Lead-acid battery residual life estimation method based on multi-magnification discharge characteristic extraction

Abstract

The application discloses a lead-acid battery residual life estimation method based on multiple discharge rate characteristic extraction, and relates to the technical field of health state detection of lead-acid batteries for substations. The application adopts a multiple rate partial discharge experiment, extracts three types of core characteristics, i.e., a constant capacity discharge termination voltage, a constant capacity discharge voltage decay rate and a discharge rate influence coefficient, calibrates the deviation of the core characteristics under a high rate due to polarization effect by using a standard rate, and finally establishes a mathematical model for estimating the residual service life of the lead-acid battery through multivariate nonlinear fitting. The application does not need to deeply discharge the battery, but can quickly extract the characteristics representing the residual health state of the battery through direct measurement data such as voltage, current and time, is suitable for the aging characteristics of the lead-acid battery for the substation, improves the estimation accuracy through rate sensitivity compensation, and realizes fast and accurate estimation of the residual life of the lead-acid battery for the substation on site while reserving the capacity margin of the standby power supply.

Description

[0001] This invention belongs to the field of health status detection technology for lead-acid batteries used in substations, specifically involving a method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction. Background Technology

[0002] Lead-acid batteries in substations, as core components of the power grid's backup power supply, bear the crucial responsibility of ensuring uninterrupted power supply to critical equipment such as relay protection and switch operations during power outages. Their remaining lifespan directly determines the reliability of the backup power supply. Traditional lead-acid batteries have a relatively short cycle life, with a theoretical cycle count only about one-third that of lithium-ion batteries. Therefore, there is still considerable room for improvement in the cycle life of lead-acid batteries, especially those using new materials, structures, and technologies, such as bipolar lead-acid batteries and lead-carbon batteries. However, from a battery perspective, due to the "weakest link" effect, any inconsistency in the charge-discharge performance of any single cell in the battery pack can lead to premature failure of the entire pack, even though most cells in the pack are still in good health.

[0003] Health status assessment technology for lead-acid batteries has become a hot research topic. The mainstream method in substations is verification discharge, which requires completely discharging the battery to a failure state to calibrate its actual capacity. For a battery with a nominal capacity of 500Ah, discharging at a 0.1C current for 10 hours is considered to confirm its capacity of 500Ah. However, this method requires the entire lead-acid battery pack to be tested off-grid, necessitating the addition of a backup power supply. Furthermore, the deep charge-discharge process is time-consuming and can exacerbate the shedding of active materials, sulfation of the plates, and irreversible damage to the battery, further shortening its remaining lifespan and increasing maintenance and replacement costs. Traditional methods often use single parameters such as the discharge process or cutoff voltage to establish a lifespan correlation model, without considering the increase in internal polarization resistance and decrease in active material utilization as the battery ages, as well as the complex and variable load current and the nonlinear increase in the voltage response sensitivity to discharge current. Existing data-driven algorithms require a large amount of historical operating data as an identifier, and iterative calculations using algorithms such as neural networks and Kalman filters cannot achieve real-time online identification of the health status of lagging batteries, resulting in premature battery failures. After long-term service, lead-acid batteries in substations commonly suffer from aging problems such as plate corrosion, active material shedding, and uneven electrolyte concentration, resulting in fundamentally different electrochemical characteristics compared to new batteries. Traditional methods, which build models based on battery characteristics without specifically compensating for rate effects, suffer from poor model generalization and are prone to battery misjudgment.

[0004] Lead-acid batteries, used as backup power, are constantly in a float charge state to respond to AC system outages. The current supplied by these batteries depends on the load requirements, and their aging condition is highly sensitive to the discharge current rate. In float charge mode, lead-acid batteries are constantly charged at low currents, which easily leads to plate corrosion, active material shedding, and lead sulfate crystal accumulation. These aging products increase the battery's internal ohmic polarization and concentration polarization resistance. Higher discharge current rates result in greater current density and faster charge migration, leading to a more significant voltage drop due to increased polarization resistance. Float charging aging reduces the utilization rate of the battery's active materials, especially at high discharge current rates, where the active materials do not have enough time to fully participate in the reaction, causing a rapid voltage drop and further amplifying the rate-sensitive characteristics. Given that current technology cannot address the core need for rapid on-site assessment of the remaining lifespan of lead-acid batteries in substations, a method is urgently needed to explore the health status of lead-acid batteries under float charging aging. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by providing a method for estimating the remaining life of lead-acid batteries based on the extraction of multi-rate discharge characteristics.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction includes: Step S1: Design the experimental steps for multi-rate partial charge-discharge test and remaining capacity calibration, and collect experimental data; Step S2: Extract core features characterizing the aging degree of lead-acid batteries based on experimental data; Step S3: Under the influence of different current magnifications, calibrate the deviation of core features caused by polarization at high current magnification using the standard current magnification; Step S4: Use the multivariate nonlinear fitting method to establish a quantitative mathematical relationship for estimating the remaining service life of lead-acid batteries based on the calibrated core characteristic parameters.

[0007] To optimize the above technical solution, the specific measures also include: Furthermore, the nominal voltage of the sealed lead-acid battery is 2V, the nominal capacity is 500Ah, the charging current is 50A, the float charge voltage is 2.23V, the equalization charge voltage is 2.3V, and the full capacity discharge cutoff voltage is 1.8V. The remaining health status assessment object of this invention is the "Shengyang" brand GFMD series valve-regulated sealed lead-acid battery used in substations.

[0008] Furthermore, the design and implementation of the charge-discharge experiment in step S1 employs an eight-channel charge-discharge tester. The tester is a Xinwei brand, and eight lead-acid batteries from the same batch were selected for multi-rate partial charge-discharge testing and remaining capacity calibration experiments. The remaining capacity calibration experiment in step S1 involves a capacity test on the selected lead-acid batteries to calibrate their remaining health status. Multiple charge-discharge cycles can activate the battery, bringing it into a stable operating state and more accurately reflecting its true capacity. Additionally, after the first charge-discharge cycle, the internal chemical state of the battery may not be completely stable, leading to capacity deviations. Three charge-discharge cycles can help average these random errors and obtain more consistent results.

[0009] Further, step S1 includes Step S11: First, let the battery rest for one hour and discharge it at a constant current rate of 0.1C to a cutoff voltage of 1.8V. Then, after the discharge is completed and the battery is rested for one hour, charge it at a constant current rate of 0.15C to a voltage of 2.35V until the charging current drops to 8A. At the same time, complete the charge and discharge experiment three times and take the discharge capacity as the actual capacity of the battery each time. The accurate value of the current remaining capacity of the battery is obtained by calculating the average of the three discharge capacities. Step S12: Conduct a multi-rate partial charge-discharge experiment. Charge the battery at a constant current of 0.15C to 2.35V, then charge at a constant voltage until the charging current drops to 8A. After charging, let the battery rest for two hours and discharge it at different target rates of 0.1C, 0.12C, 0.14C, 0.16C, and 0.18C to the midpoint of 2V to complete the multi-rate partial charge-discharge experiment. This end voltage indicates that the battery has not been fully discharged.

[0010] Furthermore, in step S2, the electrochemical performance of batteries in different health states is compared and analyzed based on experimental data such as current, voltage, and time collected during multi-rate partial charge-discharge tests. A set of characteristic parameters strongly correlated with remaining lifespan is extracted, and core features strongly correlated with battery aging are extracted. These core features include the constant-capacity discharge termination voltage. U mid Voltage decay rate dU / dt and the influence coefficient of the multiplier K i。

[0011] Furthermore, in step S2, the state of charge of the battery, the discharge termination voltage, and the remaining service life are related. Therefore, the first feature is to compare the discharge termination voltage of each battery when discharging the same capacity at different discharge rates. U midTo reduce the impact of state of charge on remaining capacity assessment, batteries in poorer health have higher polarization resistance, resulting in lower termination voltage when discharging the same capacity. Therefore, the utilization rate of active materials inside aged batteries is low during discharge, leading to a faster voltage drop within the same time period. Hence, the second characteristic is the slope of voltage drop when discharging the same capacity at different discharge rates, i.e., the voltage decay rate. dU / dt, In step S2, the battery with the worse health condition is more sensitive to changes in discharge rate, and the rate influence coefficient is... K i for

[0012] In the formula, C represents the charge / discharge current ratio, C0 0.1C This indicates the capacity discharged at a voltage of 0.1C to the midpoint of 2V. C iC Indicates i The capacity discharged at the midpoint of 2V discharge rate.

[0013] Furthermore, in step S3, under the same healthy state, the higher the discharge rate, the higher the discharge termination voltage. U mid The smaller the voltage decay rate dU / dt The larger the value, the better, because it considers the influence of different current rates and compensates for the error in quickly estimating the remaining life of lead-acid batteries at high current rates. This is because the quantitative mathematical relationship between the remaining health status of lead-acid batteries and the three core characteristics is crucial. K i The sensitivity of battery health to rate changes is quantified by partial discharge capacity. Since the internal polarization effect of the battery is weak at low rates, the extracted feature parameters can truly reflect the degree of battery aging. Therefore, 0.1C is selected as the unbiased reference rate. Using the benchmark relationship between feature parameters and remaining battery life at 0.1C, a mapping model from high-rate characteristics to low-rate characteristics is established, and the feature parameters are fitted and corrected using experimental data.

[0014] Furthermore, in step S3, the constant capacity discharge termination voltage... U mid The corrected formula is In the formula, U mid_nC This represents the measured discharge termination voltage at high rates. U eq mid_0.1C This represents the corrected equivalent constant-capacity discharge termination voltage at a 0.1C rate. a n and b nThese are coefficients fitted from high-magnification experimental data.

[0015] Furthermore, in step 3, the voltage decay rate dU / dt The corrected formula is

[0016] In the formula, dU / dt nC This represents the measured voltage decay rate at high magnification. dU / dt eq 0.1C This represents the voltage decay rate at an equivalent 0.1C rate after correction. c n and d n These are coefficients fitted from high-magnification experimental data.

[0017] Furthermore, in step S4, a quantitative mathematical relationship for estimating the remaining health status of substation lead-acid batteries based on calibrated core characteristic parameters is established through multivariate nonlinear fitting:

[0018] In the formula, U eq mid_0.1C This represents the corrected equivalent constant-capacity discharge termination voltage at 0.1C. dU / dt mid_nC This represents the voltage decay rate at the equivalent 0.1C obtained after correction. K i Defined as 0.1C multiplier and the first i The ratio of the release capacity at different multipliers. a , b , c , d , e These are the model calibration coefficients. I This represents the magnitude of the current multiplier.

[0019] The beneficial effects of this invention are: This invention proposes a method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction. Through multi-rate partial discharge experiments ranging from 0.1C to 0.18C, it integrates three core characteristics representing battery health: discharge termination voltage, voltage decay rate, and rate influence coefficient. This establishes a mathematical model for estimating the remaining life of lead-acid batteries that adapts to float charging aging characteristics. This method eliminates the need for deep discharge experiments, avoiding the vacuum period of substation backup power and irreversible battery damage. Furthermore, it accurately matches the aging characteristics of lead-acid batteries, such as increased polarization resistance and decreased utilization of active materials, through a rate sensitivity compensation mechanism, achieving high-precision assessment. Simultaneously, the proposed method relies only on directly measured parameters such as voltage, current, and time, requiring no specialized equipment or complex algorithms. It can assess battery health in real time based on discharge experiments, significantly improving the efficiency of substation on-site operation and maintenance. It avoids misjudgments caused by single-feature assessments or failure to adapt to aging mechanisms, reducing resource waste and safety hazards, and lowering operation and maintenance costs.

[0020] This invention employs multi-rate partial discharge experiments to extract three core features: the termination voltage of constant-capacity discharge, the decay rate of constant-capacity discharge voltage, and the influence coefficient of discharge rate. It utilizes a standard rate calibration to correct deviations in these core features caused by polarization effects at high rates. Finally, a mathematical model for estimating the remaining lifespan of lead-acid batteries is established through multivariate nonlinear fitting. This invention eliminates the need for deep discharge; it rapidly extracts features characterizing the battery's remaining health status using only direct measurement data such as voltage, current, and time. Adapting to the aging characteristics of lead-acid batteries in substations, it improves estimation accuracy through rate sensitivity compensation. This invention achieves rapid and accurate estimation of the remaining lifespan of lead-acid batteries in substations while maintaining backup power capacity margins. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method for estimating the remaining life of lead-acid batteries based on the extraction of multi-rate discharge characteristics.

[0022] Figure 2 It is a discharge voltage curve of lead-acid batteries used in substations at different discharge rates.

[0023] Figure 3 This is a diagram showing the discharge end voltage of lead-acid batteries in various healthy states when they discharge the same capacity at different discharge rates, according to one embodiment.

[0024] Figure 4 This is a diagram showing the voltage decay rate of lead-acid batteries in different health states when discharging the same capacity at different discharge rates, as described in one embodiment.

[0025] Figure 5 In one embodiment, lead-acid batteries in various health states are compared with the first... i A graph showing the ratio of the discharge capacity when discharged at different rates to the midpoint termination voltage.

[0026] Figure 6 This is an example of an embodiment showing the estimated remaining lifespan of a training set of lead-acid battery samples.

[0027] Figure 7 This is an example of verifying the estimated remaining lifespan of a sample of lead-acid batteries. Detailed Implementation

[0028] The invention will now be described in further detail with reference to the accompanying drawings.

[0029] In one embodiment, the present invention proposes a method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction, the process of which is as follows: Figure 1 As shown, the specific steps include: Step S1: Design the multi-rate partial charge-discharge test and remaining capacity calibration experimental steps, and collect experimental data such as current, voltage, and time.

[0030] In this embodiment, the test object is the "Shengyang" brand GFMD series valve-regulated sealed lead-acid battery used in substations. The battery has a nominal voltage of 2V, a nominal capacity of 500Ah, a charging current of 50A, a float charge voltage of 2.23V, an equalization charge voltage of 2.3V, and a full capacity discharge cutoff voltage of 1.8V.

[0031] In this embodiment, the design and implementation of the charge and discharge experiment adopted an eight-channel Xinwei charge and discharge tester, and selected eight lead-acid batteries from the same batch that were put into operation to conduct multi-rate partial charge and discharge tests and remaining capacity calibration experiments. The charge and discharge current is described by the C-rate.

[0032] In this embodiment, the remaining capacity calibration experiment is conducted as follows: The battery is left to stand for one hour, then discharged at a constant current rate of 0.1C to a cutoff voltage of 1.8V. After discharging, it is left to stand for one hour, then charged at a constant current rate of 0.15C to 2.35V, followed by constant voltage charging until the charging current drops to 8A. The complete charge-discharge experiment is repeated three times. The discharge capacity of each cycle is taken as the actual capacity of the battery. The accurate value of the current remaining capacity of the battery is obtained by averaging the three discharge capacities.

[0033] In this embodiment, the multi-rate partial charge-discharge experiment uses different current rates to discharge the fully charged battery to the 2V midpoint termination voltage. The specific test steps include: constant current charging at a 0.15C rate to 2.35V, constant voltage charging until the charging current drops to 8A, and then allowing it to rest for two hours after charging. The fully charged battery is then discharged at rates of 0.1C, 0.12C, 0.14C, 0.16C, and 0.18C respectively to the 2V midpoint termination voltage, which indicates that the battery was not fully discharged.

[0034] like Figure 2The graph shows the discharge voltage curves of a lead-acid battery at different discharge rates. The discharge rate gradually increases as the color lightens. The graph shows that the initial voltage drop increases non-linearly with increasing discharge rate. At low discharge rates, the discharge curve exhibits a clear plateau, while at higher rates, the voltage curve shows almost no plateau, exhibiting a continuous linear voltage decay. This reflects that at high discharge rates, the reaction rate of the active material cannot keep up with the discharge demand, leading to continuous polarization accumulation and a more significant polarization effect in the aging battery. Furthermore, the higher the discharge rate, the greater the absolute value of the voltage drop slope (dU / dt) over time. This is because at high discharge rates, the internal current density of the battery is high, making it easier for a lead sulfate passivation layer to form on the surface of the active material. Simultaneously, the electrolyte concentration gradient increases, leading to accelerated voltage decay. Under the premise of discharging the same capacity, the higher the discharge rate, the shorter the discharge time.

[0035] Step S2: Extract three core features characterizing the aging degree of lead-acid batteries based on experimental data.

[0036] In this embodiment, based on experimental data such as current, voltage, and time collected during multi-rate partial charge-discharge tests, the electrochemical performance of batteries in different health states is compared and analyzed, and a set of characteristic parameters strongly correlated with remaining lifespan is extracted: constant capacity discharge termination voltage. U mid Voltage decay rate dU / dt Multiplier Influence Coefficient K i .

[0037] In this embodiment, since the state of charge, discharge termination voltage, and remaining lifespan of a battery are related, the first feature is to compare the discharge termination voltages of each battery when discharging the same capacity at different discharge rates. U mid This reduces the impact of state of charge on remaining capacity assessment. For example... Figure 3 The figure shows the discharge end voltage of lead-acid batteries in different health states under different discharge rates when discharging the same capacity in one embodiment. It can be seen from the figure that the battery with the worse health state has a lower discharge end voltage when discharging the same capacity due to its larger polarization resistance.

[0038] In this embodiment, the second feature is the slope of voltage drop when discharging the same capacity at different discharge rates, i.e., the voltage decay rate. dU / dt .like Figure 4 The figure shows the voltage decay rate of lead-acid batteries in various healthy states under different discharge rates when discharging the same capacity in one embodiment. It can be seen from the figure that the voltage of the aged battery drops faster and the voltage decay rate is higher in the same discharge time. This is because the utilization rate of the active material inside the aged battery is low.

[0039] In this embodiment, the third characteristic parameter quantifies the sensitivity of the aged battery to rate changes, and is defined as the ratio of the 0.1C rate to the [missing value]. i The ratio of the released capacity at different seed ratios K i .like Figure 5 The image shows a lead-acid battery in various health states at a 0.1C rate and a [missing information - likely a specific rate or value] in one embodiment. i The graph shows the ratio of the capacity discharged when the battery is discharged to the midpoint voltage at different discharge rates. It can be seen from the graph that the worse the battery's health condition, the more sensitive it is to changes in the discharge rate, and the larger the value of this parameter.

[0040] Step S3: Considering the influence of different current magnifications, use the standard current magnification to calibrate the deviation of the core feature caused by polarization at high current magnifications; Step S4: Establish a quantitative mathematical relationship for estimating the remaining service life of lead-acid batteries based on calibrated core characteristic parameters through multivariate nonlinear fitting.

[0041] In this embodiment, Figure 6 The image shows an embodiment where the remaining lifespan is estimated based on a nonlinear mathematical relationship between three types of calibrated core features and battery health status. The estimated RMSE is 0.0854. Figure 7 The figure shows the remaining healthy life of two validation battery samples calculated using this quantitative mathematical relationship. The prediction results have a small error compared with the remaining capacity calibration experimental results.

[0042] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction, characterized in that, include Step S1: Design the experimental steps for multi-rate partial charge-discharge test and remaining capacity calibration, and collect experimental data; Step S2: Extract core features characterizing the aging degree of lead-acid batteries based on experimental data; Step S3: Under the influence of different current magnifications, calibrate the deviation of core features caused by polarization at high current magnification using the standard current magnification; Step S4: Use the multivariate nonlinear fitting method to establish a quantitative mathematical relationship for estimating the remaining service life of lead-acid batteries based on the calibrated core characteristic parameters.

2. The method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction as described in claim 1, characterized in that, The sealed lead-acid battery has a nominal voltage of 2V, a nominal capacity of 500Ah, a charging current of 50A, a float charge voltage of 2.23V, an equalization charge voltage of 2.3V, and a full-capacity discharge cutoff voltage of 1.8V.

3. The method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction as described in claim 1, characterized in that, The design and implementation of the charge-discharge experiment in step S1 uses an eight-channel charge-discharge tester, and selects eight lead-acid batteries from the same batch for multi-rate partial charge-discharge testing and remaining capacity calibration experiments. The remaining capacity calibration experiment in step S1 performs a core capacity test on the selected lead-acid batteries to calibrate their remaining health status.

4. The method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction as described in claim 3, characterized in that, Step S1 includes Step S11: First, let the battery stand for one hour and discharge it at a constant current at the target rate to the cutoff voltage of 1.8V. Then, after the discharge is completed and the battery stands for one hour, charge it at a constant current at the target rate to 2.35V until the charging current drops to 8A. At the same time, complete the charge and discharge experiment three times and take the discharge capacity as the actual capacity of the battery each time. The accurate value of the current remaining capacity of the battery is obtained by calculating the average of the three discharge capacities. Step S12: Conduct a multi-rate partial charge and discharge experiment. Charge the battery at the target rate with constant current to 2.35V, and then charge it with constant voltage until the charging current drops to 8A. After charging is completed, let it stand for two hours and discharge the fully charged battery at different target rates to the midpoint of 2V to complete the multi-rate partial charge and discharge experiment.

5. The method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction as described in claim 4, characterized in that, In step S2, the electrochemical performance of batteries in different health states is compared and analyzed based on the experimental data collected in the multi-rate partial charge-discharge test. A set of characteristic parameters strongly correlated with the remaining lifespan is extracted, and core features strongly correlated with the degree of battery aging are extracted. These core features include the constant capacity discharge termination voltage. U mid Voltage decay rate dU / dt and the influence coefficient of the multiplier K i .

6. The method for estimating the remaining life of a lead-acid battery based on multi-rate discharge feature extraction as described in claim 1, characterized in that, In step S2, the battery's state of charge, discharge termination voltage, and remaining lifespan are related. In step S2, batteries with poorer health conditions are more sensitive to changes in the discharge rate. The rate influence coefficient... K i for In the formula, C represents the charge / discharge current ratio, C0 0.1C This indicates the capacity discharged at a voltage of 0.1C to the midpoint of 2V. C iC Indicates i The capacity discharged at the midpoint of 2V discharge rate.

7. The method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction as described in claim 1, characterized in that, In step S3, under the same healthy state, the higher the discharge rate, the higher the discharge termination voltage. U mid The smaller the voltage decay rate dU / dt The larger the value, the more we can establish a mapping model from high-rate characteristics to low-rate characteristics by using the benchmark relationship between the characteristic parameters and the remaining battery life at the target rate, and then use experimental data to fit and correct the characteristic parameters.

8. The method for estimating the remaining life of lead-acid batteries based on multi-rate discharge feature extraction as described in claim 1, characterized in that, In step S3, the constant capacity discharge termination voltage U mid The corrected formula is In the formula, U mid_nC This represents the measured discharge termination voltage at high rates. U eq mid_0.1C This represents the corrected equivalent constant-capacity discharge termination voltage at a 0.1C rate. a n and b n These are coefficients fitted from high-magnification experimental data.

9. The method for estimating the remaining life of a lead-acid battery based on multi-rate discharge feature extraction as described in claim 8, characterized in that, In step 3, the voltage decay rate dU / dt The corrected formula is In the formula, dU / dt nC This represents the measured voltage decay rate at high magnification. dU / dt eq 0.1C This represents the voltage decay rate at an equivalent 0.1C rate after correction. c n and d n These are coefficients fitted from high-magnification experimental data.

10. The method for estimating the remaining life of a lead-acid battery based on multi-rate discharge feature extraction as described in claim 8, characterized in that, In step S4, a quantitative mathematical relationship for estimating the remaining health status of substation lead-acid batteries is established based on calibrated core characteristic parameters through multivariate nonlinear fitting: In the formula, U eq mid_0.1C This represents the corrected equivalent constant-capacity discharge termination voltage at 0.1C. dU / dt mid_nC This represents the voltage decay rate at the equivalent 0.1C obtained after correction. K i Defined as 0.1C multiplier and the first i The ratio of the release capacity at different multipliers. a , b , c , d , e These are the model calibration coefficients. I This represents the magnitude of the current multiplier.

Images