第16讲:滤波算法的概率论基础及其在电池容量预测中的应用【AESA王晨旭】 | |||
发表时间:2020-12-06 阅读次数: | |||
相关文献 [1] Hu X , Xu L , Lin X , et al. Battery Lifetime Prognostics[J]. Joule, 2020, 4(2). (点击下载) [2] Thrun, Sebastian. Probabilistic robotics[J]. Communications of the Acm, 2005, 45(3):52-57. (点击下载) [3] Bromiley, P. Products and Convolutions of Gaussian Probability Density Functions Density Functions[J]. Tina Memo, 2003. (点击下载) [4] Ye M , Guo H , Xiong R , et al. A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries[J]. Energy, 2018, 144(FEB.1):789-799. (点击下载) [5] Yu Q , Xiong R , Lin C , et al. Lithium-Ion Battery Parameters and State-of-Charge Joint Estimation Based on H-Infinity and Unscented Kalman Filters[J]. IEEE Transactions on Vehicular Technology, 2017, 66(10):8693-8701. (点击下载) [6] Xiong R , Mu H . Accurate state of charge estimation for lithium-ion battery using dual Uncsented Kalman filters[C]// 2017 Chinese Automation Congress (CAC). 2017. (点击下载) [7] Hu X , Xu L , Lin X , et al. Battery Lifetime Prognostics[J]. Joule, 2020, 4(2). (点击下载) [8] Xiong R. Battery Management Algorithm for Electric Vehicles[M]. Springer, 2020. [9] 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. [10] 熊瑞, 何洪文.电动车辆复合电源系统集成管理基础[M]. 北京: 化学工业出版社, 2019. |
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