Lithium-ion Battery State Of Health Research Articles

Data-driven battery state-of-health estimation and prediction using IC based features and coupled model

Accurate estimation and prediction of the lithium-ion battery state of health (SOH) play a vital role in improving the reliability and safety of battery operations. However, the complexity of operation modes and inconsistency of aging trajectories in the real-world deteriorate the functional domain of existing methods in accurate estimation and prediction. In this study, a novel data-driven framework is proposed to enhance performance in real-world operation scenarios. Accordingly, an SOH estimation method is proposed, based on incremental capacity (IC) analysis and the operation characteristics of batteries. This method is more feasible in practical applications and has a 12.89 % improvement in reflecting the SOH compared with the IC peak. Moreover, a correction model is proposed and coupled with a regression model to remedy the deviation due to battery individual adaptively. The method is verified on laboratory and EV datasets, achieving mean absolute percentage errors of 0.29 % and 3.20 % respectively, evidently lower than those of conventional methods. This study highlights the adaptability of health features in real-world operation scenarios and the promise of combining group-based models with individual-based models to optimize predictions. The proposed framework can be extensively utilized for battery residual value analysis, secondary use, analysis of system energy storage, and other applications in real-world scenarios.

Novel method for modelling and adaptive estimation for SOC and SOH of lithium-ion batteries

Equivalent circuit models of lithium-ion batteries are the foundation of accurately estimating state of charge (SOC) and state of health (SOH). To further describe the performance of the batteries between working and idling states, we present for the first time the analysis of an improved fractional-order model (IFOM). Moreover, an endogenous immune algorithm (EIA) is proposed to identify the parameters of the model. Meanwhile, considering the aging effect, an adaptive dual square root Kalman filtering (ADSRCKF) with the dormancy zone is proposed to achieve the estimation. The first filter in ADSRCKF estimates SOC and SOH in which the SOH is characterised by the ohmic resistance. The second filter adaptively updates the drifted model parameters according to the dormancy zone. Finally, the proposed method is evaluated with the random walk charging and discharging test. The experimental results illustrate that the adaptively updated parameters can better match the battery status in real time, and the proposed ADSRCKF can achieve high accuracy with strong robustness. For the initial SOC error, the root mean square error (RMSE) of SOC is less than 0.3%, and the RMSE is approximately 1% even when the initial capacity is also deviated.

Deep learning to estimate lithium-ion battery state of health without additional degradation experiments

State of health is a critical state which evaluates the degradation level of batteries. However, it cannot be measured directly but requires estimation. While accurate state of health estimation has progressed markedly, the time- and resource-consuming degradation experiments to generate target battery labels hinder the development of state of health estimation methods. In this article, we design a deep-learning framework to enable the estimation of battery state of health in the absence of target battery labels. This framework integrates a swarm of deep neural networks equipped with domain adaptation to produce accurate estimation. We employ 65 commercial batteries from 5 different manufacturers to generate 71,588 samples for cross-validation. The validation results indicate that the proposed framework can ensure absolute errors of less than 3% for 89.4% of samples (less than 5% for 98.9% of samples), with a maximum absolute error of less than 8.87% in the absence of target labels. This work emphasizes the power of deep learning in precluding degradation experiments and highlights the promise of rapid development of battery management algorithms for new-generation batteries using only previous experimental data.

Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR

Accurate estimation of the state-of-health (SOH) of lithium-ion batteries is a crucial reference for energy management of battery packs for electric vehicles. It is of great significance in ensuring safe and reliable battery operation while reducing maintenance costs of the battery system. To eliminate the nonlinear effects caused by factors such as capacity regeneration on the SOH sequence of batteries and improve the prediction accuracy and stability of lithium-ion battery SOH, a prediction model based on Variational Modal Decomposition (VMD) and Dung Beetle Optimization -Support Vector Regression (DBO-SVR) is proposed. Firstly, the VMD algorithm is used to decompose the SOH sequence of lithium-ion batteries into a series of stationary mode components. Then, each mode component is treated as a separate subsequence and modeled and predicted directly using SVR. To address the problem of difficult parameter selection for SVR, the DBO algorithm is used to optimize the parameters of the SVR model before training. Finally, the predicted values of each subsequence are added and reconstructed to obtain the final SOH prediction. In order to verify the effectiveness of the proposed method, the VMD-DBO-SVR model was compared with SVR, Empirical Mode Decomposition-Support Vector Regression (EMD-SVR), and VMD-SVR methods for SOH prediction of batteries based on the NASA dataset. Experimental results show that the proposed model has higher prediction accuracy and fitting degree, with prediction errors all within 1% and better robustness.

A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance

State-of-health (SOH) estimation of lithium-ion batteries is an important issue in electric vehicle energy management. The complication of the internal electrochemical reaction mechanism and the uncertainty of the external operating conditions pose a significant challenge to SOH estimation. This paper develops a data-driven approach to estimate the SOH of lithium-ion batteries with consideration of the battery's internal resistance, which is used as a bridge to effectively integrate the equivalent circuit model (ECM) and the data-driven method. We try to identify the internal resistance under constant current charging conditions by simplifying the ECM. The poles and offsets are extracted from the differential thermal voltammetry, differential thermal capacity, and incremental capacity curves as thermoelectric coupling features. Then the internal resistance and thermoelectric coupling features are combined as model inputs. An explanation boosting machine (EBM) is used to construct the SOH estimator according to the good fitting performance and interpretability. The model parameters of EBM are optimized by using an ant colony algorithm to improve its robustness. Finally, comparative experiments between features and the model are carried out on the Oxford dataset. The results demonstrate that the mean absolute error of the proposed method is less than 1%.

Performance simulation method and state of health estimation for lithium-ion batteries based on aging-effect coupling model

Accurate simulation of characteristics performance and state of health (SOH) estimation for lithium-ion batteries are critical for battery management systems (BMS) in electric vehicles. Battery simplified electrochemical model (SEM) can achieve accurate estimation of battery terminal voltage with less computing resources. To ensure the applicability of life-cycle usage, degradation physics need to be involved in SEM models. This work conducts deep analysis on battery degradation physics and develops an aging-effect coupling model based on an existing improved single particle (ISP) model. Firstly, three mechanisms of solid electrolyte interface (SEI) film growth throughout life cycle are analyzed, and an SEI film growth model of lithium-ion battery is built coupled with the ISP model. Then, a series of identification conditions for individual cells are designed to non-destructively determine model parameters. Finally, battery aging experiment is designed to validate the battery performance simulation method and SOH estimation method. The validation results under different aging rates indicate that this method can accurately estimate characteristics performance and SOH for lithium-ion batteries during the whole life cycle.

Accurate Prediction Approach of SOH for Lithium-Ion Batteries Based on LSTM Method

The deterioration of the health state of lithium-ion batteries will lead to the degradation of the battery performance, the reduction of the maximum available capacity, the continuous shortening of the service life, the reduction of the driving range of electric vehicles, and even the occurrence of safety accidents in electric vehicles driving. To solve the problem that the traditional battery management system is difficult to accurately manage and predict its health condition, this paper proposes the mechanism and influencing factors of battery degradation. The battery capacity is selected as the characterization of the state of health (SOH), and the long short-term memory (LSTM) model of battery capacity is constructed. The intrinsic pattern of capacity degradation is detected and extracted from the perspective of time series. Experimental results from NASA and CALCE battery life datasets show that the prediction approach based on the LSTM model can accurately predict the available capacity and the remaining useful life (RUL) of the lithium-ion battery.

State of Health Assessment of Lithium-ion Batteries Based on Deep Gaussian Process Regression Considering Heterogeneous Features

With the continuous development of the global new energy industry, lithium-ion batteries as new energy and the heart of intelligent manufacturing have attracted much attention. But the performance of lithium-ion battery systems has been deteriorating for a long time. An accurate assessment of its state of health (SOH) can effectively avoid unnecessary loss of lithium-ion batteries due to unexpected failure. Therefore, a method for evaluating the SOH of deep Gaussian process regression (DGPR) lithium-ion batteries based on the Gaussian process and deep network is presented. Firstly, the heterogeneous features reflecting the SOH of lithium-ion batteries were extracted from the aspects of charging and discharging time, the peak of incremental capacity curve (ICC), internal resistance, and energy. Then, through grey correlation analysis (GRA), significant features are introduced into the DGPR model to establish the SOH estimation method for lithium-ion batteries. Finally, the data sets provided by CALCE and NASA are used as an experimental object to compare with different data-driven models to verify the accuracy, reliability and applicability of the proposed models. The experimental results show that the method proposed in this paper has high accuracy in SOH estimation, RMSE is less than 0.7 %, and R2 is more than 98.2 % in each lithium-ion battery. It shows that this method can provide a reliable basis for the health management of lithium-ion batteries.

Data-driven state of health estimation in retired battery based on low and medium-frequency electrochemical impedance spectroscopy

Electrochemical impedance spectroscopy (EIS) is one of the critical techniques to characterize the state of health (SoH) of lithium-ion batteries, which has different performances under different SoH and dynamic working conditions. However, the identification parameters of full-frequency EIS will consume too much computing time. Considering that low and medium-frequency EIS plays a dominant role in battery aging, the present paper constructs a simplified equivalent circuit (SECM) based on low and medium-frequency EIS. Based on the fitted SECM parameters, three SECM parameters are selected as features by grey correlation analysis. Combined with the ambient temperature and SoC under dynamic conditions, the Gated recurrent unit neural network estimates the SoH of lithium-ion batteries. The proposed method has good accuracy and robustness for different SoC states and ambient temperature variations. This method's error values are less than 2 %, which proves the critical value of low and medium-frequency EIS signals in battery management systems.

Research on State-of-Health Estimation for Lithium-Ion Batteries Based on the Charging Phase

The lithium-ion battery state of health (SOH) estimation is an essential parameter to ensure the safety and stability of the life cycle of electric vehicles. Accurate SOH estimation has been an industry puzzle and a hot topic in academia. To solve the problem of low fitting accuracy of lithium-ion battery SOH estimation in a traditional neural network, a nonlinear autoregressive with exogenous input (NARX) neural network is proposed based on the charging stage. Firstly, six health factors related to the lithium-ion battery aging state are acquired at the charging stage because the charging process has better applicability and simplicity than the discharging process in actual operation. Then six health factors are pre-processed using the principal component analysis (PCA) method. The principal component of the input variable is selected as the input of the neural network, which reduces the dimension of input compared with the neural network model without principal component analysis. The correlation between the inputs is eliminated. To verify the rationality of the proposed algorithm, two public aging datasets are used to develop and validate it. Moreover, the proposed PCA-NARX method is compared with the other two neural networks. The simulation results show that the proposed method can achieve accurate SOH estimation for different types of lithium-ion batteries under different conditions. The average mean absolute error (MAE) and root mean square error (RMSE) are 0.68% and 0.94%, respectively. Compared with other neural networks, the prediction error is reduced by more than 50% on average, which demonstrates the effectiveness of the proposed SOH estimation method.

State of Health Estimation Method for Lithium-Ion Batteries via Generalized Additivity Model and Transfer Component Analysis

Battery state of health (SOH) is a momentous indicator for aging severity recognition of lithium-ion batteries and is also an indispensable parameter of the battery management system. In this paper, an innovative SOH estimation algorithm based on feature transfer is proposed for lithium-ion batteries. Firstly, sequence features with battery aging information are sufficiently extracted based on the capacity increment curve. Secondly, transfer component analysis is employed to obtain the mapping that minimizes the data distribution difference between the training set and the test set in the shared feature space. Finally, the generalized additive model is investigated to estimate the battery health status. The experimental results demonstrate that the proposed algorithm is capable of forecasting the SOH for lithium-ion batteries, and the results are more outstanding than those of several comparison algorithms. The predictive error evaluation indicators for each battery are both less than 2.5%. In addition, satisfactory SOH estimation results can also be obtained by only relying on a small amount of data as the training set. The comparative experiments using traditional features and different machine learning methods also testify to the superiority of the proposed algorithm.

A Data-Driven Comparative Analysis of Lithium-Ion Battery State of Health and Capacity Estimation

Researchers are focused toward the development of electric vehicles and hybrid electric vehicles for the preservation of the ecosystem. Energy storage, conversely, became essential with these changes in vehicular technology. This opens new research fronts and introduces complex problems that are to be investigated regarding the primary storage element. Battery modeling offers improved efficiency, safety, and reliability. Lithium-ion batteries are a popular source of energy in many applications, especially for electric vehicles because of their high performance and high energy density. However, there exist some inherent issues like complex electrochemical reactions that make accurate monitoring a challenging task. This article performs a comparative study of the existing battery state of health (SOH) monitoring and capacity estimation techniques using the NASA dataset for lithium-ion batteries. Charging cycle datasets for voltage, current, and temperature are used for feature identification and extraction. After the modeling of extracted features, the SOH and the capacity of four batteries are estimated using three data-driven techniques. Including convolutional neural network, feed-forward neural network, and long short-term memory (LSTM), and their results are compared. The comparative analysis highlights an outstanding performance and high accuracy of the LSTM-based machine learning technique because of the inherited long-term memory of the LSTM. The study, therefore, recommends the use of LSTM to researchers for battery health monitoring and capacity estimation with the highest possible accuracy.

State of Health Estimation of Lithium-Ion Batteries Using a Multi-Feature-Extraction Strategy and PSO-NARXNN

The lithium-ion battery state of health (SOH) estimation is critical for maintaining reliable and safe working conditions for electric vehicles (EVs). However, accurate and robust SOH estimation remains a significant challenge. This paper proposes a multi-feature extraction strategy and particle swarm optimization-nonlinear autoregressive with exogenous input neural network (PSO-NARXNN) for accurate and robust SOH estimation. First, eight health features (HFs) are extracted from partial voltage, capacity, differential temperature (DT), and incremental capacity (IC) curves. Then, qualitative and quantitative analyses are used to evaluate the selected HFs. Second, the PSO algorithm is adopted to optimize the hyperparameters of NARXNN, including input delays, feedback delays, and the number of hidden neurons. Third, to verify the effectiveness of the multi-feature extraction strategy, the SOH estimators based on a single feature and fusion feature are comprehensively compared. To verify the effectiveness of the proposed PSO-NARXNN, a simple three-layer backpropagation neural network (BPNN) and a conventional NARXNN are built for comparison based on the Oxford aging dataset. The experimental results demonstrate that the proposed method has higher accuracy and stronger robustness for SOH estimation, where the average mean absolute error (MAE) and root mean square error (RMSE) are 0.47% and 0.56%, respectively.

A deep learning approach to estimate the state of health of lithium-ion batteries under varied and incomplete working conditions

The reliable state of health (SOH) estimation of lithium-ion batteries ensures the safety and efficiency of electric vehicles and energy storage systems. However, when the battery is under varied and incomplete working conditions, the estimation of SOH is still challenging due to the diversity and irregularity of the health indicators. Thus, an algorithm with strongly nonlinear capability is required. In this paper, a dilated residual regression network is presented for estimating the SOH of lithium-ion batteries under varied and incomplete charging conditions. The incremental capacity and voltage are extracted as feature pairs from varied conditions with fixed charging ranges to build the feature matrix. Then the dilated convolution initially extracts the shallow features of the feature matrix, which is subsequently fed into the deep residual network to estimate the SOH of the batteries. The accuracy and robustness of the proposed method are verified by comparison with machine learning algorithms and shallow convolutional networks. The interpretability of the proposed deep model is further improved by analyzing the occlusion sensitivity map of shallow and deep layers. These efforts enhance the application of SOH estimation methods in the real world.

XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries

The instability and variable lifetime are the benefits of high efficiency and low-cost issues in lithium-ion batteries.An accurate equipment's remaining useful life prediction is essential for successful requirement-based maintenance to improve dependability and lower total maintenance costs. However, it is challenging to assess a battery's working capacity, and specific prediction methods are unable to represent the uncertainty. A scientific evaluation and prediction of a lithium-ion battery's state of health (SOH), mainly its remaining useful life (RUL), is crucial to ensuring the battery's safety and dependability over its entire life cycle and preventing as many catastrophic accidents as feasible. Many strategies have been developed to determine the prediction of the RUL and SOH of lithium-ion batteries, including particle filters (PFs). This paper develops a novel PF-based technique for lithium-ion battery RUL estimation, combining a Kalman filter (KF) with a PF to analyze battery operating data. The PF method is used as the core, and extreme gradient boosting (XGBoost) is used as the observation RUL battery prediction. Due to the powerful nonlinear fitting capabilities, XGBoost is used to map the connection between the retrieved features and the RUL. The life cycle testing aims to gather precise and trustworthy data for RUL prediction. RUL prediction results demonstrate the improved accuracy of our suggested strategy compared to that of other methods. The experiment findings show that the suggested technique can increase the accuracy of RUL prediction when applied to a lithium-ion battery's cycle life data set. The results demonstrate the benefit of the presented method in achieving a more accurate remaining useful life prediction.

State of health estimation for Li-ion battery using characteristic voltage intervals and genetic algorithm optimized back propagation neural network

The accurate estimation of lithium-ion battery state of health (SOH) is important for the battery safety condition and range. However, in most cases, the operating conditions of lithium batteries are highly random. The data length of each cycle during the actual use of lithium-ion batteries is highly random and does not satisfy the input conditions of existing SOH estimation methods. To deal with the randomness of battery data, this paper proposes a SOH estimation scheme based on BP neural network optimized by a genetic algorithm (GA-BP) and fixed characteristic voltage interval. The method selects the effective charging cycle data on random length data through fixed characteristic voltage interval as the model training set, constructs 3 different feature parameters on this training set, and trains the GA-BP model with it to achieve SOH estimation on random length data. Experiments with the Monte Carlo method on 2 publicly available datasets validated the accuracy of the method at different levels of randomization.

An Indirect State-of-Health Estimation Method Based on Improved Genetic and Back Propagation for Online Lithium-Ion Battery Used in Electric Vehicles

Lithium-ion battery state of health (SOH) estimation technology is an important part of the design of a battery monitoring system (BMS) for electric vehicles. People often use battery capacity and internal resistance as SOH estimation indicators. However, due to actual working conditions, it is difficult for electric vehicles to achieve complete charge and discharge, so the battery capacity and internal resistance cannot be monitored online. In view of the above questions, this article proposes an indirect SOH estimation method for online EV lithium-ion batteries based on arctangent function adaptive genetic algorithm combination with back propagation neural network (ATAGA-BP). Firstly, constant current drop time (CCDT), constant current drop capacity (CCDC) and maximum constant current drop rate (MCCDR) in constant voltage charging stage are used as health indicator (HI) to evaluate battery SOH in order to indirectly quantify the degradation process of lithium-ion batteries. Error point optimization and correlation verification are also carried out. Secondly, an ATAGA-BP algorithm is proposed to establish the relationship between HI and available battery capacity, and SOH estimate is made for lithium-ion batteries according to the proposed algorithm. Finally, simulation results with NASA data show the correlation between the proposed HI and lithium-ion battery capacity is above 85%, the error of SOH estimation method proposed is 3.7%, and the iteration efficiency is increased by 17.8%.

A method for state of charge and state of health estimation of lithium-ion battery based on adaptive unscented Kalman filter

The state of lithium-ion battery is a key indicator for the battery management system (BMS) of electric vehicles (EVs). State of charge (SOC) and state of health (SOH) of the power cell are the main parameters of the BMS during operation. In this paper, an adaptive unscented Kalman filter algorithm (AUKF) is presented for the joint estimation of SOC and SOH of lithium-ion batteries. Firstly, this paper develops a 2-RC equivalent circuit model and identifies the model parameters using recursive least squares algorithm with forgetting factor. Then, the SOC and SOH of the battery are estimated simultaneously by AUKF. Finally, the accuracy of the proposed method is verified under different operating conditions. The experiment results show that the maximum SOC estimation error is under 0.08% by the proposed method. Compared with the unscented Kalman filtering (UKF), it is shown that the proposed method is more accurate and reliable. An effective method is provided for state estimation for battery management system.

State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM

State-of-Health (SOH) prediction of lithium-ion batteries is crucial in battery management systems. In order to guarantee the safe operation of lithium-ion batteries, a hybrid model based on convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) and attention mechanism (AM) is developed to predict the SOH of lithium-ion batteries. By analyzing the charging and discharging process of batteries, the indirect health indicator (HI), which is highly correlated with capacity, is extracted in this paper. HI is taken as the input of CNN, and the convolution and pooling operations of CNN layers are used to extract the features of battery time series data. On this basis, a BiLSTM depth model is built in this paper to collect the data coming from CNN forward and reverse dependencies and further emphasize the correlation between the serial data by AM to obtain an accurate SOH estimate. Experimental results based on NASA PCoE lithium-ion battery data demonstrate that the proposed hybrid model outperforms other single models, with the root mean square error (RMSE) of SOH prediction results all less than 0.01, and can accurately predict the SOH of lithium-ion batteries.

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.