Electrochemical energy storage residual life estimation method and system

Abstract

The invention relates to the technical field of electrical performance monitoring, in particular to an electrochemical energy storage residual life estimation method and system. According to the method, by applying multi-stage voltage step excitation and capturing the maximum offset of the instantaneous current, the high-sensitivity characteristic reflecting the electrochemical dynamic characteristic in the energy storage monomer can be obtained, the limitation that a traditional method depends on the hysteresis index of capacity fading for evaluation is avoided, early insight of performance change is achieved, and meanwhile, the performance of the energy storage monomer is improved. The environment temperature is collected in real time, the preset correction coefficient is used for correcting the dynamic response characteristics, the interference of working temperature fluctuation on performance evaluation is effectively stripped, the consistency and comparability of characteristic data under different environment conditions are ensured, and then the deviation spectrum of the performance characteristics evolved along with time is constructed, so that the performance evaluation accuracy is improved. And the change sequences which are continuous and consistent in direction are specially identified, the irreversible decline trend caused by internal aging is accurately locked, and the influence of random noise and short-term fluctuation is filtered.

Description

Technical Field

[0001] This invention relates to the field of electrical performance monitoring technology, and in particular to a method and system for estimating the remaining lifetime of electrochemical energy storage. Background Technology

[0002] The field of electrical performance monitoring technology refers to the detection and monitoring technology of electrical performance in various electrical energy storage and conversion systems. It is widely used in batteries, electrochemical energy storage devices, and other equipment related to power transmission and storage.

[0003] Among them, the electrochemical energy storage remaining life estimation method refers to predicting the remaining life of the energy storage system by using the electrochemical performance data of the battery or energy storage system, combined with the known battery aging law, and modeling the law of capacity decay during battery charging and discharging.

[0004] Existing technologies primarily rely on analyzing and applying the patterns of battery capacity degradation. This approach has inherent flaws. The aging patterns they rely on are typically based on standard and constant laboratory operating conditions. However, in actual operation, energy storage systems often face complex temperature variations and irregular load shocks. Directly applying these laboratory-derived aging patterns to variable field environments can lead to significant deviations in assessment results. For example, an energy storage system operating in a low-temperature environment may experience a temporary reduction in usable capacity. Existing technologies may misjudge this non-permanent performance degradation as structural aging, prematurely triggering scrap warnings and causing unnecessary economic losses. Furthermore, capacity degradation is a slow, cumulative process, exhibiting a significant lag as an assessment indicator. By the time the system detects a significant capacity decrease, significant and irreversible material aging may have already occurred within the individual energy storage cells. This makes it difficult for existing technologies to provide early warnings and to allow sufficient lead time for maintenance decisions. Moreover, this method tends to use universal aging patterns, ignoring individual differences between energy storage cells in the same batch due to manufacturing variations and different operating histories, resulting in insufficient accuracy in predicting the lifespan of individual cells. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a method and system for estimating the remaining lifetime of electrochemical energy storage.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for estimating the remaining lifetime of electrochemical energy storage, comprising the following steps:

[0007] S1: By applying externally controlled multi-level voltage step excitation to both ends of the electrochemical energy storage cell under test, the terminal voltage and current data of the electrochemical energy storage cell are collected in real time during the step excitation process, a voltage and current response sequence is constructed, and temperature-sensitive features are extracted from the sequence to construct an electrochemical performance response set.

[0008] S2: Collect the temperature information of the current electrochemical energy storage cell, perform error correction on the temperature-sensitive features in the electrochemical performance response set, and generate a temperature-corrected performance response set;

[0009] S3: Construct a feature sequence for the current operating cycle by arranging the temperature-corrected performance response set of the electrochemical energy storage cell in chronological order, compare it with the same temperature-sensitive features of the electrochemical energy storage cell in the previous operating cycle, determine the shift trend of each temperature-sensitive feature, and construct a performance shift feature map.

[0010] S4: Identify the offset trend sequence in the performance offset feature map where the change direction of temperature-sensitive features remains consistent, and construct a phased degradation index sequence;

[0011] S5: Calculate the remaining lifetime estimate based on the phased degradation index sequence and total operating time of the electrochemical energy storage unit to obtain the remaining lifetime estimate result of the electrochemical energy storage.

[0012] As a further aspect of the present invention, the voltage and current response sequence includes the voltage change rate and the maximum current offset; the temperature-corrected performance response set includes the corrected voltage change rate and the corrected maximum current offset; the performance offset feature map includes the voltage change rate offset trend and the maximum current offset offset trend; the phased degradation index sequence specifically includes a trend consistency interval, a feature item identifier, and a time series index; and the electrochemical energy storage remaining lifetime estimation result includes the lifetime change trend, the current remaining lifetime value, and the estimated time node.

[0013] As a further aspect of the present invention, the step of obtaining the electrochemical performance response set specifically includes:

[0014] S111: By applying externally controlled multi-level voltage step excitation to both ends of the electrochemical energy storage cell under test, wherein the electrochemical energy storage cell is a lithium iron phosphate battery, the terminal voltage and current data of the electrochemical energy storage cell are collected in real time during the step excitation process to construct a voltage and current response sequence.

[0015] S112: Identify the voltage step occurrence segment in the voltage and current response sequence, calculate the voltage change rate in each voltage step occurrence segment, and simultaneously obtain the maximum current offset in the corresponding segment. Combine the voltage change rate and the maximum current offset of all segments as temperature-sensitive features to obtain dynamic response feature data.

[0016] S113: Call up the voltage change rate and current maximum offset of all segments in the dynamic response feature data, integrate these two temperature-sensitive features into a structured data set, merge the structured data sets obtained under multiple excitations, and construct an electrochemical performance response set.

[0017] As a further aspect of the present invention, the step of obtaining the performance response set after temperature correction specifically includes:

[0018] S211: The ambient temperature of the electrochemical energy storage cell during the response process is obtained through a temperature sensor. The temperature data collected at each time point is recorded and integrated to establish ambient temperature information.

[0019] S212: According to the temperature range matching rule, the temperature data in the ambient temperature information is compared with multiple preset temperature ranges one by one to lock the unique temperature range to which it belongs, and the corresponding preset correction coefficient is extracted from the temperature range to obtain the matching correction coefficient value.

[0020] S213: For the temperature-sensitive characteristics in the electrochemical performance response set, error correction is performed by calling the voltage change rate and the maximum current offset, and using the matching correction coefficient value for correction processing. The maximum current offset after temperature and voltage plateau effect correction is calculated to generate the temperature-corrected performance response set.

[0021] As a further aspect of the present invention, the step of obtaining the performance offset feature map specifically includes:

[0022] S311: Arrange the temperature-corrected performance response set of the electrochemical energy storage cell in chronological order to establish the characteristic sequence of the current operating cycle;

[0023] S312: Retrieve the same type of temperature-sensitive features from the previous operating cycle of the electrochemical energy storage cell, and perform a comparison of the difference and rate of change at the same index point for the feature sequence of the current operating cycle, determine the offset trend of each temperature-sensitive feature, and obtain feature offset trend information.

[0024] S313: Combine the offset trends of each temperature-sensitive feature extracted from the feature offset trend information to construct a performance offset feature map.

[0025] As a further aspect of the present invention, the steps for obtaining the phased decline indicator sequence are specifically as follows:

[0026] S411: Call the offset trend information of each temperature-sensitive feature in the performance offset feature map, make a directional judgment on the time series data of each feature, filter the sequence segments whose change direction remains unchanged in continuous period, associate them with the temperature-sensitive feature type to construct a consistent offset sequence.

[0027] S412: For each sequence segment in the consistency offset sequence, extract the temperature-sensitive feature type, the duration of the sequence segment trend, and the offset magnitude value. Integrate these three pieces of information into independent structured index items. Collect the index items of all sequence segments to obtain quantitative data on the decline trend.

[0028] S413: Based on the quantitative data of the decline trend, call all the indicator items in the set, arrange and summarize them according to the time sequence identifier attached to each indicator item, and construct a phased decline indicator sequence.

[0029] As a further aspect of the present invention, the process of determining the directionality of the time series data for each feature specifically involves:

[0030] The difference between the temperature-sensitive feature value of the current operating cycle and the same temperature-sensitive feature value of the previous operating cycle is calculated, and positive offset threshold and negative offset threshold are set.

[0031] If the result of the difference calculation is greater than the positive offset threshold, then the change direction of the temperature-sensitive feature is determined to be a positive offset.

[0032] If the difference calculation result is less than the negative offset threshold, then the change direction of the temperature sensitive feature is determined to be a negative offset;

[0033] If the result of the difference calculation is between the negative offset threshold and the positive offset threshold, then the direction of change of the temperature-sensitive feature is determined to be without offset.

[0034] As a further aspect of the present invention, the steps for obtaining the remaining lifetime estimation result of the electrochemical energy storage are specifically as follows:

[0035] S511: Based on the phased degradation index sequence of the electrochemical energy storage cell and the obtained total operating time data, the two are paired at time points to calculate the change range of the index within a specified time period of each phase, and the phased index change range information is obtained.

[0036] S512: Construct the change range information of the stage indicators into a change trend curve, perform extrapolation calculation on the decline of the future change trend curve, and calibrate it in combination with the current total running time data to generate the life trend extension result.

[0037] S513: Calculate the time span required for the lifetime trend extension result to extend from the current time point to the preset lifetime termination threshold, and use the time span value as the estimated value of the remaining lifetime of the electrochemical energy storage cell to obtain the estimated result of the remaining lifetime of the electrochemical energy storage.

[0038] As a further aspect of the present invention, the process of performing extrapolation calculations on the decline of the future trend curve specifically involves:

[0039] Extract the latest continuous data segment from the trend curve;

[0040] A decline prediction curve is established by using polynomial fitting to fit the latest continuous data segment over time.

[0041] An electrochemical energy storage remaining lifetime estimation system is provided, the system being used to execute the above-described electrochemical energy storage remaining lifetime estimation method, the system comprising:

[0042] The voltage response acquisition module applies externally controlled multi-level voltage step excitation to both ends of the electrochemical energy storage cell under test. During the step excitation process, it acquires the terminal voltage and current data of the electrochemical energy storage cell in real time, constructs a voltage and current response sequence, extracts temperature-sensitive features from the sequence, and constructs an electrochemical performance response set.

[0043] The temperature correction processing module collects the temperature information of the current electrochemical energy storage cell and corrects the error of the temperature-sensitive features in the electrochemical performance response set to generate a temperature-corrected performance response set.

[0044] The performance deviation analysis module constructs a feature sequence for the current operating cycle by arranging the temperature-corrected performance response set of the electrochemical energy storage cell in chronological order. It compares the sequence with the same temperature-sensitive features of the electrochemical energy storage cell in the previous operating cycle to determine the deviation trend of each temperature-sensitive feature and construct a performance deviation feature map.

[0045] The degradation trend identification module identifies a sequence of temperature-sensitive features in the performance offset feature map that maintains a consistent direction of change, and constructs a phased degradation indicator sequence.

[0046] The remaining lifetime estimation module calculates the remaining lifetime estimate based on the phased degradation index sequence and total operating time of the electrochemical energy storage unit, and obtains the remaining lifetime estimation result of the electrochemical energy storage.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] In this invention, by applying multi-level voltage step excitation and capturing the maximum offset of instantaneous current, highly sensitive characteristics reflecting the internal electrochemical dynamics of the energy storage cell can be obtained. This avoids the limitations of traditional methods that rely on capacity decay as a lagging indicator for evaluation, enabling early insight into performance changes. Simultaneously, by real-time acquisition of ambient temperature and correction of dynamic response characteristics using preset correction coefficients, the interference of operating temperature fluctuations on performance evaluation is effectively eliminated, ensuring the consistency and comparability of characteristic data under different environmental conditions. Furthermore, by constructing an offset spectrum of performance characteristics over time and specifically identifying continuous and consistent change sequences, the irreversible degradation trend caused by internal aging is accurately identified, filtering out the influence of random noise and short-term fluctuations. Finally, based on the latest stage's degradation index change amplitude data, polynomial fitting and extrapolation are performed to establish a degradation prediction curve that can dynamically adjust according to the recent actual aging rate of the energy storage cell itself. This approach avoids applying general aging laws and significantly improves the personalization and accuracy of remaining lifetime estimation. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a flowchart of step S1 of the present invention;

[0051] Figure 3 This is a flowchart of step S2 of the present invention;

[0052] Figure 4 This is a flowchart of step S3 of the present invention;

[0053] Figure 5 This is a flowchart of step S4 of the present invention;

[0054] Figure 6 This is a flowchart of step S5 of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0056] Please see Figure 1 This invention provides a technical solution: a method for estimating the remaining lifetime of electrochemical energy storage, comprising the following steps:

[0057] S1: By applying externally controlled multi-level voltage step excitation to both ends of the electrochemical energy storage cell under test, the terminal voltage and current data of the electrochemical energy storage cell are collected in real time during the step excitation process, a voltage and current response sequence is constructed, and temperature-sensitive features are extracted from the sequence to construct an electrochemical performance response set.

[0058] S2: Collect the temperature information of the current electrochemical energy storage cell, correct the error of the temperature-sensitive features in the electrochemical performance response set, and generate a temperature-corrected performance response set;

[0059] S3: Construct a feature sequence for the current operating cycle by arranging the temperature-corrected performance response set of the electrochemical energy storage cell in chronological order. Compare it with the same temperature-sensitive features of the electrochemical energy storage cell in the previous operating cycle to determine the shift trend of each temperature-sensitive feature and construct a performance shift feature map.

[0060] S4: Identify offset trend sequences in the performance offset feature map where the direction of change of temperature-sensitive features remains consistent, and construct a phased degradation indicator sequence;

[0061] S5: Calculate the remaining lifetime estimate based on the phased degradation index sequence and total operating time of the electrochemical energy storage unit to obtain the remaining lifetime estimate result of the electrochemical energy storage.

[0062] The voltage and current response sequence includes the voltage change rate and the maximum current offset. The temperature-corrected performance response set includes the corrected voltage change rate and the corrected maximum current offset. The performance offset feature map includes the voltage change rate offset trend and the maximum current offset offset trend. The phased degradation index sequence specifically includes the trend consistency interval, feature item identifier, and time series index. The electrochemical energy storage remaining lifetime estimation results include the lifetime change trend, the current remaining lifetime value, and the estimated time node.

[0063] Please see Figure 2 The specific steps for obtaining the electrochemical performance response set are as follows:

[0064] S111: By applying externally controlled multi-level voltage step excitation to both ends of the electrochemical energy storage cell under test, the electrochemical energy storage cell is a lithium iron phosphate battery. During the step excitation process, the terminal voltage and current data of the electrochemical energy storage cell are collected in real time to construct a voltage and current response sequence.

[0065] A lithium iron phosphate (LFP) battery with a rated capacity of 100 amp-hours and a nominal voltage of 3.2 volts was operated by applying externally controlled multi-stage voltage step excitation across the terminals of the tested electrochemical energy storage cell. The excitation signal was generated by a programmable DC power supply with an output voltage accuracy of 0.001 volts. First, the LFP battery was placed in a 25°C environment, and its open-circuit voltage was continuously monitored. When the fluctuation of the open-circuit voltage was less than 0.002 volts for 10 consecutive minutes, the open-circuit voltage was considered stable, and the recorded stable voltage was 3.200 volts. The excitation process began by applying a constant voltage of 3.250 volts to the LFP battery through the programmable DC power supply and maintaining it for 30 seconds. During this 30-second constant voltage period, the terminal voltage and current of the LFP battery were simultaneously recorded using a Hall effect current sensor with a sampling frequency of 1000 Hz connected in series with the LFP battery and a voltage sensor with a sampling frequency of 1000 Hz connected in parallel with the LFP battery. Every 0.001 seconds, the data acquisition device collects and records a voltage value and a current value. For example, at 10.001 seconds, the recorded terminal voltage is 3.250 volts and the current is 0.512 amps; at 10.002 seconds, the recorded terminal voltage is 3.250 volts and the current is 0.511 amps. After a 30-second constant voltage phase, the externally controlled programmable DC power supply linearly increases the output voltage from 3.250 volts to 3.300 volts within 0.01 seconds, completing the first voltage step. Then, it maintains the voltage at 3.300 volts for another 30 seconds, continuously collecting voltage and current data. This process is repeated, sequentially stepping the voltage to 3.350 volts, 3.400 volts, 3.450 volts, and 3.500 volts, with each voltage plateau maintained for 30 seconds. The entire multi-stage voltage step excitation process lasts about 2.5 minutes, and finally a set of hundreds of thousands of data points containing timestamps, voltage readings and current readings, arranged in chronological order, is obtained, which constructs the voltage and current response sequence.

[0066] S112: Identify the voltage step occurrence segment in the voltage and current response sequence, calculate the voltage change rate in each voltage step occurrence segment, and simultaneously obtain the maximum current offset in the corresponding segment. Combine the voltage change rate and the maximum current offset of all segments as temperature-sensitive features to obtain dynamic response feature data.

[0067] In the voltage-current response sequence, voltage data is scanned point-by-point to identify segments where voltage changes rapidly. The specific identification process is as follows: starting from the first data point in the voltage-current response sequence, the voltage difference between the current sampling point and the next adjacent sampling point is calculated, and this voltage difference is divided by the sampling time interval of 0.001 seconds to obtain the instantaneous voltage change rate. A voltage change rate threshold is preset to define the start and end of voltage steps. The voltage change rate threshold is set based on continuous voltage monitoring of brand-new, healthy lithium iron phosphate batteries of the same model under completely static conditions for one hour, recording the normal voltage fluctuation range caused by internal electrochemical reactions in the lithium iron phosphate battery without external excitation. The maximum voltage fluctuation rate monitored in the experiment was 0.05 volts per second. To ensure the accuracy of the identification, the voltage change rate threshold is set to 0.1 volts per second, which is twice the observed maximum fluctuation rate. During the scanning process, when the calculated instantaneous voltage change rate continuously exceeds 0.1 volts per second, the timestamp of the first data point exceeding the threshold is marked as the beginning of the voltage step occurrence segment; when the instantaneous voltage change rate falls back to below 0.1 volts per second, the timestamp of the first data point below the threshold is marked as the end of the voltage step occurrence segment. For example, in the process of a voltage jump from 3.250 volts to 3.300 volts, the voltage changes by 0.050 volts in 0.01 seconds. The average voltage change rate in this segment is 0.050 volts divided by 0.01 seconds, which is 5 volts per second. After identifying such a voltage step occurrence segment, the current data segment corresponding to the exact timestamp is simultaneously locked. Within this current segment, all current values ​​are traversed from beginning to end, searching for and recording the maximum current value. For example, in the above step segment, the current instantly rises from a stable value of 0.4 amps before the step to a peak value of 8.5 amps, and then falls back. Therefore, the maximum current offset is the difference between the peak current of 8.5 amps and the steady-state current of 0.4 amps immediately before the step, which is calculated to be 8.1 amps. The calculated voltage change rate (5 volts per second) and the obtained maximum current offset (8.1 amps) are stored as a data pair. The above process of segment identification, rate calculation, and offset acquisition is repeated for all identified voltage step segments (from 3.250 volts to 3.300 volts, from 3.300 volts to 3.350 volts, etc.) throughout the entire excitation process. The voltage change rates of all five segments and the corresponding maximum current offsets are collected as a set of temperature-sensitive features to obtain dynamic response characteristic data.

[0068] S113: Call the voltage change rate and current maximum offset of all segments in the dynamic response feature data, integrate these two temperature-sensitive features into a structured data set, merge the structured data sets obtained under multiple excitations, and construct an electrochemical performance response set;

[0069] The dynamic response feature data is retrieved, which includes the voltage change rate and maximum current offset for all voltage step segments. Taking a complete five-stage voltage step excitation as an example, the acquired data pairs are: [(first stage voltage change rate, first stage maximum current offset), (second stage voltage change rate, second stage maximum current offset), ..., (fifth stage voltage change rate, fifth stage maximum current offset)]. Specific values ​​are: [(5 volts per second, 8.1 amps), (5 volts per second, 7.9 amps per second), (5 volts per second, 7.7 amps per second), (5 volts per second, 7.5 amps per second), (5 volts per second, 7.3 amps per second)]. The voltage change rate and maximum current offset, two temperature-sensitive features, are integrated into a structured dataset, which is marked with the timestamp of the current excitation operation, such as "September 29th, 3:40 AM". To construct a response set that reflects the long-term performance changes of lithium iron phosphate batteries, this type of excitation operation is repeated at fixed 4-hour intervals. In the next excitation cycle, namely "September 29th, 7:40 AM", steps S111 and S112 are executed again to acquire a new set of dynamic response characteristic data, for example: [(5V / s, 8.09A), (5V / s, 7.89A), (5V / s, 7.69A), (5V / s, 7.49A), (5V / s, 7.29A)]. Subsequently, the newly acquired structured data set is merged with all previous data sets in chronological order. Specifically, the merging operation involves appending the new structured data set to the end of the list storing all historical data sets, constructing an electrochemical performance response set.

[0070] Please see Figure 3 The specific steps for obtaining the performance response set after temperature correction are as follows:

[0071] S211: The ambient temperature of the electrochemical energy storage cell during the response process is obtained through a temperature sensor. The temperature data collected at each time point is recorded and integrated to establish ambient temperature information.

[0072] The surface temperature of the lithium iron phosphate battery during the response process is acquired using a PT100 temperature sensor with an accuracy of 0.1 degrees Celsius. To ensure accuracy, the temperature sensor is tightly bonded to the center of the electrochemical energy storage cell's casing using thermally conductive silicone grease, a location that best reflects the overall temperature of the cell. The sampling frequency of the temperature sensor is set to the same 1000 Hz as the voltage and current sensors. Throughout the entire process of the S111 multi-stage voltage step excitation, the temperature sensor synchronously acquires data. Each acquired temperature data point is accompanied by a timestamp accurate to milliseconds, ensuring precise temporal correspondence with the voltage and current data. For example, at time "September 29th, 3:40:15.123", in addition to the voltage and current values, a temperature value of 25.2 degrees Celsius is also acquired. Over 150,000 temperature data points are acquired during the entire 2.5-minute excitation process. These timestamped temperature data are recorded and integrated to form a temperature time series parallel to the voltage and current response sequence. An arithmetic average of all temperature readings within a single excitation cycle is calculated to obtain a representative ambient temperature. This is done to smooth out minor temperature fluctuations that may occur within a short period, resulting in a temperature value that represents the stable operating conditions throughout the entire excitation process. For example, summing the values ​​of 150,000 temperature data points and dividing by the total number of data points yields an average temperature of 25.3 degrees Celsius. The representative ambient temperature for each excitation cycle and its corresponding excitation cycle start timestamp are stored to establish ambient temperature information.

[0073] S212: Based on the temperature range matching rules, compare the temperature data in the ambient temperature information with multiple preset temperature ranges one by one, lock the unique temperature range to which it belongs, and extract the corresponding preset correction coefficient from the temperature range to obtain the matching correction coefficient value.

[0074] The calibration process involves matching the battery across multiple pre-defined temperature ranges. The division of these ranges and the determination of corresponding correction coefficients are based on calibration experimental data demonstrating significant differences in the electrochemical characteristics of lithium iron phosphate batteries at different temperatures. The specific calibration procedure is as follows: A brand-new lithium iron phosphate battery of the same model as the one under test is placed in a high-precision temperature-controlled chamber, and the ambient temperature is sequentially set to 0°C, 10°C, 25°C, and 40°C. At each temperature point, the battery is first kept at this constant temperature for 2 hours to ensure uniform and stable internal temperature. Then, steps S111 and S112 are performed to obtain the dynamic response characteristic data of the battery at the corresponding temperature. The maximum current deviation measured at 25°C is used as the benchmark (the correction coefficient is defined as 1.000 at this point). For example, assuming a voltage step from 3.250V to 3.300V, the maximum current deviation measured at 25°C is 8.1A, while the value measured at 10°C is 6.5A. The correction factor at 10 degrees Celsius is calculated by dividing the baseline value of 8.1 amps by the measured value of 6.5 amps, resulting in approximately 1.246. This establishes a mapping relationship between temperature ranges and preset correction factors. For example, temperature range one: -10.0 degrees Celsius to 5.0 degrees Celsius, correction factor 1.450; temperature range two: 5.1 degrees Celsius to 15.0 degrees Celsius, correction factor 1.246; temperature range three: 15.1 degrees Celsius to 30.0 degrees Celsius, correction factor 1.000; temperature range four: 30.1 degrees Celsius to 45.0 degrees Celsius, correction factor 0.885. The temperature data in the ambient temperature information established in S211, such as the 25.3 degrees Celsius obtained above, is compared one by one with these preset temperature ranges. The comparison process is as follows: First, it is determined whether 25.3 is within interval one (no); then it is determined whether it is within interval two (no); next, it is determined whether it is within interval three (15.1 <= 25.3 <= 30.0) (yes). Therefore, the unique temperature interval is identified as temperature interval three. Subsequently, the corresponding preset correction coefficient of 1.000 is extracted from temperature interval three to obtain the matching correction coefficient value.

[0075] S213: Error correction is performed on the temperature-sensitive characteristics of the electrochemical performance response set. By calling the voltage change rate and the maximum current offset, and using the matching correction coefficient value for correction, the maximum current offset after temperature and voltage plateau effect correction is calculated, and the temperature-corrected performance response set is generated.

[0076] Error correction is performed on the temperature-sensitive feature of the electrochemical performance response set. A structured dataset from the electrochemical performance response set constructed in S113 is used, for example, [(5V / s, 8.1A), (5V / s, 7.9A), (5V / s, 7.7A), (5V / s, 7.5A), (5V / s, 7.3A)] obtained at an average temperature of 25.3 degrees Celsius. Simultaneously, the matching correction coefficient value of 1.000 obtained in S212 is used. The correction process only targets the temperature-sensitive feature of the maximum current offset, and does not correct the voltage change rate. The reason for not correcting the voltage change rate is that the voltage change rate is precisely controlled by an external excitation device (programmable DC power supply) and is an input signal applied to the battery; its value is not affected by the battery's own state or ambient temperature. The correction calculation formula is as follows: The explanation of each letter is as follows: This represents the maximum current offset after correction for temperature and voltage plateau effects. The subscript corr stands for "Corrected". This value is the final result of the calculation and is intended to reflect the equivalent performance of the lithium iron phosphate battery at a standard temperature of 25 degrees Celsius and a reference voltage plateau, and is expressed in amperes (A). : Represents the maximum offset of the raw current actually measured in step S112. The subscript raw means "raw". This value is the direct measurement data without any processing and will be affected by the current ambient temperature and voltage plateau. The unit is amperes (A). : Represents a specific stage in a multi-stage voltage step excitation. In the current scenario, the excitation process includes five voltage step segments from 3.250 volts to 3.500 volts. Therefore, the value of n is an integer from 1 to 5, used to distinguish the response characteristics under different voltage steps. : Represents the temperature correction factor. The subscript T indicates "Temperature". This factor is a dimensionless value used to quantify the impact of ambient temperature on the internal impedance of lithium iron phosphate batteries, thereby correcting the current response. This factor value is obtained based on the mapping relationship between the temperature range and the correction factor established in S212. : Represents the preset temperature range in S212. Based on the average ambient temperature calculated in S211, a unique temperature range is matched. For example, when the temperature is 12.0 degrees Celsius, it matches temperature range two, so m is 2. : Represents the temperature correction weighting coefficient. The subscript T indicates "Temperature". This coefficient is a dimensionless value used to adjust the proportion of the temperature correction term in the entire correction formula. The principle for setting this weight is: the greater the influence of the physical effect on the measurement result, the larger its corresponding weighting coefficient should be. Through calibration experiments on S212, it was found that when the ambient temperature changes from 25 degrees Celsius to 10 degrees Celsius, the maximum current deviation is... The variation is much greater than that between different voltage platforms (such as 3.300 volts and 3.400 volts) at a constant temperature. The magnitude of the change. This indicates that temperature is the main factor affecting this characteristic parameter, therefore it is assigned a weight as high as 0.9 to ensure that the temperature correction effect dominates the overall correction. : Represents the voltage correction factor. The subscript V stands for "Voltage". This factor is a dimensionless value used to eliminate the influence of different voltage step plateaus on the maximum current offset. This is because even at a constant temperature, the polarization effect of lithium iron phosphate batteries differs at different states of charge (approximately reflected by different voltage plateaus). : Represents the voltage correction weighting coefficient. The subscript V stands for "Voltage". This coefficient is a dimensionless value used to adjust the weight of the voltage correction term in the overall correction formula. Its setting follows the... The same principle applies. Calibration experimental data shows that the voltage plateau... The impact is minor and small. To reflect this fact and to ensure that the sum of the total weights is 1.0 ( The voltage correction weight is set to 0.1. This allows the voltage plateau correction to serve as a fine-tuning process, supplementing the correction results that are primarily determined by temperature.

[0077] Suppose that the average temperature measured at another time point is 12.0 degrees Celsius, which falls within temperature range two (5.1 degrees Celsius to 15.0 degrees Celsius). What is the matching temperature correction factor? The value was 1.246. This was the maximum offset of the original current during the first voltage step transition segment (n=1, from 3.250 V to 3.300 V) measured at that time. The voltage is 6.5 amps. This is the voltage correction factor corresponding to the voltage step section. The default value is 1.02. Calculate using the input parameters: An. By performing this correction process on the maximum current offset of all data points in the electrochemical performance response set, the original data, which is affected by the actual operating temperature and voltage plateau, is converted into equivalent performance data at the calibration temperature (25 degrees Celsius) and reference voltage plateau, generating a temperature-corrected performance response set.

[0078] Please see Figure 4 The specific steps for obtaining the performance offset feature map are as follows:

[0079] S311: Arrange the temperature-corrected performance response set of the electrochemical energy storage cell in chronological order to establish the characteristic sequence of the current operating cycle;

[0080] The temperature-corrected performance response set is strictly arranged according to the original excitation operation time sequence. The temperature-corrected performance response set contains a structured data set obtained after each excitation operation, after which the temperature effect has been eliminated. Each data set in the temperature-corrected performance response set is associated with a unique timestamp. For example, data recording starts at "September 29th, 3:40 AM," and subsequent recording points are "September 29th, 7:40 AM," "September 29th, 11:40 AM," etc., with a time interval of 4 hours. These data sets are organized into a sequence according to the most recent timestamp. Each item in this sequence represents the dynamic performance of the lithium iron phosphate battery at a specific moment under standard temperature (25 degrees Celsius). This complete and ordered sequence forms the basis for subsequent analysis of the performance degradation trend of lithium iron phosphate batteries, establishing the current operating cycle characteristic sequence. The current operating cycle characteristic sequence is represented in data structure as an ordered list, where each element is a data object containing a timestamp and five corrected maximum current offset values.

[0081] S312: Retrieve the same temperature-sensitive features of the electrochemical energy storage cell in the previous operating cycle, and perform a comparison of the difference and rate of change of the same index point for the feature sequence of the current operating cycle to determine the offset trend of each temperature-sensitive feature and obtain feature offset trend information.

[0082] Retrieve similar temperature-sensitive characteristics from the previous operating cycle of the electrochemical energy storage cell. For each data point in the characteristic sequence of the current operating cycle (except for the first data point), perform a comparison of the difference and rate of change at the same index point. The comparison process is as follows: Select the data point located at "7:40 AM on September 29th" in the characteristic sequence of the current operating cycle. The maximum temperature-corrected current offset corresponding to the first voltage step in this data point is 8.09 A. Retrieve the data point that is immediately preceding the current operating cycle in the characteristic sequence, i.e., the data point at "3:40 AM on September 29th". Find the corresponding value at the same index position (first voltage step) of 8.10 A. First, calculate the difference by subtracting the value of the previous cycle from the value of the current cycle: 8.09 A minus 8.10 A, resulting in a difference of -0.01 A. Next, calculate the rate of change by dividing the calculated difference by the value of the previous cycle: -0.01 A divided by 8.10 A, resulting in approximately -0.00123, or -0.123%. This set of calculation results (difference -0.01 amps, rate of change -0.123%) collectively describes the shift trend of this specific temperature-sensitive feature from the previous cycle to the current cycle. This operation is repeated for all five temperature-sensitive features (i.e., the maximum current shift under five different voltage steps) at the data point of "September 29th, 7:40 AM". Furthermore, for each data point in the feature sequence of the entire current operating cycle (starting from the second point), a comparison is performed with its respective preceding data point to determine the shift trend of each temperature-sensitive feature, thus obtaining feature shift trend information.

[0083] S313: Combine the offset trends of each temperature-sensitive feature extracted from the feature offset trend information to construct a performance offset feature map;

[0084] The acquired feature offset trend information is systematically combined. For any data point in the feature sequence of the current operating cycle (except the first one), S312 has calculated an offset trend (consisting of a difference and a rate of change) for each temperature-sensitive feature within it. For example, at the time point "7:40 AM on September 29th", the offset trend information for the maximum current offset of five voltage steps is combined into a record, containing: Feature 1 (offset difference -0.01 A, offset change rate -0.123%), Feature 2 (offset difference -0.01 A, offset change rate -0.127%), Feature 3 (offset difference -0.01 A, offset change rate -0.130%), Feature 4 (offset difference -0.01 A, offset change rate -0.133%), and Feature 5 (offset difference -0.01 A, offset change rate -0.135%). These five sets of information describing the offset trend are combined as a single data unit and associated with the timestamp "September 29, 7:40 AM". This information extraction and combination operation is performed on all time points in the current operating cycle feature sequence (starting from the second time point). Finally, the offset trends of each temperature-sensitive feature extracted from all time points are integrated to form a multi-dimensional, time-series arranged dataset. This dataset is the performance offset feature map, whose structure is a time series, where each element contains a timestamp and five corresponding sets of offset trend information.

[0085] Please see Figure 5 The specific steps for obtaining the phased recession indicator sequence are as follows:

[0086] S411: Call the offset trend information of each temperature-sensitive feature in the performance offset feature map, make a directional judgment on the time series data of each feature, filter the sequence segments whose change direction remains unchanged in continuous period, associate them with the temperature-sensitive feature type to construct a consistent offset sequence.

[0087] Specifically, the process of determining the directionality of time series data for each feature is as follows:

[0088] The difference between the temperature-sensitive feature value of the current operating cycle and the same temperature-sensitive feature value of the previous operating cycle is calculated, and positive offset threshold and negative offset threshold are set.

[0089] If the result of the difference calculation is greater than the positive offset threshold, the direction of change of the temperature-sensitive feature is judged as a positive offset.

[0090] If the result of the difference calculation is less than the negative offset threshold, the direction of change of the temperature-sensitive feature is judged as a negative offset.

[0091] If the result of the difference operation is between the negative offset threshold and the positive offset threshold, then the direction of change of the temperature sensitive feature is judged as no offset.

[0092] The offset trend information of each temperature-sensitive feature in the performance offset feature map is retrieved. Directional determination is performed on the time series data of each independent temperature-sensitive feature in the performance offset feature map (e.g., the difference in the maximum current offset corresponding to the first voltage step). This determination process relies on preset positive and negative offset thresholds. The setting of the positive and negative offset thresholds is based on monitoring new lithium iron phosphate batteries during the initial continuous operation phase (e.g., the first 100 cycles) to analyze the natural fluctuation range of their characteristic parameters under conditions without significant degradation. Experimental data shows that the standard deviation of the maximum current offset difference calculated within continuous cycles is 0.003 A. To effectively filter out random fluctuations caused by measurement noise and minor environmental changes, the positive offset threshold is set to three times this standard deviation, i.e., +0.009 A; the negative offset threshold is set to three times this standard deviation, i.e., -0.009 A. Now, directional determination is performed on the time series data of each feature. Take the difference calculated in S312, for example, -0.01 amps. Compare the difference of -0.01 amps with the threshold. The specific judgment logic is as follows: if the difference is greater than the positive offset threshold +0.009 amps, the change direction of the temperature-sensitive feature is judged as "positive offset"; if the difference is less than the negative offset threshold -0.009 amps (such as -0.01 amps in this example), the change direction of the temperature-sensitive feature is judged as "negative offset"; if the difference is between the negative offset threshold and the positive offset threshold (including equal to -0.009 amps or +0.009 amps), the change direction of the temperature-sensitive feature is judged as "no offset". In multiple consecutive operating cycles (for example, 10 consecutive cycles, i.e., 40 hours), if the change direction of a certain feature is always judged as "negative offset", then the sequence segment in which "negative offset" occurs continuously is identified and associated with the temperature-sensitive feature type to which the sequence segment belongs (the maximum current offset corresponding to the first voltage step) to construct a consistent offset sequence.

[0093] S412: For each sequence segment in the consistency shift sequence, extract the temperature-sensitive feature type, the duration of the sequence segment trend, and the shift magnitude value. Integrate these three pieces of information into independent structured indicators. Collect the indicators of all sequence segments to obtain quantitative data on the decline trend.

[0094] Each segment of the consistency offset sequence is analyzed. Assume a segment is identified with the following information: the temperature-sensitive feature type is "maximum current offset corresponding to the first voltage step," and the segment's direction of change is "negative offset" over 20 consecutive measurement periods from "October 15th" to "October 25th." First, the temperature-sensitive feature type, "maximum current offset corresponding to the first voltage step," is extracted. Second, the duration of the segment's trend is extracted by calculating the number of consecutive measurement periods contained in the segment, which is 20 periods. Finally, the cumulative offset amplitude within the segment is calculated. This calculation is performed by summing the raw differences obtained from each calculation within these 20 periods (e.g., -0.010 A, -0.012 A, -0.011 A, etc., all less than -0.009 A). The sum is assumed to be -0.230 A. These three pieces of information—feature type (maximum current offset corresponding to the first voltage step), duration (20 cycles), and offset magnitude (-0.230 amps)—are integrated into a single structured indicator. This information extraction and integration process is repeated for all identified consistent offset sequence segments. The indicator generated from all sequence segments is then aggregated to obtain quantitative data on the decline trend.

[0095] S413: Based on quantitative data of the decline trend, call all the indicator items in the set, arrange and summarize them according to the time sequence identifier attached to each indicator item, and construct a phased decline indicator sequence.

[0096] Based on the acquired quantitative data on degradation trends, all structured indicators in the quantitative data set are retrieved. Each structured indicator records complete information about a degradation event and can be assigned a specific time sequence identifier based on the end time of its corresponding sequence segment. For example, the end time of the sequence segment corresponding to the first indicator is "October 25th," and the end time of the sequence segment corresponding to the second indicator is "November 10th." Based on the time sequence identifier attached to each indicator, all indicators in the set are arranged and summarized from earliest to latest. The specific operation of arrangement and summarization involves creating a new ordered list, traversing all indicators, and inserting indicators into the new list according to their time sequence identifiers. This sorted and summarized set forms a new time series, where each element is a quantified, stage-specific degradation event. This series clearly depicts the specific characteristics, duration, and severity of performance degradation of lithium iron phosphate batteries at different stages since their introduction into service, constructing a stage-specific degradation indicator sequence.

[0097] Please see Figure 6 The specific steps for obtaining the remaining lifetime estimation results of electrochemical energy storage are as follows:

[0098] S511: Based on the phased degradation index sequence of electrochemical energy storage cells and the obtained total operating time data, the two are paired at time points to calculate the change range of the index within a specified time period of each phase, and obtain the phased index change range information.

[0099] Based on the phased degradation indicator sequence of electrochemical energy storage cells and the acquired total operating time data, the phased degradation indicator sequence and the total operating time data are precisely paired at specific time points. The total operating time data is a continuously accumulated time record since the lithium iron phosphate battery was put into use. For example, on "October 25th", the total operating time is 1200 hours; on "November 10th", the total operating time is 1500 hours. An indicator item is extracted from the phased degradation indicator sequence, with an offset of -0.230 amps. Its corresponding sequence segment begins at a total operating time of 800 hours and ends at a total operating time of 1200 hours. The change in the indicator within a specified time period of this phase is calculated by dividing the offset by the continuous operating time of that phase. Specifically, -0.230 amps divided by (1200 hours minus 800 hours), i.e., -0.230 amps divided by 400 hours, yields a change of -0.000575 amps per hour. This pairing and calculation is performed on each indicator item in the phased decline indicator sequence to obtain a series of indicator change magnitude values ​​at different total running length stages. These values ​​together constitute the phased indicator change magnitude information.

[0100] S512: Construct a trend curve from the information on the magnitude of changes in phased indicators, perform extrapolation calculations on the decline of future trend curves, and calibrate it in combination with the current total running time data to generate a lifespan trend extension result.

[0101] The specific process of extrapolating the decline of the future trend curve is as follows:

[0102] Extract the most recent continuous data segment from the trend curve;

[0103] A decline prediction curve is established by using polynomial fitting to fit the latest continuous data segment in time.

[0104] The information on the changes in phased indicators is plotted, with total runtime on the x-axis and the magnitude of indicator changes on the y-axis, to construct a trend curve consisting of a series of discrete points. Extrapolation is performed on the most recent continuous data segment of this curve, such as the last 30 data points. The extrapolation uses a polynomial fitting method, specifically establishing a second-order polynomial to describe the change in the degradation rate. The fitting process involves finding a second-order polynomial that minimizes the mean square error between the polynomial curve and the 30 most recent continuous data points. The least squares algorithm is used to determine the three coefficients of the polynomial, thus establishing a degradation prediction curve. After generating the degradation prediction curve, its starting point is aligned and calibrated with the latest total runtime data point to ensure that the initial value of the prediction curve is exactly the same as the last actual measurement value, thereby generating the lifetime trend extension result. The lifetime trend extension result is a curve extending from the current moment into the future, predicting how the performance degradation rate of lithium iron phosphate batteries will continue to change over time.

[0105] S513: Calculate the time span required for the lifetime trend extension result to extend from the current time point to the preset lifetime termination threshold, and use the time span value as the estimated value of the remaining lifetime of the electrochemical energy storage cell to obtain the estimated result of the remaining lifetime of the electrochemical energy storage cell.

[0106] The calculation determines the time span required for the lifetime trend extension result to extend from the current point in time to the preset lifetime termination threshold. The preset lifetime termination threshold is set based on a complete cycle life test experiment conducted on the same type of lithium iron phosphate battery. The experiment involved repeatedly charging and discharging the battery under standard conditions until its actual usable capacity decayed to 80% of its factory rated capacity. At the point where the capacity decayed to 80%, steps S111 and S112 were performed, and the value of its "maximum current offset corresponding to the first voltage step" (after temperature correction) was measured to be 4.86 amps, while this value is 8.10 amps in the battery's brand new state. Therefore, 4.86 amps was set as the lifetime termination threshold for this characteristic. The formula for calculating the remaining lifetime estimate is as follows: The explanation of each letter is as follows: : Represents the estimated remaining lifetime of the electrochemical energy storage cell. This is the final target to be calculated, indicating the length of time, in hours (h), from the current moment until the lithium iron phosphate battery reaches its end-of-life threshold. : Represents the predicted total runtime at the end of the battery's lifespan. This is a predicted value, representing the total accumulated operating time of the lithium iron phosphate battery from the start of use until its performance degrades to the preset end-of-life threshold, in hours (h). : Represents the current total runtime. This is an actual recorded value, indicating the total accumulated working time from the start of the lithium iron phosphate battery's use to the time of this lifespan prediction calculation, in hours (h).

[0107] The decay prediction curve is obtained by solving for the intersection of the decay prediction curve established in S512 and the lifespan termination threshold. This decay prediction curve is a second-order polynomial, and its general form is: Solving for the end-of-life time is to let... Thus, the following calculation formula is obtained:

[0108] The explanation of each letter is as follows: : In the second-order polynomial model, the predicted maximum current offset value at a total running time of T, expressed in amperes (A). : is the independent variable in the second-order polynomial model, representing the total running time in hours (h). : Represents the coefficient of the quadratic term in the second-order polynomial. This coefficient determines the curvature of the degradation curve, reflecting the "acceleration" of performance degradation. Its setting is not pre-set, but rather calculated by fitting the latest stage indicator change data points obtained in S511 using the least squares algorithm in S512. Its value is entirely determined by the recent actual performance degradation data of the tested battery. The unit is ampere-hour² (A / h²). : Represents the coefficient of the first-order term in the second-order polynomial. This coefficient primarily determines the linear trend of the recession curve and can be understood as the "initial velocity" of the recession. (and) Similarly, this value is calculated by least-squares fitting of actual measurement data, rather than being arbitrarily set. It reflects the main rate of recent decline. The unit is amperes per hour (A / h). : Represents the constant term of the second-order polynomial. This coefficient is the intercept of the fitted curve at T=0 (the starting point of the fitting interval). It is also the result calculated using the least squares fitting method, used to ensure the mathematical integrity of the fitted curve, and is consistent with... and Together, we determine the overall shape of the curve. The unit is ampere (A). : Represents the preset end-of-life threshold. This value is a specific maximum current offset value. When this performance indicator of the lithium iron phosphate battery degrades to this value, its lifespan is considered over. This threshold is determined through full-life experiments on benchmark batteries, and the unit is ampere (A).

[0109] Let the current total running time be... The timeframe is 3000 hours. By fitting a second-order polynomial to the latest 30 data points, the coefficients of the decay prediction curve are obtained as follows: amps / hour² amperes / hour Safe. Preset end-of-life threshold. It is 4.86 amps. Calculate the total runtime at the end of its lifespan. : Hours. Set the predicted total runtime obtained through the above calculations. The remaining lifespan is 5500 hours. Then calculate the remaining lifespan. : The remaining lifetime of the electrochemical energy storage unit is estimated using a time span of 2500 hours.

[0110] An electrochemical energy storage remaining lifetime estimation system is provided. This system is used to execute the aforementioned electrochemical energy storage remaining lifetime estimation method. The system includes:

[0111] The voltage response acquisition module applies externally controlled multi-level voltage step excitation to both ends of the electrochemical energy storage cell under test. During the step excitation process, it acquires the terminal voltage and current data of the electrochemical energy storage cell in real time, constructs a voltage and current response sequence, extracts temperature-sensitive features from the sequence, and constructs an electrochemical performance response set.

[0112] The temperature correction processing module collects the current temperature information of the electrochemical energy storage cell and corrects the error of the temperature-sensitive features in the electrochemical performance response set to generate a temperature-corrected performance response set.

[0113] The performance deviation analysis module constructs a feature sequence for the current operating cycle by arranging the temperature-corrected performance response set of the electrochemical energy storage cell in chronological order. It compares this sequence with the same temperature-sensitive features of the electrochemical energy storage cell in the previous operating cycle to determine the deviation trend of each temperature-sensitive feature and construct a performance deviation feature map.

[0114] The degradation trend identification module identifies a sequence of temperature-sensitive features in the performance offset feature map that maintains a consistent direction of change, and constructs a sequence of phased degradation indicators.

[0115] The remaining lifetime estimation module calculates the estimated remaining lifetime based on the phased degradation index sequence and total operating time of the electrochemical energy storage unit, thus obtaining the estimated remaining lifetime of the electrochemical energy storage.

[0116] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for estimating the remaining lifetime of electrochemical energy storage, characterized in that, Includes the following steps: S1: By applying externally controlled multi-level voltage step excitation to both ends of the electrochemical energy storage cell under test, the terminal voltage and current data of the electrochemical energy storage cell are collected in real time during the step excitation process, a voltage and current response sequence is constructed, and temperature-sensitive features are extracted from the sequence to construct an electrochemical performance response set. S2: Collect the temperature information of the current electrochemical energy storage cell, perform error correction on the temperature-sensitive features in the electrochemical performance response set, and generate a temperature-corrected performance response set; S3: Construct a feature sequence for the current operating cycle by arranging the temperature-corrected performance response set of the electrochemical energy storage cell in chronological order, compare it with the same temperature-sensitive features of the electrochemical energy storage cell in the previous operating cycle, determine the shift trend of each temperature-sensitive feature, and construct a performance shift feature map. S4: Identify the offset trend sequence in the performance offset feature map where the change direction of temperature-sensitive features remains consistent, and construct a phased degradation index sequence; S5: Calculate the remaining lifetime estimate based on the phased degradation index sequence and total operating time of the electrochemical energy storage unit to obtain the remaining lifetime estimate result of the electrochemical energy storage.

2. The method for estimating the remaining lifetime of electrochemical energy storage according to claim 1, characterized in that, The voltage and current response sequence includes the voltage change rate and the maximum current offset; the temperature-corrected performance response set includes the corrected voltage change rate and the corrected maximum current offset; the performance offset feature map includes the voltage change rate offset trend and the maximum current offset offset trend; the phased degradation index sequence specifically includes a trend consistency interval, feature item identifier, and time series index; and the electrochemical energy storage remaining lifetime estimation result includes the lifetime change trend, the current remaining lifetime value, and the estimated time node.

3. The method for estimating the remaining lifetime of electrochemical energy storage according to claim 1, characterized in that, The specific steps for obtaining the electrochemical performance response set are as follows: S111: By applying externally controlled multi-level voltage step excitation to both ends of the electrochemical energy storage cell under test, wherein the electrochemical energy storage cell is a lithium iron phosphate battery, the terminal voltage and current data of the electrochemical energy storage cell are collected in real time during the step excitation process to construct a voltage and current response sequence. S112: Identify the voltage step occurrence segment in the voltage and current response sequence, calculate the voltage change rate in each voltage step occurrence segment, and simultaneously obtain the maximum current offset in the corresponding segment. Combine the voltage change rate and the maximum current offset of all segments as temperature-sensitive features to obtain dynamic response feature data. S113: Call up the voltage change rate and current maximum offset of all segments in the dynamic response feature data, integrate these two temperature-sensitive features into a structured data set, merge the structured data sets obtained under multiple excitations, and construct an electrochemical performance response set.

4. The method for estimating the remaining lifetime of electrochemical energy storage according to claim 3, characterized in that, The specific steps for obtaining the temperature-corrected performance response set are as follows: S211: The ambient temperature of the electrochemical energy storage cell during the response process is obtained through a temperature sensor. The temperature data collected at each time point is recorded and integrated to establish ambient temperature information. S212: According to the temperature range matching rule, the temperature data in the ambient temperature information is compared with multiple preset temperature ranges one by one to lock the unique temperature range to which it belongs, and the corresponding preset correction coefficient is extracted from the temperature range to obtain the matching correction coefficient value. S213: For the temperature-sensitive characteristics in the electrochemical performance response set, error correction is performed by calling the voltage change rate and the maximum current offset, and using the matching correction coefficient value for correction processing. The maximum current offset after temperature and voltage plateau effect correction is calculated to generate the temperature-corrected performance response set.

5. The method for estimating the remaining lifetime of electrochemical energy storage according to claim 4, characterized in that, For correction processing using matching correction coefficient values, the formula is as follows: ； Calculate the maximum current offset after temperature and voltage plateau effect correction. ; in, This represents the maximum deviation of the original current obtained from the measurement. This represents a specific stage in a multi-stage voltage step excitation system. Represents the temperature correction factor. Represents the preset temperature range. Represents the temperature correction weighting coefficient. Represents the voltage correction factor. : Represents the voltage correction weighting coefficient.

6. The method for estimating the remaining lifetime of electrochemical energy storage according to claim 4, characterized in that, The specific steps for obtaining the performance offset feature map are as follows: S311: Arrange the temperature-corrected performance response set of the electrochemical energy storage cell in chronological order to establish the characteristic sequence of the current operating cycle; S312: Retrieve the same type of temperature-sensitive features from the previous operating cycle of the electrochemical energy storage cell, and perform a comparison of the difference and rate of change at the same index point for the feature sequence of the current operating cycle, determine the offset trend of each temperature-sensitive feature, and obtain feature offset trend information. S313: Combine the offset trends of each temperature-sensitive feature extracted from the feature offset trend information to construct a performance offset feature map.

7. The method for estimating the remaining lifetime of electrochemical energy storage according to claim 6, characterized in that, The specific steps for obtaining the phased decline indicator sequence are as follows: S411: Call the offset trend information of each temperature-sensitive feature in the performance offset feature map, make a directional judgment on the time series data of each feature, filter the sequence segments whose change direction remains unchanged in continuous period, associate them with the temperature-sensitive feature type to construct a consistent offset sequence. S412: For each sequence segment in the consistency offset sequence, extract the temperature-sensitive feature type, the duration of the sequence segment trend, and the offset magnitude value. Integrate these three pieces of information into independent structured index items. Collect the index items of all sequence segments to obtain quantitative data on the decline trend. S413: Based on the quantitative data of the decline trend, call all the indicator items in the set, arrange and summarize them according to the time sequence identifier attached to each indicator item, and construct a phased decline indicator sequence.

8. The method for estimating the remaining lifetime of electrochemical energy storage according to claim 7, characterized in that, The specific steps for obtaining the remaining lifetime estimation results of the electrochemical energy storage are as follows: S511: Based on the phased degradation index sequence of the electrochemical energy storage cell and the obtained total operating time data, the two are paired at time points to calculate the change range of the index within a specified time period of each phase, and the phased index change range information is obtained. S512: Construct the change range information of the stage indicators into a change trend curve, perform extrapolation calculation on the decline of the future change trend curve, and calibrate it in combination with the current total running time data to generate the life trend extension result. S513: Calculate the time span required for the lifetime trend extension result to extend from the current time point to the preset lifetime termination threshold, and use the time span value as the estimated value of the remaining lifetime of the electrochemical energy storage cell to obtain the estimated result of the remaining lifetime of the electrochemical energy storage.

9. The method for estimating the remaining lifetime of electrochemical energy storage according to claim 8, characterized in that, The process of extrapolating the decline of the future trend curve is as follows: Extract the latest continuous data segment from the trend curve; A decline prediction curve is established by using polynomial fitting to fit the latest continuous data segment over time.

10. A system for estimating the remaining lifetime of electrochemical energy storage, characterized in that, The electrochemical energy storage remaining lifetime estimation method according to any one of claims 1-9, wherein the system comprises: The voltage response acquisition module applies externally controlled multi-level voltage step excitation to both ends of the electrochemical energy storage cell under test. During the step excitation process, it acquires the terminal voltage and current data of the electrochemical energy storage cell in real time, constructs a voltage and current response sequence, extracts temperature-sensitive features from the sequence, and constructs an electrochemical performance response set. The temperature correction processing module collects the temperature information of the current electrochemical energy storage cell and corrects the error of the temperature-sensitive features in the electrochemical performance response set to generate a temperature-corrected performance response set. The performance deviation analysis module constructs a feature sequence for the current operating cycle by arranging the temperature-corrected performance response set of the electrochemical energy storage cell in chronological order. It compares the sequence with the same temperature-sensitive features of the electrochemical energy storage cell in the previous operating cycle to determine the deviation trend of each temperature-sensitive feature and construct a performance deviation feature map. The degradation trend identification module identifies a sequence of temperature-sensitive features in the performance offset feature map that maintains a consistent direction of change, and constructs a phased degradation indicator sequence. The remaining lifetime estimation module calculates the remaining lifetime estimate based on the phased degradation index sequence and total operating time of the electrochemical energy storage unit, and obtains the remaining lifetime estimation result of the electrochemical energy storage.

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