Li-ion Battery State Of Charge Estimation Research Articles

Study on SOC Estimation of Li-ion Battery Based on the Comparison of UKF Algorithm and AUKF Algorithm

In this study, a state of charge (SOC) estimate technique for lithium-ion batteries is presented using an adaptive traceless Kalman filter (AUKF). First, the battery’s second-order RC equivalent circuit model is created, and its parameters are identified. Next, in contrast to the traceless Kalman filter (UKF) algorithm, which ignores the time-varying characteristics of the system noise when estimating the lithium-ion battery’s state of charge, the AUKF-based SOC estimation method is formed from the perspective of adaptive noise adjustment (SOC). The results of testing the AUKF algorithm in real-world settings demonstrate that it has great estimate accuracy and stability and that its estimation outcomes outperform those of the UKF method.

Estimation of state of charge for hybrid unmanned aerial vehicle Li-ion power battery for considering rapid temperature change

State of Charge (SOC) estimation of power battery is essential for hybrid Unmanned Aerial Vehicle (UAV) systems. During the flight of a hybrid UAV, the ambient temperature changes rapidly. The battery parameters also change with the temperature, which deteriorates the accuracy of SOC estimation. Given the above problems, this paper proposes a bipolar equivalent circuit model of a Li-ion battery and its R, C parameter identification method based on the particle swarm optimization algorithm, considering the impact of rapid changes in ambient temperature on battery parameters during the flight of a hybrid UAV. On this basis, a Fast Temperature Correction Adaptive Particle Filter (FTC-APF) SOC estimation method is proposed. This method not only considers the rapid change of temperature to update the actual capacity, Open Circuit Voltage (OCV), and Dual Polarization (DP) model parameters in real time but also weakens the impact of the uncertainty noise generated by external disturbances or sensors on the SOC estimation of Li-ion battery by predicting the process noise and updating the observation noise. The experimental results show that, compared with the existing research, the proposed method effectively achieves the accurate estimation of SOC of the hybrid UAV lithium-ion battery under the condition of rapid temperature change.

SOC Estimation of Li-ion Battery using on Variational Mode Decomposition and Transformer-Generative Adversarial Network

Accurate estimation of the state of charge (SOC) of lithium battery is crucial to improve the dynamic performance and energy utilization of batteries. The method, the existing neural network are used to estimate SOC, has the problems of low accuracy and poor stability under complex working conditions. A new algorithm are proposed to estimate the SOC, which combines Transformer and Generative Adversarial Network (GAN), and the Variational Modal Decomposition (VMD). Firstly, as the excellent prediction ability of Transformer, Transformer is used as the generative network of GAN. Secondly, VMD is used to decompose the SOC historical data into six subsets to increase the input features. Finally, DST work data from the University of Maryland CALCE dataset is used for model training, and the VMD-Transformer-GAN algorithm is compared with LSTM, GRU, and BiLSTM algorithms for experiments. The experimental results show that the VMD-Transformer-GAN algorithm algorithmic estimation model has high stability and accuracy, which verifies the feasibility of the improved scheme.

Estimating battery state-of-charge with a few target training data by meta-learning

This study proposes a meta-learning battery state-of-charge (SOC) estimation approach to reduce the amount of target battery data required for training a Li-ion battery SOC estimation by applying deep-learning. The proposed approach reduces the training data required for the target battery by improving the pre-training performance. Multiple other batteries are used as pre-training data to allow generalization to the target battery. The performance of meta-learning SOC estimation is compared with that of transfer learning using one battery data for pre-training. The meta-learning performance does not depend on the similarity between the pre-training data and target battery data. The proposed meta-learning SOC estimation accuracy is verified based on battery test data from various driving cycles (US06, urban dynamometer driving schedule (UDDS), and LA92), to reflect actual electric vehicle driving patterns. Using only a small amount of target battery data and nine gradient steps, the proposed meta-learning SOC estimation algorithm achieves a mean squared error (MSE) of 0.0176% and a mean absolute error (MAE) of 1.0075%. These results show that the proposed method can adapt more quickly than transfer learning (with SOC estimation errors of 3.1378% in terms of the MSE and 15.1327% in terms of the MAE under the same conditions).

Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review

As one of the critical state parameters of the battery management system, the state of charge (SOC) of lithium batteries can provide an essential reference for battery safety management, charge/discharge control, and the energy management of electric vehicles (EVs). To analyze the application of deep learning in electric vehicles’ power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, as well as the structural characteristics and advantages and disadvantages of lithium battery SOC estimation in deep learning methods. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Second, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of three types of deep learning methods were obtained using the structure of the neural network used for training as the classification criterion; further, the selection of the deep learning structure for SOC estimation was discussed. Finally, the challenges and future development directions of lithium battery SOC estimation using the deep learning method were explained. Over all, this review provides insights into deep learning for EVs’ Li-ion battery SOC estimation in the future.

SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture

State-of-charge (SOC) is critical to the safe operation and energy management of electric vehicles. Data-driven SOC estimation algorithms all require a period of data to ensure convergence of the estimation results and cannot accurately estimate the SOC values near the starting point. We propose a SOC estimation method based on the U-Net architecture that can handle variable-length input data and output equal-length SOC estimation results, including accurate SOC of the starting point. Symmetric padding convolutional layer was proposed to address the boundary effect of Convolutional Neural Networks (CNN) and improve the accuracy of SOC estimation at the edges. We also propose a total variation loss function, which improves the stability of the estimation only by optimizing the loss function without increasing the model complexity, and significantly reduces the maximum error. The model was trained using dynamic drive cycle data at five constant temperatures, and the model has high accuracy at both constant and variable temperature conditions. The proposed method can estimate the SOC at constant temperatures with mean absolute error (MAE) within 1.1% and root-mean-square error (RMSE) within 1.4%. This method also can estimate SOC at varying temperatures with MAE within 1.5% and RMSE within 1.8% under different driving conditions.

SOC Estimation of Li-ION Battery Based on Improved EKF Algorithm

The state of charge (SOC) is one of the important performance indicators of battery, which provides an important basis for the management and control of Battery Management System (BMS). In view of the characteristics of lithium iron phosphate battery, considering the model accuracy and calculation amount, the equivalent circuit model of improved PNGV was selected. Based on that, an improved Extended Kalman Filter (EKF) algorithm was adopted to estimate the state of charge (SOC) of Li-ion battery, which covariance matrix was modified by the Levenberg-Marquardt method. At the end of this paper, the SOC estimation algorithm was verified by MATLAB simulations. The results show that compared with the standard EKF, the improved EKF has higher estimation accuracy and anti-interference ability, and has better convergence in the estimation process.

Li-ion battery SOC estimation method using a Neural Network trained with data generated by a P2D model

Abstract The State Of Charge (SOC) estimation of a Li-ion battery is still an open problem. The most classical method, Coulomb Counting (CC) is vulnerable to current measurement bias. Measuring the Open-Circuit Voltage (OCV) allows to correct the error accumulated by the CC method, but only after the battery has been unsolicited long enough. Regarding these deficiencies, advanced SOC estimation methods try to combine current and voltage information, and are either based on an Extended Kalman Filter (EKF), which represents a certain algorithmic complexity and is hard to calibrate, or on black-box methods. In particular, methods using a Neural Network (NN) have been investigated in the literature, but take usually into account only instantaneous information, failing to represent the dynamic of ion diffusion in the electrodes. By considering also a close-past current integral as an input, this paper proposes a NN model able to correct initial SOC estimation errors and handle current measurement bias, and achieving a better estimation performance than a classical NN model taking only instantaneous information as an input.

Implementation of an improved Coulomb-Counting Algorithm Based on a Piecewise SOC-OCV Relationship for SOC Estimation of Li-Ion Battery

Considering the expanding use of mobile devices equipped with rechargeable batteries, especially Li-ion batteries that have higher power and energy density, the battery management function becomes increasingly important. In fact, the accuracy of the amount of remaining charges estimation is critical as it affects the device autonomy. Therefore, the battery State-Of-Charge (SOC) is defined to indicate its estimated available capacity. In this paper, a method for Li-ion battery SOC estimation based on an enhanced Coulomb-counting is proposed to be implemented for multimedia applications. Assuming that Coulomb-counting suffers from cumulative errors due to the initial SOC and the measurements uncertainties errors, we used a piece-wise linear SOC-OCV relationship and periodic re-calibration to overcome these limitations. This solution has been implemented and validated on a hardware platform based on PIC18F MCU Family. The measurement results were correlated with theoretical ones and the method has shown a reliable estimation since accuracy is less than 2%.

Modeling and state of charge estimation of lithium-ion battery

Modeling and state of charge (SOC) estimation of lithium-ion (Li-ion) battery are the key techniques of battery pack management system (BMS) and critical to its reliability and safety operation. An auto-regressive with exogenous input (ARX) model is derived from RC equivalent circuit model (ECM) due to the discrete-time characteristics of BMS. For the time-varying environmental factors and the actual battery operating conditions, a variable forgetting factor recursive least square (VFFRLS) algorithm is adopted as an adaptive parameter identification method. Based on the designed model, a SOC estimator using cubature Kalman filter (CKF) algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure. In the battery tests, experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter (EKF) algorithm, which is widely used for Li-ion battery SOC estimation, and the maximum estimation error is about 2.3%.

State of charge estimation for Li-ion battery based on model from extreme learning machine

Lithium-ion (Li-ion) battery state of charge (SOC) estimation is important for electric vehicles (EVs). The model-based state estimation method using the Kalman filter (KF) variants is studied and improved in this paper. To establish an accurate discrete model for Li-ion battery, the extreme learning machine (ELM) algorithm is proposed to train the model using experimental data. The estimation of SOC is then compared using four algorithms: extended Kalman filter (EKF), unscented Kalman filter (UKF), adaptive extended Kalman filter (AEKF) and adaptive unscented Kalman filter (AUKF). The comparison of the experimental results shows that AEKF and AUKF have better convergence rate, and AUKF has the best accuracy. The comparison from the radial basis function neural network (RBF NN) model also verifies that the ELM model has lighter computation load and smaller estimation error in SOC estimation process. In general, the performance of Li-ion battery SOC estimation is improved by the AUKF algorithm applied on the ELM model.

Online State-of-Charge Estimation of Li-Ion Battery Based on the Second-Order RC Model

The real-timely estimation of the SOC (state of charge) is the key technology in Li-ion battery management system. In this paper, to overcome the error of the SOC estimation of Extended Kalman filter (EKF), a new estimation method based on modified-strong tracking filter (MSTF) is applied to SOC estimation of Li-ion battery, based on the second-order RC equivalent circuit model. Experiments are made to compare the new filter with the EKF and Coulomb counting approach (Ah). The simulation results demonstrate that the new filter algorithm MSTF used in this paper has higher filtering accuracy under the same conditions.

Comparison between two model-based algorithms for Li-ion battery SOC estimation in electric vehicles

Accurate battery State of Charge (SOC) estimation is of great significance for safe and efficient energy utilization for electric vehicles. This paper presents a comparison between a novel robust extended Kalman filter (REKF) and a standard extended Kalman filter (EKF) for Li-ion battery SOC indication. The REKF-based method is formulated to explicitly compensate for the battery modeling uncertainty and linearization error often involved in EKF, as well as to provide robustness against the battery system noise to some extent. Evaluation results indicate that both filters have a good average performance, given appropriate noise covariances, owing to a small average modeling error. However, in contrast, the REKF-based SOC estimation method possesses slightly smaller root-mean-square (RMS) error. In the worst case, the robustness characteristics of the REKF result in an obviously smaller error bound (around by 1%). Additionally, the REKF-based approach shows superior robustness against the noise statistics, leading to a better tolerance to inappropriate tuning of the process and measurement noise covariances.