Health Estimation Of Lithium-ion Batteries Research Articles

State of Health Estimation of Lithium-Ion Battery Based on Constant-Voltage Charging Reconstruction

State of health (SOH) estimation is essential for life evaluation and health management of lithium-ion battery (LIB). This paper proposes a novel SOH estimator by using the partial constant-voltage (CV) charging data. First, a thorough analysis is performed over different CV health indicators (HIs) in terms of the HI-SOH correlation as well as the robustness to CV partialness and disturbances, and the CV capacity is proved to be more informative and robust for SOH estimation. Second, to tackle the practical challenge arising from CV charging partialness, a novel CV phase reconstruction method combining Q-V modeling and open-circuit voltage (OCV) estimation iteratively is proposed to predict the CV capacity authentically based on the available partial CV data. The extracted CV capacity is further used to estimate the battery SOH precisely. The proposed method is validated with long-term degradation experiments performed on NCA cells. Results suggest that the proposed method manifests itself with a high estimation accuracy, a low requirement on the charging completeness, and a high robustness to cell inconsistency.

State of health estimation for lithium-ion batteries using Gaussian process regression-based data reconstruction method during random charging process

State of health (SOH) estimation is a critical technology to guarantee the safe and reliable operation of battery energy systems. Data-driven methods have been widely studied in the field of lithium-ion battery SOH estimation. However, random charging in real operating scenarios will result in difficult extraction of health features, which in turn limits the online application of data-driven methods. In this paper, a SOH estimation method using a piece of random charging data is proposed. In random charging scenarios, the health features screened within the predefined charging range are easily lost. For this reason, the partial charging data is reconstructed by the Gaussian Process Regression (GPR) model to intelligently supplement the health features that are not sampled. In terms of health feature selection, this paper selects the charging time sequence within a voltage interval as the input of the Long-Short-Term Memory (LSTM) estimation model, so that the complicated feature extraction work can be avoided, such as the peak of incremental capacity (IC) curve, the slope of the charging voltage and so on. The experimental results show that the data reconstruction method based on the GPR model can reconstruct the missing charging data accurately under a large number of simulated random charging data, and the reconstruction error of the charging data under two types of aging paths is less than 4.30%. Finally, using most known health features and a few reconstructed health features within certain charging intervals, the proposed GPR-LSTM method achieves a root-mean-squared percentage error (RMSPE) of 2.51% in SOH estimation for the two well-known laboratory datasets, and compared to 3.89% for the IC peak feature-based method, a reduction by 1.47%. Meanwhile, for a real-world battery data, the RMSPE of the proposed method is below 3%, illustrating the usefulness of the proposed method under the on-road conditions.

Health estimation of lithium-ion batteries with voltage reconstruction and fusion model

Accurate state of health (SOH) estimation is crucial for Lithium-ion battery in electric vehicles (EVs). This work proposes a battery SOH estimation method based on voltage reconstruction and fusion models. Firstly, a voltage curve reconstruction method based on importance sampling is proposed to solve the V–Q curve. Then, feature factors related to SOH are extracted and their correlation with SOH is analyzed. Furthermore, a SOH estimation fusion model is established based on improved Support Vector Regression (SVR) and Convolutional Neural Network (CNN). Finally, the accuracy of the proposed algorithm is verified in 20% and 30% small sample scenarios, respectively. The experimental results show that the numerical evaluation indicators of the proposed method are superior to Gauss Process Regression (GPR), CNN, whale optimization algorithm-SVR (WOA-SVR) and Long Short Term Memory (LSTM) neural network, which indicates that the proposed method has good performance.

Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment

Battery health evaluation is vital for ensuring the security and reliability of lithium-ion batteries. However, the currently proposed methods generally require high-quality input data for feature extraction in online applications. To overcome this obstacle, this paper proposes a rapid online health estimation method only based on partial constant-voltage (CV) charging segment. Firstly, through primary analysis of battery test data, the evolution of CV charging current is confirmed to be correlated with battery capacity. Subsequently, the current evolution constant of CV charging phase is mathematically formulated and quantitatively characterized using a novel health indicator (HI). Besides, charging time and charging capacity are also extracted as HIs to comprehensively capture the CV charging behavior and enhance the robustness of data-driven models. Considering the user's charging habits, an optimized CV segment is determined, enabling a significant reduction in data size and coverage. Finally, three data-driven methods are employed to construct health estimation models by using the extracted HIs, and the best performance is achieved by Gaussian process regression with MAE and RMSE lower than 0.8% and 1%, respectively. Remarkably, the proposed method demonstrates superiority in dealing with sparse sampling, and satisfactory results with 2.9% error under the sparsity of 10 s are obtained.

A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery

Accurate estimation of the state of health (SOH) of lithium-ion batteries holds significant importance in guaranteeing the stable and secure functioning of electric vehicles. However, existing neural network-based methods suffer from limitations in capturing long-term serial relationships and extracting degenerate features. In light of these challenges, we propose a novel sequence-free framework for performing the SOH estimation task. Technically, a global–local context embedding module is proposed to learn both global- and local-range information context by two convolutional streams with different depths. With this module, a discriminatory feature learning can be guided. By integrating it into the convolution neural network, a novel time series prediction network, called improved convolution neural network (ICNN) is presented, which can effectively establish the mapping relationship between battery charging/discharging curves and battery SOH. Through rigorous experimentation on the CACLE dataset and NASA dataset, we demonstrate the efficacy of our proposed method, achieving mean absolute errors (MAEs) of 0.54% and 1.20% respectively. Our findings highlight the superiority of the proposed method compared to commonly used neural network methods in the domain of battery SOH estimation.

A feature reuse based multi-model fusion method for state of health estimation of lithium-ion batteries

Precise estimation of lithium-ion batteries’s state of health (SOH) is crucial for their safe and stable operation. The existing SOH estimation methods mainly focus on a single model with multi-features. Hence, the potential of each feature cannot be fully exploited. To improve the accuracy of prediction model, a feature reuse(FR) based multi-model fusion method is proposed in this paper. Firstly, four features are extracted by analyzing the battery aging process from multiple perspectives such as sample entropy and time(micro/macro), particularly a new feature is proposed to reflect changes in battery energy. The four health factors are as first-level input and second-level feature reuse. Secondly, the preliminary SOH predictions are generated respectively by using k-nearest neighbor regression, random forest regression, linear regression, and extremely randomized tree regression models with different feature inputs. Finally, the initial SOH predictions were fused with the input features from the first stage using a Bayesian linear regression algorithm. Inspired by the advantages of feature reuse method and multi-model fusion, the feature reuse method is applied for fusing Bayesian regression multi-model. To verify the effectiveness of the proposed model, comparative experiments are carried on the Oxford and CALCE battery degradation dataset. Comparing with the single model estimation and methods without feature reuse, the proposed method has better accuracy and stronger robustness in SOH estimation.

Accurate state of health estimation for lithium-ion batteries under random charging scenarios

Accurate state of health (SOH) knowledge is critical for reliable operations of lithium-ion batteries. However, short-term random charging operations of lithium-ion batteries are not conducive to reliable SOH estimation. To solve it, a charging voltage prediction and machine learning based estimation are employed to supply precise estimation. Firstly, the correlated feature variables in terms of SOH are determined by analyzing the raw charging voltage distribution. Then, the wide range charging voltage is predicted via the constructed polynomials and short-term measures. Next, the extreme learning machine algorithm is employed to achieve online SOH estimation. Finally, the feasibility of the proposed voltage estimation method is verified at different aging cycles and different charging intervals, and the reliability of SOH estimation is investigated in the full lifetime range and at different state of charge (SOC) charging intervals. The experimental results manifest that the proposed method can supply reliable SOH estimation with the error of less than 2.02% based on only the short-term random charging scenarios, furnishing reliable and safe operation guideline for lithium-ion battery systems.

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.

Linear Model for Online State of Health Estimation of Lithium-Ion Batteries Using Segmented Discharge Profiles

The pressing need to reduce CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions has triggered the exponential growth of electric vehicles powered by lithium-ion batteries (LIBs). Accurate real-time State of Health (SOH) diagnosis of LIBs is paramount to ensure reliable operation of these batteries through their entire service life. This paper presents an intuitive multiple linear regression algorithm using short segment of voltage measurements from a battery’s constant current discharge profile to determine its SOH accurately with a Root Mean Square Error (RMSE) of less than 4%. The result from this work shows that voltage segments of just 0.02 V is sufficient to provide SOH prediction with less than 3% RMSE. However, the accuracy of the model is shown to be dependent on the specific voltage range of data used. To ensure robust SOH estimation and for practicality reasons, voltage segment of 0.1 V (about 13% SOC), within a range of 3.6 V to 3.9 V for nominal LIBs cell voltage (about 54 to 91% SOC), is recommended to be used for the SOH estimation. In this work, Nickel Cobalt Aluminum Oxides and Lithium Cobalt Oxide chemistry type are used for validation. The use of small voltage decay segments provides the capability for online SOH estimation.

A vision transformer-based deep neural network for state of health estimation of lithium-ion batteries

Accurate state-of-health (SOH) estimation of lithium-ion batteries (LIBs) is crucial to ensure the safety and reliability of electric vehicles. Deep learning has become a popular method for SOH estimation. However, this has mostly relied on convolutional neural networks (CNNs) and recurrent neural networks (RNNs), not fully exploring the potentialities of the method. This paper proposes a new procedure for SOH estimation of LIBs based on vision transformer networks (VITs). By analyzing the training speed and accuracy for different sampling points, an adaptive algorithm is designed to choose the most appropriate sampled data for the VIT, guiding battery data collection in actual systems, and reducing manual work during neural network training. Moreover, the VIT is improved by adding a dimension transformation layer, a multilayer perceptron (MLP) and a trainable regression token. Experiments carried out on two different datasets revealed that the proposed framework is able to reach an accuracy better than 0.01, which is superior to that achieved with other available techniques. The new approach has high robustness, good accuracy and applicability, and the VIT has great potential in SOH estimation.

An improved variable forgetting factor recursive least square-double extend Kalman filtering based on global mean particle swarm optimization algorithm for collaborative state of energy and state of health estimation of lithium-ion batteries

An improved variable forgetting factor recursive least square-double extend Kalman filtering based on global mean particle swarm optimization algorithm for collaborative state of energy and state of health estimation of lithium-ion batteries

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

Energy storage is an important technical means to increase the consumption of renewable energy and reduce greenhouse gas emissions. Electrochemical energy storage, represented by lithium-ion batteries, has a promising developmental prospect. The performance of lithium-ion batteries continues to decline in the process of application, and the differences between batteries are increasing. Therefore, accurate estimation of the state of health (SOH) of batteries is the key to the safe and efficient operation of energy storage systems. In this paper, the electrochemical impedance spectroscopy (EIS) characteristics of Li-ion batteries under different states of charge and health were studied. Three groups of Li-ion battery impedance module values under different frequencies were selected as characteristic parameters, and the SOH estimation model of Li-ion batteries was built by using the support vector regression (SVR) algorithm. The results show that: the model with the second group of frequency-point combinations as characteristic parameters takes into account both accuracy and efficiency; the cumulative time of the characteristic frequency test and SOH evaluation of lithium-ion batteries is less than 10 s; and this technology has good engineering application value.

State of health estimation for lithium-ion batteries on few-shot learning

State of health estimation for lithium-ion batteries on few-shot learning

Data-Driven State of Health Estimation for Lithium-Ion Batteries Based on Universal Feature Selection

A simple yet effective health indicator (HI)-based data-driven model forecasting the state of health (SOH) of lithium-ion batteries (LIBs) and thus enabling their efficient management is developed. Five HIs with high physical significance and predictive power extracted from voltage, current, and temperature profiles are used as model inputs. The generalizability and robustness of the proposed ridge regression–based linear regularization model are assessed using three NASA datasets containing information on the behavior of batteries over a wide range of temperatures and discharge rates. The maximum mean absolute error, maximum root-mean-square error, and maximum mean absolute percentage error of the SOH for the three groups of batteries are determined as 0.7%, 0.86%, and 2.1%, respectively. Thus, the developed model exhibits high accuracy in estimating the SOH of LIBs under multiworking conditions and is sufficiently robust to be applicable to low-quality datasets obtained under other conditions.

State of health estimation for lithium-ion batteries based on hybrid attention and deep learning

State of health estimation for lithium-ion batteries based on hybrid attention and deep learning

Ensemble Learning and Voltage Reconstruction Based State of Health Estimation for Lithium-Ion Batteries With Twenty Random Samplings

Accurate estimation of state of health (SOH) is critical for the safe and efficient operation of lithium-ion batteries in electric transport tools. However, the random charge/discharge behaviors complicate online SOH estimation and discount estimation accuracy. To overcome this difficulty, this study presents an ensemble learning and voltage reconstruction-based SOH estimation framework through the incorporation of individual estimators and with the consideration of limited charging data. First, by analyzing more than 100 000 charging behaviors, the difficulty of feature extraction is addressed based on voltage distribution. Then, a voltage shape fitting method combing mechanistic and prognostic model is developed to reconstruct the constant current charge voltage, and the model parameters are identified by the moth-flame optimization algorithm. Next, the extreme learning machine and random forest are leveraged to estimate SOH preliminarily from the random discontinuous charging points with preferable diversity and high efficiency. On this basis, an induced ordered weighted averaging operator is exploited to efficiently integrate the individual learners and adaptively update the weight of each learner, thereby achieving better estimation than individual ones. The experimental results manifest that the SOH can be reliably estimated within an error of 3.42% using only 20 random samplings.

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.

State of health estimation of lithium-ion batteries based on remaining area capacity

Accurate state of health (SOH) estimation can ensure the safe and reliable operation of the battery and prolong its service life. A new SOH evaluation method including the concepts of characteristic probability (CP) and remaining area capacity (RAC) are introduced in the framework of probability density function (PDF). Battery SOH evaluation models are respectively established for the lithium-iron phosphate (LFP) battery module and nickel-cobalt-aluminium (NCA) cell based on laboratory data at 1/3C-rate and 1C-rate at the sampling frequency of 1 min, and the effects of different CP values on the RAC - SOH models are investigated. The results show that there is a strong linear positive correlation between RAC and SOH whether charge and discharge conditions, while the model with the maximum PDF peak height as health factor is not ideal to assess battery SOH during discharging. The smaller the CP value, the better the effect of the RAC - SOH model. The R2 values of RAC - SOH models of NCA cell and LFP battery module are up to 0.99 at appropriate characteristic probability. The RMSE between the real and the estimated value of NASA battery SOH is not >1.2 % based on the RAC - SOH model. The improved PDF method has accurate and robust performance for the SOH estimation of different batteries.

Partial Charging-Based Health Feature Extraction and State of Health Estimation of Lithium-Ion Batteries

The state-of-health (SOH) is a critical indicator in the battery management system of lithium-ion batteries (LIBs). Charging feature-based SOH estimators have attracted much attention due to the stability and controllability of working conditions. However, the uncertainty raised from partial charging substantially rolls up the difficulties in feature extraction. A new incremental capacity (IC) feature-based SOH estimation method using the partial constant current (CC) charging data of LIB is proposed in this article. First, the linear model between the specific features of interest (FOI) and the SOH is calibrated by fitting the revised Lorentzian function-based voltage-capacity (RL-VC) model with the full-range CC charging data. Second, the corresponding FOI is extracted from the IC curves by fitting the RL-VC model with the partial CC data. Then, the SOH can be directly interpolated based on the calibrated FOI-SOH linear model. The estimated SOH accuracy holds within 5% when different partial CC datasets are used, which indicates the proposed SOH estimator can be reliably implemented in practical applications.

Online State of Health Estimation of Lithium-Ion Batteries Based on Charging Process and Long Short-Term Memory Recurrent Neural Network

Accurate state of health (SOH) estimation is critical to the operation, maintenance, and replacement of lithium-ion batteries (LIBs), which have penetrated almost every aspect of our life. This paper introduces a new approach to accurately estimate the SOH for rechargeable lithium-ion batteries based on the corresponding charging process and long short-term memory recurrent neural network (LSTM-RNN). In order to learn the mapping function without employing battery models and filtering techniques, the LSTM-RNN is initially fed into the health indicators (HIs) extracted from the charging process and trained to encode the dependencies of the related data sequence. Subsequently, the trained LSTM-RNN can properly estimate online SOHs of LIBs using extracted HIs. We experiment on two public datasets for model construction, validation, and comparison. Conclusively, the trained LSTM-RNN achieves an overall root mean square error (RMSE) lower than 1% on the cases with the same discharging current rate and an RMSE of 1.1198% above 80% SOH on another testing case that underwent a different discharging current rate.