Lithium-ion Battery State Of Health Estimation Research Articles

Estimation of SOH of Lithium-Ion Batteries Based on PSO-Bi GRU-Attention Network

The State of Health (SOH) of lithium-ion batteries is a critical parameter that characterizes their actual lifespan, and its accurate assessment ensures the safe and reliable operation of batteries. However, in practical applications, SOH cannot be directly measured. To further improve the accuracy of SOH estimation for lithium-ion batteries, this study employs the Particle Swarm Optimization (PSO) algorithm to search for the optimal hyperparameters of the Bidirectional Gated Recurrent Unit (Bi GRU) neural network, enabling the prediction of time series information. Additionally, Attention Mechanism (AM) is integrated to allocate weights to the prediction results, resulting in the SOH prediction for lithium-ion batteries. The propose model is validated using the B0005 battery from the NASA lithium battery dataset. Experimental results demonstrate that, compared to the Bi GRU-Attention and Bi GRU models, the propose model reduces the Root Mean Square Error (RMSE) by 52.34% and 66.88%, respectively.

SOH Estimation for Lithium-Ion Batteries Based on Health Indicators Extraction and MKRVR

Aiming at the problems of difficult feature extraction and low State of Health (SOH) prediction accuracy of lithium-ion batteries, this paper proposes a SOH estimation approach that involves extracting Health Indicators (HIs) and utilizing mixed kernel function relevance vector regression (MKRVR). In order to accurately and efficiently extract battery HIs, the paper first examines the limitations of extracting HIs based on Incremental Capacity (IC) curves and puts forward an alternative method of HIs extraction based on voltage-capacity (V-C) curves. Following correlation analysis of the HIs, the paper establishes the MKRVR model that combines a range of kernel functions to estimate SOH. To determine the hyper-parameters and weight coefficients of the MKRVR model, the paper integrates Differential Evolution (DE) and Levy flight into the Gray Wolf Optimizer (GWO) to enhance the population diversity and random search ability of GWO. Finally, the paper conducts experimental validation utilizing three distinct battery datasets. The results indicate that the proposed approach outperforms Support Vector Regression (SVR) and Relevance Vector Regression (RVR), with evaluation index values of MAE, MSE, and RMSE all below 1%. Moreover, the coefficients of determination exceed 0.95, which demonstrates the superiority of this approach over other methods.

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.

State-of-health estimation for lithium-ion battery using model-based feature optimization and deep extreme learning machine

Accurate and reliable estimation of state of health (SOH) is essential for the safe and efficient operation of battery energy storages. However, random charging/discharging behaviors and ambient conditions complicate the online SOH estimation. Aiming at an accurate and robust online SOH estimation for lithium-ion batteries, this paper proposes a SOH estimation method using model-based feature optimization and improved machine learning. First, an empirical model-based voltage reconstruction method is proposed to reconstruct the voltage curve for solving disturbance-free different time (DT) curves under measurement noises and high C-rates and extract model-associated health features (HFs). Then, an optimal charging voltage window (CVW) determination method is proposed, which extracts the CVW-associated HFs by determining the optimal CVW from partial charging voltage ranges. Finally, a set of informative and multi-attribute HFs are extracted to train an improved deep extreme learning machine (DELM) mode for online SOH estimation. The proposed method is validated based on three datasets of batteries with different materials. The results demonstrate high accuracy and strong robustness to partial voltage range, noise corruption, and ambient temperature fluctuation.

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.

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.

A Polak-Ribière-Polyak Conjugate Gradient Algorithm Optimized Broad Learning System for Lithium-ion Battery State of Health Estimation

Accurate state of health (SOH) estimation plays a significant role in the battery management system. This paper investigates a Polak-Ribière-Polyak conjugate gradient (PRPCG) algorithm optimized broad learning system (BLS) for lithium-ion battery SOH estimation. Firstly, effective health indicators (HIs) are extracted from the voltage curve in the constant current charge process. Secondly, a hybrid four layers BLS structure with mapped feature nodes and enhancement nodes connecting to the output is established to build both the linear and nonlinear relationships between the HIs and SOH, in which only the output weights require to be trained. Again, the PRPCG algorithm is adopted for searching optimal output weights without matrix inverse calculation during the training process. Furthermore, certain Gaussian noises are added to enhance the training data for solving the locally low accuracy problem. Finally, under the Oxford battery degradation data set, experiments validate the investigated algorithm has high accuracy in SOH estimation with the mean absolute error below 1%. The enhanced data can efficiently improve the model generalization ability.

Lithium-Ion Battery State of Health Estimation Based on Electrochemical Impedance Spectroscopy and Cuckoo Search Algorithm Optimized Elman Neural Network

Abstract In order to ensure the driving safety of electric vehicles and avoid potential failures, it is important to properly estimate the state of health (SOH) of lithium-ion batteries. In this paper, a method of lithium-ion battery SOH estimation based on electrochemical impedance spectroscopy (EIS) and an algorithm fused by Elman neural network and cuckoo search (CS-Elman) is proposed. First, by extracting 19 features of EIS and using principal component analysis to reduce dimension, we obtain four principal components as model inputs. Second, CS algorithm optimizes the weights and thresholds of Elman algorithm. Next, we use the CS-Elman model to estimate the battery SOH and verify the model with the remaining battery data. In addition, we propose a variable temperature estimation model and verify the feasibility of the model between 25 °C and 45 °C. Finally, the experimental results show that the mean absolute error of the method is less than 1.36%.

An Automatic Weak Learner Formulation for Lithium-Ion Battery State of Health Estimation

Current pulses are convenient to be actively implemented by a battery management system. However, the short-term features (STF) from current pulses originate from various sensors with uneven qualities, which hinder one powerful and strong learner with STF for the battery state of health (SOH) estimation. This article, thus, proposes an optimized weak learner formulation procedure for lithium-ion battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto front-based selection strategy is designed to select the representative solutions from the nondominated solutions fed by a knee point driven evolutionary algorithm, which guarantees both the diversity and accuracy of the weak learners. Afterward, the weak learners, whose coefficients are obtained by self-adaptive differential evolution, are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of the SOH estimation. The validation of the proposed method is proved by LiFePO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${_4}$</tex-math></inline-formula> /C batteries under accelerated cycling ageing test including one mission profile providing primary frequency regulation service to the grid and one constant current profile.

Lithium-ion battery SoH estimation based on incremental capacity peak tracking at several current levels for online application

In this paper, an extension to high C-rates of State of Health (SoH) diagnostic methods based on Incremental Capacity (IC) peak tracking is proposed. A set of eleven NCA Lithium-ion batteries who went under different ageing protocol is used. Charge and discharge cycles are performed at C/20, C/10, C/5 and C/2, and then used for IC analysis. Correlations between the variations of IC peaks and SoH are presented and modelized, and shown to be accurate estimators for all tested C-rates.

A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs

An accurate lithium-ion battery state of health (SOH) estimate is a key factor in guaranteeing the reliability of electronic equipment. This paper proposes a new method that is based on an indirect enhanced health indicator (HI) and uses support vector regression (SVR) to estimate SOH values. First, three original features that can describe the dynamic changes of the battery charging and discharging processes are extracted. Considering the coupling relationship between pairs of the original health indicators, we use the differential evolution (DE) algorithm to optimize their corresponding feature parameters and combine them to form an enhanced health indicator. Second, this paper modifies the kernel function of the SVR model to describe the trend of SOH as the number of cycles increases, with simultaneous hyperparameters optimization via DE algorithm. Third, the proposed model and other published methods are compared in terms of accuracy on the same NASA datasets. We also evaluated the generalization performance of the model in dynamic discharging experiments. The simulation results demonstrate that the proposed method can provide more accurate SOH estimation values.

Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction

SOH (state of health) estimation is important for battery management. Since the electrochemical reaction inside LIBS (lithium-ion battery system) is extremely complex and the external working environment is uncertain, it is difficult to achieve accurate determination of SOH. To improve the accuracy of SOH estimation, we propose a SOH estimation method for lithium-ion battery based on XGBoost algorithm with accuracy correction. We extract several features, including average voltage, voltage difference, current difference, and temperature difference, to describe the aging process of batteries. Due to the higher prediction accuracy and generalization ability of ensemble learning algorithm, the XGBoost model is established to estimate the SOH of lithium-ion battery. Then, the estimation values are corrected by Markov chain. Compared with the methods by XGBoost, random forest, k-nearest neighbor algorithm (KNN), SVM, linear regression, our proposed method shows an accuracy improvement by 10% to 20%. Additionally, the errors of our method are also superior to the others in terms of the average absolute error, root mean square error, and root mean square error.

Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance

Real-time state-of-health (SoH) estimation is often difficult to obtain due to the unavailability of capacity measurements in real-time monitoring. The equivalent internal resistance (EIR), which is easily obtained and closely related to battery deterioration, is studied as a possible solution for achieving real-time and reliable SoH estimation for lithium-ion batteries. A novel real-time SoH estimation method based on the EIR is introduced for lithium-ion batteries. First, an experimental study of the relationship between the EIR and battery degradation is implemented, and this study is used to develop an empirical description of battery degradation using the EIR vector. Second, a fast extraction method for identifying the EIR in real time is proposed by leveraging the relationship between the EIR vector and state of charge (SoC). Third, a support vector regression (SVR)-based method for real-time SoH estimation is introduced by characterizing the hidden relationship between the EIR vector and battery SoH. The proposed method is demonstrated using laboratory test data. The results show that the proposed method can predict the battery SoH in real time with good accuracy and robustness.

Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries

In order to provide an accurate State-Of-Health (SOH) estimation, a novel estimation method is proposed in this paper. In this work, some battery SOH relate features are selected theoretically, proved and then re-screened mathematically. These features can reflect the battery degeneration from different aspects. Also, a new training set design idea is proposed for Least Squares Support Vector Machine algorithm, thereby a model that is suitable for lithium-ion Battery SOH estimation under multi-working conditions can be built. Several lithium-ion battery degeneration testing datasets from National Aeronautics and Space Administration Ames Prognostics Center of Excellence are used to validate the proposed method. Results demonstrate both the superiority of the proposed method and its potential applicability as an effective SOH estimation method for embedded Battery Management System.

A new prognostics method for state of health estimation of lithium-ion batteries based on a mixture of Gaussian process models and particle filter

State of health (SOH) estimation for batteries is a key component in the prognostics and health management (PHM) of battery driven systems. Due to the complicated operating conditions, it is necessary to implement the prognostics under uncertain situations. In this paper, a novel integrated approach based on a mixture of Gaussian process (MGP) model and particle filtering (PF) is presented for lithium-ion battery SOH estimation under uncertain conditions. Instead of directly assuming a certain state space model for capacity degradation, in this paper, the distribution of the degradation process is learnt from the inputs based on the available capacity monitoring data. To capture the time-varying degradation behavior, the proposed method fuses the training data from different battery conditions as the multiple inputs for the distribution learning using the MGP model. Then, a recursive updating of the distribution parameters is conducted. By exploiting the distribution information of the degradation model parameters, the PF can be implemented to predict the battery SOH. Experiments and comparison analysis are provided to demonstrate the efficiency of the proposed approach.

Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries

Two novel methods to estimate the state of charge (SOC) and state of health (SOH) of a lithium-ion battery are presented. Based on a detailed deduction, a dual filter consisting of an interaction of a standard Kalman filter and an Unscented Kalman filter is proposed in order to predict internal battery states. In addition, a support vector machine (SVM) algorithm is implemented and coupled with the dual filter. Both methods are verified and validated by cell measurements in form of cycle profiles as well as storage and cycle ageing tests. A SOC estimation error below 1% and accurate resistance determination are presented.