第05讲:神经网络、深度学习及其在电池状态估计领域的应用【AESA陈铖】 | |||
发表时间:2020-07-02 阅读次数: | |||
相关文献 [1] C. Chen, R. Xiong, R. Yang, W.X. Shen and F. Sun. “State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter,” Journal of Cleaner Production, vol. 234, 2019, pp. 1153–1164. (点击下载) [2] R. Yang, R. Xiong, W. Shen and X. Lin, “Extreme learning machine based thermal model for lithium-ion batteries of electric vehicles under external short circuit,” Engineering, 2020, doi.org/ 10.1016/j.eng.2020.08.015. (点击下载) [3] Y. Zhang, R. Xiong, H. He and M. Pecht, “Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries,” IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 5695–5705, 2018. (点击下载) [4] W. He, N. Williard, C. Chen and M. Pecht. “State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation,” Electrical Power and Energy Systems, vol. 62, 2014, pp. 783–791. (点击下载) [5] M. Charkhgard and M. Farrokhi, “State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF,” IEEE Trans. Ind. Electron., vol. 57, no. 12, pp. 4178–4187, 2010. (点击下载) [6] Xiong R. Battery Management Algorithm for Electric Vehicles[M]. Springer, 2020. [7] Xiong R, Shen W. Advanced battery management technologies for electric vehicles[M]. John Wiley & Sons, 2019. [8] 熊瑞. 动力电池管理系统核心算法[M]. 北京:机械工业出版社,2018. |
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