Estimation Of Lithium-ion Batteries Research Articles

A Novel 2-RC Equivalent Model Based on the Self-Discharge Effect for Accurate State-Of-Charge Estimation Of Lithium-Ion Batteries

A Novel 2-RC Equivalent Model Based on the Self-Discharge Effect for Accurate State-Of-Charge Estimation Of Lithium-Ion Batteries

A Novel Square-Root Adaptive Unscented Kalman Filtering Method for Accurate State-of-Charge Estimation of Lithium-ion Batteries

A Novel Square-Root Adaptive Unscented Kalman Filtering Method for Accurate State-of-Charge Estimation of Lithium-ion Batteries

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

Efforts to decarbonize the world have shown a quick increase in electric vehicles (EVs), limiting increasing pollution. During this electric transportation revolution, lithium-ion batteries (LIBs) play a vital role in storing energy. To determine the range of an electric vehicle (EV), the state of charge and the state of health (SOH) of the battery pack is essential. Access to high-quality data on battery parameters is a crucial challenge for researchers working in the energy storage domain due primarily to confidentiality constraints on manufacturers of batteries and EVs. This paper proposes a hybrid framework for predicting the state of a lithium-ion battery for electric vehicles (EV). Electric vehicles are growing worldwide because of their environmental and sustainability advantages. Batteries are replacing fossil fuels in electric vehicles. In order to prevent failure, Li-ion batteries in electric vehicles should be operated and controlled in a controlled and progressive manner to ensure increased efficiency and safety. An extreme gradient boosting (XGBoost) algorithm is used in this paper to estimate the state of health (SOH) of lithium-ion batteries used in electric vehicles. The model is subjected to error analysis to optimize the battery’s performance parameter. The model undergoes an error analysis to optimize its performance parameters. Furthermore, a state of health (SOH) estimation method based on the extreme gradient boosting algorithm with accuracy correction is proposed here to improve the accuracy of state of health (SOH) estimation for lithium-ion batteries. To describe the aging process of batteries, we extract several features such as average voltages, voltage differences, current differences, and temperature differences. The extreme gradient boosting (XGBoost) model for estimating the state of health (SOH) of lithium-ion batteries is based on the ensemble learning algorithm’s higher prediction accuracy and generalization ability. Experimental results suggest that the boundary gradient lifting algorithm model is capable of more accurate prediction.

A cubature Kalman filter for online state-of-charge estimation of lithium-ion battery using a gas-liquid dynamic model

A novel state of charge (SOC) estimation method is proposed based on a gas-liquid dynamic model using a Cubature Kalman filter (CKF) with state constraints to improve the accuracy of estimations. The constraints derived from the principle of aerodynamic are introduced, which does not need the temperature correction coefficient and can achieve rapid SOC convergence due to its unique iterative form. Non-linear Kalman filter used to eliminate the violent jitter of the original algorithm when switching working conditions are also incorporated. Then, the proposed method is tested using a cylindrical-format ternary lithium-ion battery with a nominal capacity of 2.6 Ah and the estimated SOC were compared with the experimental results. As a result, comparative studies of two non-linear filters revealed that the CKF has the most outstanding performance based on concerning estimation accuracy, recovery time from an initial offset, and computational time. That is 36.4 % more accurate and the convergence time is 97.5 % shorter than the Extended Kalman filter (EKF) when the initial offset is 60 %, respectively.

Adaptive robust unscented Kalman filter-based state-of-charge estimation for lithium-ion batteries with multi-parameter updating

State-of-charge (SOC) estimation is one of the key technologies for the development and application of battery management system (BMS). To achieve high accuracy in SOC estimation, which can adapt to any conditions, an adaptive robust unscented Kalman filter (ARUKF) based on multi-parameter update is proposed herein. First, the DP battery model is applied to replicate the dynamic behavior of lithium-ion batteries, and the model parameters are identified online by the improved forgetting factor recursive least square (IFFRLS). Then, the Institute of Geodesy and Geophysics (IGGIII) weight function is introduced into unscented Kalman filter (UKF) as the form of a robust factor to adjust the weights of observation residuals, and receding horizon based adaptive filter tuning is employed to obtain the time-varying noise covariance. Subsequently, joint estimation of battery model parameters and SOC with capacity updating is implemented which can suppress the system disturbance caused by outliers, mistuning, unknown initial value, and aging. Finally, the superior performance of accuracy and convergence is verified by cycle and aging tests. The maximum absolute error of SOC estimation under the proposed method is kept within 2%. The convergence speed of SOC estimation utilizing ARUKF is nearly 80 s faster than that of robust UKF (RUKF) and UKF under a 20% SOC initial error.

Differential Capacity as a Tool for SOC and SOH Estimation of Lithium Ion Batteries Using Charge/Discharge Curves, Cyclic Voltammetry, Impedance Spectroscopy, and Heat Events: A Tutorial

State-of-charge (SOC) and state-of-health (SOH) of different cell chemistries were investigated using long-time cycle tests. This practical guide illustrates how differential capacity dQ/dU (capacitance) obtained from discharge curves, impedance spectra, and cyclic voltammograms can be used for the instant diagnosis of lithium-ion batteries without fully charging and discharging the cell. The increase of dU/dQ is an early indicator of upcoming heat events and deep discharge. The criterion dQ/dU = dU/dQ = 1 could serve as an indicator for “full charge”. The frequency response of capacitance correlates with the available charge of the battery and reflects overcharge events and deep discharges long before the battery fails. It is not necessary to measure down to extremely low frequencies because the charge transfer pseudocapacitance of around 10 Hz reflects well the SOC. Computer-aided calculation methods for the evaluation of measurements in industrial environments and for the training of students are presented.

State-of-health estimation for lithium-ion batteries using differential thermal voltammetry and Gaussian process regression

State-of-health estimation for lithium-ion batteries using differential thermal voltammetry and Gaussian process regression

State of health estimation based on modified Gaussian process regression for lithium-ion batteries

State of health (SOH) estimation of lithium-ion batteries is a challenging and crucial task for consumer electronics, electric vehicles, and micro-rids. This study presents a data-driven battery SOH estimation method based on a novel integrated Gaussian process regression (GPR) model. First, the aging characteristics of batteries are analyzed from multiple perspectives, and three health indicators (HIs) are extracted from battery charging and discharging curves. Then, the Pearson correlation analysis method is used to quantitatively analyze the correlation between the selected HIs and SOH. Next, a novel compound kernel function is proposed for battery SOH estimation, and different pairs of mean function and kernel function chosen from four mean functions and sixteen kernel functions are used to construct GPR models, and their estimation accuracy is compared subsequently. Finally, four different batteries with various initial health conditions from the NASA battery dataset are used to verify the performance of the proposed method. Experiments show that the method proposed in this paper has satisfactory estimation results in terms of accuracy, generalization ability, and robustness. Specifically, its estimated mean-absolute-error (MAE) and root-mean-square-error (RMSE) is only 1.7%, and 2.41%, respectively.

State-of-Health Estimation for Lithium-Ion Batteries Using Domain Adversarial Transfer Learning

Lithium-ion batteries are the main energy source of devices, and the estimation of their state-of-health (SOH) has become a hot point in prognostics and health management. However, many existing methods assume that training and testing data follow the same distribution. The model based on dataset under one working condition may be ineffective for the dataset under another working condition due to the distribution discrepancy. Thus, this article proposes a novel battery health prognostic model based on transfer learning. First, a novel transfer learning-based prognostic model, called deep domain adversarial network, is developed for SOH estimation of Lithium-ion batteries. Second, an unsupervised feature alignment metric is proposed, where maximum mean discrepancy and correlation alignment are considered simultaneously. Moreover, a generative adversarial learning is developed to guide the feature generator to provide the domain-invariant features. Finally, a novel feature generator, called dense bidirectional gated recurrent unit, is proposed to extract discriminate features from sensor signals. The effectiveness of DDAN for SOH estimation is verified on a battery dataset. The experimental results indicate that DDAN can effectively predict SOH of Lithium-ion battery, and significantly improve the performance of feature learning and knowledge transferring.

Feature Extraction from Charging Profiles for State of Health Estimation of Lithium-ion Battery

Abstract Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance to ensure the reliability and safety of battery management systems (BMS). The difficulty of modelling the complex degradation mechanism has made the data-driven methods gain much attention in battery SOH prediction. To improve the estimation accuracy of battery SOH, extracting the suitable health indicators is still a challenging work. In this work, the health indication features are attracted from the charging voltage profile based on the experimental data measured under constant current charging mode. Subsequently, the Pearson correlation coefficient is used to evaluate the relationships between the extracted health features and battery capacity, thus selecting the most effective health features for establishing the prediction models. Finally, the battery SOH is estimated using a Gaussian process regression (GPR) method. The estimation results with R 2 of 1 and lower mean absolute error (MAE) and maximum error (MAX) provide higher accuracy based on the extracted health feature.

A novel Bayesian multivariate linear regression model for online state-of-health estimation of Lithium-ion battery using multiple health indicators

An accurate state of health is critically significant to evaluate the battery aging level and to ensure electric vehicle security and reliability. This paper presents a novel Bayesian-based method to quantify battery capacity degradation using multiple health indicators extracted from the battery Thevenin model at different temperatures. In this method, the linearity and monotonicity between the extracted health indicators and capacity degradation are analyzed by exploiting the correlation analysis. To estimate the battery's state of health (SOH) accurately, the Bayesian multivariate linear regression is introduced to develop an online state of health estimator. Experimental tests are conducted on two batteries with the same specifications to verify the efficiency of the proposed method. Additionally, the interval estimation of numerical results contains reasonable uncertainty expression, which validates the robustness and reliability of the proposed method.

An Improved Gated Recurrent Unit Neural Network for State-of-Charge Estimation of Lithium-Ion Battery

State-of-charge (SOC) estimation of lithium-ion battery is a key parameter of the battery management system (BMS). However, SOC cannot be obtained directly. In order to predict SOC accurately, we proposed a recurrent neural network called gated recurrent unit network that is based on genetic algorithm (GA-GRU) in this paper. GA was introduced to optimize the key parameters of the model, which can improve the performance of the proposed network. Furthermore, batteries were tested under four dynamic driving conditions at five temperatures to establish training and testing datasets. Finally, the proposed method was validated on dynamic driving conditions and compared with other deep learning methods. The results show that the proposed method can achieve high accuracy and robustness.

Constant current charging time based fast state-of-health estimation for lithium-ion batteries

The state of health (SOH) estimation is critical for a battery management system's safe operation. Considering feature extraction, time-consuming, model/calculation complexity problems, a battery SOH estimation method based on constant current charging time (CCCT) is proposed in this paper. Unlike previous works, it is proved that CCCT can perfectly replace incremental capacity peak area. Since no filtering process is required in this method, the validity of the feature is maximally preserved. The random forest regression is combined to form accurate and fast SOH estimation. The proposed method is validated with the Oxford and CALCE datasets, collected from different batteries under different conditions. The average root-mean-square error of 8 cells for SOH estimation is 0.52%. Compared with the incremental capacity analysis (ICA)-based SOH estimation method, the prediction accuracy of the proposed method is improved by 41.6%, and fewer data are utilized. Besides, the time needed for the model training and prediction of the proposed method is less than 1 s. Additionally, the proposed method is proved to have good adaptability to different voltage ranges and charging/discharging conditions.

State-of-Charge Estimation of Lithium-ion Batteries Using LSTM Deep Learning Method

State-of-Charge Estimation of Lithium-ion Batteries Using LSTM Deep Learning Method

A Novel Trigger Mechanism for a Dual-Filter to Improve the State-of-Charge Estimation of Lithium-Ion Batteries

Abstract State-of-charge (SOC) estimation is essential in the energy management of electric vehicles. In the context of SOC estimation, a dual filter based on the equivalent circuit model represents an important research direction. The trigger for parameter filter in a dual filter has a significant influence on the algorithm, despite which it has been studied scarcely. The present paper, therefore, discusses the types and characteristics of triggers reported in the literature and proposes a novel trigger mechanism for improving the accuracy and robustness of SOC estimation. The proposed mechanism is based on an open-loop model, which determines whether to trigger the parameter filter based on the model voltage error. In the present work, particle filter (PF) is used as the state filter and Kalman filter (KF) as the parameter filter. This dual filter is used as a carrier to compare the proposed trigger with three other triggers and single filter algorithms, including PF and unscented Kalman filter (UKF). According to the results, under different dynamic cycles, initial SOC values, and temperatures, the root-mean-square error of the SOC estimated using the proposed algorithm is at least 34.07% lower than the value estimated using other approaches. In terms of computation time, the value is 4.67%. Therefore, the superiority of the proposed mechanism is demonstrated.

A Variable-length scale Parameter Dependent State of Charge Estimation of Lithium Ion Batteries by Kalman Filters

A Variable-length scale Parameter Dependent State of Charge Estimation of Lithium Ion Batteries by Kalman Filters

Applying Set Membership Strategy in State of Charge Estimation for Lithium-ion Battery

As state of charge is one important variable to monitor the later battery management system, and as traditional Kalman filter can be used to estimate the state of charge for Lithium-ion battery on basis of probability distribution on external noise. To relax this strict assumption on external noise, set membership strategy is proposed to achieve our goal in case of unknown but bounded noise. External noise with unknown but bounded is more realistic than white noise. After equivalent circuit model is used to describe the Lithium-ion battery charging and discharging properties, one state space equation is constructed to regard state of charge as its state variable. Based on state space model about state of charge, two kinds of set membership strategies are put forth to achieve the state estimation, which corresponds to state of charge estimation. Due to external noise is bounded, i.e. external noise is in a set, we construct interval and ellipsoid estimation for state estimation respectively in case of external noise is assumed in an interval or ellipsoid. Then midpoint of interval or center of the ellipsoid are chosen as the final value for state of charge estimation. Finally, one simulation example confirms our theoretical results.

Physics-Informed Recurrent Neural Network With Fractional-Order Gradients for State-of-Charge Estimation of Lithium-Ion Battery

As a typical machine learning algorithm, neural networks (NNs) has been designed and developed for battery management system (BMS) with artificial intelligence. State of charge (SOC) estimation of lithium-ion battery (LIB) is the basis of BMS so as to widely employ NNs, and recurrent neural network (RNN) is usually selected to describe the time-series characteristics of LIB. However, RNN is a data-driven statistic black box, which cannot reveal electrochemical principle and learn inner Knowledge of LIB. This paper introduces fractionalorder gradients for RNN to improve its backpropagation process, so that network updates weights instructed by the fractionalorder characteristics of LIB. Our work provides two backpropagation patterns with fractional-order gradient descent and momentum for RNN, respectively, both resulting in a physicsinformed RNN for SOC estimation of LIB. The proposed physicsinformed RNN can conduct training in which the gradients and the loss of network is informed by the physical fractional-order laws of LIB. Experimental results under operation conditions of federal urban driving schedule (FUDS) are presented with satisfying SOC estimation accuracy. Furtherly, physics-informed RNN proposed in this paper is not limited to SOC estimation, but also other state estimation or even fault prognosis for LIB.

Hfcm-Lstm: A Novel Hybrid Framework for State-of-Health Estimation of Lithium-Ion Battery

Hfcm-Lstm: A Novel Hybrid Framework for State-of-Health Estimation of Lithium-Ion Battery

Distributed State-of-Charge Estimation for Lithium-Ion Batteries with Random Sensor Failure Under Dynamic Event-Triggering Protocol

Distributed State-of-Charge Estimation for Lithium-Ion Batteries with Random Sensor Failure Under Dynamic Event-Triggering Protocol