Lithium-ion Battery State Of Health Research Articles

High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model

The state of health (SOH) of lithium-ion batteries plays a crucial role in maintaining the stability of electric vehicle systems. To address the issue of low accuracy in existing prediction models, this article introduces an enhanced grey wolf algorithm for optimizing the hybrid kernel least squares support vector regression machine used in lithium-ion battery SOH prediction. This research extracted four key health features from the raw data of each battery in the Cycle dataset, which is publicly accessible. Data preprocessing of health features involved Pearson correlation analysis and Hampel filtering techniques. The framework of least squares support vector regression constructs a hybrid kernel function of polynomial kernel function and radial basis function. The integration of differential evolution and the law of survival of the fittest into the grey wolf algorithm enhances its optimization ability. The improved grey wolf algorithm optimizes the parameters of the hybrid kernel least squares support vector regression machine, improving the accuracy and robustness of the model. After data validation, it is known that the optimal average absolute error value predicted by the model can reach 0.32 %. This indicates that the proposed method is effective and feasible.

A hybrid multilayerperceptron-extremegradientboost approach for precise state of charge and state of health assessment

Lithium ion batteries are popularly used in various energy storage systems. Accurate battery's State of Charge (SoC) and State of Health (SoH) estimate is essential for energy storage system's safety. This manuscript proposes a hybrid approach for estimating SoC and SoH of lithium ion battery. The proposed hybrid technique is the joint execution of both the Multi-Layer Perceptron (MLP) with Extreme Gradient boosting (XGBoost) algorithm. Hence, it is named as MLP-XG Boost Algorithm. The major objective of the proposed system is to improve the safety of energy storage systems and to achieve the highly accurate SoH. The MLP algorithm is utilized to analyze and trained the battery data. The XG Boosting algorithm is utilized to preprocessed and estimate the SoC and SoH of a lithium-ion battery. The proposed method's effectiveness is assessed using the MATLAB platform and contrasted with other methods that are currently in use. In every system now in use, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), the proposed approach produces better results. From the result it is concluded that the proposed method based Mean Square Error and Mean Absolute Error Root is less than 1 at various temperatures such as 0∘C, 25∘Cand 45∘C and it is better and efficient.

State of Health Estimation for Lithium-Ion Battery Based on Sample Transfer Learning under Current Pulse Test

Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods.

Health State Assessment of Lithium-Ion Batteries Based on Multi-Health Feature Fusion and Improved Informer Modeling

Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this end, this paper proposes a health state assessment method for lithium-ion batteries based on health feature extraction and an improved Informer model. First, multiple features that can reflect the SOH of lithium-ion batteries were extracted from the charging and discharging time, the peak value of incremental capacity curve (ICC), and the inflection point value of differential voltage curve, etc., and the correlation between multiple health features and the health state was evaluated by gray correlation analysis. Then, an improved Informer model is proposed to establish a health state estimation method for lithium-ion batteries. Finally, the proposed algorithm is tested and validated using publicly available battery charge/discharge datasets and compared with other algorithms. The results show that the method in this paper can realize high-precision SOH prediction with a root-mean-square error (RMSE) of 0.011, and the model fit reaches more than 98%.

Lithium-ion battery SOH prediction based on VMD-PE and improved DBO optimized temporal convolutional network model

Accurately estimating the state of health (SOH) in lithium-ion batteries is crucial for optimizing performance, cost management, enhancing safety, extending lifespan, and promoting environmental sustainability. However, due to factors such as capacity regeneration, battery capacity degradation data exhibits nonlinear and non-stationary characteristics, making it challenging to accurately predict SOH. In order to improve prediction accuracy, this paper proposes a novel SOH prediction model based on VMD-PE-IDBO-TCN. Firstly, the variational mode decomposition-permutation entropy (VMD-PE) method is innovatively introduced to decompose the original battery SOH data and reorganize it into a series of subsequences, reducing the impact of non-stationary components on the prediction results. Moreover, temporal convolutional network (TCN) prediction models are used for each subsequence, and an improved dung beetle optimization (IDBO) algorithm is used to optimize the TCN parameters, where this IDBO algorithm incorporates SPM chaotic mapping, golden sine strategy, and adaptive Gaussian-Cauchy mixed mutation perturbation. The IDBO algorithm rapidly and accurately identifies the optimal parameter combination, significantly enhancing the predictive performance of the TCN model. Finally, the predicted values of each subsequence are superimposed to obtain the final prediction result. Two different lithium-ion battery datasets from NASA and CALCE are used for experimental validation. Experimental results show that the proposed model has higher prediction accuracy, with a maximum RMSE of only 0.0072 and a maximum MAPE of only 0.67 %, and has good adaptability to different types of batteries and batteries operating at high temperatures.

Joint Estimation of SOC and SOH for Lithium-Ion Batteries Based on Dual Adaptive Central Difference H-Infinity Filter

The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence and insufficient robustness in the existing joint estimation algorithms of SOC and SOH, a Dual Adaptive Central Difference H-Infinity Filter algorithm is proposed. Firstly, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for parameter identification, and an inner loop with multiple updates of the parameter estimation vector is added to improve the accuracy of parameter identification. Secondly, the capacity is selected as the characterization of SOH, and the open circuit voltage and capacity are used as the state variables for capacity estimation to improve its convergence speed. Meanwhile, considering the interaction between SOC and SOH, the state space equations of SOC and SOH estimation are established. Moreover, the proposed algorithm introduces a robust discrete H-infinity filter equation to improve the measurement update on the basis of the central differential Kalman filter with good accuracy, and combines the Sage–Husa adaptive filter to achieve the joint estimation of SOC and SOH. Finally, under Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) conditions, the SOC estimation errors are 0.5% and 0.63%, and the SOH maximum estimation errors are 0.73% and 0.86%, indicating that the proposed algorithm has higher accuracy compared to the traditional algorithm. The experimental results at different initial values of capacity and SOC demonstrate that the proposed algorithm showcases enhanced convergence speed and robustness.

A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions

Accurately estimating the state of charge (SOC), state of energy (SOE), and state of health (SOH) online is a critical and urgent concern in the management of lithium-ion batteries for electric vehicle applications, particularly in terms of safety and reliability. This paper develops a hybrid neural network, abbreviated as CNN-SAM-LSTM model, which combines a convolutional neural network, self-attention mechanism, and long-short term memory neural network to jointly estimate the state parameters of lithium-ion batteries, including SOC, SOE, and SOH. Additionally, a joint loss function considering homoscedastic uncertainty is developed to optimize weight adjustments for the training losses associated with the three state parameters. Experimental data collected under UDDS, BBDST and CC discharge conditions are employed to showcase the effectiveness of the proposed CNN-SAM-LSTM model. The results demonstrate that the proposed model is capable of simultaneously and accurately estimating SOC, SOE, and SOH for lithium-ion batteries under different dynamical operating conditions. Moreover, when applied to randomly segmented data, the proposed model exhibits robustness, effectively handling deviations from random discharge segments.

A Deep Learning Method for the Health State Prediction of Lithium-Ion Batteries Based on LUT-Memory and Quantization

The precise determination of the state of health (SOH) of lithium-ion batteries is critical in the domain of battery management systems. The proposed model in this research paper emulates any deep learning or machine learning model by utilizing a Look Up Table (LUT) memory to store all activation inputs and their corresponding outputs. The operation that follows the completion of training is referred to as the LUT memory preparation procedure. This method’s lookup process supplants the inference process entirely and simply. This is achieved by discretizing the input data and features before binarizing them. The term for the aforementioned operation is the LUT inference method. This procedure was evaluated in this study using two distinct neural network architectures: a bidirectional long short-term memory (LSTM) architecture and a standard fully connected neural network (FCNN). It is anticipated that considerably greater efficiency and velocity will be achieved during the inference procedure when the pre-trained deep neural network architecture is inferred directly. The principal aim of this research is to construct a lookup table that effectively establishes correlations between the SOH of lithium-ion batteries and ensures a degree of imprecision that is tolerable. According to the results obtained from the NASA PCoE lithium-ion battery dataset, the proposed methodology exhibits a performance that is largely comparable to that of the initial machine learning models. Utilizing the error assessment metrics RMSE, MAE, and (MAPE), the accuracy of the SOH prediction has been quantitatively evaluated. The indicators mentioned above demonstrate a significant degree of accuracy when predicting SOH.

Research on State of Health Estimation of Lithium Batteries Based on Electrochemical Impedance Spectroscopy and CNN-VIT Models

Abstract With the development of electric vehicles, the demand for lithium-ion batteries has been increasing annually. Accurately estimating the state of health (SOH) of lithium-ion batteries is crucial for their efficient and reliable use. Most of the existing research on SOH estimation is based on parameters such as current, voltage, and temperature, which are prone to fluctuations. Estimating the SOH of lithium-ion batteries based on electrochemical impedance spectroscopy (EIS) and data-driven approaches has been proven effective. In this paper, we explore a novel SOH estimation model for lithium batteries based on EIS and Convolutional Neural Network (CNN)-Vision Transformer (VIT). The EIS data are treated as a grayscale image, eliminating the need for manual feature extraction and simultaneously capturing both local and global features in the data. To validate the effectiveness of the proposed model, a series of simulation experiments are conducted, comparing it with various traditional machine learning models in terms of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The simulation results demonstrate that the proposed model performs best overall in the testing dataset at three different temperatures. This confirms that the model can accurately and stably estimate the SOH of lithium-ion batteries without requiring manual feature extraction and knowledge of battery aging temperature.

State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine

Lithium-ion battery State of Health (SOH) estimation is an essential issue in battery management systems. In order to better estimate battery SOH, Extreme Learning Machine (ELM) is used to establish a model to estimate lithium-ion battery SOH. The Swarm Optimization algorithm (PSO) is used to automatically adjust and optimize the parameters of ELM to improve estimation accuracy. Firstly, collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity curve. Use Grey Relation Analysis (GRA) method to analyze the correlation between battery capacity and five characteristic quantities. Then, an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics, and a PSO is introduced to optimize the parameters of the capacity estimation model. The proposed method is validated by the degradation experiment of the lithium-ion battery under different conditions. The results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation, and the average absolute percentage error is less than 1%.

Bayesian Approach to Estimate Distribution of Relaxation Times for Lithium-Ion Batteries

Electrochemical impedance spectroscopy (EIS) has emerged as a powerful tool for analyzing the state of lithium-ion batteries due to its ability to provide rich electrochemical information. Traditionally, equivalent circuit and physical models have been utilized for interpreting EIS data. However, these methods possess inherent drawbacks: equivalent circuit models are non-unique, while physical models can be challenging to implement. As an alternative, the distribution of relaxation times (DRT) has garnered increasing attention in recent years. The DRT method enables the analysis of EIS data without predefining a model's shape, allowing for the decomposition of internal characteristics in complex systems, such as lithium-ion batteries. Despite its advantages, DRT estimation is an ill-posed inverse problem that often requires regularization or fine-tuning based on empirical choices, which can lead to the misinterpretation of EIS data.To address these challenges, we propose a Bayesian approach that enables the estimation of DRT while considering uncertainties, even in the presence of observational errors in EIS data. Moreover, the proposed method can assist in estimating the state of health (SOH) of the battery, which provides valuable insights into the battery's overall performance, longevity, and safety. To validate the efficacy of our proposed method, we apply it to synthetic EIS data and compare its performance with existing DRT estimation methods. The results show that the proposed Bayesian approach offers a more robust and accurate estimation of DRT. We also demonstrate the practical applicability of the proposed method by applying it to real lithium-ion battery EIS experimental data, where it successfully extracts meaningful information about the battery's internal state and SOH. In summary, the proposed DRT-based Bayesian method has the potential to enable a robust diagnosis of the internal state and SOH of lithium-ion batteries through EIS measurements. By incorporating this approach, researchers and industry practitioners can gain a deeper understanding of battery performance, facilitating the development of improved diagnostic techniques and battery management strategies to enhance the performance and reliability of lithium-ion batteries. This, in turn, could contribute to the broader adoption of sustainable energy solutions, such as electric vehicles and renewable energy storage systems.This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grants funded by the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20214910100070). Figure 1

State of health estimation of lithium-ion batteries based on multi-feature extraction and temporal convolutional network

Accurate estimation of the state of health (SOH) is vital for maintaining the safe and stable operation of lithium-ion batteries. However, the utilization of extant data-driven methods for SOH estimation often poses a dichotomy between the diversity of feature selection and the intricacy of network models. This study proposes an estimation methodology combining multi-feature extraction with temporal convolutional network (TCN). The experimental curves of charging and discharging, alongside the incremental capacity curve of lithium-ion batteries, were subjected to principal component analysis to extract Class I features. Class II features were derived by performing empirical mode decomposition on the battery capacity decay curve, thereby securing multi-feature data. Moreover, a channel attention module based on TCN was utilized to process multi-dimensional features and select appropriate weights. Concurrently, to enhance the adaptive threshold training ability of the model with multiple input parameters, a residual shrinkage network was introduced. The SOH estimation of lithium-ion batteries was ascertained by training and processing these multi-features using an improved TCN. The results were subsequently compared with long short-term memory and conventional TCN models. The proposed model demonstrated a mean absolute percentage error of 1.47 % in estimating the SOH of lithium-ion batteries.

Improved LSTM based state of health estimation using random segments of the charging curves for lithium-ion batteries

Battery State of Health (SOH) estimation is crucial for providing valuable information for optimizing battery usage and improving battery efficiency. Considering the uncertainties in battery charging behavior during practical usage, this paper proposes an ensemble model based on an improved long short-term memory (LSTM) neural network. The model takes random segments of charging curves as input to estimate the SOH of lithium-ion batteries. In this paper, a multi-layer LSTM network with attention mechanism is proposed as the base learner. Then, multiple base learners are trained using different parts of the charging curve segments. The BP neural network is used to integrate the SOH estimation value of each base learner to obtain the final SOH estimation value. The accuracy and robustness of the proposed method are validated using the Oxford lithium-ion battery dataset and NASA battery degradation dataset. Additionally, this paper investigates the influence of charging segment length and the number of sampling points on the estimation results.

State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree

To ensure the stable, reliable, and safe operation of lithium-ion batteries, the state of health (SOH) serves as a crucial basis for battery safety monitoring and operational maintenance. However, the complex working environment of batteries leads to the presence of a significant amount of noise data, abnormal data, and data discontinuities, posing challenges for battery feature extraction and SOH estimation. To address this issue, this paper proposes a two-stage transformation-based feature extraction method combined with the gradient boosting decision tree (GBDT) algorithm. The two-stage transformation feature extraction method utilizes singular spectrum analysis (SSA) and fast Fourier transform (FFT) to extract time-domain and frequency-domain health features from the battery data, respectively. The GBDT ensemble learning algorithm is employed to estimate the SOH of lithium-ion batteries, leveraging the capability to effectively uncover and learn relevant features by combining multiple weak learners into a strong model. Furthermore, correlation analysis and ablation experiments are conducted to validate the effectiveness of the two-stage feature extraction method. Additionally, four sets of comparative experiments are designed to demonstrate the superior performance of the GBDT method compared to four other machine learning methods. The experimental results show that the mean absolute error (MAE) and root mean square error (RMSE) of SOH estimation using the proposed method do not exceed 3 % on the NASA and CALCE datasets.

State-of-health estimation of lithium-ion batteries using a novel dual-stage attention mechanism based recurrent neural network

Accurate estimation of the state of health (SOH) of lithium-ion batteries is an important guarantee to ensure safe and reliable operation of lithium-ion battery systems. However, the complex aging mechanism inside the battery makes it difficult to measure the battery SOH directly. In this paper, a SOH estimation method based on a novel dual-stage attention-based recurrent neural network (DARNN) and health feature (HF) extraction from time varying charging process is proposed. Firstly, the constant current charging time, the maximum temperature time, the isochronous voltage difference, and the isochronous current were extracted as lithium-ion battery HFs, and their correlations with SOH are verified by spearman correlation coefficient. Secondly, the DARNN is proposed to capture the time-dependent and temporal features of the input sequence and to accurately predict SOH. Finally, the proposed estimation method is validated on the NASA battery dataset. The results show that the method can accurately estimate SOH for lithium-ion batteries. The mean square error and the mean absolute percentage error of the method are <0.5 %.

Jellyfish optimized recurrent neural network for state of health estimation of lithium-ion batteries

The state of health (SOH) of lithium-ion batteries is considered an important health indicator to evaluate different features such as efficiency, robustness, and accuracy. The SOH of the lithium-ion battery is investigated to determine the advent of failures to mitigate the battery risk, avoid uneven fault occurrences and ensure safe battery operation. Nonetheless, the accurate assessment of SOH is difficult due to the occurrence of capacity degradation and performance deviation with temperature and aging impacts. Therefore, this work presents an improved hybrid recurrent neural network (RNN) and jellyfish optimization (JFO) to estimate the SOH of lithium-ion batteries. A total of 124 lithium-ion battery cells classified in three batches, namely ‘2017-05-12’, ‘2017-06-30’, and ‘2018-04-12’ from the MIT Stanford- Toyota Research Institute battery database, are used for the proposed work. The systematic sampling method is utilized for developing a 31-dimensional data format. The 31-dimensional data format is developed considering various critical battery parameters such as capacity, voltage, current, and temperature. The JFO is applied to achieve the best suitable RNN model hyperparameters for accurate estimation outcomes. The validation of the hybrid RNN-JFO method is conducted with other JFO-optimized neural network (NN) models, such as feedforward neural network (FFNN) and backpropagation neural network (BPNN). Compared with FFNN-JFO and BPNN-JFO models, the experiment conducted with the RNN-JFO method delivered accurate outcomes. For the batches ‘2017-05-12’, ‘2017-06-30’, and ‘2018-04-12’, the average root mean square error (RMSE) error achieved with the RNN-JFO model was 0.0529, 0.2674, and 0.1638. The SOH estimation outcomes confirm the effectiveness of the RNN-JFO method for SOH estimation on different lithium-ion batteries.

Estimation of State of Health and Knee Point Identification in Lithium-ion Batteries Using BES-GRU

Accurately assessing the State of Health (SOH) of batteries and conducting knee point detection is of utmost importance in prolonging their lifespan. However, the generalization ability and robustness of individual methods are limited. To address this, a novel lithium-ion battery SOH estimation model is proposed, incorporating the Bald Eagle Search (BES) optimization algorithm in conjunction with the Gate Recurrent Unit (GRU) neural network. The selection of highly correlated health indicators (HI) with battery SOH is performed using the Pearson correlation coefficient. The BES algorithm is employed to optimize the selection of parameters for GRU networks. The model reliability is verified using the MIT-Stanford lithium-ion battery dataset. The results illustrate that the proposed BES-GRU model effectively predicts the SOH of batteries and identifies knee points, effectively improving the estimation accuracy.

A new method for determining SOH of lithium batteries using the real-part ratio of EIS specific frequency impedance

The State of Health (SOH) is a crucial parameter for evaluating the stability of battery operation. The SOH can be investigated via electrochemical impedance spectroscopy (EIS), which is useful for nondestructive battery testing and studying the working mechanism of batteries. The traditional EIS method used for estimating the SOH of lithium-ion batteries requires a long test time, complex circuit structure, complex calculation, or huge history data, making it inconvenient for practical applications. This paper presents a straightforward, convenient, and efficient method for estimating the SOH of Li-ion batteries, reducing the measurement time (3 min) compared with full-range EIS measurement (30 min) by a factor of 10. This method is based on the ratio between the real part of the zero-crossing of a Nyquist plot (impedance at 0.99 Hz in this paper) and the real part of the low-frequency inflection point of the Nyquist plot (impedance at 1976 Hz in this paper). This method is validated by different battery experiments. The obtained results show that this indicator does not significantly change with the battery charging capacity, and could be used to estimate the Li-ion battery SOH with an average relative error of 4.46 % through a second-order fitting function, which is of utmost importance for practical applications.

State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network

Lithium-ion batteries (LIBs) need to maintain high energy efficiency and power level in several application scenario. Accurate state of health (SOH) forecast is essential for designing a safe and reliable battery management systems (BMS). Temporal convolutional network (TCN) is a prevailing deep learning method for estimating the SOH of lithium-ion batteries. However, the hyperparameters in the network are usually difficult to predefine, which poses a challenge for the SOH estimation accuracy in real-world. To solve this problem, this paper proposes a data-driven estimation approach, where the TCN is combined with the modified flower pollination algorithm (MFPA) to determine the network topology. After hyperparameter optimization, the external sensor raw data and identified ohmic resistances trajectories in the equivalent circuits model (ECM) are both input to the TCN model to estimate SOH of LIBs. In contrast to prior approaches for feature extraction, this method is not only conductive to improve SOH estimation accuracy, but also can reduce on-board estimation computing burden. We carry out experiments on the same type of cells from NASA public data resources. The experimental results systematically validate the superiority of the proposed method, which covers high estimation accuracy, great robustness to varied training set and satisfied universality to different batteries.

Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy

With the increasing application of lithium-ion batteries, accurate estimation of the state of health (SOH) of lithium-ion batteries is of great significance for the safe operation of lithium-ion battery systems and the reduction of operation and maintenance costs. The complex physical and chemical reactions inside the lithium battery and the complex external working conditions make it challenging to achieve an accurate health-state estimation and life prediction. Therefore, the accurate estimation of the SOH of lithium-ion batteries is an important issue. At present, electrochemical impedance spectroscopy (EIS) is widely used in the study of battery-power impedance characteristics and battery-state estimation due to its advantage of nondestructive measurement. For this reason, this paper summarizes the research progress of lithium-ion SOH estimation based on EIS in recent years and details it layer by layer, mainly from two aspects: first, the quantitative relationship model between the characteristic parameters and SOH is established by constructing a frequency domain-equivalent circuit model. Secondly, we construct a quantitative relationship model between EIS data and SOH using the data-driven method. Finally, the advantages and disadvantages of different methods and estimation accuracy are analyzed and compared, and the future estimation of SOH based on EIS is prospected.