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Author

Chao Qin

Bio: Chao Qin is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Extended Kalman filter & State of charge. The author has an hindex of 4, co-authored 4 publications receiving 99 citations.

Papers
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Journal ArticleDOI
01 Feb 2021-Energy
TL;DR: A novel capacity estimation method realized by combining model-based and data-driven methods based on a sequential extended Kalman filter (SEKF), to improve the accuracy, and reliability of capacity estimation.

82 citations

Journal ArticleDOI
TL;DR: The estimation method based on fractional charging curves is developed to estimate the battery capacity during vehicle charging, and the estimation results serve as the feedback using the first Kalman filter to update the model parameters of the discrete Arrhenius aging model.

69 citations

Journal ArticleDOI
15 Oct 2019-Energy
TL;DR: A method based on charging curve sections which can be easily achieved for electric vehicles by using the complete charging curves and the corresponding capacities in experiments as the training data for a certain battery type.

40 citations

Journal ArticleDOI
Xin Lai, Chao Qin, Wenkai Gao, Yuejiu Zheng, Yi Wei 
TL;DR: In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC) estimation, where the solid phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks.
Abstract: In this paper, an improved equivalent circuit model (ECM) considering partial electrochemical properties is developed for accurate state-of-charge (SOC). In the proposed model, the solid-phase diffusion process is calculated by a simple equation about particle surface SOC, and the double layer is simulated by two resistance-capacitance (RC) networks. To improve the global accuracy of the model, a subarea parameter-identification method based on particle swarm optimization is proposed, in order to determine the optimal model parameters in the entire SOC area. Then, an SOC estimator is developed based on extended kalman filter. The comparative study shows that a model considering solid-phase diffusion with two RC networks is the best choice. Finally, experimental results show that the accuracy of the proposed model is one times higher than that of the traditional ECM in the low SOC area, and is able to estimate SOC with errors less than 1% in the entire SOC area. Furthermore, estimation results of two types of batteries under two working conditions indicate that the developed model and SOC estimator have satisfactory global accuracy and guaranteed robustness with low computational complexity, which can be applied in real-time situations.

24 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, a comprehensive review on the mechanism and evolutionary process of internal short circuit (ISC) is provided, including modeling and simulation experiments and the methods of detection and diagnosis.

115 citations

Journal ArticleDOI
TL;DR: A novel framework incorporating the concepts of transfer learning and network pruning is proposed to build compact Convolutional Neural Network models on a relatively small dataset with improved estimation performance, which outperforms other models in terms of accuracy and computational efficiency.

100 citations

Journal ArticleDOI
TL;DR: The existing characteristic parameters in defining battery SOH at cell-level and pack-level are reviewed, and some suggestions for SOH definitions are proposed, and the impact of external influencing factors on battery degradation is introduced to lay a foundation for Soh estimation.

91 citations

Journal ArticleDOI
01 Feb 2022-Energy
TL;DR: A comprehensive overview of second-life Li-ion batteries through exploring relevant literature is provided in this paper , where the fundamental of battery degradation and experimental approaches are first surveyed, followed by the obstacles and methods of reusing and recycling Li-ION battery, related applications, cost issues, and business models of second life Liion batteries are discussed.

88 citations

Journal ArticleDOI
TL;DR: In this article , the basic framework and types, standards and methods, and technical challenges of LCA are comprehensively reviewed, and the carbon footprint in the battery production and recycling stages is conducted under the current and future energy mixes.

86 citations