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.

Review A Review of Fuel Cell System Technology: From Fuel Cell Stack to System Integration Weiqun Ren 1,*, Jun Shen 2, Xuebing Li 1, and Changqing Du 2 1 Dongfeng Commercial Vehicle Co. Ltd., 10 Dongfeng Avenue, Wuhan, China 2 Wuhan University of Technology, Wuhan, China * Correspondence: renweiqun@tsinghua.org.cn; Tel.: +86-139-1820-4209     Received: 14 October 2022 Accepted: 8 November 2022 Published: 18 December 2022   Abstract: The technology of hydrogen fuel cell vehicles (FCV) is the ultimate direction of clean energy vehicle development, and commercial vehicles are the most important application area for fuel cell commercialization. This paper summarizes the key components, technologies and development trends of the fuel cell stack, fuel cell system and vehicle integration at home and abroad, and points out that key materials (such as bipolar plates and membrane electrodes), key system components (such as air compressors and ejectors), high-power modular integration technologies, and fuel cell control technologies are the main factors influencing the commercialization of FCVs. Particularly, we put the main fouce on variable ejector or multi-stage ejector technology, integrated and optimized control technology, and multi-energy cooperative control technology due to their crucial roles in meeting the industrial development of FCVs in China. Furthermore, some guidance opinions are also provided for reference about the development and industrialization of FCVs.

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