A lithium battery life prediction method and device based on data analysis

Abstract

The present invention discloses a lithium battery life prediction method and device based on data analysis. The method includes obtaining the charging current, discharging current, battery voltage, and timestamp of each complete charge and discharge cycle; performing data analysis based on the charging current, discharging current, and timestamp to construct a charge and discharge current sequence; performing voltage fluctuation amplitude analysis based on the timestamp, charge and discharge current sequence, and battery voltage to obtain voltage mutation characteristics; performing fluctuation characteristic analysis based on the voltage mutation characteristics to obtain voltage fluctuation characteristics; performing correlation analysis based on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence; calculating a voltage attenuation factor based on the voltage fluctuation characteristics and the optimal charge and discharge current sequence; constructing a life prediction model based on the optimal charge and discharge current sequence and the voltage attenuation factor, and calculating the remaining number of cycles, future battery voltage, and battery life based on the life prediction model. This method accurately predicts the life of lithium batteries.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and device for predicting the life of a lithium battery based on data analysis. Background Art

[0002] Lithium batteries, as highly efficient chemical energy storage devices, play a vital role in numerous areas of modern society. From portable electronic devices to electric vehicles to large-scale energy storage systems, lithium batteries are ubiquitous. Their high energy density, long cycle life, and relatively low self-discharge rate make them one of the most popular energy storage solutions. However, as the scope of lithium battery applications expands, performance requirements are becoming increasingly stringent. In particular, the cycle stability and lifespan of lithium batteries are directly related to product safety and user costs. Batteries inevitably experience capacity decay over long-term use, which not only affects battery efficiency but also poses potential safety risks such as overheating or explosion. Therefore, accurately predicting the lifespan of lithium batteries is crucial for optimizing battery management systems (BMS), extending battery life, reducing maintenance costs, and minimizing environmental pollution.

[0003] Methods based on changes in direct current resistance (DCR) provide an effective and intuitive approach to assessing the state of health (SOH) and remaining useful life (RUL) of lithium batteries. First, an accurate initial DCR baseline is established after the battery is first commissioned or fully recovered, serving as the basis for subsequent analysis. As the battery continues to operate, an online monitoring system collects real-time operating parameters, including temperature, voltage, and current. The DCR is automatically measured at the end of each charge and discharge cycle. This data is compared with the initial baseline to identify resistance growth trends due to aging. Utilizing a specially designed data processing algorithm, this collected data is deeply analyzed. This not only compares DCR differences at different time points but also, combined with machine learning models, predicts future DCR trends and, consequently, estimates the rate of battery aging. Based on the DCR change and a pre-defined aging model, the current battery state of health is calculated, and a threshold is defined to determine whether the battery has entered the aging stage. Furthermore, statistical methods are employed to predict the battery's remaining useful life based on the available DCR change rate and other variables.

[0004] However, existing methods for predicting the health status and remaining service life of lithium batteries based on direct current resistance (DCR) mainly rely on the DCR value obtained through charging and discharging equipment tests to predict battery life. This ignores the storage time and working status differences of the battery and cannot accurately capture the actual aging process of the battery. As a result, the prediction results are not accurate enough and it is difficult to truly reflect the actual life of the lithium battery. Summary of the Invention

[0005] The present invention provides a lithium battery life prediction method and device based on data analysis, so as to achieve accurate prediction of lithium battery life.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a lithium battery life prediction method based on data analysis, comprising:

[0007] Obtain the charge current, discharge current, battery voltage and timestamp of each complete charge and discharge cycle;

[0008] Performing data analysis based on the charging current, the discharging current, and the timestamp to construct a charging and discharging current sequence;

[0009] Perform voltage fluctuation amplitude analysis based on the timestamp, the charge and discharge current sequence, and the battery voltage to obtain a voltage mutation feature;

[0010] Performing a fluctuation characteristic analysis based on the voltage mutation characteristics to obtain voltage fluctuation characteristics;

[0011] Performing correlation analysis on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence;

[0012] Calculating a voltage attenuation factor based on the voltage fluctuation characteristics and the optimal charge and discharge current sequence;

[0013] A life prediction model is constructed according to the optimal charge and discharge current sequence and the voltage attenuation factor, and the remaining number of cycles, future battery voltage and battery life are calculated according to the life prediction model.

[0014] In an optional implementation, performing data analysis based on the charging current, the discharging current, and the timestamp to construct a charging and discharging current sequence includes:

[0015] Calculating the time interval between adjacent timestamps and comparing the time interval with a preset time window; if the time interval is less than the time window, determining that the corresponding charging current and discharging current are invalid data;

[0016] If the time interval is greater than the time window, the charging current and the discharging current are determined to be valid data;

[0017] All the valid data are combined to construct a charge and discharge current sequence.

[0018] In an optional embodiment, performing voltage fluctuation amplitude analysis based on the timestamp, the charge and discharge current sequence, and the battery voltage to obtain a voltage mutation feature includes:

[0019] Recording the battery voltage corresponding to the charge and discharge current sequence as the standard voltage;

[0020] Perform a difference operation on the standard voltages at adjacent timestamps to obtain the voltage fluctuation amplitude;

[0021] Comparing the voltage fluctuation amplitude with a preset voltage fluctuation threshold, and if the voltage fluctuation amplitude is less than the voltage fluctuation threshold, determining the voltage fluctuation amplitude as invalid fluctuation data;

[0022] If the voltage fluctuation amplitude is greater than the voltage fluctuation threshold, determining the voltage fluctuation amplitude as valid fluctuation data;

[0023] Sorting and combining all the valid fluctuation data according to corresponding timestamps to form a voltage fluctuation amplitude sequence;

[0024] The voltage fluctuation amplitudes of adjacent time stamps in the voltage fluctuation amplitude sequence are subjected to a difference operation, and all the difference results are sorted and combined to obtain a voltage mutation feature.

[0025] In an optional embodiment, performing fluctuation characteristic analysis based on the voltage mutation characteristic to obtain the voltage fluctuation characteristic includes:

[0026] Applying wavelet transform to process the timestamp corresponding to the voltage mutation feature to obtain an approximate wavelet coefficient sequence;

[0027] Multiplying the approximate wavelet coefficient sequence with the voltage mutation characteristic to obtain an optimal fluctuation sequence;

[0028] A mutation characteristic calculation is performed based on the optimal fluctuation sequence to obtain a voltage fluctuation characteristic.

[0029] In an optional embodiment, performing correlation analysis on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence includes:

[0030] Calculate the correlation coefficient between the charge current and discharge current at any time stamp in the charge and discharge current sequence of each charge and discharge cycle and the charge current and discharge current at other times in the same sequence;

[0031] Selecting the charge and discharge current sequence with the largest correlation coefficient as the optimal charge and discharge current sequence;

[0032] The correlation coefficient is calculated using the following formula:

[0033]

[0034] Where, Indicates the time interval is The correlation coefficient between the charge and discharge current series, Indicates the first The charging or discharging current of each time stamp, Indicates The time interval between timestamps is The charging current or discharging current, represents the number of timestamps in the charge and discharge current sequence, Indicates the time interval between two corresponding timestamps. Indicates the average value of the charging current or discharging current.

[0035] In an optional embodiment, the calculating the voltage attenuation factor according to the voltage fluctuation characteristics and the optimal charge and discharge current sequence includes:

[0036] Add a preset time interval to each timestamp in the optimal charge and discharge current sequence to obtain the future moment;

[0037] Using the charging current and discharging current in the optimal charging and discharging current sequence as the future charging current and future discharging current corresponding to the future moment;

[0038] According to the future charging current, the future discharging current and the voltage fluctuation characteristics, a voltage attenuation factor at a future moment is calculated using linear regression analysis;

[0039] The voltage attenuation factor is calculated using the following formula:

[0040]

[0041] in, Indicates the future moment The voltage attenuation factor, Indicates the future moment The charging current or discharging current, Indicates the pre-stored voltage attenuation factor at the current moment, Indicates the voltage fluctuation characteristics, and Represents the pre-stored linear regression coefficients.

[0042] In an optional embodiment, constructing a life prediction model according to the optimal charge and discharge current sequence and the voltage attenuation factor, and calculating the future battery voltage, remaining number of cycles, and battery life according to the life prediction model includes:

[0043] The lifespan pre-storage model is as follows:

[0044]

[0045]

[0046]

[0047] in, Indicates the next moment to the future moment The number of moments, Indicates the future moment The voltage attenuation factor, Represents the maximum current in the optimal charge and discharge current sequence, Indicates the battery voltage at the current moment, Indicates the voltage fluctuation characteristics, Indicates the future moment The future battery voltage, Indicates the future moment The remaining number of cycles, Indicates the future moment battery life.

[0048] In a second aspect, the present invention provides a lithium battery life prediction device based on data analysis, comprising:

[0049] Data acquisition module, used to obtain the charging current, discharging current, battery voltage and timestamp of each complete charge and discharge cycle;

[0050] a current sequence construction module, configured to perform data analysis based on the charging current, the discharging current, and the timestamp to construct a charging and discharging current sequence;

[0051] a voltage mutation analysis module, configured to analyze the voltage fluctuation amplitude according to the timestamp, the charge and discharge current sequence, and the battery voltage to obtain a voltage mutation feature;

[0052] A voltage fluctuation analysis module is used to perform fluctuation characteristic analysis based on the voltage mutation characteristics to obtain voltage fluctuation characteristics;

[0053] An optimal sequence analysis module, configured to perform correlation analysis on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence;

[0054] an attenuation factor calculation module, configured to calculate a voltage attenuation factor based on the voltage fluctuation characteristics and the optimal charge and discharge current sequence;

[0055] A life prediction module is configured to construct a life prediction model based on the optimal charge and discharge current sequence and the voltage attenuation factor, and to calculate the remaining number of cycles, future battery voltage, and battery life based on the life prediction model. In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any of the above-described methods for predicting the life of a lithium battery based on data analysis.

[0056] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned lithium battery life prediction methods based on data analysis.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention discloses a lithium battery life prediction method based on data analysis, comprising obtaining the charging current, discharging current, battery voltage and the timestamp of the recorded data for each complete charge and discharge cycle; performing data analysis based on the charging current, the discharging current and the timestamp to construct a charge and discharge current sequence; performing voltage fluctuation amplitude analysis based on the timestamp, the charge and discharge current sequence and the battery voltage to obtain a voltage mutation feature; performing fluctuation characteristic analysis based on the voltage mutation feature to obtain a voltage fluctuation feature; performing correlation analysis based on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence; calculating a voltage attenuation factor based on the voltage fluctuation feature and the optimal charge and discharge current sequence; constructing a life prediction model based on the optimal charge and discharge current sequence and the voltage attenuation factor, and calculating the remaining number of cycles, future battery voltage and battery life based on the life prediction model. The present invention collects the charging current, discharging current, battery voltage and timestamp in each complete charge and discharge cycle of the lithium battery, then constructs a charge and discharge current sequence based on this data and analyzes the voltage mutation characteristics and voltage fluctuation characteristics, and performs correlation analysis to determine the optimal charge and discharge current sequence. According to the analysis results, the voltage attenuation factor is calculated and a life prediction model is constructed to predict the battery life, thereby achieving accurate prediction of the lithium battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of a lithium battery life prediction method based on data analysis provided by the first embodiment of the present invention;

[0060] Figure 2 It is a structural diagram of a lithium battery life prediction device based on data analysis provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0062] Reference Figure 1 The first embodiment of the present invention provides a lithium battery life prediction method based on data analysis, comprising the following steps:

[0063] S11, obtaining the charging current, discharging current, battery voltage and the timestamp of the recorded data for each complete charge and discharge cycle;

[0064] S12, performing data analysis based on the charging current, the discharging current, and the timestamp to construct a charging and discharging current sequence;

[0065] S13, performing voltage fluctuation amplitude analysis based on the timestamp, the charge and discharge current sequence, and the battery voltage to obtain a voltage mutation feature;

[0066] S14, performing a fluctuation characteristic analysis based on the voltage mutation characteristic to obtain a voltage fluctuation characteristic;

[0067] S15, performing correlation analysis on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence;

[0068] S16, calculating a voltage attenuation factor according to the voltage fluctuation characteristics and the optimal charge and discharge current sequence;

[0069] S17, constructing a life prediction model according to the optimal charge and discharge current sequence and the voltage attenuation factor, and calculating the remaining number of cycles, future battery voltage and battery life according to the life prediction model.

[0070] In step S11 , the charging current, discharging current, battery voltage and the timestamp of the recorded data of each complete charge and discharge cycle are obtained.

[0071] First, charging and discharging current data is obtained by current sensors installed in the battery's charging and discharging circuits. These sensors are Hall-effect sensors or shunts that accurately measure the current flowing through the battery and transmit this data to the battery management system (BMS). Battery voltage monitoring relies on voltage sensors, which are typically connected directly to the positive and negative terminals of the battery to ensure accurate measurement of the battery voltage level at each moment. Timestamps are generated by the system's built-in high-precision clock module, which accurately timestamps each recorded data item, ensuring that all data points correspond to specific points in time.

[0072] In step S12, data analysis is performed based on the charging current, the discharging current and the timestamp to construct a charging and discharging current sequence.

[0073] In a specific embodiment, performing data analysis based on the charging current, the discharging current, and the timestamp to construct a charging and discharging current sequence includes:

[0074] Calculating the time interval between adjacent timestamps and comparing the time interval with a preset time window; if the time interval is less than the time window, determining that the corresponding charging current and discharging current are invalid data;

[0075] If the time interval is greater than the time window, the charging current and the discharging current are determined to be valid data;

[0076] All the valid data are combined to construct a charge and discharge current sequence.

[0077] Specifically, first, the battery management system (BMS) performs time stamping on adjacent and Process and calculate the time interval between them :

[0078]

[0079] Then, the time interval With the preset time window Compare. Preset time window It is a threshold value set based on the actual application scenario, which is used to filter out data loss or inconsistency caused by sensor failure or other abnormal conditions. Less than the time window , the corresponding charge current and discharge current data points are considered invalid, because this indicates that there may be data acquisition problems or signal interruptions during this period; conversely, if Greater than , then these current data are determined to be valid data, indicating that the data collection during this period is normal and continuous.

[0080] For example, assuming the preset time window Set to 1 second, and the interval between two adjacent timestamps If the time is 0.5 seconds, the charge current and discharge current in this time period are marked as invalid data and will not be included in the final charge and discharge current sequence. If the value is 2 seconds, the corresponding data is considered valid and will be retained.

[0081] After the aforementioned screening process, all valid data is combined to form a complete charge and discharge current sequence. This sequence includes not only the charge and discharge current at each moment, but also the corresponding timestamp information, allowing accurate tracking of the temporal dynamics of current changes. This constructed charge and discharge current sequence truly reflects the battery's current fluctuations throughout the entire charge and discharge cycle.

[0082] Through rigorous screening and verification of raw data, we ensure that the dataset used for subsequent analysis has a high degree of reliability and integrity. Only when the data meets certain continuity and stability standards can the validity and accuracy of subsequent analysis results be guaranteed.

[0083] In step S13, voltage fluctuation amplitude analysis is performed based on the timestamp, the charge and discharge current sequence, and the battery voltage to obtain voltage mutation characteristics.

[0084] In a specific embodiment, the performing voltage fluctuation amplitude analysis based on the timestamp, the charge and discharge current sequence, and the battery voltage to obtain a voltage mutation feature includes:

[0085] Recording the battery voltage corresponding to the charge and discharge current sequence as the standard voltage;

[0086] Perform a difference operation on the standard voltages at adjacent timestamps to obtain the voltage fluctuation amplitude;

[0087] Comparing the voltage fluctuation amplitude with a preset voltage fluctuation threshold, and if the voltage fluctuation amplitude is less than the voltage fluctuation threshold, determining the voltage fluctuation amplitude as invalid fluctuation data;

[0088] If the voltage fluctuation amplitude is greater than the voltage fluctuation threshold, determining the voltage fluctuation amplitude as valid fluctuation data;

[0089] Sorting and combining all the valid fluctuation data according to corresponding timestamps to form a voltage fluctuation amplitude sequence;

[0090] The voltage fluctuation amplitudes of adjacent time stamps in the voltage fluctuation amplitude sequence are subjected to a difference operation, and all the difference results are sorted and combined to obtain a voltage mutation feature.

[0091] Specifically, the system first records the battery voltage corresponding to the charge and discharge current sequence as the standard voltage These standard voltages are based on actual measurements at different time points in each charge and discharge cycle, ensuring the authenticity and accuracy of the data. Next, the system performs a difference operation on the standard voltages at adjacent timestamps to calculate the voltage fluctuation amplitude. :

[0092]

[0093] Where, Indicates the timestamp To timestamp The voltage fluctuation amplitude when and Respectively in timestamp and In this way, the system can quantify the voltage variation within each time interval.

[0094] The system then compares these voltage fluctuation amplitudes with the preset voltage fluctuation thresholds. The preset voltage fluctuation threshold is a parameter set based on experience and experiments to distinguish normal small fluctuations from large fluctuations that may indicate battery abnormalities. Less than the voltage fluctuation threshold , then the voltage fluctuation is determined to be invalid fluctuation data, because it may be caused by measurement noise or other non-critical factors; on the contrary, if Greater than , it is judged as valid fluctuation data, which indicates that significant voltage changes have indeed occurred during this time period.

[0095] All voltage fluctuation data that are judged to be valid will be sorted and combined according to the corresponding timestamps to form a voltage fluctuation amplitude sequence This sequence not only contains the specific value of each effective voltage fluctuation, but also preserves the time order in which they occur, so that the temporal dynamics of the voltage changes can be tracked.

[0096] In order to further extract the voltage mutation characteristics, the system will perform a difference operation on the voltage fluctuation amplitudes of adjacent timestamps in the voltage fluctuation amplitude sequence and sort and combine all the difference results. Specifically, for each effective voltage fluctuation amplitude , calculate its fluctuation amplitude with the next effective voltage The difference between :

[0097]

[0098] The results of these secondary differences The data are organized into a new sequence called the voltage mutation characteristic sequence, which reflects the temporal trend of voltage fluctuations, especially those drastic changes that occur in a short period of time.

[0099] Through multi-level screening and analysis of voltage data, key features that reflect the battery's health status are extracted. Voltage mutation characteristics provide important clues about the battery's operating conditions, such as whether there is overcharge or overdischarge, and whether there is a risk of internal short circuits.

[0100] In step S14, a fluctuation characteristic analysis is performed based on the voltage mutation characteristic to obtain a voltage fluctuation characteristic.

[0101] In a specific embodiment, performing fluctuation characteristic analysis based on the voltage mutation characteristic to obtain the voltage fluctuation characteristic includes:

[0102] Applying wavelet transform to process the timestamp corresponding to the voltage mutation feature to obtain an approximate wavelet coefficient sequence;

[0103] Multiplying the approximate wavelet coefficient sequence with the voltage mutation characteristic to obtain an optimal fluctuation sequence;

[0104] A mutation characteristic calculation is performed based on the optimal fluctuation sequence to obtain a voltage fluctuation characteristic.

[0105] Specifically, the system first applies wavelet transform to process the timestamps corresponding to the voltage mutation characteristics to obtain an approximate wavelet coefficient sequence. The original voltage mutation characteristics are decomposed into an approximate wavelet coefficient sequence and a detail wavelet coefficient sequence of different frequency components through the following discrete wavelet transform function:

[0106]

[0107] in, Indicates the preset decomposition scale level, represents the approximate wavelet coefficients, represents the detail wavelet coefficients, Indicates the voltage mutation characteristics.

[0108] Next, the system multiplies the obtained approximate wavelet coefficient sequence with the voltage mutation characteristic to obtain the optimal fluctuation sequence. It can be expressed by the following formula:

[0109]

[0110] Where, Represents the timestamp in the optimal volatility sequence The corresponding optimal volatility element, represents the approximate wavelet coefficients, and This multiplication operation not only preserves the temporal information of the voltage mutation characteristics, but also enhances those parts identified as important in the wavelet transform, thereby constructing a more representative fluctuation sequence.

[0111] Finally, the mutation characteristics are calculated based on the optimal fluctuation sequence to obtain the final voltage fluctuation characteristics. The voltage fluctuation characteristics are calculated using the following formula:

[0112]

[0113] in, Indicates the voltage fluctuation characteristics, represents the number of elements of the optimal fluctuation sequence, Represents the timestamp in the optimal volatility sequence The corresponding optimal volatility element, Indicates a timestamp.

[0114] By deeply analyzing the characteristics of voltage fluctuations, the system can extract deeper fluctuation characteristics that reflect the complex physical and chemical processes within the battery. For example, certain fluctuation patterns may be associated with battery aging mechanisms or indicate impending failure.

[0115] In step S15 , correlation analysis is performed based on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence.

[0116] In a specific embodiment, performing correlation analysis on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence includes:

[0117] Calculate the correlation coefficient between the charge current and discharge current at any time stamp in the charge and discharge current sequence of each charge and discharge cycle and the charge current and discharge current at other times in the same sequence;

[0118] Selecting the charge and discharge current sequence with the largest correlation coefficient as the optimal charge and discharge current sequence;

[0119] The correlation coefficient is calculated using the following formula:

[0120]

[0121] Where, Indicates the time interval is The correlation coefficient between the charge and discharge current series, Indicates the first The charging or discharging current of each time stamp, Indicates The time interval between timestamps is The charging current or discharging current, represents the number of timestamps in the charge and discharge current sequence, Indicates the time interval between two corresponding timestamps. Indicates the average value of the charging current or discharging current.

[0122] Specifically, the system first calculates the correlation coefficient between the charging current and discharging current at any time stamp in the charging and discharging current sequence of each charging and discharging cycle and the charging current and discharging current at other times in the same sequence. Represents the difference between two timestamps and can be a fixed or varying value. It is used to evaluate the current correlation under different time delays.

[0123] Assume there is a charge and discharge current sequence , which includes Current measurement value at each time stamp. , the system will calculate the correlation coefficient as shown in the following formula :

[0124]

[0125] Where, Indicates the time interval is The correlation coefficient between the charge and discharge current series, Indicates the first The charging or discharging current of each time stamp, Indicates The time interval between timestamps is The charging current or discharging current, represents the number of timestamps in the charge and discharge current sequence, Indicates the time interval between two corresponding timestamps. Indicates the average value of the charging current or discharging current.

[0126] Using the above formula, the system can calculate the current correlation coefficients at different time intervals. These correlation coefficients reflect the similarity between the charge and discharge currents at different time points: a correlation coefficient close to 1 indicates a strong positive correlation, close to -1 indicates a strong negative correlation, and close to 0 indicates almost no correlation.

[0127] Next, the system compares all calculated correlation coefficients and selects the charge / discharge current sequence with the largest correlation coefficient as the optimal charge / discharge current sequence. Selecting the largest correlation coefficient selects the current pattern that exhibits the greatest consistency and stability across different time points. This pattern not only represents the most typical current behavior during the charge / discharge process but also reveals the optimal configuration of the battery's internal operating state.

[0128] By performing correlation analysis on charge and discharge current sequences, the system can identify the current pattern that best reflects the battery's health and performance characteristics. The optimal charge and discharge current sequence not only provides important clues about the battery's operating conditions but can also be used to optimize charging strategies, predict battery life, and identify potential problems in advance. For example, if the current in a certain charge and discharge pattern consistently exhibits high stability, it may be indicative of the battery operating under optimal conditions. Conversely, if the current fluctuates significantly in certain patterns, this may indicate a fault risk or other abnormal phenomenon.

[0129] In step S16, a voltage attenuation factor is calculated according to the voltage fluctuation characteristics and the optimal charge and discharge current sequence.

[0130] In a specific embodiment, the calculating the voltage attenuation factor according to the voltage fluctuation characteristics and the optimal charge and discharge current sequence includes:

[0131] Add a preset time interval to each timestamp in the optimal charge and discharge current sequence to obtain the future moment;

[0132] Using the charging current and discharging current in the optimal charging and discharging current sequence as the future charging current and future discharging current corresponding to the future moment;

[0133] According to the future charging current, the future discharging current and the voltage fluctuation characteristics, a voltage attenuation factor at a future moment is calculated using linear regression analysis;

[0134] The voltage attenuation factor is calculated using the following formula:

[0135]

[0136] in, Indicates the future moment The voltage attenuation factor, Indicates the future moment The charging current or discharging current, Indicates the pre-stored voltage attenuation factor at the current moment, Indicates the voltage fluctuation characteristics, and Represents the pre-stored linear regression coefficients.

[0137] Specifically, the system first adds a preset time interval to each timestamp in the optimal charge and discharge current sequence to obtain a future moment. This time interval is set based on the application scenario, for example, it can be 1 minute or shorter, used to predict battery behavior in the short term. This future moment serves as the basis for subsequent analysis.

[0138] Next, the system uses the charge and discharge currents in the optimal charge and discharge current sequence as the future charge and discharge currents corresponding to the future moment. This is because the current pattern changes relatively little in the short term, so it can be assumed that the current optimal current pattern will still be applicable in the short term.

[0139] In order to obtain the voltage attenuation factor at a future time, the system uses linear regression analysis method, combined with the future charging current or future discharging current and voltage fluctuation characteristics to make a prediction. Linear regression is a statistical method used to establish a linear relationship between variables to predict the change of the dependent variable (voltage attenuation factor). The linear regression coefficients pre-stored in the system and It is the best fitting parameter obtained by training historical data.

[0140] The specific voltage attenuation factor calculation formula is as follows:

[0141]

[0142] in, Indicates the future moment The voltage attenuation factor, Indicates the future moment The charging current or discharging current, Indicates the pre-stored voltage attenuation factor at the current moment, Indicates the voltage fluctuation characteristics, and Represents the pre-stored linear regression coefficients.

[0143] Through the above formula, the system can predict the voltage attenuation factor at future times This prediction not only takes into account future current changes but also incorporates voltage fluctuation characteristics, making the prediction more accurate and reliable. The voltage attenuation factor reflects the aging trend of the battery over time under different charge and discharge conditions.

[0144] By combining voltage fluctuation characteristics with the optimal charge and discharge current sequence, the system can predict future voltage decay in advance. Accurate prediction of the voltage decay factor helps identify potential aging issues, optimize charge and discharge strategies, extend battery life, and improve overall system performance and safety.

[0145] In step S17, a life prediction model is constructed according to the optimal charge and discharge current sequence and the voltage attenuation factor, and the remaining number of cycles, future battery voltage and battery life are calculated according to the life prediction model.

[0146] In a specific embodiment, constructing a life prediction model according to the optimal charge and discharge current sequence and the voltage attenuation factor, and calculating the future battery voltage, remaining number of cycles, and battery life according to the life prediction model includes:

[0147] The lifespan pre-storage model is as follows:

[0148]

[0149]

[0150]

[0151] in, Indicates the next moment to the future moment The number of moments, Indicates the future moment The voltage attenuation factor, Represents the maximum current in the optimal charge and discharge current sequence, Indicates the battery voltage at the current moment, Indicates the voltage fluctuation characteristics, Indicates the future moment The future battery voltage, Indicates the future moment The remaining number of cycles, Indicates the future moment battery life.

[0152] Specifically, the system first builds a life prediction model to describe the voltage change of the battery at different time points. This model takes into account the changes from the current moment to the future moment. The cumulative effect of voltage attenuation is calculated by the following formula at the future moment Future battery voltage:

[0153]

[0154] in, Indicates the next moment to the future moment The number of moments, Indicates the future moment The voltage attenuation factor, Indicates the battery voltage at the current moment, Indicates the future moment The future battery voltage.

[0155] Next, to calculate the future time The remaining number of cycles , the system introduces the maximum current and voltage fluctuation characteristics Maximum current Derived from the optimal charge and discharge current sequence, it indicates the maximum current value that the battery can reach during the charge and discharge process. Voltage fluctuation characteristics It reflects the degree of voltage mutation. The remaining number of cycles can be calculated by the following formula:

[0156]

[0157] in, Indicates the future moment The voltage attenuation factor, Represents the maximum current in the optimal charge and discharge current sequence, Indicates the voltage fluctuation characteristics, Indicates the future moment The remaining number of cycles.

[0158] Finally, to evaluate future moments Battery life , the system combines the number of remaining cycles with the difference between the current and predicted battery voltage. The battery life can be calculated using the following formula:

[0159]

[0160] in, Indicates the battery voltage at the current moment, Indicates the future moment The future battery voltage, Indicates the future moment The remaining number of cycles, Indicates the future moment battery life.

[0161] By building a lifetime prediction model and utilizing optimal charge and discharge current sequences and voltage decay factors, the system can comprehensively assess battery performance changes over future use. This prediction not only helps identify potential aging issues but also optimizes charge and discharge strategies, extending battery life and improving overall system performance and safety.

[0162] The following describes the working process of the present invention using a common scenario as an example. Figure 1 , a lithium battery life prediction method based on data analysis, comprising the following steps:

[0163] First, the battery management system collects complete charge and discharge data through Hall effect sensors and voltage sensors, including charging current, discharging current, battery voltage and corresponding timestamps.

[0164] The battery management system then analyzes the acquired data, using a preset time window of one second. After screening, all data with a time interval greater than one second is determined to be valid. Ultimately, a charge and discharge current sequence containing 500 valid data points is constructed, covering the entire charge and discharge cycle.

[0165] The system recorded the battery voltage corresponding to these 500 valid data points as the standard voltage. By comparing the standard voltages at adjacent timestamps, it found that 30 voltage fluctuations exceeded the preset voltage fluctuation threshold. These data were considered valid voltage fluctuation data and combined into a voltage fluctuation amplitude sequence in chronological order. After further processing, a voltage mutation feature sequence consisting of these valid voltage fluctuation amplitudes was obtained, showing a significant voltage change trend.

[0166] The system then performed a wavelet transform on the voltage mutation signature, extracting an approximate wavelet coefficient sequence. This was then multiplied by the voltage mutation signature to produce the optimal fluctuation sequence. Calculations based on this sequence revealed a voltage fluctuation signature with a value of 0.025, indicating slight signs of aging within the battery but still within normal operating range.

[0167] Then, the system calculated the correlation coefficient of the charge and discharge current sequence and found that when the time interval was 5 minutes, the correlation coefficient reached a maximum value of 0.98. Therefore, the charge and discharge current sequence under this time interval was selected as the optimal charge and discharge current sequence.

[0168] Based on the above voltage fluctuation characteristics and the optimal charge and discharge current sequence, the voltage attenuation factor at the future moment (the next hour) is predicted to be 0.97, indicating that the battery will have a slight voltage drop in the next hour.

[0169] Finally, in step S17, a lifetime prediction model is constructed based on the optimal charge and discharge current sequence and the voltage decay factor. The model predicts that within the next hour, the battery voltage will drop from the current 3.8 volts to 3.75 volts, with an estimated remaining number of 400 cycles. The battery lifetime, expressed as the product of the number of charge and discharge cycles and the voltage difference, is estimated to support approximately 150 additional units of workload.

[0170] In summary, through a series of carefully designed data acquisition and analysis steps, the charge and discharge behavior of lithium-ion batteries can be comprehensively monitored and evaluated. Starting with the acquisition of precise charge current, discharge current, and battery voltage data, combined with high-precision timestamp recording, the accuracy and temporal relevance of each piece of data are ensured. Subsequently, through effective screening and processing of this data, a reliable charge and discharge current sequence is constructed. Further analysis of voltage fluctuation characteristics identifies significant voltage trends, revealing possible signs of aging within the battery. Correlation analysis is then used to determine the optimal charge and discharge current pattern, and statistical methods such as linear regression are used to predict future voltage decay. Finally, using a lifespan prediction model, the remaining number of cycles and expected lifespan of the battery over a period of time can be calculated, demonstrating that this method accurately predicts the lifespan of lithium batteries.

[0171] Reference Figure 2 The second embodiment of the present invention provides a lithium battery life prediction device based on data analysis, comprising:

[0172] Data acquisition module, used to obtain the charging current, discharging current, battery voltage and timestamp of each complete charge and discharge cycle;

[0173] a current sequence construction module, configured to perform data analysis based on the charging current, the discharging current, and the timestamp to construct a charging and discharging current sequence;

[0174] a voltage mutation analysis module, configured to analyze the voltage fluctuation amplitude according to the timestamp, the charge and discharge current sequence, and the battery voltage to obtain a voltage mutation feature;

[0175] A voltage fluctuation analysis module is used to perform fluctuation characteristic analysis based on the voltage mutation characteristics to obtain voltage fluctuation characteristics;

[0176] An optimal sequence analysis module, configured to perform correlation analysis on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence;

[0177] an attenuation factor calculation module, configured to calculate a voltage attenuation factor based on the voltage fluctuation characteristics and the optimal charge and discharge current sequence;

[0178] A life prediction module is configured to construct a life prediction model based on the optimal charge and discharge current sequence and the voltage attenuation factor, and to calculate the remaining number of cycles, future battery voltage, and battery life based on the life prediction model. It should be noted that the data analysis-based lithium battery life prediction device provided in an embodiment of the present invention is configured to execute all steps of the data analysis-based lithium battery life prediction method of the above-mentioned embodiment. The operating principles and beneficial effects of the two methods correspond one to another, and thus will not be further described.

[0179] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a lithium battery life prediction program based on data analysis. When the processor executes the computer program, the steps in each of the above-mentioned lithium battery life prediction method embodiments based on data analysis are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as a lithium battery life prediction module based on data analysis.

[0180] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0181] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0182] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0183] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0184] If the module / unit integrated into the electronic device 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 present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0185] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0186] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A lithium battery life prediction method based on data analysis, characterized in that: Executed by a computer, including: Obtain the charge current, discharge current, battery voltage and timestamp of each complete charge and discharge cycle; Performing data analysis based on the charging current, the discharging current, and the timestamp to construct a charging and discharging current sequence; Perform voltage fluctuation amplitude analysis based on the timestamp, the charge and discharge current sequence, and the battery voltage to obtain a voltage mutation feature; Performing a fluctuation characteristic analysis based on the voltage mutation characteristics to obtain voltage fluctuation characteristics; Performing correlation analysis on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence; Calculating a voltage attenuation factor based on the voltage fluctuation characteristics and the optimal charge and discharge current sequence; Constructing a life prediction model according to the optimal charge and discharge current sequence and the voltage attenuation factor, and calculating the remaining number of cycles, future battery voltage, and battery life according to the life prediction model; The performing of correlation analysis on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence includes: Calculate the correlation coefficient between the charge current and discharge current at any time stamp in the charge and discharge current sequence of each charge and discharge cycle and the charge current and discharge current at other times in the same sequence; Selecting the charge and discharge current sequence with the largest correlation coefficient as the optimal charge and discharge current sequence; The correlation coefficient is calculated using the following formula: Where, Indicates the time interval is The correlation coefficient between the charge and discharge current series, Indicates the first The charging or discharging current of each time stamp, Indicates The time interval between timestamps is The charging current or discharging current, represents the number of timestamps in the charge and discharge current sequence, Indicates the time interval between two corresponding timestamps. Indicates the average value of the charging current or discharging current.

2. The lithium battery life prediction method based on data analysis according to claim 1, characterized in that: The performing data analysis according to the charging current, the discharging current and the timestamp to construct a charging and discharging current sequence includes: Calculating the time interval between adjacent timestamps and comparing the time interval with a preset time window; if the time interval is less than the time window, determining that the corresponding charging current and discharging current are invalid data; If the time interval is greater than the time window, the charging current and the discharging current are determined to be valid data; All the valid data are combined to construct a charge and discharge current sequence.

3. The lithium battery life prediction method based on data analysis according to claim 1, characterized in that: The performing voltage fluctuation amplitude analysis according to the timestamp, the charge and discharge current sequence, and the battery voltage to obtain a voltage mutation feature includes: Recording the battery voltage corresponding to the charge and discharge current sequence as the standard voltage; Perform a difference operation on the standard voltages at adjacent timestamps to obtain the voltage fluctuation amplitude; Comparing the voltage fluctuation amplitude with a preset voltage fluctuation threshold, and if the voltage fluctuation amplitude is less than the voltage fluctuation threshold, determining the voltage fluctuation amplitude as invalid fluctuation data; If the voltage fluctuation amplitude is greater than the voltage fluctuation threshold, determining the voltage fluctuation amplitude as valid fluctuation data; Sorting and combining all the valid fluctuation data according to corresponding timestamps to form a voltage fluctuation amplitude sequence; The voltage fluctuation amplitudes of adjacent time stamps in the voltage fluctuation amplitude sequence are subjected to a difference operation, and all the difference results are sorted and combined to obtain a voltage mutation feature.

4. The lithium battery life prediction method based on data analysis according to claim 1, characterized in that: The performing fluctuation characteristic analysis based on the voltage mutation characteristic to obtain the voltage fluctuation characteristic includes: Applying wavelet transform to process the timestamp corresponding to the voltage mutation feature to obtain an approximate wavelet coefficient sequence; Multiplying the approximate wavelet coefficient sequence with the voltage mutation characteristic to obtain an optimal fluctuation sequence; A mutation characteristic calculation is performed based on the optimal fluctuation sequence to obtain a voltage fluctuation characteristic.

5. The lithium battery life prediction method based on data analysis according to claim 1, characterized in that: The calculating of the voltage attenuation factor according to the voltage fluctuation characteristics and the optimal charge and discharge current sequence includes: Add a preset time interval to each timestamp in the optimal charge and discharge current sequence to obtain the future moment; Using the charging current and discharging current in the optimal charging and discharging current sequence as the future charging current and future discharging current corresponding to the future moment; According to the future charging current, the future discharging current and the voltage fluctuation characteristics, a voltage attenuation factor at a future moment is calculated using linear regression analysis; The voltage attenuation factor is calculated using the following formula: in, Indicates the future moment The voltage attenuation factor, Indicates the future moment The charging current or discharging current, Indicates the pre-stored voltage attenuation factor at the current moment, Indicates the voltage fluctuation characteristics, and Represents the pre-stored linear regression coefficients.

6. The lithium battery life prediction method based on data analysis according to claim 1, characterized in that: The method of constructing a life prediction model according to the optimal charge and discharge current sequence and the voltage attenuation factor, and calculating the future battery voltage, the remaining number of cycles, and the battery life according to the life prediction model, includes: The lifespan pre-storage model is as follows: in, Indicates the next moment to the future moment The number of moments, Indicates the future moment The voltage attenuation factor, Represents the maximum current in the optimal charge and discharge current sequence, Indicates the battery voltage at the current moment, Indicates the voltage fluctuation characteristics, Indicates the future moment The future battery voltage, Indicates the future moment The remaining number of cycles, Indicates the future moment battery life.

7. A lithium battery life prediction device based on data analysis, characterized in that: A method for predicting the life of a lithium battery based on data analysis according to any one of claims 1 to 6, comprising: Data acquisition module, used to obtain the charging current, discharging current, battery voltage and timestamp of the recorded data for each complete charge and discharge cycle; a current sequence construction module, configured to perform data analysis based on the charging current, the discharging current, and the timestamp to construct a charging and discharging current sequence; a voltage mutation analysis module, configured to analyze the voltage fluctuation amplitude according to the timestamp, the charge and discharge current sequence, and the battery voltage to obtain a voltage mutation feature; A voltage fluctuation analysis module is used to perform fluctuation characteristic analysis based on the voltage mutation characteristics to obtain voltage fluctuation characteristics; An optimal sequence analysis module, configured to perform correlation analysis on the charge and discharge current sequence to obtain an optimal charge and discharge current sequence; an attenuation factor calculation module, configured to calculate a voltage attenuation factor based on the voltage fluctuation characteristics and the optimal charge and discharge current sequence; The life prediction module is used to construct a life prediction model according to the optimal charge and discharge current sequence and the voltage attenuation factor, and calculate the remaining number of cycles, future battery voltage and battery life according to the life prediction model.

8. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the lithium battery life prediction method based on data analysis as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the lithium battery life prediction method based on data analysis as described in any one of claims 1 to 6.

Images