Battery fault detection method and device, vehicle, electronic equipment and storage medium

Abstract

The invention discloses a battery fault detection method and device, a vehicle, electronic equipment and a storage medium. Battery parameters and a real curve of a battery in a charging mode are acquired; performing one-dimensional linear fitting calculation on the battery parameters to obtain a prediction curve; calling an error algorithm, and performing error calculation on the prediction curve and the real curve to obtain a target error; and when it is determined that the target error exceeds the error threshold, determining that the battery has a fault. According to the scheme, whether the battery has the electric leakage phenomenon or not is judged through the target error between the prediction curve and the real curve, and the electric leakage phenomenon is caused by the cell fault of the battery, so that the cell fault of the battery can be further determined through the electric leakage phenomenon of the battery without focusing on the type of the cell fault of the battery; the method breaks away from a traditional causal analysis method based on a fault tree, is higher in detection accuracy, and achieves the recognition of a fault cell at the early stage of the generation of a cell fault, and carries out the early warning.

Description

Battery fault detection methods and devices, vehicles, electronic devices and storage media Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a battery fault detection method and apparatus, vehicle, electronic device and storage medium. Background Technology

[0002] During the charging process of a lithium battery, the phenomenon where the amount of charge deposited does not match the actual amount of charge is called leakage. The causes of lithium battery leakage may include various types of cell failures such as abnormal aging of the cell, lithium plating, and internal short circuits. These cell failures may pose battery safety hazards.

[0003] In order to identify and address cell failures in their early stages and eliminate potential safety hazards in lithium batteries, existing fault detection methods for lithium batteries typically rely on voltage data during the charging and discharging process. They use traditional fault tree analysis and other causal analysis methods to determine the specific type of cell failure and then assess whether the lithium battery is leaking current.

[0004] Since a lithium battery is a multi-coupled nonlinear system, the changes in voltage data are often complex. If the causal analysis of cell failure is performed solely based on voltage data, cell failures may be missed, resulting in low accuracy of battery failure detection. Consequently, it may be impossible to identify and issue early warnings for faulty cells in the early stages of cell failure. Summary of the Invention

[0005] This disclosure provides a battery fault detection method and apparatus, vehicle, electronic device, and storage medium. Its main objective is to address the problem that relying solely on voltage data for causal analysis of cell faults may result in missed faults, leading to low accuracy in battery fault detection and consequently, the inability to identify and warn of faulty cells in their early stages.

[0006] According to a first aspect of this disclosure, a battery fault detection method is provided, comprising:

[0007] Obtain battery parameters and actual curves in charging mode. The battery parameters include ampere-hours, and the actual curve is used to characterize the one-dimensional linear relationship between the battery's actual remaining capacity and the battery's open-circuit voltage.

[0008] A one-dimensional linear fitting calculation is performed on the battery parameters to obtain a prediction curve, which is used to characterize the one-dimensional linear relationship between the ampere-hours and the predicted remaining battery capacity.

[0009] An error algorithm is invoked to calculate the error between the predicted curve and the true curve, and a target error is obtained. The target error is the minimum error between the predicted curve and the true curve.

[0010] If the target error exceeds the error threshold, the battery is determined to be faulty.

[0011] In some embodiments provided in this disclosure, the battery parameters further include a DC internal resistance sequence, current, and cell voltage;

[0012] In some embodiments provided in this disclosure, the step of performing a one-dimensional linear fitting calculation on the battery parameters to obtain a predicted curve includes:

[0013] The open-circuit voltage calculation algorithm is invoked to perform one-dimensional linear calculations on the DC internal resistance sequence, the current, and the individual cell voltage to obtain the predicted open-circuit voltage sequence.

[0014] Based on the mapping relationship between the battery's open-circuit voltage and remaining capacity, the predicted remaining capacity corresponding to the predicted open-circuit voltage sequence is determined sequentially, and all predicted remaining capacities constitute a predicted remaining capacity sequence.

[0015] The prediction curve is obtained by performing a one-dimensional linear fit between the ampere-hour sequence and the predicted remaining power sequence.

[0016] In some embodiments provided in this disclosure, the step of calling the open-circuit voltage calculation algorithm to perform one-dimensional linear calculations on the DC internal resistance sequence, the current, and the individual cell voltage to obtain the predicted open-circuit voltage sequence includes:

[0017] Each DC internal resistance value in the DC internal resistance value sequence is multiplied by the current to obtain the voltage sequence;

[0018] The predicted open-circuit voltage sequence is obtained by summing each voltage value in the voltage sequence with the individual cell voltage.

[0019] In some embodiments provided in this disclosure, the step of performing a one-dimensional linear fit between the ampere-hour sequence and the predicted remaining power sequence to obtain the prediction curve includes:

[0020] The ampere-hour sequence is determined as the independent variable;

[0021] The predicted remaining power sequence is determined as the dependent variable;

[0022] The prediction curve is obtained by performing a one-dimensional linear fit on the independent variable and the dependent variable.

[0023] In some embodiments provided in this disclosure, the step of invoking an error algorithm to calculate the error between the predicted curve and the true curve to obtain the target error includes:

[0024] According to the preset interval size, multiple real value sequences are obtained from the real curve; one interval corresponds to one real value sequence.

[0025] According to the preset interval size, multiple predicted value sequences are obtained from the prediction curve; wherein the number of actual value sequences is the same as the number of predicted value sequences;

[0026] The error algorithm is invoked to calculate the error for each true value sequence and the corresponding predicted value sequence, thereby obtaining the error corresponding to each true value sequence.

[0027] The minimum error among the errors corresponding to each true value sequence is determined as the target error.

[0028] In some embodiments provided in this disclosure, the step of invoking the error algorithm to calculate the error for each true value sequence and the corresponding predicted value sequence to obtain the error corresponding to each true value sequence includes:

[0029] Determine the number of true values ​​corresponding to each of the true value sequences;

[0030] In each sequence of true values ​​and its corresponding sequence of predicted values, the absolute value of the difference between each true value and its corresponding predicted value is calculated to obtain the set of absolute values ​​corresponding to each sequence of true values.

[0031] The sum of all absolute values ​​in each set of absolute values ​​is calculated to obtain the sum of the absolute values ​​corresponding to each sequence of true values.

[0032] The ratio of the sum of the absolute values ​​corresponding to each true value sequence to the corresponding number of true values ​​is determined as the error corresponding to each true value sequence.

[0033] In some embodiments provided in this disclosure, determining the minimum error among the errors corresponding to each true value sequence as the target error includes:

[0034] The optimization algorithm is invoked to find the minimum error among the errors corresponding to each true value sequence;

[0035] The minimum value of the error is determined as the target error.

[0036] In some embodiments provided in this disclosure, obtaining the battery parameters and actual curves in charging mode includes:

[0037] Determine multiple preset resistance value ranges for the battery's DC internal resistance;

[0038] The correlation coefficient algorithm is called to calculate the correlation coefficient between the actual remaining power and the open circuit voltage within each preset resistance range, so as to obtain the correlation coefficient corresponding to each preset resistance range; the correlation coefficient is used to characterize the correlation between the actual remaining power and the open circuit voltage within the preset resistance range.

[0039] The target correlation coefficient is determined based on the comparison between the correlation coefficient corresponding to each preset resistance range and the preset correlation coefficient threshold.

[0040] The preset resistance range corresponding to the target correlation coefficient is determined as the target resistance range;

[0041] Within the target resistance range, the battery parameters and actual curves of the battery in the charging state are obtained.

[0042] According to a second aspect of this disclosure, a battery fault detection device is provided, comprising:

[0043] The acquisition unit is used to acquire battery parameters and the actual curve of the battery in charging mode. The battery parameters include the ampere-hours, and the actual curve is used to characterize the one-dimensional linear relationship between the actual remaining capacity of the battery and the open circuit voltage of the battery.

[0044] The first calculation unit is used to perform one-dimensional linear fitting calculation on the battery parameters to obtain a prediction curve, which is used to characterize the one-dimensional linear relationship between the ampere-hours and the predicted remaining battery capacity.

[0045] The second calculation unit is used to call the error algorithm to calculate the error between the predicted curve and the true curve to obtain the target error, which is the minimum error between the predicted curve and the true curve.

[0046] The determining unit is used to determine that the battery is faulty when the target error exceeds an error threshold.

[0047] In some embodiments provided in this disclosure, the battery parameters further include a DC internal resistance sequence, current, and cell voltage;

[0048] The first computing unit includes:

[0049] The first calculation module is used to call the open-circuit voltage calculation algorithm to perform one-dimensional linear calculations on the DC internal resistance sequence, the current, and the individual cell voltage to obtain the predicted open-circuit voltage sequence.

[0050] The first determining module is used to determine the predicted remaining power corresponding to the predicted open circuit voltage sequence in sequence based on the mapping relationship between the battery's open circuit voltage and the remaining power, and all the predicted remaining power constitutes the predicted remaining power sequence.

[0051] The fitting module is used to perform a one-dimensional linear fitting between the ampere-hour sequence and the predicted remaining power sequence to obtain the prediction curve.

[0052] In some embodiments provided in this disclosure, the first computing module is used for:

[0053] Each DC internal resistance value in the DC internal resistance value sequence is multiplied by the current to obtain the voltage sequence;

[0054] The predicted open-circuit voltage sequence is obtained by summing each voltage value in the voltage sequence with the individual cell voltage.

[0055] The fitting module is used for:

[0056] The ampere-hour sequence is determined as the independent variable;

[0057] The predicted remaining power sequence is determined as the dependent variable;

[0058] The prediction curve is obtained by performing a one-dimensional linear fit on the independent variable and the dependent variable.

[0059] In some embodiments provided in this disclosure, the second computing unit includes:

[0060] The first acquisition module is used to acquire multiple real value sequences from the real curve according to a preset interval size; one interval corresponds to one real value sequence.

[0061] The second acquisition module is used to acquire multiple predicted value sequences from the prediction curve according to the preset interval size; wherein the number of the true value sequences is the same as the number of the predicted value sequences;

[0062] The second calculation module is used to call the preset error algorithm to calculate the error for each true value sequence and the corresponding predicted value sequence, so as to obtain the error corresponding to each true value sequence.

[0063] The second determining module is used to determine the minimum error among the errors corresponding to each true value sequence as the target error.

[0064] In some embodiments provided in this disclosure, the second computing module is used for:

[0065] Determine the number of true values ​​corresponding to each of the true value sequences;

[0066] In each sequence of true values ​​and its corresponding sequence of predicted values, the absolute value of the difference between each true value and its corresponding predicted value is calculated to obtain the set of absolute values ​​corresponding to each sequence of true values.

[0067] The sum of all absolute values ​​in each set of absolute values ​​is calculated to obtain the sum of the absolute values ​​corresponding to each sequence of true values.

[0068] The ratio of the sum of the absolute values ​​corresponding to each true value sequence to the corresponding number of true values ​​is determined as the error corresponding to each true value sequence.

[0069] In some embodiments provided in this disclosure, the second determining module is used to:

[0070] The optimization algorithm is invoked to find the minimum error among the errors corresponding to each true value sequence;

[0071] The minimum value of the error is determined as the target error.

[0072] In some embodiments provided in this disclosure, the acquisition unit includes:

[0073] The third determining module is used to determine multiple preset resistance value ranges of the battery's DC internal resistance;

[0074] The third calculation module is used to call the correlation coefficient algorithm to calculate the correlation coefficient between the actual remaining power and the open circuit voltage within each preset resistance range, and to obtain the correlation coefficient corresponding to each preset resistance range; the correlation coefficient is used to characterize the correlation between the actual remaining power and the open circuit voltage within the preset resistance range.

[0075] The fourth determining module is used to determine the target correlation coefficient based on the comparison results between the correlation coefficient corresponding to each preset resistance range and the preset correlation coefficient threshold.

[0076] The fifth determining module is used to determine the preset resistance range corresponding to the target correlation coefficient as the target resistance range;

[0077] The third acquisition module is used to acquire the battery parameters and actual curves of the battery in the charging state within the target resistance range.

[0078] According to a third aspect of this disclosure, a vehicle is provided that includes a battery fault detection device as described in the second aspect above.

[0079] According to a fourth aspect of this disclosure, an electronic device is provided, comprising:

[0080] At least one processor; and

[0081] A memory communicatively connected to the at least one processor; wherein,

[0082] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0083] According to a fifth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0084] According to a sixth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0085] This disclosure provides a battery fault detection method, apparatus, vehicle, electronic device, and storage medium. It acquires battery parameters and a true curve in charging mode. The battery parameters include ampere-hours (Ah-hours). The true curve represents a one-dimensional linear relationship between the battery's actual remaining capacity and its open-circuit voltage. A one-dimensional linear fitting calculation is performed on the battery parameters to obtain a predicted curve, which represents a one-dimensional linear relationship between the Ah-hours and the predicted remaining capacity. An error algorithm is invoked to calculate the error between the predicted curve and the true curve, obtaining a target error, which is the minimum error between the predicted and true curves. If the target error exceeds an error threshold, a battery fault is determined. This solution determines whether the battery has leakage by using the target error between the predicted and true curves. Since leakage is caused by cell faults, the presence of leakage can further confirm the presence of cell faults, rather than focusing on the specific cell fault type. This departs from traditional fault tree-based causal analysis methods, resulting in higher detection accuracy and enabling early identification and warning of faulty cells.

[0086] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0087] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0088] Figure 1 is a schematic flowchart of a battery fault detection method provided in an embodiment of this disclosure;

[0089] Figure 2 is a schematic diagram of a real curve and a predicted curve provided in an embodiment of this disclosure;

[0090] Figure 3 is a schematic diagram of another real curve and predicted curve provided in an embodiment of this disclosure;

[0091] Figure 4 is a flowchart illustrating another battery fault detection method provided in an embodiment of this disclosure;

[0092] Figure 5 is a schematic flowchart of a method for calculating and predicting open-circuit voltage sequences provided in this disclosure;

[0093] Figure 6 is a schematic flowchart of a method for calculating target error according to an embodiment of this disclosure;

[0094] Figure 7 is a flowchart illustrating an error calculation method provided in an embodiment of this disclosure;

[0095] Figure 8 is a schematic diagram of a battery fault detection device provided in an embodiment of this disclosure;

[0096] Figure 9 is a schematic diagram of another battery fault detection device provided in an embodiment of this disclosure;

[0097] Figure 10 is a schematic block diagram of an example electronic device 700 provided in an embodiment of this disclosure. Detailed Implementation

[0098] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0099] The following description, with reference to the accompanying drawings, outlines a battery fault detection method, apparatus, vehicle, electronic device, and storage medium according to embodiments of the present disclosure.

[0100] Figure 1 is a schematic flowchart of a battery fault detection method provided in an embodiment of this disclosure. This solution is applied in a vehicle. The method can be executed by an information prompting control device or equipment, which can be configured in a server, processor, or main control chip. For example, it can be deployed in the vehicle's battery management system (BMS), etc. As shown in Figure 1, the method includes the following steps:

[0101] Step 101: Obtain the battery parameters and true curve of the battery in charging mode. The battery parameters include the ampere-hours, and the true curve is used to characterize the one-dimensional linear relationship between the battery's actual remaining capacity and the battery's open-circuit voltage.

[0102] In some embodiments provided in this disclosure, the true curve is obtained based on the battery's actual remaining capacity and actual open-circuit voltage. In practical applications, when the battery is in a normal charging state, there is a one-dimensional linear relationship between the battery's remaining capacity (State-of-Charge, SOC) and open-circuit voltage (OCV). Therefore, the true curve theoretically presents a smooth curve that is approximately a straight line with a small change in slope, as shown in Figure 2. Figure 2 is a schematic diagram of a true curve and a predicted curve provided by an embodiment of this disclosure, where the solid line in Figure 2 represents the true curve. However, when the battery experiences leakage due to a cell failure, there is no one-dimensional linear relationship between SOC and OCV. In this case, the true curve no longer presents an approximately straight line but may be a rugged curve, as shown in Figure 3. Figure 3 is a schematic diagram of another true curve and a predicted curve provided by an embodiment of this disclosure. Compared to the true curve in Figure 2, the slope of the true curve in Figure 3 fluctuates more when the OCV is in the range of 70 to 100.

[0103] Step 102: Perform a one-dimensional linear fitting calculation on the battery parameters to obtain a prediction curve. The prediction curve is used to characterize the one-dimensional linear relationship between the ampere-hours and the predicted remaining battery capacity.

[0104] In practical applications, since battery parameters vary under different working conditions or environments, it is necessary to use a predicted curve as a reference to determine whether the slope of the real curve changes significantly within a certain range. By comparing the error between the real curve and the predicted curve to see if it exceeds the error threshold, it can be determined whether the battery has experienced leakage.

[0105] In some embodiments provided in this disclosure, the prediction curve is obtained by performing a one-dimensional linear fitting calculation on the battery parameters. Specifically, based on the definition of SOC, a one-dimensional linear relationship can be determined between SOC and ampere-hours (AH), and a mapping relationship exists between SOC and OCV. Therefore, a one-dimensional linear relationship between AH and OCV can be determined through the mapping relationship between SOC and OCV.

[0106] In this embodiment, battery parameters include cell voltage (Vcell), current (I), and direct current resistance (DCR). Therefore, the predicted OCV can be calculated through the quantitative relationship between Vcell, I, and DCR. Thus, the predicted SOC can be determined by predicting OCV based on the mapping relationship between SOC and OCV. Since there is a one-dimensional linear relationship between AH and OCV, there is also a one-dimensional linear relationship between the predicted SOC and AH. Therefore, a one-dimensional linear fit can be performed on the predicted SOC and AH to obtain the prediction curve. For a better understanding of the prediction curve, please refer to Figures 2 and 3. The prediction curve is a straight line with a fixed slope, and the slope is close to the slope of the true curve.

[0107] Step 103: Call the error algorithm to calculate the error between the predicted curve and the true curve to obtain the target error, which is the minimum error between the predicted curve and the true curve.

[0108] In some embodiments provided in this disclosure, in order to further determine the degree of matching between AH and the predicted SOC change, the difference between the predicted curve and the true curve is quantified. It is necessary to calculate the error between the predicted curve and the true curve. The error algorithm can be a preset error algorithm, or it can call any of the following algorithms: Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), etc. as the error between the predicted curve and the true curve.

[0109] In practical applications, the magnitude of the Mean Error (MAE) varies across different intervals between the predicted and actual curves. In intervals where the difference between the predicted and actual curves is large, the corresponding MAE is larger; conversely, in intervals where the difference is small, the MAE is smaller. If the minimum error between the predicted and actual curves is greater than an error threshold, then all errors between the predicted and actual curves are greater than the error threshold. Therefore, to simplify the judgment process, but not limited to, the minimum error between the predicted and actual curves can be used as the target error, and compared with the error threshold to determine whether the battery is leaking.

[0110] Step 104: If the target error exceeds the error threshold, the battery is determined to be faulty.

[0111] In some embodiments provided in this disclosure, since the target error is the minimum error between the predicted curve and the true curve, when the target error exceeds the error threshold, it can be determined that all errors between the predicted curve and the true curve are greater than the error threshold. Ideally, AH and predicted SOC should exhibit a one-dimensional linear relationship. However, considering sampling errors (voltage, current sensors, etc.) and OCV curve deformation caused by normal battery aging, the one-dimensional linear fitting of AH and predicted SOC will have certain errors. Therefore, the error between the predicted curve and the true curve is greater than zero. Furthermore, the error threshold is an empirical value. It can be flexibly selected and set within a range greater than zero, depending on the actual situation. For example, when calculating the MAE between the predicted curve and the true curve, the threshold range of MAE can be set to 0.5. When the MAE between the predicted curve and the true curve is greater than 0.5, it can be considered that the battery corresponding to the predicted curve has leakage.

[0112] The battery fault detection method disclosed herein acquires battery parameters and a true curve in charging mode. The battery parameters include ampere-hours (Ah-hours). The true curve represents a one-dimensional linear relationship between the battery's actual remaining capacity and its open-circuit voltage. A one-dimensional linear fitting calculation is performed on the battery parameters to obtain a predicted curve, which represents a one-dimensional linear relationship between the Ah-hours and the predicted remaining capacity. An error algorithm is invoked to calculate the error between the predicted curve and the true curve, obtaining a target error, which is the minimum error between the predicted and true curves. If the target error exceeds an error threshold, the battery is determined to be faulty. This method determines whether the battery has leakage by using the target error between the predicted and true curves. Since leakage is caused by cell faults, the presence of leakage can further confirm the presence of cell faults, rather than focusing on the specific cell fault type. This departs from traditional fault tree-based causal analysis methods, resulting in higher detection accuracy and enabling early identification and warning of faulty cells.

[0113] In practical applications, the correlation coefficient between the actual remaining capacity and the open-circuit voltage varies depending on the range of the battery's DC internal resistance. The correlation coefficient characterizes the degree of linear correlation between two different variables. In this embodiment, if the correlation coefficient between the actual remaining capacity and the open-circuit voltage is too small within a preset range of the battery's DC internal resistance, it indicates that there is no linear relationship between the actual remaining capacity and the open-circuit voltage within that preset resistance range, and the battery fault detection method provided in this disclosure fails. Therefore, when obtaining the battery parameters and the actual curve in charging mode, it is necessary to obtain the battery parameters and the actual curve in charging mode within a preset resistance range where there is a linear relationship between the actual remaining capacity and the open-circuit voltage. The specific implementation method includes: determining multiple preset resistance ranges for the DC internal resistance of the battery. For example, the resistance range of a single battery cell is usually [0.5, 3.0], which can be divided into preset resistance ranges such as [0.5, 1.0), [1.0, 1.5), [1.5, 2.0), [2.0, 2.5], and [2.5, 3.0]. A correlation coefficient algorithm is called to calculate the correlation coefficient between the actual remaining charge and the open-circuit voltage within each resistance range, obtaining the correlation coefficient corresponding to each preset resistance range. Based on the comparison between the correlation coefficient corresponding to each preset resistance range and the correlation coefficient threshold, a target correlation coefficient is determined. The larger the correlation coefficient, the stronger the linear correlation between the actual remaining charge and the open-circuit voltage. The stronger the linear correlation, the more accurate the battery fault detection method of this disclosure. Therefore, the preset correlation coefficient threshold should be set relatively large. For example, the correlation coefficient ranges from 0 to 1, with a stronger correlation indicating a closer correlation to 1. In this disclosure, the preset correlation coefficient threshold can be set to 0.95 to 0.99. The battery fault detection method provided in this embodiment can only be applied when the correlation coefficient meets this range. After determining the target correlation coefficient, the preset resistance range corresponding to the target correlation coefficient is determined as the target resistance range. Within the target resistance range, the battery parameters and the actual curve of the battery in the charging state are obtained.

[0114] To better understand step 102 above, Figure 4 is a flowchart of another battery fault detection method provided in this embodiment of the present disclosure. As shown in Figure 4, the method includes the following steps:

[0115] Step 201: Call the open-circuit voltage calculation algorithm to perform one-dimensional linear calculations on the DC internal resistance sequence, the current, and the individual cell voltage to obtain the predicted open-circuit voltage sequence.

[0116] In the battery charging mode, there is always a one-dimensional linear relationship between the DC internal resistance sequence, current, and individual cell voltage. To better understand this one-dimensional linear relationship, Figure 5 is a flowchart illustrating a method for calculating and predicting the open-circuit voltage sequence provided in this disclosure. As shown in Figure 5, the method includes the following steps.

[0117] Step 301: Multiply each DC internal resistance value in the DC internal resistance value sequence with the current to obtain a voltage sequence.

[0118] Step 302: Add each voltage value in the voltage sequence to the individual cell voltage to obtain the predicted open-circuit voltage sequence.

[0119] To better illustrate the above steps, please refer to formula (1) provided in the embodiments of this disclosure.

[0120] OCV = Vcell + I×DCR formula (1)

[0121] Where OCV represents open-circuit voltage, Vcell represents cell voltage, I represents current and is negative, and DCR is a variable of DC resistance within a preset resistance range.

[0122] To better understand formula (1), we can refer to the one-dimensional linear equation y = b + kx, where OCV is equivalent to the dependent variable y in the one-dimensional linear equation, Vcell is equivalent to the intercept b in the one-dimensional linear equation, I is equivalent to the slope k in the one-dimensional linear equation, and DCR is equivalent to the independent variable x in the one-dimensional linear equation. Therefore, according to the above formula (1), we can calculate the open-circuit voltage corresponding to each DC resistance value within the preset resistance range in sequence, and all open-circuit voltages form a predicted open-circuit voltage sequence.

[0123] Step 202: Based on the mapping relationship between the battery's open-circuit voltage and remaining power, determine the predicted remaining power corresponding to each of the predicted open-circuit voltage sequences in sequence, and all predicted remaining power constitutes a predicted remaining power sequence.

[0124] Since there is a mapping relationship between the battery's open-circuit voltage and remaining capacity, i.e., SOC = f(OCV), after calculating the preset OCV, the predicted SOC corresponding to each predicted OCV can be calculated sequentially according to SOC = f(OCV), and all predicted SOCs can form a predicted SOC sequence. For example, the predicted SOC corresponding to each predicted OCV can be found sequentially using a pre-obtained mapping table between SOC and OCV. Alternatively, the predicted SOC corresponding to each predicted OCV can be calculated sequentially according to the formula SOC = f(OCV). The specific method for calculating the predicted SOC is not disclosed and is not limited in this embodiment.

[0125] Step 203: Perform a one-dimensional linear fit between the ampere-hour sequence and the predicted remaining power sequence to obtain the prediction curve.

[0126] In practical applications, linear fitting requires setting the data sequences corresponding to the independent variables and the dependent variables. Since the independent variable of the true curve is usually the true OCV (i.e., the x-axis of the true curve records the true OCV data), and the dependent variable is usually the true SOC (i.e., the y-axis of the true curve records the true SOC data), to better reflect the difference between the true curve and the predicted curve, the ampere-hour series is determined as the independent variable of the predicted curve, making the ampere-hour series correspond to the true OCV; the predicted remaining power series is determined as the dependent variable of the predicted curve, making the predicted remaining power series correspond to the true SOC; then, a one-dimensional linear fit is performed on the ampere-hour series and the predicted SOC to obtain the predicted curve.

[0127] Through the calculation methods described in steps 201 to 203 above, a predicted curve can be obtained. This predicted curve serves as a reference curve for the true curve, further determining whether a one-dimensional linear relationship still exists between the true OCV and the true SOC in the true curve. If a one-dimensional linear relationship exists between the true OCV and the true SOC in the true curve, the battery is normal. If no one-dimensional linear relationship exists between the true OCV and the true SOC in the true curve, the battery has leakage, indicating a cell fault. Furthermore, this calculation method can be applied to each individual battery cell or cell within a battery pack to determine whether a battery cell or cell is faulty, enabling precise location of the faulty cell.

[0128] In some embodiments provided in this disclosure, in order to quantify the degree of matching between the predicted curve and the true curve, the following methods may be used, but are not limited to, as shown in Figure 6. Figure 6 is a flowchart illustrating a method for calculating the target error provided in an embodiment of this disclosure.

[0129] Step 401: Obtain multiple real value sequences from the real curve according to the preset interval size; one interval corresponds to one real value sequence.

[0130] In some embodiments provided in this disclosure, since the range of the true curve is large, the slope of the true curve in each different range may be different. Therefore, the true curve can be divided into multiple different intervals according to the preset interval size, and multiple true values ​​can be obtained from one interval. The true values ​​in the same interval form the true value sequence corresponding to that interval.

[0131] Step 402: According to the preset interval size, obtain multiple predicted value sequences from the prediction curve; wherein the number of the true value sequences is the same as the number of the predicted value sequences.

[0132] In some embodiments provided in this disclosure, since the range of the prediction curve is large, the slope of the prediction curve in each different range may be different. Therefore, the prediction curve can be divided into multiple different intervals according to the preset interval size, and multiple prediction values ​​can be obtained from one interval. The prediction values ​​of the same interval form the prediction value sequence corresponding to that interval.

[0133] Step 403: Invoke the error algorithm to calculate the error for each true value sequence and the corresponding predicted value sequence, and obtain the error corresponding to each true value sequence.

[0134] To better understand the error calculation process, Figure 7 is a flowchart of an error calculation method provided by an embodiment of this disclosure. As shown in Figure 7, the method includes the following steps.

[0135] Step 501: Determine the number of true values ​​corresponding to each true value sequence;

[0136] Step 502: In each true value sequence and its corresponding predicted value sequence, the absolute value of the difference between each true value and its corresponding predicted value is calculated to obtain the set of absolute values ​​corresponding to each true value sequence.

[0137] Step 503: Accumulate all absolute values ​​in each set of absolute values ​​to obtain the sum of absolute values ​​corresponding to each true value sequence;

[0138] Step 504: The ratio of the sum of the absolute values ​​corresponding to each true value sequence to the corresponding number of true values ​​is determined as the error corresponding to each true value sequence.

[0139] For a better explanation of the above steps, please refer to formula (2) provided in the embodiments of this disclosure.

[0140]

[0141] Where MAE represents the mean absolute error, n represents the number of true or predicted values, and X 真 X represents the true value. 预 This represents the predicted value.

[0142] Step 404: Determine the minimum error among the errors corresponding to each true value sequence as the target error.

[0143] After obtaining the error corresponding to each true value sequence, an optimization algorithm can be invoked to find the minimum error among the errors corresponding to each true value sequence; and the minimum error is determined as the target error. The optimization algorithm can employ, but is not limited to, enumeration methods.

[0144] The method provided in steps 401 to 404 above can be used to calculate the target error between the predicted curve and the true curve, thereby quantifying the degree of difference between the predicted curve and the true curve and making the battery fault detection results more accurate.

[0145] In summary, the embodiments disclosed herein achieve the following effects:

[0146] 1. A predicted curve is obtained by performing a one-dimensional linear fitting on the battery parameters. This predicted curve serves as a reference curve for the actual curve. Further analysis is conducted to determine whether a one-dimensional linear relationship still exists between the actual OCV and the actual SOC in the actual curve. If a one-dimensional linear relationship exists between the actual OCV and the actual SOC in the actual curve, the battery is normal. If no one-dimensional linear relationship exists, the battery exhibits leakage, indicating a cell fault. Furthermore, this calculation method can be applied to each individual battery cell or cell within a battery pack to determine if a cell or cell is faulty, enabling precise location of the faulty cell.

[0147] 2. By calculating errors, the degree of difference between the predicted curve and the actual curve can be quantified, making the battery fault detection results more accurate.

[0148] Corresponding to the battery fault detection method described above, this invention also proposes a battery fault detection device. Since the device embodiments of this invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to in the method embodiments described above, and will not be repeated here.

[0149] Figure 8 is a schematic diagram of a battery fault detection device provided in an embodiment of this disclosure. As shown in Figure 8, it includes: an acquisition unit 61, a first calculation unit 62, a second calculation unit 63, and a determination unit 64.

[0150] The acquisition unit 61 is used to acquire battery parameters in charging mode and acquire a true curve. The battery parameters include ampere-hours, and the true curve is used to characterize the one-dimensional linear relationship between the battery's actual remaining capacity and the battery's open-circuit voltage.

[0151] The first calculation unit 62 is used to perform one-dimensional linear fitting calculation on the battery parameters to obtain a prediction curve. The prediction curve is used to characterize the one-dimensional linear relationship between the ampere-hours and the predicted remaining battery capacity.

[0152] The second calculation unit 63 is used to call the error algorithm to calculate the error between the predicted curve and the true curve to obtain the target error, wherein the target error is the minimum error between the predicted curve and the true curve.

[0153] The determining unit 64 is used to determine that the battery is faulty when the target error exceeds the error threshold.

[0154] The battery fault detection device disclosed herein acquires battery parameters and a true curve in charging mode. The battery parameters include ampere-hours (Ah-hours). The true curve represents a one-dimensional linear relationship between the battery's actual remaining capacity and its open-circuit voltage. A one-dimensional linear fitting calculation is performed on the battery parameters to obtain a predicted curve, which represents a one-dimensional linear relationship between the Ah-hours and the predicted remaining capacity. An error algorithm is invoked to calculate the error between the predicted curve and the true curve, obtaining a target error, which is the minimum error between the predicted and true curves. If the target error exceeds an error threshold, the battery is determined to be faulty. This solution determines whether the battery has leakage by using the target error between the predicted and true curves. Since leakage is caused by cell faults, the presence of leakage can further confirm the presence of cell faults, rather than focusing on the specific cell fault type. This departs from traditional fault tree-based causal analysis methods, resulting in higher detection accuracy and enabling early identification and warning of faulty cells.

[0155] In some embodiments provided in this disclosure, the battery parameters further include a DC internal resistance sequence, current, and cell voltage;

[0156] Furthermore, in one possible implementation of this embodiment, as shown in FIG9, the first computing unit 62 includes:

[0157] The first calculation module 621 is used to call the open-circuit voltage calculation algorithm to perform one-dimensional linear calculations on the DC internal resistance sequence, the current, and the individual cell voltage to obtain the predicted open-circuit voltage sequence.

[0158] The first determining module 622 is used to determine the predicted remaining power corresponding to the predicted open circuit voltage sequence in sequence based on the mapping relationship between the battery's open circuit voltage and the remaining power, and all the predicted remaining power constitutes the predicted remaining power sequence.

[0159] The fitting module 623 is used to perform a one-dimensional linear fitting between the ampere-hour sequence and the predicted remaining power sequence to obtain the prediction curve.

[0160] Furthermore, in one possible implementation of this embodiment, the first calculation module 621 is used for:

[0161] Each DC internal resistance value in the DC internal resistance value sequence is multiplied by the current to obtain the voltage sequence;

[0162] The predicted open-circuit voltage sequence is obtained by summing each voltage value in the voltage sequence with the individual cell voltage.

[0163] Furthermore, in one possible implementation of this embodiment, the fitting module 623 is used for:

[0164] The ampere-hour sequence is determined as the independent variable;

[0165] The predicted remaining power sequence is determined as the dependent variable;

[0166] The prediction curve is obtained by performing a one-dimensional linear fit on the independent variable and the dependent variable.

[0167] Furthermore, in one possible implementation of this embodiment, as shown in FIG9, the second computing unit 63 includes:

[0168] The first acquisition module 631 is used to acquire multiple real value sequences from the real curve according to a preset interval size; one interval corresponds to one real value sequence.

[0169] The second acquisition module 632 is used to acquire multiple predicted value sequences from the prediction curve according to the preset interval size; wherein the number of the true value sequences is the same as the number of the predicted value sequences;

[0170] The second calculation module 633 is used to call the error algorithm to calculate the error for each true value sequence and the corresponding predicted value sequence, so as to obtain the error corresponding to each true value sequence.

[0171] The second determining module 634 is used to determine the minimum error among the errors corresponding to each true value sequence as the target error.

[0172] Furthermore, in one possible implementation of this embodiment, the second calculation module 633 is used for:

[0173] Determine the number of true values ​​corresponding to each of the true value sequences;

[0174] In each sequence of true values ​​and its corresponding sequence of predicted values, the absolute value of the difference between each true value and its corresponding predicted value is calculated to obtain the set of absolute values ​​corresponding to each sequence of true values.

[0175] The sum of all absolute values ​​in each set of absolute values ​​is calculated to obtain the sum of the absolute values ​​corresponding to each sequence of true values.

[0176] The ratio of the sum of the absolute values ​​corresponding to each true value sequence to the corresponding number of true values ​​is determined as the error corresponding to each true value sequence.

[0177] Furthermore, in one possible implementation of this embodiment, the second determining module 634 is used to:

[0178] The optimization algorithm is invoked to find the minimum error among the errors corresponding to each true value sequence;

[0179] The minimum value of the error is determined as the target error.

[0180] Furthermore, in one possible implementation of this embodiment, as shown in FIG9, the acquisition unit 61 includes:

[0181] The third determining module 611 is used to determine multiple preset resistance value ranges of the battery's DC internal resistance;

[0182] The third calculation module 612 is used to call the correlation coefficient algorithm to calculate the correlation coefficient between the actual remaining power and the open circuit voltage within each preset resistance range, and to obtain the correlation coefficient corresponding to each preset resistance range; the correlation coefficient is used to characterize the correlation between the actual remaining power and the open circuit voltage within the preset resistance range.

[0183] The fourth determining module 613 is used to determine the target correlation coefficient based on the comparison results between the correlation coefficient corresponding to each preset resistance range and the preset correlation coefficient threshold.

[0184] The fifth determining module 614 is used to determine the preset resistance range corresponding to the target correlation coefficient as the target resistance range;

[0185] The third acquisition module 615 is used to acquire the battery parameters and the actual curve of the battery in the charging state within the target resistance range.

[0186] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0187] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0188] Figure 10 illustrates a schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0189] As shown in Figure 10, device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 702 or a computer program loaded from storage unit 708 into RAM (Random Access Memory) 703. RAM 703 can also store various programs and data required for the operation of device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via bus 704. I / O (Input / Output) interface 705 is also connected to bus 704.

[0190] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0191] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as a battery fault detection method. For example, in some embodiments, the battery fault detection method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the aforementioned battery fault detection method by any other suitable means (e.g., by means of firmware).

[0192] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0193] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0194] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0195] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0196] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0197] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0198] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0199] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0200] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A battery fault detection method, characterized in that, include: The battery parameters and actual curves in charging mode are obtained. The battery parameters include the ampere-hours. The actual curve is used to characterize the one-dimensional linear relationship between the actual remaining capacity of the battery and the open-circuit voltage of the battery. The battery parameters are subjected to one-dimensional linear fitting calculation to obtain a predicted curve. The predicted curve is used to characterize the one-dimensional linear relationship between the ampere-hours and the predicted remaining capacity of the battery. An error algorithm is invoked to calculate the error between the predicted curve and the true curve, and a target error is obtained. The target error is the minimum error between the predicted curve and the true curve. If the target error exceeds the error threshold, the battery is determined to be faulty.

2. The method according to claim 1, characterized in that, The battery parameters also include a DC internal resistance sequence, current, and individual cell voltage. The step of performing a one-dimensional linear fitting calculation on the battery parameters to obtain a predicted curve includes: calling an open-circuit voltage calculation algorithm to perform a one-dimensional linear calculation on the DC internal resistance sequence, the current, and the individual cell voltage to obtain a predicted open-circuit voltage sequence; based on the mapping relationship between the battery's open-circuit voltage and remaining capacity, sequentially determining the predicted remaining capacity corresponding to each of the predicted open-circuit voltage sequences, with all predicted remaining capacity constituting a predicted remaining capacity sequence; and performing a one-dimensional linear fitting between the ampere-hour sequence and the predicted remaining capacity sequence to obtain the predicted curve.

3. The method according to claim 2, characterized in that, The method of calling the open-circuit voltage calculation algorithm to perform one-dimensional linear calculation on the DC internal resistance value sequence, the current, and the individual unit voltage to obtain the predicted open-circuit voltage sequence includes: multiplying each DC internal resistance value in the DC internal resistance value sequence by the current to obtain a voltage sequence; and summing each voltage value in the voltage sequence with the individual unit voltage to obtain the predicted open-circuit voltage sequence.

4. The method according to claim 2, characterized in that, The step of performing a one-dimensional linear fit between the ampere-hour sequence and the predicted remaining power sequence to obtain the prediction curve includes: determining the ampere-hour sequence as the independent variable; determining the predicted remaining power sequence as the dependent variable; and performing a one-dimensional linear fit between the independent variable and the dependent variable to obtain the prediction curve.

5. The method according to claim 1, characterized in that, The step of invoking the error algorithm to calculate the error between the predicted curve and the true curve to obtain the target error includes: obtaining multiple true value sequences from the true curve according to a preset interval size; one interval corresponds to one true value sequence; obtaining multiple predicted value sequences from the predicted curve according to the preset interval size; wherein the number of true value sequences is the same as the number of predicted value sequences; invoking the error algorithm to calculate the error for each true value sequence and the corresponding predicted value sequence respectively; and determining the minimum error among the errors corresponding to each true value sequence as the target error.

6. The method according to claim 5, characterized in that, The step of invoking the error algorithm to calculate the error for each true value sequence and its corresponding predicted value sequence includes: determining the number of true values ​​for each true value sequence; calculating the absolute value of the difference between each true value and its corresponding predicted value in each true value sequence and its corresponding predicted value sequence to obtain a set of absolute values ​​for each true value sequence; summing all the absolute values ​​in each set of absolute values ​​to obtain a sum of absolute values ​​for each true value sequence; and determining the ratio of the sum of absolute values ​​for each true value sequence to the number of true values ​​for that sequence as the error for each true value sequence.

7. The method according to claim 5, characterized in that, The step of determining the minimum error among the errors corresponding to each true value sequence as the target error includes: calling an optimization algorithm to find the minimum error among the errors corresponding to each true value sequence; and determining the minimum error as the target error.

8. The method according to claim 1, characterized in that, The process of acquiring battery parameters and actual curves in charging mode includes: determining multiple preset resistance ranges for the battery's DC internal resistance; calling a correlation coefficient algorithm to calculate the correlation coefficient between the actual remaining charge and open-circuit voltage within each preset resistance range, obtaining the correlation coefficient corresponding to each preset resistance range; the correlation coefficient is used to characterize the correlation between the actual remaining charge and open-circuit voltage within the preset resistance range; determining a target correlation coefficient based on the comparison result between the correlation coefficient corresponding to each preset resistance range and a preset correlation coefficient threshold; determining the preset resistance range corresponding to the target correlation coefficient as the target resistance range; and acquiring the battery parameters and actual curves in charging mode within the target resistance range.

9. A battery fault detection device, characterized in that, include: The acquisition unit is used to acquire battery parameters in charging mode and acquire a true curve. The battery parameters include ampere-hours, and the true curve is used to characterize the one-dimensional linear relationship between the battery's actual remaining capacity and the battery's open-circuit voltage. The first calculation unit is used to perform one-dimensional linear fitting calculation on the battery parameters to obtain a prediction curve, which is used to characterize the one-dimensional linear relationship between the ampere-hours and the predicted remaining battery capacity. The second calculation unit is used to call the error algorithm to calculate the error between the predicted curve and the true curve to obtain the target error, which is the minimum error between the predicted curve and the true curve. The determining unit is used to determine that the battery is faulty when the target error exceeds an error threshold.

10. A vehicle, characterized in that, The vehicle includes the battery fault detection device as described in claim 9.

11. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

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