期刊
JOURNAL OF POWER SOURCES
卷 259, 期 -, 页码 166-176出版社
ELSEVIER
DOI: 10.1016/j.jpowsour.2014.02.095
关键词
Adaptive extended Kalman filter; State-of-charge; State-of-power; Parameter update; Lithium-ion battery; Electrified vehicles
资金
- National High Technology Research and Development Program of China [2012AA111603, 2013BAG05B00, 2011AA11A209]
- National Natural Science Foundation of China [51276022]
- Higher School Discipline Innovation Intelligence Plan (111plan)
- Program for New Century Excellent Talents in University [NCET-11-0785]
Battery state-of-charge (SoC) and state-of-power capability (Sop) are two of the most significant decision factors for energy management system in electrified vehicles. This paper tries to make two contributions to the existing literature. (1) Based on the adaptive extended Kalman filter algorithm, a data-driven joint estimator for battery SoC and SoP against varying degradations has been developed. (2) To achieve accurate estimations of SoC and SoP in the whole calendar-life of battery, the need for model parameter updates with lowest computation burden has been discussed and studied. The robustness of the joint estimator against dynamic loading profiles and varying health conditions is evaluated. We subsequently used data from cells that have different aging levels to assess the robustness of the SoC and SoP estimation algorithm. The results show that battery SoP has close relationship with its aging levels. And the prediction precision would be significantly improved if recalibrating the parameter of battery capacity and resistance timely. What's more, the method reaches accuracies for new and aged battery cells in electrified vehicle applications of better than 97.5%. (C) 2014 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据