Ic Curve Research Articles

Rapid Screening for Retired Batteries Based on Lithium-Ion Battery IC Curve Prediction

Rapid Screening for Retired Batteries Based on Lithium-Ion Battery IC Curve Prediction

A Lithium-Ion Battery Remaining Useful Life Prediction Method with A New Algorithm Based on Incremental Capacity Analysis

Safe use of lithium-ion batteries requires accurately assessing state of charge (SoC), state of health (SOH) and capacity estimation techniques. Due to numerous charge and discharge cycles, lithium ion batteries undergo a degradation process during their use leading to failures, accidents, and fire. Traditional ICA/DVA methods have been used to overcome these issues, but they are subject to changes in battery resistance and polarization processes during battery aging. Evaluation of the SoC as a function of incremental capacity is proposed in this work to overcome this problem. This article used a new algorithm to perform, through simulations carried out with Matlab® software, incremental capacity analysis for a preventive estimate of remaining useful life (RUL). In addition, the comparison between IC curves and the SoC here used fully represents the relationship between the IC values and the internal parameters of the battery. The validity of the proposed algorithm against the phenomenon of battery aging was evaluated based on experimental data from NASA's PCoE research center.

Lithium-Ion Battery Capacity Estimation Based on Incremental Capacity Analysis and Deep Convolutional Neural Network

Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%.

Capacity degradation analysis and knee point prediction for lithium-ion batteries

Analyzing capacity degradation characteristics and accurately predicting the knee point of capacity are crucial for the safety management of lithium-ion batteries (LIBs). However, the degradation mechanism of LIBs is complex. A key but challenging problem is how to clarify the degradation mechanism and predict the knee point. According to the external characteristics such as capacity decline gradievnt and the peak value of increment capacity curve (IC curve), the capacity degradation can be divided into four stages, including initial decline stage, slow decline stage, transition stage and high-speed decline stage. The degradation mechanism of LIBs is compared from the longitudinal and horizontal aspects, respectively. Among them, the battery usage from the initial stage to the end of life (EOL) is longitudinal analysis. The battery under different conditions, such as charging and discharging, different discharge rate, different cathode material degradation mechanism is horizontal analysis. Moreover, a method based on neural network is proposed to predict the knee point. Two features are used to predict the capacity and cycle of the knee point, which are the gradient of the capacity degradation curve and the difference of the IC curve with the maximum correlation. The experimental results show that a two-dimensional surface can be obtained using only the first 100 cycles, which can provide a reference for the position of the knee point accurately prediction.

State of health estimation of lithium-ion batteries based on the regional triangle

A novel fast state of health (SOH) evaluation method for lithium-ion battery is proposed. The concepts of the regional voltage and regional triangle are introduced based on the incremental capacity analysis (ICA). The ICA method is only used to obtain the voltage value corresponding to the main peak on the IC curve. After that, the voltage value corresponding to the main peak is determined on the original charging curve and is defined as the Vpeak. And then a regional triangle is defined by the Vpeak and the two terminal voltages of a certain regional voltage interval with the Vpeak as the midpoint on the original charging curve. The two edge lengths containing the highest voltage vertex of the triangle are defined as the regional edge lengths and are used as the health factors for SOH evaluation. The SOH models at different sampling frequencies are established using the charging voltage data of nickel–cobalt–aluminum (NCA) lithium-ion batteries and lithium iron phosphate (LFP) battery modules at different current rates. The results show that the SOH has a linear positive correlation with either of the two health factors and the maximum R-square (R2) of linear fitting can reach >0.99. The proposed health factors still have high R2 with SOH modeling in the narrow regional voltage interval at sampling frequency as low as one point every 5 min whether NCA or LFP, suggesting that the regional triangle method has a potential for SOH online evaluation of batteries in engineering.

A Data-Driven LiFePO4 Battery Capacity Estimation Method Based on Cloud Charging Data from Electric Vehicles

The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are mainly developed by experimental data under well-controlled charge–discharge processes, which are seldom available for practical battery health monitoring under realistic conditions due to uncertainties in environmental and operational conditions. In this paper, a novel method to estimate the capacity of large-format LiFePO4 batteries based on real data from electric vehicles is proposed. A comprehensive dataset consisting of 85 vehicles that has been running for around one year under diverse nominal conditions derived from a cloud platform is generated. A classification and aggregation capacity prediction method is developed, combining a battery aging experiment with big data analysis on cloud data. Based on degradation mechanisms, IC curve features are extracted, and a linear regression model is established to realize high-precision estimation for slow-charging data with constant-current charging. The selected features are highly correlated with capacity (Pearson correlation coefficient < 0.85 for all vehicles), and the MSE of the capacity estimation results is less than 1 Ah. On the basis of protocol analysis and mechanism studies, a feature set including internal resistance, temperature, and statistical characteristics of the voltage curve is constructed, and a neural network (NN) model is established for multi-stage variable-current fast-charging data. Finally, the above two models are integrated to achieve capacity prediction under complex and changeable realistic working conditions, and the relative error of the capacity estimation method is less than 0.8%. An aging experiment using the battery, which is the same as those equipped in the vehicles in the dataset, is carried out to verify the methods. To the best of the authors’ knowledge, our study is the first to verify a capacity estimation model derived from field data using an aging experiment of the same type of battery.

Study on field-based superconducting cable for magnetic energy storage devices

During the design of superconducting magnets, the Ic(B) curve is widely used to evaluate the critical current density distribution in the magnet, safety factor is determined by the lowest value of critical current density and operating current. However, other parts of the magnets will work in over-safe state, which leads to a huge waste of superconducting properties as well as budget problem. This article presents a Field-based cable to improve the utilizing rate of superconducting magnets in SMES system. The quantity of HTS tapes are determined by the magnetic field distribution. By this approach, the cost of HTS materials can be potentially reduced. Firstly, the main motivation as well as the entire design method are introduced. Then, a typical no-insulation coil is designed and fabricated based on Field-based stacked cable to validate the feasibility of this method. Both charging/discharging test and the magnetic field test are carried out to explore the working mechanism of this coil. The unique working performance on time constant and field distribution are analyzed in detail.

The JAK Inhibitor Pacritinib Suppresses the Checkpoint Proteins Pvrig and Tigit Expression on T Cells in Multiple Myeloma Patients

Introduction: Immunotherapies blocking immune checkpoints have been effective in cancer treatment, but these drugs have significant toxicities. Previously, we have shown the JAK1/2 inhibitor ruxolitinib has beneficial effects for treating multiple myeloma (MM) patients. We have also found that it reduces expression of two immune checkpoint proteins, PD-L1 and B7-H3, in MM bone marrow (BM). Pacritinib is another JAK inhibitor that specifically targets JAK2, but not JAK1. We hypothesized pacritinib may inhibit other immune checkpoints in MM. The immune checkpoint proteins TIGIT and PVRIG have been shown to be over-expressed in MM BM, and we evaluated whether pacritinib could reduce expression of these checkpoint inhibitors and the effect of this JAK inhibitor on MM growth. Methods: The human MM cell lines U266, RPMI8226 and MM1s were obtained from ATCC. Mononuclear cells (MCs) were isolated from MM patients' BM aspirates. Cell viability was quantified using the MTS cell proliferation assay. We determined the IC curves for the MM cell lines RPMI8226, U266, and MM1S and fresh BMMCs from the MM patients using this JAK inhibitor and other anti-MM drugs. Specifically, we examined the anti-proliferative effects of pacritinib alone and in combination with dexamethasone, pomalidomide or bortezomib using these 3 MM cell lines and 1 patient's fresh MM BMMCs. In addition, 5 fresh MMBMMCs (3 patients with progressive disease [PD] and 2 patients in complete remission [CR]) were treated with pacritinib alone for 48 hours in vitro, followed by flow cytometric analysis to determine TIGIT and PVRIG expression using an Attune flow cytometer. Results: MM tumor cells from the 3 human myeloma cell lines were exposed to increasing concentrations of pacritinib from 0.1 to 100 µM. The results of anti-MM effects on cell viability showed that pacritinib alone inhibited MM tumor cell proliferation in all three MM cell lines (RPMI8226, U266 and MM1S) in a concentration dependent manner. We also found the viability in one PD patient was significantly decreased when pacritinib was combined with dexamethasone or bortezomib compared to these drugs alone, but there was no effect when this JAK inhibitor was combined with pomalidomide. Next, flow cytometric analysis showed PVRIG and TIGIT expression in MM BMMCs were significantly decreased after pacritinib treatment in vitro for 48 hours in both PD and CR patients, demonstrating that pacritinib inhibited PVRIG and TIGIT in MMBM. Specifically, pacritinib reduced the expression of both PVRIG and TIGIT on CD8+ T cells. Thus, blockade of PVRIG and TIGIT on CD8+ T cells with pacritinib may restore T cell anti-tumor activity. Summary: We demonstrated that pacritinib can inhibit expression of PVRIG and TIGIT in MM BM, and specifically blocks PVRIG and TIGIT expression on CD8+ T cells. We also show the anti-MM effects of pacritinib both alone and in combination with other anti-MM drugs. Pacritinib can be potentially beneficial for treating myeloma patients due to its inhibitory role on PVRIG and TIGIT expression which may enhance tumor cell killing by CD8+ T cells.

Evaluation of Repotrectinib's Anti-MM Effects in Combination with Bortezomib, Carfilzomib, Cyclophosphamide, and Dexamethasone in Vitro and Ex Vivo

Introduction: The proteasome inhibitors bortezomib and carfilzomib are anticancer drugs which are indicated for the treatment of multiple myeloma (MM). The alkylating agent cyclophosphamide is also an effective drug against MM. Glucocorticoids including dexamethasone induce MM cell apoptosis and are effective in MM. However, the disease remains incurable with currently available therapies. Repotrectinib (Turning Point Therapeutics, San Diego, CA) is a novel next-generation ROS1/TRK/ALK-tyrosine kinase inhibitor (TKI). Repotrectinib also inhibits the Janus kinase (JAK) 2 and indirectly down-regulates STAT5 phosphorylation in the SET2 cancer cell line harboring a JAK2 V617F tyrosine kinase mutation (Drilon et al 2018). We have previously shown the preclinical anti-MM effects of JAK1/2 inhibitors in combination with corticosteroids and other anti-MM drugs in vitro and in vivo. Although activation of JAK2 and PI3K signaling is often detected in hematological malignancies, there is no data regarding the potential anti-MM effects of repotrectinib alone or in combination with other anti-MM drugs. Thus, we evaluated the anti-myeloma effects of repotrectinib alone and in combination with bortezomib, carfilzomib, cyclophosphamide, and dexamethasone in vitro using MM cell lines and ex vivo using the human MM xenograft model LAG- λ1. Methods: The human MM cell lines U266, RPMI8226 and MM1S were obtained from ATCC. MCs were isolated from MM patients' BM aspirates. Cell viability was quantified using the MTS assay. For the ex vivo studies, cells were isolated from the MM xenograft LAG-λ1 after it was established in SCID mice. We determined the IC curves using the MM cell lines and fresh BMMCs from 6 MM patients. Next, we combined repotrectinib with other active anti-MM drugs (carfilzomib, bortezomib, cyclophosphamide, and dexamethasone) using these same three MM cell lines and BMMCs from 6 MM patients to evaluate the anti-proliferative effects of these combinations. Results: We first evaluated the anti-MM effects of repotrectinib in combination with bortezomib or carfilzomib by using the MM1S, RPMI-8226, and U266 MM cell lines. Specifically, the results with repotrectinib in combination with carfilzomib showed enhanced anti-MM effects when compared to repotrectinib alone or carfilzomib alone. We also determined the anti-MM effects of repotrectinib alone and in combination with carfilzomib by using isolated LAG- λ1 MM xenograft cells. Repotrectinib alone inhibited tumor cell proliferation in a concentration dependent manner (IC50 4.5 µM) and in combination with carfilzomib synergistically inhibited LAG- λ1 MM tumor cell proliferation. These results were not seen when it was combined with bortezomib. We studied BMMCs from 3 PD and 3 CR patients using a cell proliferation assay. The results showed that repotrectinib alone minimally inhibited MM tumor cell proliferation among patients with PD. Repotrectinib combined with carfilzomib showed enhanced anti-proliferative effects compared with either drug alone among BMMCs from PD patients but not those in CR. In contrast, bortezomib did not show this enhanced anti-proliferative effect. We also determined the anti-MM effects of repotrectinib in combination with cyclophosphamide using ex vivo and in vivo studies. The results showed that repotrectinib in combination with cyclophosphamide enhanced the inhibition of LAG- λ1 MM tumor cell and fresh MM BMMC proliferation compared with repotrectinib or cyclophosphamide alone. We further determined the anti-MM effects of repotrectinib in combination with dexamethasone. The results showed that repotrectinib in combination with dexamethasone reduced LAG- λ1 MM cell proliferation in a concentration dependent manner and enhanced inhibition in 2 PD patients' samples but not the other PD patient or 3 CR patients' samples. Summary Single agent repotrectinib decreased MM tumor cell proliferation in all 3 MM cell lines and reduced LAG- λ1 tumor cell proliferation ex vivo. Repotrectinib also reduced proliferation of BMMCs from MM patients with PD but not those in CR. Repotrectinib in combination with carfilzomib, cyclophosphamide or dexamethasone showed enhanced anti-proliferative effects compared with the drugs alone. The results suggest that this kinase inhibitor may have activity in treating MM patients in combination with other active anti-MM agents. Further evaluation is warranted.

Prediction of Li-ion battery state of health based on data-driven algorithm

Li-ion battery state of health (SOH) is a key parameter for characterizing actual battery life. SOH cannot be measured directly. In order to further improve the accuracy of Li-ion battery SOH estimation, a combined model based on health feature parameters combined with EMD-ICA-GRU is proposed to predict the SOH of Li-ion batteries. The capacity regeneration phenomenon and data noise are decomposed by empirical mode decomposition (EMD), and then the SOH-related health indicators are deeply mined using incremental capacity analysis (ICA), and the peaks of IC curves and their corresponding voltages are extracted as the input of the model. Then, gated recurrent units (GRUs) are formed into a combined SOH estimation model by adaptive weighting factors. Finally, it is validated against the NASA lithium battery dataset. Experimental results show that the mean squared error (MSE) of the proposed combined model can reach about 0.3%, and it has stronger generalization and prediction accuracy than other algorithms driven by independent estimation data.

A reliable data-driven state-of-health estimation model for lithium-ion batteries in electric vehicles

The implementation of a precise and low-computational state-of-health (SOH) estimation algorithm for lithium-ion batteries represents a critical challenge in the practical application of electric vehicles (EVs). The complicated physicochemical property and the forceful dynamic nonlinearity of the degradation mechanism require data-driven methods to substitute mechanistic modeling approaches to evaluate the lithium-ion battery SOH. In this study, an incremental capacity analysis (ICA) and improved broad learning system (BLS) network-based SOH estimation technology for lithium-ion batteries are developed. First, the IC curves are drawn based on the voltage data of the constant current charging phase and denoised by the smoothing spline filter. Then, the Pearson correlation coefficient method is used to select the critical health indicators from the features extracted from the IC curves. Finally, the lithium-ion battery SOH is assessed by the SOH estimation model established by an optimized BLS network, where the BLS network is formed through its L2 regularization parameter and the enhancement nodes’ shrinkage scale filtrated by a particle swarm optimization algorithm. The experimental results demonstrate that the proposed method can effectively evaluate the SOH with strong robustness as well as stability to the degradation and disturbance of in-service and retired lithium-ion batteries.

SOH prediction of lithium battery based on IC curve feature and BP neural network

Precise battery SOH (state of health) prediction and monitoring are of extreme importance for the future intelligent battery management system (BMS). In this paper, battery discharge experiments at different temperatures were carried out. A battery SOH prediction model based on incremental capacity analysis and BP neural network is proposed to predict battery SOH at different ambient temperatures. By analyzing the correlation between the characteristics of IC curve and SOH, the mapping relationship between temperature and IC curve characteristics is established by using the least square method, and the SOH prediction model at different temperatures is obtained. At the same time, combined with ICA, an online real-time correction prediction model is established, and the characteristic data is continuously updated to ensure the SOH prediction accuracy under different aging states. Finally, the feasibility of the prediction method proposed in this paper is verified by comparing the model test results and experimental results, the average error of the model prediction results is 1.16%. Thus, by establishing the relationship between temperature and IC curve characteristics, the battery SOH at different temperatures can be predicted.

A novel health indicator for online health estimation of lithium-ion batteries using partial incremental capacity and dynamic voltage warping

Real-time state of health (SoH) estimation is essential to guarantee the reliability and safety of a lithium-ion battery system. However, the incomplete charge/discharge process, which is encountered inevitably as diverse customer habits and complex operation conditions, severely limits the use of traditional health indicators (HIs) in a battery management system (BMS). In this study, a novel HI, namely incremental capacity–dynamic voltage warping (IC–DVW), is extracted from partial online discharge voltage and current data. Ideally, IC–DVW shows a strong correlation with the actual capacity fading state as well as good robustness and adaptability to incomplete discharge data. The linear correlation between IC–DVW and capacity is further enhanced by adopting the Box–Cox transformation. In addition, to improve the performance of the partial IC curve, we propose a novel interpolation method, namely keep-raw-data interpolation (KRDI), and combine it with the Savitzky–Golay filter to obtain clear and smooth IC curves. Finally, we verify the robustness of the proposed HI to different voltage ranges, known cycle data, and battery samples. Experimental studies confirm the high accuracy and adaptability of the proposed HI in degradation modeling and online health estimation, especially under frequently occurring incomplete discharge conditions.

Data-Driven ICA-Bi-LSTM-Combined Lithium Battery SOH Estimation

Lithium battery state of health (SOH) is a key parameter to characterize the actual battery life. SOH cannot be directly measured. In order to further improve the accuracy of SOH estimation of lithium batteries, a model combining incremental capacity analysis (ICA) and bidirectional long- and short-term memory (Bi-LSTM) neural networks based on health characteristic parameters is proposed to predict the SOH of lithium-ion batteries. First, the health characteristic parameters are initially selected from the lithium battery charging curve, and the health characteristics are extracted by the Pearson correlation coefficient, including the charging time of constant current, charging time of constant voltage, voltage change rate from 300 s to 1000 s, 200s of voltage per cycle at a time. Second, ICA was used to deeply mine the deep associations related to SOH and the peaks of IC curves and their corresponding voltages were extracted as additional inputs to the model. Then, Bi-LSTM is used to form a combined SOH estimation model through adaptive weighting factors. Finally, the verification is based on the 5th battery parameters of the NASA lithium battery data set. The experimental results show that the proposed combined model reduces the mean square error by 55.17%, 49.28%, and 41.47%, respectively, compared with single models such as BP neural network (BPNN), LSTM, and gated recurrent neural network (GRU).

Capacity estimation based on incremental capacity and Gaussian process regression for retired lithium-ion batteries

Fast capacity estimation for retired batteries is necessary when batteries are recycled for echelon utilization. Here, a fast capacity estimation method is proposed for retired LiFePO4 battery. First, a full survey of battery pack and cells degradation after a long period of service is studied. Then the filtered ICA is used to study degradation variation phenomenon of retired batteries, the relationship between IC curve feature and remaining capacity was studied. Finally, a fast capacity estimation using incremental capacity and Gaussian process regression is proposed. Our results show high efficiency and accuracy of the proposed method.

Rapid determination of lithium-ion battery degradation: High C-rate LAM and calculated limiting LLI

Herein, incremental capacity-differential voltage (IC-DV) at a high C-rate (HC) is used as a non-invasive diagnostic tool in lithium-ion batteries, which inevitably exhibit capacity fading caused by multiple mechanisms during charge/discharge cycling. Because battery degradation modes are complex, the simple output of capacity fading does not yield any useful data in that respect. Although IC and DV curves obtained under restricted conditions (<0.1C, 25 °C) were applied in non-invasive analysis for accurate observation of degradation symptoms, a facile, rapid diagnostic approach without intricate, complex calculations is critical in on-board applications. Herein, LiNi0.5Mn0.3Co0.2O2 (NMC532)/graphite pouch cells were cycled at 4 and 6C and the degradation characteristics, i.e., loss of active materials (LAM) and loss of lithium inventory (LLI), were parameterized using the IC-DV curves. During the incremental current cycling, the initial steep LAM and LLI slopes underwent gradual transitions to gentle states and revealed the gap between low- and high-current measurements. A quantitative comparison of LAM at high and low C-rate showed that a ICHC revealed the relative amount of available reaction region limited by cell polarization. However, this did not provide a direct relationship for estimating the LAM at a low C-rate. Conversely, the limiting LLI, which is calculated at a C-rate approaching 0, was obtained by extrapolating the LLI through more than two points measured at high C-rate, and therefore, the LLI at 0.1C was accurately determined using rapid cycling.

A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression

Remaining useful life (RUL) is a critical metric of lithium-ion battery prognostic and health management. Accurate prediction of RUL is of great significance to the safety and reliability of lithium-ion batteries, which is able to provide useful reference information for maintenance. In this work, a novel method fusing the incremental capacity analysis (ICA) and Gaussian process regression (GPR) for RUL prediction of lithium-ion batteries is proposed. Firstly, the IC curve, which has higher sensitivity than the traditional charge/discharge curve, is used to analyze the performance degradation process of the lithium-ion battery. Then the peak value of the IC curve and the regional area under the peak value of the IC curve are extracted as health indicators (HIs). Secondly, the RUL prediction framework of lithium-ion batteries based on ICA and GPR is established, and the uncertainty expression of the prediction results is given. Finally, the experimental results show that the HIs extracted in this paper can effectively reflect the degradation state of the battery, and the proposed method has high accuracy in predicting RUL.

Study on Capacity Estimation Methods of Second-Life Application Batteries

For the capacity estimation problem of cells in series-retired battery modules, this paper proposed three different methods from the perspective of data-driven, battery curve matching and recession characteristics for different applications. Firstly, based on the premise that the battery history data are available, the features of the IC curve are selected as input for the linear regression models. To avoid multicollinearity among features, we apply a filter-based feature selection method to eliminate redundant features. The results show that the average errors with Multiple Linear Regression are within 1.5%. Secondly, for the situation with a lack of historical operating data, the battery-curve-matching-based method is proposed based on the Dynamic Time Warping algorithm. This method could achieve the curve matching between the reference cell and target cell, and then the curve contraction coefficients can be obtained. The result shows that the method’s average error is 2.34%. Thirdly, whereas the tougher situation is that only part of the battery curve is available, we present a substitute method based on the battery degradation mechanism. This method can estimate most of the battery plant capacity through the partial battery curve. The result shows that the method’s average error is within 2%. Lastly, we contrast the applicability and limitations of every method based on the retired battery test data after deep cycling aging.

State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression

Accurate state-of-health (SOH) estimation for lithium-ion batteries is of great significance for future intelligent battery management systems. This study proposes a novel method combining voltage-capacity (VC)-model-based incremental capacity analysis (ICA) with support vector regression (SVR) for battery SOH estimation. For accurate and efficient capture of IC curves, 18 VC models are first compared, and then, suitable models are selected for two types of batteries with different chemistries, enabling multitype health features to be obtained by parameterizing the VC models. After correlation analysis of these extracted health features with the reference battery capacity, the SVR algorithm is adopted to construct SOH estimation models. Finally, four aging datasets are employed for validation of the proposed method. The experimental results show that the SVR models achieve high accuracy in SOH estimation, i.e., the respective mean absolute errors (MAEs) and root mean square errors (RMSEs) of all batteries are limited to within 1.1%. Moreover, the method is robust against different initial aging statuses and cycle conditions of the batteries: after migration and fine-tuning, both the MAEs and RMSEs can be confined to within 2.3% by utilizing the established SVR models.

Recognition and Diagnosis Method of Accelerated Aging of Lithium-Ion Battery Based on Logistic Regression

Aiming at the accelerated aging problem that may occur during the use of high specific energy lithium-ion batteries, this article proposes a method to judge the accelerated aging of lithium-ion batteries. Taking the IC curve and DV curve as the starting point, the complete characteristic curve of the new battery is used as a comparison benchmark, and the battery characteristic curves of different aging stages are compared and analyzed, and the parameters that have obvious changes before and after the accelerated aging of the battery are extracted as the characteristics that the battery has accelerated aging. It is important to establish the relevant characteristic parameter matrix, and use the logistic regression method to train the accelerating aging model of the battery to realize the diagnosis of accelerating aging fault. In view of the fact that lithium batteries rarely undergo a complete charging process, the characteristic parameter sets established in this paper are based on the IC curve and the DV curve in the 15–75% SOC range, which have certain practicability.