caused by fixed step-size iteration is corrected by interpolation, thereby improving the accuracy of prediction.

[0114] One feasible implementation method for determining the discharge cutoff temperature includes: obtaining the current health and current internal resistance of the battery to be predicted; determining the mapping table correction coefficient based on the current health and current internal resistance; correcting the third data mapping table based on the mapping table correction coefficient to obtain a corrected data mapping table; and determining the discharge cutoff temperature by interpolation based on the corrected data mapping table and the predicted state of charge limit window.

[0115] For example, the current health and internal resistance of the battery to be predicted are collected through the BMS. The current health reflects the proportion of capacity decay and active material loss of the battery to be predicted, while the current internal resistance reflects the battery polarization and ion transport capability. A decrease in health and an increase in internal resistance will directly change the state of charge limit window, which is the core basis for correcting the third data mapping table.

[0116] For example, a mapping correction coefficient that adapts to the current health level and current internal resistance is calculated using a preset aging correction model. The data in the third data mapping table is then corrected based on these correction coefficients to obtain a corrected data mapping table.

[0117] Optionally, an aging correction model can be constructed using multiple aging test data.

[0118] In this feasible implementation, the modified data mapping table is more closely aligned with the actual electrochemical characteristics of the battery to be predicted. Based on the interpolation calculation of the modified data mapping table, the actual thermal state of the battery to be predicted can be matched more accurately, thereby improving the accuracy of the prediction.

[0119] S306. Based on the current state of charge and discharge cutoff temperature, look up the table in the first data mapping table to obtain the predicted SOE of the battery to be predicted.

[0120] One feasible implementation method, after obtaining the predicted SOE, may further include: acquiring the discharge operating parameters and real-time temperature of the battery to be predicted; determining whether the battery to be predicted is in a power-off state based on the discharge operating parameters; if not, acquiring the real-time state of charge of the battery to be predicted; and updating the predicted SOE based on the real-time state of charge, the current state of charge, the real-time temperature, and the predicted temperature.

[0121] For example, after determining the predicted SOE, the operating status of the battery to be predicted is continuously monitored, and predictions are continuously made to obtain prediction results that conform to the actual operating conditions of the battery to be predicted.

[0122] Optionally, the discharge operating parameters include, but are not limited to, at least one of the following: discharge current, discharge power, and discharge circuit on / off signal. Based on these discharge operating parameters, it can be determined whether the battery to be predicted is powered down. For example, if the discharge current is ≤0A, the discharge power is ≤0W, or the discharge circuit disconnection signal is high, then the battery to be predicted is determined to be powered down.

[0123] For example, if the battery to be predicted is left undisturbed, its operating state will change according to the actual operating conditions, resulting in an error between the actual SOE and the predicted SOE. The predicted SOE is dynamically updated to adapt to the actual operating conditions of the battery to be predicted.

[0124] In this feasible implementation, the SOE of the battery to be predicted is updated in real time according to the operating status of the battery to be predicted, which conforms to the actual operating conditions of the battery to be predicted, thereby improving the accuracy of SOE.

[0125] One feasible implementation method is to update the predicted SOE by: determining a first difference between the current state of charge and the real-time state of charge; if the first difference is equal to the unit change in state of charge, determining a second difference between the real-time temperature and the predicted temperature; and updating the predicted SOE based on the second difference.

[0126] For example, the first difference between the current state of charge and the real-time state of charge. This represents the change in state of charge corresponding to the energy released by the battery to be predicted after the predicted SOE is calculated.

[0127] For example, it is determined whether the cell to be predicted has run another iteration of simulation with the same step size based on whether the first difference is equal to the unit change in state of charge. The predicted SOE of the cell to be predicted is updated once each time a step is run.

[0128] For example, the second difference represents the gap between the actual temperature and the predicted temperature. The larger the second difference, the greater the error in the previous prediction, and the more necessary it is to perform a correction.

[0129] In this feasible implementation, the error of the prediction result is calculated in real time, and targeted corrections are made based on the error, thereby improving the accuracy of the prediction.

[0130] One feasible implementation method is to update the predicted SOE by the following steps: if the absolute value of the second difference is greater than or equal to a preset threshold, then determine the third temperature rise based on the temperature rise data between the real-time state of charge and the current state of charge, and update the predicted SOE based on the real-time temperature, real-time state of charge, third temperature rise, first data mapping table, and third data mapping table; if the absolute value of the second difference is less than the preset threshold, then update the predicted SOE based on the real-time temperature, real-time state of charge, and first data mapping table.

[0131] For example, a preset threshold (e.g., 3°C) serves as the dividing line for determining whether the predicted temperature error is too large. If the absolute value of the second difference is greater than or equal to the preset threshold, it indicates that the predicted temperature error is too large, and the predicted temperature needs to be corrected and the SOE re-predicted.

[0132] To illustrate with a scenario example: In the previous predicted State of Charge (SOE), the predicted state of charge (SOC) of the battery was 80%, and the predicted temperature was 25°C. After a unit change in SOE, the actual measured temperature was 29°C. The absolute value of this second difference was 4°C, exceeding the preset threshold of 3°C. Therefore, the previously calculated predicted temperature had too large an error. In this case, a third temperature rise is determined based on the temperature rise data between 80% and 75%. It can be understood that the temperature rise data between 80% and 75% best reflects the real-time operating conditions of the battery, and the third temperature rise calculated based on this data has higher accuracy.

[0133] Below, in conjunction with Figure 8 This section explains how to dynamically update SOE.

[0134] Figure 8 This is a schematic diagram illustrating the dynamic updating of the SOE provided in an embodiment of this application. Figure 8As shown, it is determined whether the battery to be predicted is in its initial power-on state. If so, the discharge cutoff temperature is calculated based on the current temperature and the second data mapping table. The predicted SOE is obtained by looking up the first data mapping table based on the discharge cutoff temperature and the current state of charge. If not, the second temperature rise, representing the reaction temperature rise rate, is determined based on multiple historical temperature rise data. The predicted SOC and the lower limit window of the predicted SOC are iteratively determined until the predicted SOC is less than or equal to the lower limit window. If the predicted SOC is equal to the lower limit window, the predicted SOE is obtained by looking up the first data mapping table. If the predicted SOC is less than the lower limit window, the predicted SOC is corrected by interpolation and the predicted SOE is obtained by looking up the first data mapping table. The system continuously monitors whether the battery to be predicted is powered off. If it is powered off, the SOE update ends. If it is not powered off, it is determined whether the second difference is greater than or equal to a preset threshold. If so, the second temperature rise is re-determined, and the predicted SOE is updated based on the re-determined second temperature rise. If not, the previous second temperature rise is reused to update the predicted SOE.

[0135] In this feasible implementation, the error of the prediction result is calculated in real time, and the temperature rise is corrected and the SOE is updated accordingly based on the error. This can be adapted to the actual operating conditions of the battery to be predicted, thereby improving the accuracy of the prediction.

[0136] Figure 9 This is a schematic diagram of a battery usable discharge energy prediction device provided in an embodiment of this application. Figure 9 As shown, the battery available discharge energy prediction device 90 may include: an acquisition module 91, a construction module 92, a prediction module 93, and a search module 94.

[0137] The acquisition module 91 is used to determine the current temperature, current state of charge, and multiple historical discharge test data of the battery to be predicted in response to the prediction request of the battery's available discharge energy (SOE).

[0138] Module 92 is used to construct a first data mapping table between temperature, state of charge, and SOE based on multiple historical discharge test data.

[0139] The prediction module 93 is used to determine the discharge cutoff temperature of the battery to be predicted based on the current temperature and the current state of charge.

[0140] The lookup module 94 is used to look up the predicted SOE of the battery to be predicted from the first data mapping table based on the current state of charge and discharge cutoff temperature.

[0141] Optionally, module 91 can be executed. Figure 2 S201 in the embodiment.

[0142] Optionally, Module 92 can be executed. Figure 2S202 in the embodiment.

[0143] Optionally, prediction module 93 can perform... Figure 2 S203 in the embodiment.

[0144] Optionally, the lookup module 94 can be executed. Figure 2 S204 in the embodiment.

[0145] Based on the above implementation method, the discharge cutoff temperature close to the actual value of the battery is calculated. The predicted SOE is obtained by looking up a table based on the predicted discharge cutoff temperature and the current state of charge. Compared with the prediction based solely on the current battery temperature, the influence of battery temperature rise on SOE during discharge can be simulated, so that the predicted SOE is closer to the actual operating conditions of the battery, thereby improving the accuracy of SOE prediction.

[0146] It should be noted that the battery available discharge energy prediction device shown in the embodiments of this application can execute the technical solution shown in the above method embodiments, and its implementation principle and beneficial effects are similar, so they will not be described again here.

[0147] In one possible implementation, the prediction module 93 is specifically used for:

[0148] Determine the current power-on state of the battery to be predicted, whether the current power-on state is the initial power-on state or not the initial power-on state;

[0149] If the current power-on state is the initial power-on state, then the second data mapping table of the temperature and temperature rise rate of the battery to be predicted is determined, and the discharge cutoff temperature is determined according to the current temperature and the second data mapping table. The second data mapping table is constructed based on the uncontrolled discharge test data from multiple historical discharge test data. The temperature rise rate is the rate at which the temperature of the battery to be predicted increases relative to the battery discharge capacity.

[0150] If the current power-on state is not the initial power-on state, then the third data mapping table of the temperature and charge state limit window of the battery to be predicted is determined, and the discharge cutoff temperature is determined according to the current temperature, the current charge state and the third data mapping table. The third data mapping table is constructed based on the temperature-controlled discharge test data from multiple historical discharge test data.

[0151] In one possible implementation, the prediction module 93 is specifically used for:

[0152] The first temperature rise of the battery to be predicted from the current state of charge to the discharge cutoff is determined based on the current temperature and the second data mapping table.

[0153] The sum of the current temperature and the first temperature rise is determined as the discharge cutoff temperature.

[0154] In one possible implementation, the prediction module 93 is specifically used for:

[0155] Determine multiple historical temperature rise data for the battery to be predicted;

[0156] The temperature rise curve of the battery to be predicted is obtained by fitting multiple historical temperature rise data.

[0157] Based on the temperature rise curve, determine the second temperature rise corresponding to the unit change in state of charge;

[0158] The discharge cutoff temperature is determined based on the current temperature, current state of charge, second temperature rise, and third data mapping table.

[0159] In one possible implementation, the prediction module 93 is specifically used for:

[0160] The sum of the current temperature and the second temperature rise is determined as the predicted temperature after undergoing a unit change in state of charge.

[0161] The predicted state of charge limit window is determined based on the predicted temperature and the third data mapping table;

[0162] The difference between the current state of charge and the unit change in state of charge is determined as the predicted state of charge.

[0163] If the predicted state of charge is less than or equal to the predicted state of charge limit window, the discharge cutoff temperature is determined based on the predicted temperature.

[0164] If the predicted state of charge is greater than the predicted state of charge limit window, the predicted temperature, predicted state of charge, and predicted state of charge limit window are iteratively updated by unit state of charge change until the updated predicted state of charge is less than or equal to the updated predicted state of charge limit window, and the discharge cutoff temperature is determined based on the updated predicted temperature.

[0165] In one possible implementation, the prediction module 93 is specifically used for:

[0166] If the predicted state of charge is equal to the predicted state of charge lower limit window, then the predicted temperature is determined as the discharge cutoff temperature.

[0167] If the predicted state of charge is less than the predicted state of charge limit window, the discharge cutoff temperature is determined by interpolation based on the third data mapping table and the predicted state of charge limit window.

[0168] Figure 10 This is a schematic diagram of another battery usable discharge energy prediction device provided in an embodiment of this application. Figure 9 Based on the illustrated embodiments, as Figure 10As shown, the battery's available discharge energy prediction device 90 also includes: a calculation module 95 and an update module 96.

[0169] Calculation module 95 is used for:

[0170] Obtain the current health status and current internal resistance of the battery to be predicted;

[0171] Determine the mapping table correction coefficient based on the current health status and current internal resistance;

[0172] The third data mapping table is corrected according to the mapping table correction coefficient to obtain the corrected data mapping table.

[0173] Based on the corrected data mapping table and the predicted state of charge limit window, the discharge cutoff temperature is determined by interpolation.

[0174] Update module 96, used for:

[0175] Obtain the discharge operating parameters and real-time temperature of the battery to be predicted;

[0176] Determine whether the battery to be predicted is in a powered-off state based on the discharge operating parameters;

[0177] If not, obtain the real-time state of charge of the battery to be predicted;

[0178] The predicted SOE is updated based on the real-time state of charge, the current state of charge, the real-time temperature, and the predicted temperature.

[0179] In one possible implementation, the update module 96 is specifically used for:

[0180] Determine the first difference between the current state of charge and the real-time state of charge;

[0181] If the first difference is equal to the unit change in state of charge, then the second difference between the real-time temperature and the predicted temperature is determined.

[0182] Update the predicted SOE based on the second difference.

[0183] In one possible implementation, the update module 96 is specifically used for:

[0184] If the absolute value of the second difference is greater than or equal to the preset threshold, the third temperature rise is determined based on the temperature rise data between the real-time state of charge and the current state of charge, and the predicted SOE is updated based on the real-time temperature, real-time state of charge, third temperature rise, first data mapping table and third data mapping table.

[0185] If the absolute value of the second difference is less than the preset threshold, the predicted SOE is updated based on the real-time temperature, real-time state of charge, and the first data mapping table.

[0186] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 11 As shown, the electronic device includes:

[0187] The electronic device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke logical instructions stored in the memory 292 to execute the methods of the above embodiments.

[0188] Furthermore, the logic instructions in the aforementioned memory 292 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0189] The memory 292, as a non-volatile computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, it implements the methods in the above-described method embodiments.

[0190] The memory 292 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 292 may include high-speed random access memory and may also include non-volatile memory.

[0191] Based on the above implementation method, the discharge cutoff temperature close to the actual value of the battery is calculated. The predicted SOE is obtained by looking up a table based on the predicted discharge cutoff temperature and the current state of charge. Compared with the prediction based solely on the current battery temperature, the influence of battery temperature rise on SOE during discharge can be simulated, so that the predicted SOE is closer to the actual operating conditions of the battery, thereby improving the accuracy of SOE prediction.

[0192] This application provides a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in the foregoing embodiments.

[0193] Based on the above implementation method, the discharge cutoff temperature close to the actual value of the battery is calculated. The predicted SOE is obtained by looking up a table based on the predicted discharge cutoff temperature and the current state of charge. Compared with the prediction based solely on the current battery temperature, the influence of battery temperature rise on SOE during discharge can be simulated, so that the predicted SOE is closer to the actual operating conditions of the battery, thereby improving the accuracy of SOE prediction.

[0194] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in the foregoing embodiments.

[0195] Based on the above implementation method, the discharge cutoff temperature close to the actual value of the battery is calculated. The predicted SOE is obtained by looking up a table based on the predicted discharge cutoff temperature and the current state of charge. Compared with the prediction based solely on the current battery temperature, the influence of battery temperature rise on SOE during discharge can be simulated, so that the predicted SOE is closer to the actual operating conditions of the battery, thereby improving the accuracy of SOE prediction.

[0196] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0197] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps; they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages, which do not necessarily complete at the same time but can be executed at different times. The execution order of these sub-steps or stages is also not necessarily sequential but can be alternated or carried out in turn with other steps or at least some of the sub-steps or stages of other steps.

[0198] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0199] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0200] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. The processor can be any suitable hardware processor, such as CPU, GPU, FPGA, DSP, and ASIC. The storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0201] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0202] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0203] 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 claims.

[0204] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for predicting the usable discharge energy of a battery, characterized in that, include: In response to a request to predict the available discharge energy (SOE) of a battery, the current temperature, current state of charge, and multiple historical discharge test data of the battery to be predicted are determined. Based on the aforementioned historical discharge test data, a first data mapping table is constructed between temperature, state of charge, and SOE. Based on the current temperature and the current state of charge, determine the discharge cutoff temperature of the battery to be predicted; Based on the current state of charge and the discharge cutoff temperature, the predicted SOE of the battery to be predicted is obtained by looking up the table in the first data mapping table.

2. The method according to claim 1, characterized in that, Determining the discharge cutoff temperature of the battery to be predicted based on the current temperature and the current state of charge includes: Determine the current power-on state of the battery to be predicted, wherein the current power-on state is either the initial power-on state or a non-initial power-on state; If the current power-on state is the initial power-on state, then a second data mapping table of temperature and temperature rise rate of the battery to be predicted is determined, and the discharge cutoff temperature is determined according to the current temperature and the second data mapping table. The second data mapping table is constructed based on the uncontrolled discharge test data in the multiple historical discharge test data. The temperature rise rate is the rate at which the temperature of the battery to be predicted increases relative to the battery discharge capacity. If the current power-on state is not the initial power-on state, then a third data mapping table of the temperature and charge state limit window of the battery to be predicted is determined, and the discharge cutoff temperature is determined according to the current temperature, the current charge state and the third data mapping table. The third data mapping table is constructed based on the temperature-controlled discharge test data in the multiple historical discharge test data.

3. The method according to claim 2, characterized in that, Determining the discharge cutoff temperature based on the current temperature and the second data mapping table includes: The first temperature rise of the battery to be predicted from the current state of charge to the discharge cutoff is determined based on the current temperature and the second data mapping table. The sum of the current temperature and the first temperature rise is determined as the discharge cutoff temperature.

4. The method according to claim 2, characterized in that, Determining the discharge cutoff temperature based on the current temperature, the current state of charge, and the third data mapping table includes: Determine multiple historical temperature rise data points for the battery to be predicted; The temperature rise curve of the battery to be predicted is obtained by fitting the multiple historical temperature rise data. Based on the temperature rise curve, determine the second temperature rise corresponding to the unit change in state of charge; The discharge cutoff temperature is determined based on the current temperature, the current state of charge, the second temperature rise, and the third data mapping table.

5. The method according to claim 4, characterized in that, The discharge cutoff temperature is determined based on the current temperature, the current state of charge, the second temperature rise, and the third data mapping table, including: The sum of the current temperature and the second temperature rise is determined as the predicted temperature after undergoing the unit change in state of charge. The predicted state of charge limit window is determined based on the predicted temperature and the third data mapping table; The difference between the current state of charge and the unit change in state of charge is determined as the predicted state of charge. If the predicted state of charge is less than or equal to the predicted state of charge limit window, then the discharge cutoff temperature is determined based on the predicted temperature. If the predicted state of charge is greater than the predicted state of charge limit window, the predicted temperature, the predicted state of charge, and the predicted state of charge limit window are iteratively updated by a unit change in state of charge until the updated predicted state of charge is less than or equal to the updated predicted state of charge limit window, and the discharge cutoff temperature is determined based on the updated predicted temperature.

6. The method according to claim 5, characterized in that, Determining the discharge cutoff temperature based on the predicted temperature includes: If the predicted state of charge is equal to the predicted state of charge lower limit window, then the predicted temperature is determined as the discharge cutoff temperature; If the predicted state of charge is less than the predicted state of charge limit window, the discharge cutoff temperature is determined by interpolation based on the third data mapping table and the predicted state of charge limit window.

7. The method according to claim 6, characterized in that, Based on the third data mapping table and the predicted state of charge limit window, the discharge cutoff temperature is determined by interpolation, including: Obtain the current health status and current internal resistance of the battery to be predicted; Determine the mapping table correction coefficient based on the current health level and the current internal resistance; The third data mapping table is corrected according to the correction coefficient of the mapping table to obtain the corrected data mapping table; The discharge cutoff temperature is determined by interpolation based on the corrected data mapping table and the predicted state of charge limit window.

8. The method according to any one of claims 1-7, characterized in that, After obtaining the predicted SOE of the battery to be predicted by looking up the first data mapping table, the method further includes: Obtain the discharge operating parameters and real-time temperature of the battery to be predicted; Determine whether the battery to be predicted is in a powered-off state based on the discharge operating parameters. If not, then obtain the real-time state of charge of the battery to be predicted; The predicted SOE is updated based on the real-time state of charge, the current state of charge, the real-time temperature, and the predicted temperature.

9. The method according to claim 8, characterized in that, The predicted SOE is updated based on the real-time state of charge, the current state of charge, the real-time temperature, and the predicted temperature, including: Determine a first difference between the current state of charge and the real-time state of charge; If the first difference is equal to the unit change in state of charge, then the second difference between the real-time temperature and the predicted temperature is determined. The predicted SOE is updated based on the second difference.

10. The method according to claim 9, characterized in that, Update the predicted SOE based on the second difference, including: If the absolute value of the second difference is greater than or equal to a preset threshold, then the third temperature rise is determined based on the temperature rise data between the real-time state of charge and the current state of charge, and the predicted SOE is updated based on the real-time temperature, the real-time state of charge, the third temperature rise, the first data mapping table, and the third data mapping table. If the absolute value of the second difference is less than a preset threshold, the predicted SOE is updated based on the real-time temperature, the real-time state of charge, and the first data mapping table.

11. A device for predicting the available discharge energy of a battery, characterized in that, include: The acquisition module is used to determine the current temperature, current state of charge, and multiple historical discharge test data of the battery to be predicted in response to the prediction request of the battery's available discharge energy (SOE). The construction module is used to construct a first data mapping table between temperature, state of charge, and SOE based on the multiple historical discharge test data. The prediction module is used to determine the discharge cutoff temperature of the battery to be predicted based on the current temperature and the current state of charge. The lookup module is used to look up the predicted SOE of the battery to be predicted from the first data mapping table based on the current state of charge and the discharge cutoff temperature.

12. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-10.

13. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-10.

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