Method, device and equipment for predicting residual charging time of battery system and storage medium

Abstract

The invention relates to a battery system residual charging time prediction method and device, equipment, a storage medium and a computer program product. The method comprises the steps that structural data and initial state information of a current battery system are acquired, a system equivalent model of the current battery system is established according to the structural data and the initial state information, and the current battery system comprises at least two single batteries; obtaining an initial charging current and a charging mode of the current battery system, and determining charging current prediction data and prediction state data in the corresponding charging mode according to the initial charging current and the system equivalent model; and according to the charging current prediction data and the prediction state data, determining the residual charging time when the current battery system is charged to the charging cut-off condition in the corresponding charging mode. By adopting the method, the state change of the charging process of the battery system under different charging modes can be considered, and the residual charging time under different charging working conditions can be accurately predicted.

Description

Technical Field

[0001] The present application relates to the technical field of battery charging prediction, and particularly to a method, device, equipment, storage medium and computer program product for predicting the remaining charging time of a battery system. Background Art

[0002] With the popularization of power-consuming equipment such as new energy vehicles and energy storage systems, and the development of various charging (fast charging, slow charging) technologies, users' demand for estimating charging time and the accuracy of the estimation results are increasing day by day, which is directly related to the convenience of vehicle use planning, charging cost optimization and user experience, or related to the planning of energy storage systems to participate in peak shaving and valley filling, power market trading, etc. Therefore, the technology for estimating the remaining charging time of a battery system is very important.

[0003] Existing related charging time estimation technologies generally divide the battery charging process into several intervals, and estimate the charging time for each interval to determine the total remaining charging time; or establish a battery charging model to simulate the charging state in real time to determine the remaining charging time. However, in actual scenarios, the former does not consider the state changes during the system charging process, resulting in a large estimation error; the latter cannot quickly predict the charging termination state and time of the battery, and has poor flexibility and adaptability to different charging scenarios. Summary of the Invention

[0004] Based on this, it is necessary to provide a battery charging prediction method, device, equipment, computer-readable storage medium and computer program product that can consider the state changes during the system charging process and has strong adaptability to different charging modes for the above technical problems.

[0005] In a first aspect, the present application provides a method for predicting the remaining charging time of a battery system. The method includes:

[0006] Obtain the structure data and initial state information of the current battery system, and establish a system equivalent model of the current battery system according to the structure data and the initial state information, where the current battery system includes at least two single cells;

[0007] Obtain the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model;

[0008] Determine the remaining charging time of the current battery system when it is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

[0009] In one embodiment, obtaining the initial charging current and charging mode of the current battery system, and determining the charging current prediction data and prediction status data corresponding to the charging mode according to the initial charging current and the system equivalent model includes:

[0010] In response to detecting that the charging mode of the current battery system is static charging, execute the static charging prediction step;

[0011] Static charging prediction step: Obtain a preset fixed current, and predict the status data at the first moment after the current input according to the preset fixed current, the status data at the current moment, and the system equivalent model;

[0012] Repeat executing the static charging prediction step until the cut-off charging condition is satisfied.

[0013] In one embodiment, obtaining the initial charging current and charging mode of the current battery system, and determining the charging current prediction data and prediction status data corresponding to the charging mode according to the initial charging current and the system equivalent model further includes:

[0014] In response to detecting that the charging mode of the current battery system is dynamic charging, obtain the corresponding dynamic charging strategy and execute the dynamic charging prediction step;

[0015] Dynamic charging prediction step: Predict the status data at the second moment after the current input according to the dynamic charging strategy, the status data at the current moment, the current charging current, and the system equivalent model;

[0016] Repeat executing the dynamic charging prediction step until the cut-off charging condition is satisfied.

[0017] In one embodiment, the dynamic charging prediction step further includes:

[0018] Predict the charging current at the second moment according to the status data at the second moment;

[0019] Predict the status data at the third moment after the charging current input at the second moment according to the dynamic charging strategy, the status data at the second moment, the charging current at the second moment, and the system equivalent model.

[0020] In one embodiment, the cut-off charging condition includes at least one of the following:

[0021] The battery system triggers the charging safety boundary, the single cell triggers the charging safety boundary, and the remaining battery power of the battery system is not less than the target power.

[0022] In one embodiment, the obtaining of the structural data and initial state information of the current battery system, and establishing the system equivalent model of the current battery system according to the structural data and the initial state information includes:

[0023] Establishing a single cell equivalent model for each single cell in the current battery system;

[0024] Based on the structural data and the single cell equivalent model of each single cell, establishing the system equivalent model of the current battery system.

[0025] In a second aspect, the present application also provides a device for predicting the remaining charging time of a battery system. The device includes:

[0026] A first data acquisition module, configured to acquire the structural data and initial state information of the current battery system, and establish the system equivalent model of the current battery system according to the structural data and the initial state information. The current battery system includes at least two single cells;

[0027] A second data acquisition module, configured to acquire the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model;

[0028] A charging prediction module, configured to determine the remaining charging time when the current battery system is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

[0029] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0030] Acquiring the structural data and initial state information of the current battery system, and establishing the system equivalent model of the current battery system according to the structural data and the initial state information. The current battery system includes at least two single cells;

[0031] Acquiring the initial charging current and charging mode of the current battery system, and determining the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model;

[0032] Determining the remaining charging time when the current battery system is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

[0033] Fourthly, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0034] Obtain the structural data and initial state information of the current battery system, and establish a system equivalent model of the current battery system according to the structural data and the initial state information, where the current battery system includes at least two single cells;

[0035] Obtain the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model;

[0036] Determine the remaining charging time when the current battery system is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

[0037] Fifthly, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0038] Obtain the structural data and initial state information of the current battery system, and establish a system equivalent model of the current battery system according to the structural data and the initial state information, where the current battery system includes at least two single cells;

[0039] Obtain the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model;

[0040] Determine the remaining charging time when the current battery system is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

[0041] A method, device, equipment, storage medium and computer program product for predicting the remaining charging time of a battery system provided by the embodiments of the present application can more realistically predict the charging state of the current battery system when it is charged to the cut-off charging condition under different charging modes, and further make the process simulation of charging the current battery system from the initial state or low state of charge to the charging cut-off condition more in line with the actual situation, thereby improving the prediction accuracy of the remaining charging time when the current battery system is charged to the cut-off charging condition under the corresponding charging mode and enhancing the user experience. Description of the Drawings

[0042] Figure 1Schematic flowchart of the method for predicting the remaining charging time of a battery system in an embodiment;

[0043] Figure 2 Structural block diagram of the system equivalent model in an embodiment;

[0044] Figure 3 Schematic flowchart of the method for predicting the remaining charging time of a battery system in another embodiment;

[0045] Figure 4 Structural block diagram of the device for predicting the remaining charging time of a battery system in an embodiment. Detailed implementation manners

[0046] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] The method for predicting the remaining charging time of a battery system provided by an embodiment of the present application can be applied to a terminal or a server. Among them, the terminal communicates with the server through a network. The terminal can be, but is not limited to, various vehicle-mounted terminals, personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0048] In one embodiment, as Figure 1 shown, a method for predicting the remaining charging time of a battery system is provided. The method includes:

[0049] S1. Obtain the structural data and initial state information of the current battery system, and establish a system equivalent model of the current battery system according to the structural data and the initial state information. The current battery system includes at least two single cells;

[0050] S2. Obtain the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction state data under the corresponding charging mode according to the initial charging current and the system equivalent model;

[0051] S3. Determine the remaining charging time when the current battery system is charged to the cut-off charging condition under the corresponding charging mode according to the charging current prediction data and the prediction state data.

[0052] Specifically, the battery system may include one or more battery packs with the same or different power for providing power and energy in electric vehicles and hybrid electric vehicles. It can be in the form of a battery pack, a battery module, or a battery device. Whether it is a battery pack, a battery module, or a battery device, it includes at least two single cells. A battery pack is an integrated overall battery unit formed by integrating multiple battery modules.

[0053] The charging mode may include an offline static charging mode or an online dynamic charging mode. The offline static charging mode may include constant current charging, multi-stage constant current charging, etc.; the online dynamic charging mode may include online super-fast charging, lithium-free fast charging, and multi-objective optimal charging considering safety boundaries such as temperature potential. The state information may include information such as the current power, temperature, and voltage of the current battery system. The initial state information may include information such as the current power, current temperature, and current voltage of the current battery system. Establishing a system equivalent model of the current battery system based on the structure data and the initial state information can simulate the state when charging the current battery system including multiple single cells connected in series and / or in parallel. The initial charging current usually refers to the charging current at the current moment when establishing the system equivalent model. Based on the initial charging current and the system equivalent model, the battery system after inputting the current can be simulated, improving the authenticity of the predicted charging current data and predicted state data, more realistically predicting the charging state of the current battery system when charged to the cut-off charging condition under different charging modes, and further making the process simulation of charging the current battery system from the initial state or low state of charge (SOC) to the charging cut-off condition more in line with the actual situation, thereby improving the prediction accuracy of the remaining charging time when the current battery system is charged to the cut-off charging condition under the corresponding charging mode and enhancing the user experience.

[0054] In one embodiment, S1 includes:

[0055] S11. Establish a single-cell equivalent model for each single cell in the current battery system;

[0056] S12. Based on the single-cell equivalent model of each single cell according to the structure data, establish a system equivalent model of the current battery system.

[0057] Specifically, referring to Figure 2 , a thermoelectrically coupled battery single-cell equivalent model can be established. In this embodiment, a thermoelectrically coupled segmented Rint model is established. The segmented Rint model is a lithium-ion battery model. It decouples the positive and negative electrodes of the lithium-ion battery through a reference electrode and equivalent it to an ideal voltage source, a positive internal resistance, and a negative internal resistance. The circuit principle of the model is as follows:

[0058]

[0059]

[0060]

[0061]

[0062] Among them, V ca 、V an and U t are the positive terminal voltage, negative terminal voltage and full cell terminal voltage of the current battery cell respectively, OCV ca 、OCV an and U ocv are the positive open circuit voltage, negative open circuit voltage and full cell open circuit voltage of the current battery cell respectively, R ca and R an are the positive internal resistance and negative internal resistance of the current battery cell respectively, I bat is the current passing through the current battery cell, with discharge defined as positive and charge as negative.

[0063] The heat generation and heat dissipation principle of the battery thermoelectric coupling lumped thermal model is as follows:

[0064]

[0065]

[0066] Among them, is the heat generation per unit time, T bat is the temperature of the current battery cell, is the heat transfer per unit time, h represents the heat transfer coefficient, S represents the surface area of the current battery cell, m represents the mass of the battery, T amb represents the ambient temperature. Therefore, the change in battery temperature over time is:

[0067]

[0068] Exemplarily, based on the single cell model, an equivalent model of a 3P6S (6 series modules, with three parallel single cells in each group, that is, after three single cells in each group are connected in parallel to form a battery module, 6 battery modules are connected in series) battery system can be established, where B m,n represents the nth battery cell in the mth series module, R sb and R bp represent the series contact internal resistance and parallel contact internal resistance in the battery respectively. I pack,kRepresents the current passing through the battery system at time k. In this embodiment, by first establishing a single-cell equivalent model and then integrating it into a system-level model based on the system structure (series-parallel topology, contact internal resistance), the model's ability to characterize parameter discreteness is significantly improved. The single-cell model can accurately capture the dynamic response of the single cell (such as polarization voltage, temperature rise characteristics), while the system model can accurately predict the overall voltage, current, and temperature distribution of the system by superimposing the single-cell behavior and considering the influence of connection impedance. This method is not only applicable to battery modules with exactly the same single-cell parameters under ideal conditions, but also can simulate complex scenarios such as actual inconsistency differences by introducing assumptions about the parameter distribution between single cells (such as normal distribution, Weibull distribution, etc.), parameter anomalies, etc., making it closer to the actual scenario, so that the simulation of the charging process of the current battery system is more in line with the actual situation, thereby improving the prediction accuracy of the remaining charging time. It should be noted that the established thermoelectric coupling polarization Rint model is only an example of the system equivalent model in some embodiments of the present disclosure, and is not actually limited. Other implementation manners using the method for establishing the system equivalent model of the present disclosure are all covered in some embodiments of the present disclosure.

[0069] In one embodiment, the charging cut-off condition includes at least one of the following:

[0070] The battery system triggers the charging safety boundary, the single-cell battery triggers the charging safety boundary, and the remaining battery power of the battery system is not less than the target power.

[0071] Specifically, when simulating the charging process of the current battery system to the charging cut-off condition in different charging modes, it can be judged in real time according to the state data of the current battery system. Determine whether the charging cut-off condition is met. The charging cut-off condition may include that the battery system triggers the charging safety boundary, for example, whether the highest temperature T max of the battery system exceeds the set system temperature safety threshold T thr ; the charging cut-off condition may also include that the single-cell battery triggers the charging safety boundary, for example, whether the highest voltage V max of the single-cell battery exceeds the set system potential safety threshold V thr ; the charging cut-off condition may also include that the remaining battery power of the battery system is not less than the target power, for example, whether the battery system power SOC pack reaches the set power target SOC f。When the cut-off charging condition is met during the charging process of the current battery system, charging termination is performed, and the time from the initial state of the current battery system to the charging termination state is determined as the remaining charging time. When the cut-off charging condition is not met during the charging process of the current battery system, continue to perform the charging prediction of the current battery system to the cut-off charging condition under different charging modes until the cut-off charging condition is met. Through such a setting of the cut-off charging condition, the charging limit conditions of the battery system and battery cells in actual applications can be simulated, and the matching degree with the actual application scenario is high.

[0072] In one embodiment, S2 includes:

[0073] S211. In response to detecting that the charging mode of the current battery system is static charging, perform the static charging prediction step;

[0074] S212. Static charging prediction step: Obtain a preset fixed current, and predict the state data at the first moment after the current input according to the preset fixed current, the state data at the current moment, and the system equivalent model;

[0075] S213. Repeat the static charging prediction step until the cut-off charging condition is met.

[0076] Specifically, the static charging mode may include a charging mode that does not depend on the current state of the battery system. The static charging mode may include constant current charging, multi-stage constant current charging, etc. The preset fixed current may include a given offline charging current curve, and charging may be performed according to the preset offline charging current curve. The first moment generally refers to the next moment after the current static charging current is input, that is, the state data such as temperature, voltage, and internal potential of the current battery system at the next moment after the current input can be predicted according to the preset fixed current, the state data at the current moment, and the system equivalent model. By repeating the above static charging prediction step, the state data corresponding to each moment during the process of charging the current battery system from the initial state to the cut-off charging condition according to the preset fixed current in the static charging mode can be obtained, and the charging process simulation of the current battery system in the static charging mode under the current input is completed. Through the above charging prediction scheme for the current battery system in the static charging mode, the change of the state data of the current battery system after the current input in the static charging mode can be obtained, the actual situation of the battery system after the current input can be simulated, and the accuracy and reliability of the remaining charging time prediction in the static charging mode scenario can be improved.

[0077] In one embodiment, S2 further includes:

[0078] S221. In response to detecting that the charging mode of the current battery system is dynamic charging, obtain the corresponding dynamic charging strategy and perform the dynamic charging prediction step;

[0079] S222. Dynamic charging prediction step: Predict the state data at the second moment after the current input according to the dynamic charging strategy, the state data at the current moment, the current charging current, and the system equivalent model;

[0080] S223. Repeatedly execute the dynamic charging prediction step until the cut-off charging condition is satisfied.

[0081] Specifically, the dynamic charging strategy usually refers to a dynamic strategy that adjusts the current at the next moment in real time according to the state of the current battery system at the current moment. The dynamic charging strategy may include an online super-fast charging strategy, a non-lithium deposition fast charging strategy, and a multi-objective optimal charging strategy that considers safety boundaries such as temperature potential. The second moment usually refers to the next moment after the current dynamic charging current is input. The state data such as the temperature, voltage, and internal potential of the current battery system at the next moment after the current dynamic charging current is input can be predicted according to the dynamic charging strategy, the actual charging current or the predicted charging current at the current moment, the state data at the current moment, and the system equivalent model. By repeatedly executing the above dynamic charging prediction step, the state data corresponding to each moment during the process of charging the current battery system from the initial state to the cut-off charging condition according to the dynamic charging strategy in the dynamic charging mode can be obtained, and the charging process simulation of the current battery system in the dynamic charging mode under the condition of current input can be completed. Through the above charging prediction scheme for the current battery system in the dynamic charging mode, the change of the state data of the current battery system after the current input in the dynamic charging mode can be obtained, the actual situation of the battery system after the current input can be simulated, and the accuracy and reliability of the prediction of the remaining charging time in the dynamic charging mode scenario can be improved.

[0082] In one embodiment, the above dynamic charging prediction step further includes:

[0083] Predict the charging current at the second moment according to the state data at the second moment;

[0084] Predict the state data at the third moment after the charging current at the second moment is input according to the dynamic charging strategy, the state data at the second moment, the charging current at the second moment, and the system equivalent model.

[0085] Specifically, according to the dynamic charging strategy, the state data at the current moment, the current charging current, and the system equivalent model, the state data at the second moment after the current input can be predicted. After obtaining the state data at the second moment after the current input, the state data at the second moment can be used as a feedback signal for input, the charging current at the second moment can be predicted, and then based on the dynamic charging strategy, the state data at the second moment, the charging current at the second moment, and the system equivalent model, the state data at the third moment after the charging current input at the second moment can be predicted. The third moment generally can refer to the next moment of the second moment. By adopting such a technical solution, the predicted state data can be used as feedback information to continuously optimize and update the state data at the next moment, so as to obtain a more accurate state change after the dynamic charging current input, providing a reliable basis for predicting the remaining charging time in the dynamic charging mode scenario.

[0086] In some application scenarios, the entire charging process of the current battery system can be simulated according to different charging modes. It can be determined whether the charging mode of the current battery system is static charging or dynamic charging.

[0087] As Figure 3 shown, if the charging mode of the current battery system is static charging, the preset fixed current for static charging can be obtained. According to the preset fixed current, the state data at the current moment, and the system equivalent model, the state data such as the temperature, voltage, and internal potential of the current battery system at the next moment after the current input can be predicted. Then it is judged whether the cut-off charging condition is satisfied. For example, it is judged whether the maximum temperature T max of the battery system exceeds the set system temperature safety threshold T thr , it is judged whether the maximum voltage V max of the single battery exceeds the set system potential safety threshold V thr , and it is judged whether the state of charge SOC pack of the battery system reaches the set state of charge target SOC f . If the judgment result is "yes", the charging is terminated; if the judgment result is "no", the above-mentioned static charging prediction steps are repeated, and the state data corresponding to each moment during the process of charging the current battery system from the initial state to the cut-off charging condition according to the preset fixed current in the static charging mode can be obtained, completing the simulation of the charging process of the current battery system in the static charging mode under the condition of current input.

[0088] If the charging mode of the current battery system is dynamic charging, the dynamic charging strategy, the true state and the true charging current of the current battery system can be obtained. Based on the dynamic charging strategy, the true charging current at the current moment, the state data at the current moment and the system equivalent model, the state data such as the temperature, voltage and internal potential of the current battery system at the second moment after the input of the current dynamic charging current can be predicted. Then, it is judged whether the cut-off charging condition is satisfied. For example, it is judged whether the maximum temperature T max of the battery system exceeds the set system temperature safety threshold T thr , it is judged whether the maximum voltage V max of a single battery exceeds the set system potential safety threshold V thr , it is judged whether the state of charge SOC pack of the battery system reaches the set target state of charge SOC f . If the judgment result is "yes", the charging is terminated; if the judgment result is "no", the state data at the second moment can be used as a feedback signal for input, the charging current at the second moment can be predicted, and then based on the dynamic charging strategy, the state data at the second moment, the charging current at the second moment and the system equivalent model, the state data at the third moment after the input of the charging current at the second moment can be predicted. Then, continue to judge whether the cut-off charging condition is satisfied. If the judgment result is "yes", the charging is terminated; if the judgment result is "no", the dynamic charging prediction steps can be repeated according to the dynamic charging strategy, the predicted charging current at the current moment, the state data at the current moment and the system equivalent model, and the state data corresponding to each moment during the process of charging the current battery system from the initial state to the cut-off charging condition according to the dynamic charging strategy in the dynamic charging mode can be obtained, and the charging process simulation of the current battery system in the dynamic charging mode under the condition of current input is completed. By adopting the above application scenarios including the static charging mode and the dynamic charging mode, the accurate estimation of the remaining charging time of the corresponding battery system can be realized based on different charging conditions, and a decision-making basis with high precision and high robustness can be provided for the charging planning of new energy vehicles and energy storage systems.

[0089] Refer to Table 1 and Table 2 below. Table 1 shows the comparison data between the estimated remaining charging time and the actual value of the battery system at an ambient temperature of 0°C, and Table 2 shows the comparison data between the estimated remaining charging time and the actual value of the battery system at an ambient temperature of 25°C. Table 1 gives the comparison data between the estimated remaining charging time and the actual value of the battery system under the conditions that the ambient temperature is 0°C and the initial SOC of the battery system is 0, 0.2, and 0.4 respectively. It can be seen that the estimated error of the remaining charging time using this technical solution does not exceed 5% at an ambient temperature of 0°C. Table 2 gives the comparison data between the estimated remaining charging time and the actual value of the battery system under the conditions that the ambient temperature is 25°C and the initial SOC of the battery system is 0, 0.2, and 0.4 respectively. It can be seen that the estimated error of the remaining charging time using this technical solution does not exceed 4% at an ambient temperature of 25°C.

[0090] Table 1 Comparison Data between the Estimated Remaining Charging Time and the Actual Value of the Battery System at an Ambient Temperature of 0°C

[0091]

[0092] Table 2 Comparison Data between the Estimated Remaining Charging Time and the Actual Value of the Battery System at an Ambient Temperature of 25°C

[0093]

[0094] By adopting a charging prediction method for different charging modes, it is possible to more realistically predict the charging state of the current battery system when charging to the cut-off charging condition under different charging modes. Furthermore, the process simulation of charging the current battery system from the initial state or a low state of charge to the charging cut-off condition is more in line with the actual situation, thereby improving the prediction accuracy of the remaining charging time when the current battery system is charged to the cut-off charging condition under the corresponding charging mode and enhancing the user experience.

[0095] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of the steps or stages in other steps or other steps.

[0096] Based on the same inventive concept, an embodiment of the present application further provides a device for predicting the remaining charging time of a battery system for implementing the method for predicting the remaining charging time of the battery system involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for predicting the remaining charging time of the battery system provided below can refer to the limitations on the method for predicting the remaining charging time of the battery system in the above text, and will not be elaborated here.

[0097] In one embodiment, as Figure 4 shown, a device for predicting the remaining charging time of a battery system is provided, including: a first data acquisition module, configured to acquire the structural data and initial state information of the current battery system, and establish a system equivalent model of the current battery system according to the structural data and the initial state information, where the current battery system includes at least two single cells; a second data acquisition module, configured to acquire the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model; a charging prediction module, configured to determine the remaining charging time when the current battery system is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

[0098] Further, the second data acquisition module is further configured to, in response to detecting that the charging mode of the current battery system is static charging, execute a static charging prediction step; and is configured to execute the static charging prediction step: acquire a preset fixed current, and predict the state data at the first moment after the current input according to the preset fixed current, the state data at the current moment, and the system equivalent model; and is further configured to repeatedly execute the static charging prediction step until the cut-off charging condition is satisfied.

[0099] Further, the second data acquisition module is further configured to, in response to detecting that the charging mode of the current battery system is dynamic charging, acquire a corresponding dynamic charging strategy and execute a dynamic charging prediction step; and is configured to execute the dynamic charging prediction step: predict the state data at the second moment after the current input according to the dynamic charging strategy, the state data at the current moment, the current charging current, and the system equivalent model; and is further configured to repeatedly execute the dynamic charging prediction step until the cut-off charging condition is satisfied.

[0100] Further, the dynamic charging prediction step further includes: predicting the charging current at the second moment according to the state data at the second moment; predicting the state data at the third moment after the charging current at the second moment is input according to the dynamic charging strategy, the state data at the second moment, the charging current at the second moment, and the system equivalent model.

[0101] Further, the cut-off charging condition includes at least one of the following: the battery system triggers a charging safety boundary, a single battery triggers a charging safety boundary, and the remaining power of the battery system is not less than the target power.

[0102] Further, the first data acquisition module is further configured to establish an equivalent model for each single battery in the current battery system; and to establish a system equivalent model of the current battery system based on the structural data and the equivalent model of each single battery.

[0103] Each module in the above battery system remaining charging time prediction device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0104] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0105] Obtain the structural data and initial state information of the current battery system, and establish a system equivalent model of the current battery system according to the structural data and the initial state information. The current battery system includes at least two single batteries;

[0106] Obtain the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model;

[0107] Determine the remaining charging time of the current battery system when it is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

[0108] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0109] In response to detecting that the charging mode of the current battery system is static charging, execute the static charging prediction step;

[0110] Static charging prediction step: Obtain a preset fixed current, and predict the state data at the first moment after the current input according to the preset fixed current, the state data at the current moment, and the system equivalent model;

[0111] Repeat the static charging prediction step until the cut-off charging condition is met.

[0112] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0113] In response to detecting that the charging mode of the current battery system is dynamic charging, obtain the corresponding dynamic charging strategy and execute the dynamic charging prediction step;

[0114] Dynamic charging prediction step: According to the dynamic charging strategy, the state data at the current moment, the current charging current, and the system equivalent model, predict the state data at the second moment after the current input;

[0115] Repeat executing the dynamic charging prediction step until the cut-off charging condition is satisfied.

[0116] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0117] Predict the charging current at the second moment according to the state data at the second moment;

[0118] According to the dynamic charging strategy, the state data at the second moment, the charging current at the second moment, and the system equivalent model, predict the state data at the third moment after the charging current input at the second moment.

[0119] In one embodiment, the cut-off charging condition includes at least one of the following:

[0120] The battery system triggers the charging safety boundary, the single battery triggers the charging safety boundary, and the remaining power of the battery system is not less than the target power.

[0121] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0122] Establish a single equivalent model for each single battery in the current battery system;

[0123] Based on the single equivalent model of each single battery according to the structural data, establish the system equivalent model of the current battery system.

[0124] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0125] Obtain the structural data and initial state information of the current battery system, and establish the system equivalent model of the current battery system according to the structural data and the initial state information. The current battery system includes at least two single batteries;

[0126] Obtain the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction status data corresponding to the charging mode according to the initial charging current and the system equivalent model;

[0127] Determine the remaining charging time when the current battery system is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction status data.

[0128] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0129] In response to detecting that the charging mode of the current battery system is static charging, execute the static charging prediction step;

[0130] Static charging prediction step: Obtain a preset fixed current, and predict the status data at the first moment after the current input according to the preset fixed current, the status data at the current moment, and the system equivalent model;

[0131] Repeat the static charging prediction step until the cut-off charging condition is satisfied.

[0132] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0133] In response to detecting that the charging mode of the current battery system is dynamic charging, obtain the corresponding dynamic charging strategy and execute the dynamic charging prediction step;

[0134] Dynamic charging prediction step: Predict the status data at the second moment after the current input according to the dynamic charging strategy, the status data at the current moment, the current charging current, and the system equivalent model;

[0135] Repeat the dynamic charging prediction step until the cut-off charging condition is satisfied.

[0136] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0137] Predict the charging current at the second moment according to the status data at the second moment;

[0138] Predict the status data at the third moment after the charging current input at the second moment according to the dynamic charging strategy, the status data at the second moment, the charging current at the second moment, and the system equivalent model.

[0139] In one embodiment, the cut-off charging condition includes at least one of the following:

[0140] The battery system triggers the charging safety boundary, the single cell triggers the charging safety boundary, and the remaining power of the battery system is not less than the target power.

[0141] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0142] Establish an equivalent model for each single cell in the current battery system;

[0143] Based on the equivalent model of each single cell according to the structure data, establish an equivalent model for the current battery system.

[0144] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0145] Obtain the structure data and initial state information of the current battery system, and establish an equivalent model for the current battery system according to the structure data and the initial state information, where the current battery system includes at least two single cells;

[0146] Obtain the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the equivalent model of the system;

[0147] Determine the remaining charging time when the current battery system is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

[0148] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0149] In response to detecting that the charging mode of the current battery system is static charging, execute the static charging prediction step;

[0150] Static charging prediction step: Obtain a preset fixed current, and predict the state data at the first moment after the current input according to the preset fixed current, the state data at the current moment, and the equivalent model of the system;

[0151] Repeat executing the static charging prediction step until the cut-off charging condition is satisfied.

[0152] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0153] In response to detecting that the charging mode of the current battery system is dynamic charging, obtain the corresponding dynamic charging strategy and execute the dynamic charging prediction step;

[0154] Dynamic charging prediction step: Predict the state data at the second moment after the current input according to the dynamic charging strategy, the state data at the current moment, the current charging current, and the system equivalent model;

[0155] Repeat the execution of the dynamic charging prediction step until the cut-off charging condition is met.

[0156] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0157] Predict the charging current at the second moment according to the state data at the second moment;

[0158] Predict the state data at the third moment after the charging current input at the second moment according to the dynamic charging strategy, the state data at the second moment, the charging current at the second moment, and the system equivalent model.

[0159] In one embodiment, the cut-off charging condition includes at least one of the following:

[0160] The battery system triggers the charging safety boundary, the single cell triggers the charging safety boundary, and the remaining power of the battery system is not less than the target power.

[0161] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0162] Establish a single cell equivalent model for each single cell in the current battery system;

[0163] Based on the single cell equivalent model of each single cell according to the structure data, establish the system equivalent model of the current battery system.

[0164] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0165] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0166] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0167] The above-described embodiments merely represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for predicting the remaining charging time of a battery system, characterized in that, The method includes: Obtaining the structural data and initial state information of the current battery system, and establishing a system equivalent model of the current battery system according to the structural data and the initial state information, where the current battery system includes at least two single cells; Obtaining the initial charging current and charging mode of the current battery system, and determining the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model; Determining the remaining charging time when the current battery system is charged to the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

2. The method according to claim 1, characterized in that The obtaining the initial charging current and charging mode of the current battery system, and determining the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model includes: In response to detecting that the charging mode of the current battery system is static charging, performing a static charging prediction step; Static charging prediction step: Obtaining a preset fixed current, and predicting the state data at the first moment after the current input according to the preset fixed current, the state data at the current moment, and the system equivalent model; Repeatedly performing the static charging prediction step until the cut-off charging condition is satisfied.

3. The method according to claim 1, wherein The obtaining the initial charging current and charging mode of the current battery system, and determining the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model further includes: In response to detecting that the charging mode of the current battery system is dynamic charging, obtaining the corresponding dynamic charging strategy and performing a dynamic charging prediction step; Dynamic charging prediction step: Predicting the state data at the second moment after the current input according to the dynamic charging strategy, the state data at the current moment, the current charging current, and the system equivalent model; Repeatedly performing the dynamic charging prediction step until the cut-off charging condition is satisfied.

4. The method according to claim 3, characterized in that, The dynamic charging prediction step further includes: Predicting the charging current at the second moment according to the state data at the second moment; Predicting the state data at the third moment after the charging current input at the second moment according to the dynamic charging strategy, the state data at the second moment, the charging current at the second moment, and the system equivalent model.

5. The method according to any one of claims 1-4, characterized in that, The cut-off charging condition includes at least one of the following: The battery system triggers the charging safety boundary, a single cell triggers the charging safety boundary, and the remaining power of the battery system is not less than the target power.

6. The method according to claim 1, characterized in that The obtaining the structural data and initial state information of the current battery system, and establishing a system equivalent model of the current battery system according to the structural data and the initial state information includes: Establishing a single cell equivalent model for each single cell in the current battery system; Establishing a system equivalent model of the current battery system based on the single cell equivalent model of each single cell according to the structural data.

7. A device for predicting the remaining charging time of a battery system, characterized in that, The device includes: The first data acquisition module is configured to acquire the structure data and initial state information of the current battery system, and establish a system equivalent model of the current battery system according to the structure data and the initial state information, where the current battery system includes at least two single cells; The second data acquisition module is configured to acquire the initial charging current and charging mode of the current battery system, and determine the charging current prediction data and prediction state data corresponding to the charging mode according to the initial charging current and the system equivalent model; The charging prediction module is configured to determine the remaining charging time when the current battery system reaches the cut-off charging condition corresponding to the charging mode according to the charging current prediction data and the prediction state data.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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