Power battery performance early warning method and system, medium, program product and terminal

Abstract

The invention provides a power battery performance early warning method and system, a medium, a program product and a terminal, and the method comprises the steps: collecting the accumulated input electric quantity of a plurality of power batteries of the same type at a continuous sampling moment, and carrying out the interval segmentation processing based on a preset segmentation interval threshold value and an accumulated electric quantity threshold value; discrete instantaneous data is converted into a staged battery performance sampling interval, so that instantaneous fluctuation interference is effectively isolated, and the stability and reliability of anomaly detection are improved; an output energy compensation strategy is combined to correct the accumulative output electric quantity of a battery performance sampling interval termination point, the amplification effect of SOC accumulative errors on energy conversion efficiency calculation is eliminated, and the accuracy of energy conversion efficiency calculation is ensured; according to the energy conversion efficiency of each battery performance sampling interval of each power battery, an average efficiency reference curve of the same type of power batteries is constructed, through deviation comparison with the average efficiency reference curve, a progressive degradation trend is identified, and the early anomaly detection sensitivity is improved.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a method, system, medium, program product and terminal for early warning of power battery performance. Background Technology

[0002] With the widespread application of new energy vehicles and energy storage systems, the safety and reliability of power batteries, as core components, are of paramount importance. In existing technologies, anomaly detection in power batteries primarily relies on the Battery Management System (BMS) to monitor real-time parameters such as current, voltage, and temperature, and to issue alarms based on preset thresholds or fault diagnosis rules. However, this method has the following significant drawbacks:

[0003] (1) Instantaneous parameter fluctuations lead to frequent false alarms and missed alarms: During the charging and discharging process, the battery is affected by factors such as operating conditions and ambient temperature. The current, voltage and other parameters are prone to instantaneous abnormal fluctuations (such as voltage drop during low temperature start-up and current spike under pulse load). Traditional threshold alarm mechanisms lack dynamic adaptive capabilities, which can easily misjudge normal operating conditions as faults or miss hidden abnormalities, resulting in insufficient reliability.

[0004] (2) Progressive degradation is difficult to identify effectively: Progressive abnormalities such as slow increase in battery internal resistance and capacity decay usually do not trigger instantaneous threshold alarms, resulting in potential faults not being detected in time, affecting battery life and safety.

[0005] (3) Lack of full life cycle energy characteristics analysis: Existing methods focus more on short-term data and lack monitoring of long-term charge and discharge energy characteristics, efficiency changes and other trends of batteries, making it difficult to assess the health status of batteries.

[0006] (4) Imperfect SOC compensation algorithm: Traditional SOC (State of Charge) estimation is easily affected by factors such as temperature and aging, resulting in large deviations in energy calculation, which affects the accurate management and abnormal judgment of the battery.

[0007] (5) Low sensitivity of early anomaly detection: Traditional methods have a weak ability to identify early battery anomalies (such as micro short circuits and local overheating), and often only trigger alarms after the fault has worsened, which increases safety risks. Summary of the Invention

[0008] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a power battery performance early warning method, system, medium, program product and terminal to solve the technical problems in the prior art such as frequent false alarms and missed alarms caused by instantaneous parameter fluctuations, imperfect SOC compensation algorithm and low sensitivity of early anomaly detection.

[0009] To achieve the above and other related objectives, the first aspect of this application provides a power battery performance early warning method, comprising: collecting the cumulative input power of multiple power batteries at multiple consecutive sampling times; wherein, the power batteries are of the same type; performing interval segmentation processing on the cumulative input power of each power battery at each consecutive sampling time according to a preset segmentation interval threshold and a preset cumulative power threshold to obtain multiple battery performance sampling intervals for each power battery; calculating the energy conversion efficiency of each battery performance sampling interval for each power battery according to an output energy compensation strategy; constructing an average efficiency benchmark curve for power batteries of the same type based on the energy conversion efficiency of each battery performance sampling interval for each power battery; and performing anomaly detection on the performance trend of a target power battery of the same type based on the average efficiency benchmark curve for power batteries of the same type, so as to output a corresponding early warning signal based on the anomaly detection result.

[0010] In some embodiments of the first aspect of this application, the calculation method of the output energy compensation strategy includes:

[0011] W out_adj_n_i =W out_i_n_i +Q rated ×SOC _n_i ;

[0012] Among them, W out_adj_n_i W represents the cumulative output power after compensation and correction at the termination point of the performance sampling interval of the nth power battery for the i-th battery; out_i_n_i Q represents the cumulative output power corresponding to the termination point of the performance sampling interval of the nth power battery at the i-th battery; rated Indicates the rated capacity of the same type of power battery; SOC _n_i This represents the remaining charge at the termination point of the i-th battery performance sampling interval of the n-th power battery; i represents the battery performance sampling interval; and n represents the power battery.

[0013] In some embodiments of the first aspect of this application, the method for calculating the energy conversion efficiency of each battery performance sampling interval of each power battery includes:

[0014]

[0015] Where, η n-i W represents the energy conversion efficiency of the i-th battery performance sampling interval of the n-th power battery; out_adj_n_i W represents the cumulative output power after compensation and correction at the termination point of the performance sampling interval of the nth power battery for the i-th battery; out_adj_n_i-1 W represents the cumulative output capacity after compensation and correction at the termination point of the (i-1)th battery performance sampling interval of the nth power battery;in_n_i W represents the cumulative input charge corresponding to the termination point of the performance sampling interval of the nth power battery at the i-th battery; in_n_i-1 This represents the cumulative input power corresponding to the termination point of the (i-1)th battery performance sampling interval of the nth power battery; i represents the battery performance sampling interval; and n represents the power battery.

[0016] In some embodiments of the first aspect of this application, the method for constructing an average efficiency benchmark curve for power batteries of the same type includes: calculating the average efficiency benchmark of power batteries of the same type in each battery performance sampling interval based on the energy conversion efficiency of each battery performance sampling interval of each power battery; and constructing an average efficiency benchmark curve for power batteries of the same type based on the average efficiency benchmark of power batteries of the same type in each battery performance sampling interval.

[0017] In some embodiments of the first aspect of this application, the method of detecting anomalies in the performance trend of a target power battery of the same type based on the average efficiency benchmark curve of the same type of power battery, and outputting a corresponding warning signal based on the anomaly detection result, includes: calculating the efficiency residual of the target power battery of the same type in each battery performance sampling interval based on the average efficiency benchmark curve of the same type of power battery; detecting anomalies in the performance trend of the target power battery of the same type based on the efficiency residual of the target power battery of the same type in each battery performance sampling interval and based on a preset static tolerance threshold, and outputting a corresponding warning signal based on the anomaly detection result.

[0018] In some embodiments of the first aspect of this application, the method for calculating the efficiency residual of the target type of power battery in various battery performance sampling intervals includes:

[0019]

[0020] Where, Δη i ' represents the efficiency residual of the target type of power battery in the i-th battery performance sampling interval; η i 'Represents the energy conversion efficiency of the target type of power battery in the i-th battery performance sampling interval; This represents the benchmark average efficiency of the same type of power battery in the i-th battery performance sampling interval.

[0021] To achieve the above and other related objectives, a second aspect of this application provides a power battery performance early warning system, comprising: a data acquisition module for acquiring the cumulative input power of multiple power batteries at multiple consecutive sampling times; wherein the power batteries are of the same type; a dynamic segmentation module for performing interval segmentation processing on the cumulative input power of each power battery at each consecutive sampling time according to a preset segmentation interval threshold and a preset cumulative power threshold, so as to obtain multiple battery performance sampling intervals for each power battery; a segmented efficiency calculation module for calculating the energy conversion efficiency of each battery performance sampling interval of each power battery according to an output energy compensation strategy; a benchmark curve construction module for constructing an average efficiency benchmark curve of the same type of power battery based on the energy conversion efficiency of each battery performance sampling interval of each power battery; and an anomaly detection module for performing anomaly detection on the performance trend of a target power battery of the same type according to the average efficiency benchmark curve of the same type of power battery, so as to output a corresponding early warning signal based on the anomaly detection result.

[0022] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power battery performance warning method as described above.

[0023] To achieve the above and other related objectives, a fourth aspect of this application provides a computer program product comprising computer program code, which, when executed on a computer, causes the computer to implement the power battery performance early warning method as described above.

[0024] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the power battery performance warning method as described above.

[0025] As described above, the power battery performance early warning method, system, medium, program product, and terminal of this application have the following characteristics:

[0026] Beneficial effects:

[0027] (1) By collecting the cumulative input power of multiple power batteries of the same type at continuous sampling time, and performing interval segmentation based on the preset segmentation interval threshold and cumulative power threshold, the discrete instantaneous data is transformed into a phased battery performance sampling interval, which expands the sample coverage, avoids the interference of single-point instantaneous fluctuations on the overall judgment, and effectively isolates the interference of initial data fluctuations such as battery start-up and shutdown and instantaneous load change on battery performance trend analysis. While ensuring data continuity, the segmented accumulation strategy can improve the stability and reliability of anomaly detection and avoid false alarms and missed alarms.

[0028] (2) By using an output energy compensation strategy, the cumulative output power of each battery performance sampling interval at its termination point is compensated and corrected. This avoids the underestimation (during charging) or overestimation (during discharging) of output energy caused by fluctuations in battery energy storage state in traditional methods. It also eliminates the amplification effect of SOC cumulative error on energy conversion efficiency calculation, ensuring the accuracy of energy conversion efficiency calculation. A health assessment model with energy conversion efficiency as the core is constructed, breaking through the limitations of traditional methods that rely on instantaneous parameters (current, voltage). It directly reflects the dynamic characteristics of energy conversion inside the battery and can track the real performance changes of the power battery throughout its entire life cycle. This has important value for battery state assessment, energy efficiency optimization, and life prediction.

[0029] (3) Based on the energy conversion efficiency of each battery performance sampling range of each power battery, an average efficiency benchmark curve of the same type of power battery is constructed, and a dynamic scale of normal performance of the same type of power battery is established. By comparing the deviation of the energy conversion efficiency of the target power battery of the same type from the average efficiency benchmark curve, the gradual degradation trend (such as the efficiency decline trend caused by capacity decay and increased internal resistance) can be identified. This dual mechanism of "compensation correction + benchmark comparison" improves the sensitivity of early anomaly detection. Attached Figure Description

[0030] Figure 1 The diagram shown is a flowchart illustrating a power battery performance early warning method according to an embodiment of this application.

[0031] Figure 2 The diagram shows a fitted curve of the energy conversion efficiency of a power battery without interval segmentation.

[0032] Figure 3 The diagram shows a fitted curve of the energy conversion efficiency of the power battery after interval segmentation.

[0033] Figure 4 The diagram shows a fitted curve of the energy conversion efficiency of the power battery without compensation correction.

[0034] Figure 5 The diagram shows a fitted curve of the energy conversion efficiency of the power battery after compensation and correction.

[0035] Figure 6 This is a schematic diagram showing the comparison between the energy conversion efficiency curve of the power battery and the average efficiency benchmark curve in an embodiment of the present invention.

[0036] Figure 7 The diagram shown is a schematic block diagram of a power battery performance early warning system according to an embodiment of this application.

[0037] Figure 8The diagram shown is a structural schematic of an electronic terminal according to an embodiment of this application. Detailed Implementation

[0038] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0039] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0040] <1> SOC (State of Charge): The state of charge indicates the percentage of the battery's current remaining charge relative to its maximum usable capacity, ranging from 0% to 100% (0% indicates fully discharged, 100% indicates fully charged).

[0041] <2> BMS (Battery Management System): An electronic system used to monitor, control, and manage the operating status of a battery pack. It collects key parameters such as voltage, current, and temperature in real time to achieve functions such as battery protection, state estimation, energy management, and data communication.

[0042] In existing technologies, abnormal detection of power batteries mainly relies on the battery management system (BMS) to monitor real-time parameters such as current, voltage, and temperature, and to issue alarms based on preset thresholds or fault diagnosis rules. However, this method has the following significant drawbacks:

[0043] (1) Instantaneous parameter fluctuations lead to frequent false alarms and missed alarms: During the charging and discharging process, the battery is affected by factors such as operating conditions and ambient temperature. The current, voltage and other parameters are prone to instantaneous abnormal fluctuations (such as voltage drop during low temperature start-up and current spike under pulse load). Traditional threshold alarm mechanisms lack dynamic adaptive capabilities, which can easily misjudge normal operating conditions as faults or miss hidden abnormalities, resulting in insufficient reliability.

[0044] (2) Progressive degradation is difficult to identify effectively: Progressive abnormalities such as slow increase in battery internal resistance and capacity decay usually do not trigger instantaneous threshold alarms, resulting in potential faults not being detected in time, affecting battery life and safety.

[0045] (3) Lack of full life cycle energy characteristics analysis: Existing methods focus more on short-term data and lack monitoring of long-term charge and discharge energy characteristics, efficiency changes and other trends of batteries, making it difficult to assess the health status of batteries.

[0046] (4) Imperfect SOC compensation algorithm: Traditional SOC (State of Charge) estimation is easily affected by factors such as temperature and aging, resulting in large deviations in energy calculation, which affects the accurate management and abnormal judgment of the battery.

[0047] (5) Low sensitivity of early anomaly detection: Traditional methods have a weak ability to identify early battery anomalies (such as micro short circuits and local overheating), and often only trigger alarms after the fault has worsened, which increases safety risks.

[0048] To address the problems in the prior art, this invention provides a power battery performance early warning method, system, medium, program product, and terminal, which solves the technical problems in the prior art such as frequent false alarms and missed alarms caused by instantaneous parameter fluctuations, imperfect SOC compensation algorithms, and low sensitivity of early anomaly detection.

[0049] To facilitate understanding of the embodiments of this application, firstly, in conjunction with Figure 1 Detailed explanation. Figure 1 A flowchart illustrating the power battery performance early warning method in this embodiment of the invention is shown. The power battery performance early warning method in this embodiment mainly includes the following steps:

[0050] S101: Collect the cumulative input power of multiple power batteries at multiple consecutive sampling times; wherein, all power batteries are of the same type.

[0051] In this embodiment, the sampling time can be 1 minute or at the end of a complete charge-discharge cycle. The cumulative input power, cumulative output power, and remaining power at the current sampling time are recorded, and the recorded data is stored in a local or cloud database. All power batteries are of the same type, meaning they are batteries with the same cell model and system model.

[0052] In this embodiment, the cumulative input power is the total electrical energy absorbed by the power battery from an external source (such as a charging station) during the charging process. The cumulative output power is the total electrical energy released by the power battery to a load (such as an electric vehicle) during the discharging process. State of Charge (SOC) represents the percentage of the battery's current remaining capacity relative to its maximum usable capacity.

[0053] S102: Based on the preset segmentation interval threshold and the preset cumulative power threshold, the cumulative input power of each power battery at each continuous sampling time is segmented into intervals to obtain multiple battery performance sampling intervals for each power battery.

[0054] In this embodiment, based on a preset segmentation interval threshold and a preset cumulative power threshold, the cumulative input power of each power battery at each continuous sampling time is segmented into intervals to obtain multiple battery performance sampling intervals for each power battery, and the cumulative input power, cumulative output power, and remaining power corresponding to the termination point of each battery performance sampling interval of each power battery are obtained.

[0055] In this embodiment, the preset segmentation interval threshold includes a first segmentation interval threshold and a second segmentation interval threshold, wherein the second segmentation interval threshold is less than the first segmentation interval threshold; wherein, the method of performing interval segmentation processing on the cumulative input power of each power battery at each continuous sampling time according to the preset segmentation interval threshold and the preset cumulative power threshold to obtain multiple battery performance sampling intervals for each power battery includes:

[0056] (1) When the cumulative input power is less than or equal to the preset cumulative power threshold, the cumulative input power of the power battery at multiple consecutive sampling times is segmented based on the first segmentation interval threshold.

[0057] (2) When the cumulative input power is greater than the preset cumulative power threshold, the cumulative input power of the power battery at multiple consecutive sampling times is segmented based on the second segmentation interval threshold to obtain multiple battery performance sampling intervals for each power battery.

[0058] In this embodiment, the above-mentioned interval segmentation processing strategy is adopted for each power battery of the same type. For example, the first segmentation interval threshold is 50,000 kWh, the second segmentation interval threshold is 10,000 kWh, and the preset cumulative energy threshold is 200,000 kWh. When the cumulative input energy is ≤200,000 kWh, a battery performance sampling interval is divided every 50,000 kWh. When the cumulative input energy is >200,000 kWh, a battery performance sampling interval is divided every 10,000 kWh, thus obtaining multiple battery performance sampling intervals with different energy spans. When the cumulative input energy = 500,000 kWh, the multiple battery performance sampling intervals are: 0~5, 5~10, 10~15, 15~20, 20~21, 21~22, ..., 49~50. The set of cumulative input energy corresponding to the termination point of each battery performance sampling interval = {50,000, 100, 150, 200, 210, 220, ..., 500,000 kWh}.

[0059] In this embodiment, the first segmented interval threshold is suitable for the initial stage where battery performance fluctuates significantly, while the second segmented interval threshold is suitable for the later stage where fine monitoring is required, in order to capture the inflection point of battery degradation in the later stage. Each battery performance sampling interval contains a complete energy dataset from the start point to the end point of the interval. The complete energy dataset includes multiple sampling times, the cumulative input energy at the corresponding sampling time, the cumulative output energy at the corresponding sampling time, and the remaining energy at the corresponding sampling time.

[0060] like Figure 2 The diagram shows a fitted curve of the energy conversion efficiency of a power battery without interval segmentation. Figure 3 The diagram shows a fitted curve of the energy conversion efficiency of the power battery after segmentation. Figure 2 As shown, when the power battery is not segmented into intervals, the raw data is affected by instantaneous operating conditions (such as start-stop and sudden load changes), resulting in a sharp upward fluctuation in the initial stage, followed by a volatile downward trend. The trend is masked by "noise," making it difficult to identify the true performance pattern. Figure 3 As shown, by setting a preset segmentation interval threshold, the data is divided into multiple battery performance sampling intervals. Each battery performance sampling interval corresponds to a specific stage of the battery life cycle, effectively filtering out the interference of short-term fluctuations.

[0061] It is worth noting that the aging process of power batteries exhibits non-linear characteristics, with slow and fluctuating degradation in the early stages, and accelerated and potentially abrupt degradation in the later stages. Traditional equal-interval sampling (such as fixed charge-discharge cycles or fixed energy intervals) cannot adapt to this characteristic. This leads to significant efficiency fluctuations within densely sampled intervals during early oversampling, masking the true degradation trend, and sparse sampling during later undersampling, which can easily miss inflection points. In contrast, this application uses a larger interval threshold (such as 50,000 kWh) to quickly aggregate data in the initial stage (low charge stage), reducing data processing volume, effectively suppressing interference from data fluctuations during the battery activation period, and avoiding misjudgments caused by oversensitivity. In the later stage (high charge stage), a finer interval threshold (such as 10,000 kWh) is switched to significantly improve data resolution, which is beneficial for capturing efficiency degradation inflection points and subtle performance degradation trends.

[0062] S103: Calculate the energy conversion efficiency of each power battery in each battery performance sampling range according to the output energy compensation strategy.

[0063] In this embodiment, the calculation method of the output energy compensation strategy includes:

[0064] W out_adj_n_i =W out_i_n_i +Q rated ×SOC _n_i Formula (1)

[0065] Among them, Wout_adj_n_i W represents the cumulative output power after compensation and correction at the termination point of the performance sampling interval of the nth power battery for the i-th battery; out_i_n_i Q represents the cumulative output power corresponding to the termination point of the performance sampling interval of the nth power battery at the i-th battery; rated Indicates the rated capacity of the same type of power battery; SOC _n_i This represents the remaining charge at the termination point of the i-th battery performance sampling interval of the n-th power battery; i represents the battery performance sampling interval; and n represents the power battery.

[0066] In this embodiment, after segmenting the cumulative input power of various power batteries of the same type into intervals, the cumulative input power, cumulative output power, and remaining power corresponding to the termination point of each battery performance sampling interval of each power battery are obtained. Based on the cumulative output power and remaining power corresponding to the termination point of each battery performance sampling interval of each power battery, compensation and correction are performed on the cumulative output power corresponding to the termination point of each battery performance sampling interval of each power battery.

[0067] In this embodiment, for example, there are N power batteries, namely power battery 1, power battery 2, power battery 3, ..., power battery N. Since the cumulative input power of each power battery at the same sampling time is basically the same, but the remaining power of each power battery at the same sampling time is different, the cumulative output power of each power battery at the same sampling time will deviate. Therefore, the cumulative input power of the N power batteries of the same type (power battery 1, power battery 2, power battery 3, ..., power battery N) is divided into intervals. Each power battery is divided into M battery performance sampling intervals, namely battery performance sampling interval 1, battery performance sampling interval 2, battery performance sampling interval 3, ..., battery performance sampling interval M. The cumulative input power of all power batteries is basically the same in each battery performance sampling interval, but the cumulative output power is different.

[0068] In this embodiment, for the termination point of each battery performance sampling interval, the cumulative output power and remaining power of each power battery are recorded to compensate and correct the cumulative output power. For example, for a battery performance sampling interval with a cumulative input power of 50,000 to 100,000 kWh, the cumulative output power and remaining power of the nth power battery at the termination point of this battery performance sampling interval are recorded to compensate and correct the cumulative output power. That is, when the cumulative input power is 100,000 kWh, a corresponding sampling time is identified, and the cumulative output power at that sampling time is obtained and compensated and corrected. Wherein, SOC... _n_i ∈[0,1].

[0069] It is worth noting that traditional energy conversion efficiency calculation methods divide the cumulative output energy by the cumulative input energy, ignoring the unreleased stored energy (i.e., the energy corresponding to the State of Charge) that exists in the battery throughout the charge-discharge cycle. This results in some input energy being stored in the battery during charging (SOC increases) and not being included in the output energy, leading to an underestimation of output energy. During discharging, the released energy includes historical stored energy (SOC decreases), causing an overestimation of output energy. This application, however, compensates for and corrects the cumulative output energy at the termination point of each battery performance sampling interval. This allows for dynamic tracking of real-time changes in the power battery's energy storage, avoiding the underestimation (during charging) or overestimation (during discharging) of output energy caused by fluctuations in battery energy storage state, as in traditional methods. This ensures that the calculated energy conversion efficiency accurately reflects the actual energy conversion process, avoids the accumulation of stage-specific energy errors, and improves the accuracy and stability of energy conversion efficiency calculation. It can track the real performance changes of the power battery throughout its entire life cycle, which is of significant value for battery state assessment, energy efficiency optimization, and lifespan prediction.

[0070] In this embodiment, the method for calculating the energy conversion efficiency of each power battery in each battery performance sampling interval includes:

[0071]

[0072] Where, η n-i W represents the energy conversion efficiency of the i-th battery performance sampling interval of the n-th power battery; out_adj_n_i W represents the cumulative output power after compensation and correction at the termination point of the performance sampling interval of the nth power battery for the i-th battery; out_adj_n_i-1 W represents the cumulative output capacity after compensation and correction at the termination point of the (i-1)th battery performance sampling interval of the nth power battery; in_n_i W represents the cumulative input charge corresponding to the termination point of the performance sampling interval of the nth power battery at the i-th battery; in_n_i-1 This represents the cumulative input power corresponding to the termination point of the (i-1)th battery performance sampling interval of the nth power battery; i represents the battery performance sampling interval; and n represents the power battery.

[0073] In this embodiment, for the nth power battery, the energy conversion efficiency of the i-th battery performance sampling interval of the nth power battery is calculated based on the cumulative input power corresponding to the termination point of two adjacent battery performance sampling intervals and the cumulative output power after compensation and correction. For example, the multiple battery performance sampling intervals divided for each power battery are: 0-5, 5-10, 10-15, 15-20, 20-21, 21-22, ..., 49-50. The energy conversion efficiency of the second battery performance sampling interval of the first power battery is calculated, i.e., the energy conversion efficiency of the battery performance sampling interval with a cumulative input power of 50,000 to 100,000 kWh. The cumulative output power after compensation and correction corresponding to the termination point of 100,000 kWh is 80,000 kWh. The battery performance sampling interval with a cumulative input power of 0 to 50,000 kWh, i.e., the first battery performance sampling interval, has a cumulative output power after compensation and correction corresponding to the termination point of 50,000 kWh of 40,000 kWh. Therefore, the energy conversion efficiency of the second battery performance sampling interval of the first power battery is calculated.

[0074] In this embodiment, for battery performance sampling interval i, the energy conversion efficiency of the i-th battery performance sampling interval for each power battery is calculated, which is {η 1-i η 2-i η 3-i , ...η n-i …,η N-i By calculating the energy conversion efficiency of the power battery in different battery performance sampling ranges, the efficiency changes of the power battery in different charging and discharging stages can be dynamically monitored. If the efficiency in a certain range drops significantly, it may indicate battery aging, material degradation, or system failure, facilitating timely maintenance.

[0075] In this embodiment, as Figure 4 As shown, a schematic diagram of the fitted curve of the energy conversion efficiency of the power battery without compensation is presented. Figure 5 The diagram shows a fitted curve of the energy conversion efficiency of the power battery after compensation and correction. Figure 4 As shown, the fitted curve of the uncompensated energy conversion efficiency fluctuates drastically with increasing input energy, especially in the later stages, exhibiting a precipitous drop. This indicates that the original data is severely affected by random noise (such as temperature fluctuations and sudden load changes). Such fluctuations can cause the system to mistakenly identify efficiency fluctuations under normal operating conditions as abnormal, increasing ineffective maintenance costs. Figure 5 As shown, the fitted curve of the energy conversion efficiency after compensation and correction has a significantly reduced overall fluctuation. The SOC compensation and correction effectively smooths out short-term noise interference, improves the stability of energy conversion, and reduces invalid warnings.

[0076] S104: Based on the energy conversion efficiency of each power battery in each battery performance sampling range, construct the average efficiency benchmark curve of the same type of power battery.

[0077] In this embodiment, the method for constructing the average efficiency benchmark curve of the same type of power battery includes:

[0078] (1) Calculate the average efficiency benchmark of the same type of power battery in each battery performance sampling interval based on the energy conversion efficiency of each battery performance sampling interval.

[0079] (2) Based on the average efficiency benchmark of the same type of power battery in each battery performance sampling range, construct the average efficiency benchmark curve of the same type of power battery.

[0080] In this embodiment, the energy conversion efficiency of each power battery in the i-th battery performance sampling interval is respectively {η 1-i η 2-i η 3-i , ...η n-i …,η N-i The calculation method for the average efficiency benchmark of the same type of power battery in the i-th battery performance sampling interval includes:

[0081]

[0082] in, η represents the baseline average efficiency of similar power batteries in the i-th battery performance sampling interval; N represents the number of power batteries; η represents the number of power batteries. n-i This represents the energy conversion efficiency of the i-th battery performance sampling interval of the n-th power battery; i represents the battery performance sampling interval; and n represents the power battery.

[0083] In this embodiment, as Figure 6 The diagram illustrates a comparison between the energy conversion efficiency curve and the average efficiency benchmark curve of a power battery in an embodiment of the present invention. The solid line represents the energy conversion efficiency curve of the power battery, and the dashed line represents the average efficiency benchmark curve. An average efficiency benchmark curve for the same type of power battery is constructed by fitting the average efficiency benchmark values ​​across various battery performance sampling intervals. Figure 6 As shown, when the energy conversion efficiency of the same type of power battery is lower than the average efficiency benchmark in the corresponding battery performance sampling range, it indicates that the power battery has begun to show abnormalities. From the subsequent data, the abnormalities of the power battery are gradually expanding.

[0084] S105: Based on the average efficiency benchmark curve of similar power batteries, perform anomaly detection on the performance trend of the target similar power battery, and output the corresponding warning signal based on the anomaly detection results.

[0085] In this embodiment, the method of detecting anomalies in the performance trend of the target power battery of the same type based on the average efficiency benchmark curve of similar power batteries, and outputting corresponding early warning signals based on the anomaly detection results, includes:

[0086] (1) Based on the average efficiency benchmark curve of the same type of power battery, calculate the efficiency residual of the target power battery of the same type in each battery performance sampling interval.

[0087] (2) Based on the efficiency residual of the target power battery of the same type in each battery performance sampling range, and based on the preset static tolerance threshold, perform anomaly detection on the performance trend of the target power battery of the same type, and output the corresponding warning signal according to the anomaly detection result.

[0088] In this embodiment, the abnormal state of the target power battery of the same type can be determined based on the average efficiency benchmark curve of the same type of power battery. The target power battery of the same type is divided into battery performance sampling intervals according to a preset segmentation interval threshold and a preset cumulative charge threshold. Based on the output energy compensation strategy, the energy conversion efficiency of the target power battery of the same type in each battery performance sampling interval is calculated. Based on the energy conversion efficiency of the target power battery of the same type in each battery performance sampling interval, the efficiency residual of the target power battery of the same type in each battery performance sampling interval is calculated.

[0089] In this embodiment, the method for calculating the efficiency residual of the target type of power battery in each battery performance sampling interval includes:

[0090]

[0091] Where, Δη i ' represents the efficiency residual of the target type of power battery in the i-th battery performance sampling interval; η i 'Represents the energy conversion efficiency of the target type of power battery in the i-th battery performance sampling interval; This represents the benchmark average efficiency of the same type of power battery in the i-th battery performance sampling interval.

[0092] In this embodiment, a preset static tolerance threshold is set for each battery performance sampling interval. If the absolute value of the efficiency residual of the target similar power battery in the i-th battery performance sampling interval is greater than the preset static tolerance threshold of the i-th battery performance sampling interval, i.e., |Δη i '|>∈ i ,∈ iThis represents the preset static tolerance threshold for the i-th battery performance sampling interval, which is used to determine if the target similar power battery has an efficiency anomaly in the i-th battery performance sampling interval. If the absolute value of the efficiency residual of the target similar power battery in the i-th battery performance sampling interval is less than or equal to the preset static tolerance threshold for the i-th battery performance sampling interval, i.e., |Δη i '|≤∈ i The target type of power battery is determined to have normal efficiency in the i-th battery performance sampling interval.

[0093] In this embodiment, the preset static tolerance threshold ∈i for the i-th battery performance sampling interval is set based on statistical analysis. i =k·σ i , where σ i Let represent the efficiency standard deviation of the i-th battery performance sampling interval, and k represent an empirical factor used to amplify the standard deviation and determine the sensitivity of judging anomalies. Usually, k = 1.96 is taken, which corresponds to the 95% confidence interval under a normal distribution.

[0094] In this embodiment, by setting different preset static tolerance thresholds in each battery performance sampling interval, abnormal characteristics of different battery degradation stages can be matched to ensure that the threshold mechanism remains effective in complex environments.

[0095] It is worth noting that the power battery performance early warning method of this application has the following advantages:

[0096] (1) By collecting the cumulative input power of multiple power batteries of the same type at continuous sampling time, and performing interval segmentation based on the preset segmentation interval threshold and cumulative power threshold, the discrete instantaneous data is transformed into a phased battery performance sampling interval, which expands the sample coverage, avoids the interference of single-point instantaneous fluctuations on the overall judgment, and effectively isolates the interference of initial data fluctuations such as battery start-up and shutdown and instantaneous load change on battery performance trend analysis. While ensuring data continuity, the segmented accumulation strategy can improve the stability and reliability of anomaly detection and avoid false alarms and missed alarms.

[0097] (2) By using an output energy compensation strategy, the cumulative output power of each battery performance sampling interval at its termination point is compensated and corrected. This avoids the underestimation (during charging) or overestimation (during discharging) of output energy caused by fluctuations in battery energy storage state in traditional methods. It also eliminates the amplification effect of SOC cumulative error on energy conversion efficiency calculation, ensuring the accuracy of energy conversion efficiency calculation. A health assessment model with energy conversion efficiency as the core is constructed, breaking through the limitations of traditional methods that rely on instantaneous parameters (current, voltage). It directly reflects the dynamic characteristics of energy conversion inside the battery and can track the real performance changes of the power battery throughout its entire life cycle. This has important value for battery state assessment, energy efficiency optimization, and life prediction.

[0098] (3) Based on the energy conversion efficiency of each battery performance sampling range of each power battery, an average efficiency benchmark curve of the same type of power battery is constructed, and a dynamic scale of normal performance of the same type of power battery is established. By comparing the deviation of the energy conversion efficiency of the target power battery of the same type from the average efficiency benchmark curve, the gradual degradation trend (such as the efficiency decline trend caused by capacity decay and increased internal resistance) can be identified. This dual mechanism of "compensation correction + benchmark comparison" improves the sensitivity of early anomaly detection.

[0099] The power battery performance early warning method of this application can be embedded in the following platforms for implementation:

[0100] (1) Vehicle-side embedded BMS controller: a low-power microcontroller is embedded and the above algorithm is deployed.

[0101] (2) Cloud data analysis platform: Upload efficiency data to the cloud for batch processing and trend modeling.

[0102] (3) Remote monitoring and maintenance system: receives abnormal alarms in real time and automatically generates health assessment reports.

[0103] In the embodiments of this application, terms such as "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, the first segmentation interval threshold and the second segmentation interval threshold are only used to distinguish different segmentation interval thresholds and do not limit their order. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or execution order, and that terms such as "first" and "second" do not necessarily imply that they are different.

[0104] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0105] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0106] Figure 7 This is a schematic block diagram of the power battery performance early warning system provided in an embodiment of this application. Figure 7 As shown, the power battery performance early warning system 700 includes:

[0107] The data acquisition module 701 is used to collect the cumulative input power of multiple power batteries at multiple consecutive sampling times; wherein, all power batteries are of the same type.

[0108] The dynamic segmentation module 702 is used to perform interval segmentation processing on the cumulative input power of each power battery at each continuous sampling time according to the preset segmentation interval threshold and the preset cumulative power threshold, so as to obtain multiple battery performance sampling intervals for each power battery.

[0109] The segmented efficiency calculation module 703 is used to calculate the energy conversion efficiency of each power battery in each battery performance sampling range according to the output energy compensation strategy.

[0110] The baseline curve construction module 704 is used to construct an average efficiency baseline curve for the same type of power battery based on the energy conversion efficiency of each battery performance sampling range of each power battery.

[0111] The anomaly detection module 705 performs anomaly detection on the performance trend of the target power battery of the same type based on the average efficiency benchmark curve of the same type of power battery, and outputs a corresponding warning signal based on the anomaly detection result.

[0112] It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0113] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0114] Figure 8 This is a schematic block diagram of an electronic terminal provided in an embodiment of this application. The electronic terminal includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the power battery performance warning method described above. Figure 8 As shown, the electronic terminal 800 includes at least one processor 801, a memory 802, at least one network interface 803, and a user interface 805. The various components in the device are coupled together via a bus system 804. It is understood that the bus system 804 is used to implement communication between these components. In addition to a data bus, the bus system 804 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 8 The general will label all buses as bus systems.

[0115] The user interface 805 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0116] It is understood that memory 802 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0117] In this embodiment of the invention, the memory 802 is used to store various types of data to support the operation of the electronic terminal 800. Examples of this data include: any executable program for operation on the electronic terminal 800, such as the operating system 8021 and application program 8022; the operating system 8021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 8022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The implementation of the power battery performance warning method provided in this embodiment of the invention can be included in the application program 8022.

[0118] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by processor 801. Processor 801 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 801 or by instructions in software form. The processor 801 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 801 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 801 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0119] In an exemplary embodiment, the electronic terminal 800 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to perform the aforementioned method.

[0120] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to implement the power battery performance early warning method as described above.

[0121] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the power battery performance early warning method as described above.

[0122] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0123] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0124] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0128] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs, DVDs), or semiconductor media (e.g., solid-state disks, SSDs, etc.).

[0129] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0131] In summary, this application provides a method, system, medium, program product, and terminal for early warning of power battery performance. By collecting the cumulative input charge of multiple similar power batteries at continuous sampling times, and performing interval segmentation based on preset segmentation interval thresholds and cumulative charge thresholds, discrete instantaneous data is transformed into staged battery performance sampling intervals. This effectively isolates instantaneous fluctuation interference and improves the stability and reliability of anomaly detection. Combined with an output energy compensation strategy, the cumulative output charge at the end point of the battery performance sampling interval is corrected, eliminating the amplification effect of SOC cumulative error on energy conversion efficiency calculation and ensuring the accuracy of energy conversion efficiency calculation. Based on the energy conversion efficiency of each battery performance sampling interval of each power battery, an average efficiency benchmark curve for similar power batteries is constructed. By comparing the deviation from the average efficiency benchmark curve, progressive degradation trends are identified, improving the sensitivity of early anomaly detection. Therefore, this application effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0132] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for early warning of power battery performance, characterized in that, include: Collect the cumulative input power of multiple power batteries at multiple consecutive sampling times; wherein, all power batteries are of the same type; Based on the preset segmentation interval threshold and the preset cumulative power threshold, the cumulative input power of each power battery at each continuous sampling time is segmented into intervals to obtain multiple battery performance sampling intervals for each power battery. Based on the output energy compensation strategy, the energy conversion efficiency of each power battery in each battery performance sampling range is calculated; Based on the energy conversion efficiency of each power battery in each battery performance sampling range, an average efficiency benchmark curve for the same type of power battery is constructed. Based on the average efficiency benchmark curve of similar power batteries, anomaly detection is performed on the performance trend of the target similar power battery, and corresponding early warning signals are output based on the anomaly detection results.

2. The power battery performance early warning method according to claim 1, characterized in that, The calculation method for the output energy compensation strategy includes: W out_adj_n_i =W out_i_n_i +Q rated ×SOC _n_i ; Among them, W out_adj_n_i W represents the cumulative output power after compensation and correction at the termination point of the performance sampling interval of the nth power battery for the i-th battery; out_i_n_i Q represents the cumulative output capacity corresponding to the termination point of the performance sampling interval of the nth power battery at the i-th battery; rated Indicates the rated capacity of the same type of power battery; SOC _n_i This represents the remaining charge at the termination point of the i-th battery performance sampling interval of the n-th power battery; i represents the battery performance sampling interval; and n represents the power battery.

3. The power battery performance early warning method according to claim 2, characterized in that, Methods for calculating the energy conversion efficiency of each power battery across different battery performance sampling intervals include: Where, η n-i This represents the energy conversion efficiency of the i-th battery performance sampling interval of the n-th power battery; W out_adj_n_i W represents the cumulative output power after compensation and correction at the termination point of the performance sampling interval of the nth power battery for the i-th battery; out_adj_n_i-1 W represents the cumulative output capacity after compensation and correction at the termination point of the (i-1)th battery performance sampling interval of the nth power battery; in_n_i W represents the cumulative input charge corresponding to the termination point of the performance sampling interval of the nth power battery at the i-th battery; in_n_i-1 This represents the cumulative input power corresponding to the termination point of the (i-1)th battery performance sampling interval of the nth power battery; i represents the battery performance sampling interval; and n represents the power battery.

4. The power battery performance early warning method according to claim 1, characterized in that, Methods for constructing average efficiency benchmark curves for similar power batteries include: Based on the energy conversion efficiency of each power battery in each battery performance sampling range, calculate the average efficiency benchmark of the same type of power battery in each battery performance sampling range. Based on the average efficiency benchmark of the same type of power battery in each battery performance sampling range, an average efficiency benchmark curve of the same type of power battery is constructed.

5. The power battery performance early warning method according to claim 1, characterized in that, Based on the average efficiency benchmark curve of similar power batteries, anomaly detection is performed on the performance trend of the target similar power battery, and corresponding early warning signals are output based on the anomaly detection results. The methods include: Based on the average efficiency benchmark curve of the same type of power battery, calculate the efficiency residual of the target power battery of the same type in each battery performance sampling interval. Based on the efficiency residuals of the target type of power battery in each battery performance sampling range, and based on the preset static tolerance threshold, anomaly detection is performed on the performance trend of the target type of power battery, and corresponding early warning signals are output according to the anomaly detection results.

6. The power battery performance early warning method according to claim 5, characterized in that, Methods for calculating the efficiency residuals of target-type power batteries across various battery performance sampling intervals include: Where, Δη i ' represents the efficiency residual of the target type of power battery in the i-th battery performance sampling interval; η i 'Represents the energy conversion efficiency of the target type of power battery in the i-th battery performance sampling interval; This represents the benchmark average efficiency of the same type of power battery in the i-th battery performance sampling interval.

7. A power battery performance early warning system, characterized in that, include: The data acquisition module is used to collect the cumulative input power of multiple power batteries at multiple consecutive sampling times; wherein, all power batteries are of the same type. The dynamic segmentation module is used to segment the cumulative input power of each power battery at each continuous sampling time according to the preset segmentation interval threshold and the preset cumulative power threshold, so as to obtain multiple battery performance sampling intervals for each power battery. The segmented efficiency calculation module is used to calculate the energy conversion efficiency of each power battery in each battery performance sampling range according to the output energy compensation strategy. The baseline curve construction module is used to construct the average efficiency baseline curve of the same type of power battery based on the energy conversion efficiency of each battery performance sampling range. The anomaly detection module detects anomalies in the performance trend of the target power battery based on the average efficiency benchmark curve of similar power batteries, and outputs corresponding warning signals based on the anomaly detection results.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the power battery performance early warning method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes computer program code, which, when run on a computer, causes the computer to implement the power battery performance early warning method as described in any one of claims 1 to 6.

10. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the power battery performance early warning method as described in any one of claims 1 to 6.

Images