The accurate estimation of the state of charge (SOC) and state of health (SOH) is of great significance to energy management and safety in electric vehicles. To achieve a good trade-off between real-time capability and estimation accuracy, a collaborative estimation algorithm for SOC and SOH is presented based on the Thevenin equivalent circuit model, which combines the recursive least squares method with a forgetting factor and the extended Kalman filter. First, the parameter identification accuracy is studied under a dynamic stress test (DST) and the federal urban driving schedule (FUDS) test at different ambient temperatures (0 °C, 25 °C, and 45 °C). Secondly, the FUDS test is used to verify the SOC estimation accuracy. Thirdly, two batteries with different aging degrees are used to validate the proposed SOH estimation algorithm. Subsequently, the accuracy of the SOC estimation algorithm is studied, considering the influence of updating the SOH. The proposed SOC estimation algorithm can achieve good performance at different ambient temperatures (0 °C, 25 °C, and 45 °C), with a maximum error of less than 2.3%. The maximum error for the SOH is less than 4.3% for two aged batteries at 25 °C, and it can be reduced to 1.4% after optimization. Furthermore, calibrating the capacity as the SOH changes can effectively improve the SOC estimation accuracy over the whole battery life.

The lithium-ion battery state of health (SOH) estimation is critical for maintaining reliable and safe working conditions for electric vehicles (EVs). However, accurate and robust SOH estimation remains a significant challenge. This paper proposes a multi-feature extraction strategy and particle swarm optimization-nonlinear autoregressive with exogenous input neural network (PSO-NARXNN) for accurate and robust SOH estimation. First, eight health features (HFs) are extracted from partial voltage, capacity, differential temperature (DT), and incremental capacity (IC) curves. Then, qualitative and quantitative analyses are used to evaluate the selected HFs. Second, the PSO algorithm is adopted to optimize the hyperparameters of NARXNN, including input delays, feedback delays, and the number of hidden neurons. Third, to verify the effectiveness of the multi-feature extraction strategy, the SOH estimators based on a single feature and fusion feature are comprehensively compared. To verify the effectiveness of the proposed PSO-NARXNN, a simple three-layer backpropagation neural network (BPNN) and a conventional NARXNN are built for comparison based on the Oxford aging dataset. The experimental results demonstrate that the proposed method has higher accuracy and stronger robustness for SOH estimation, where the average mean absolute error (MAE) and root mean square error (RMSE) are 0.47% and 0.56%, respectively.

Accurate state of health (SOH) estimation of lithium-ion batteries is critical for maintaining reliable and safe working conditions for electric vehicles (EVs). The machine learning-based method with health features (HFs) is encouraging for health prognostics. However, the machine learning method assumes that the training and testing data have the same distribution, which restricts its application for different types of batteries. Thus, in this paper, a deep learning neural network and fine-tuning-based transfer learning strategy are proposed for accurate and robust SOH estimation toward different types of batteries. First, a universal HF extraction strategy is proposed to obtain four highly related HFs. Second, a deep learning neural network consisting of long short-term memory (LSTM) and fully connected layers is established to model the relationship between the HFs and SOH. Third, the fine-tuning-based transfer learning strategy is exploited for SOH estimation of various types of batteries. The proposed methods are comprehensively verified using three open-source datasets. Experimental results show that the proposed deep learning neural network with the HFs can estimate the SOH accurately in a single dataset without using the transfer learning strategy where the mean absolute error (MAE) and root mean square error (RMSE) are constrained to 1.21% and 1.83%. For the transfer learning between different aging datasets, the overall MAE and RMSE are limited to 1.09% and 1.41%, demonstrating the reliability of the fine-tuning strategy.

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.

Accurate estimation of the lithium-ion batteries (LiBs) state of health (SOH) is vital for ensuring the safety and dependability of electric vehicles (EVs) throughout their life cycle. However, the traditional neural network estimation has low fitting accuracy, and most research in this field is based solely on normal temperature states. To address this, we propose extracting health factors (HFs) from battery data under different temperature states, employing the Gaussian process regression algorithm (GPR) to estimate SOH. We also examine the influence of various kernel functions on the estimation results of the GPR algorithm.