期刊
ENERGY
卷 218, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.119490
关键词
Lithium-ion battery; Remaining useful life prediction; Random variable discharge current; Wiener process model; Unscented particle filter
资金
- National Natural Science Foundation of China [61873175, 71601022]
- Key Project B Class of Beijing Natural Science Fund [KZ201710028028]
- Capacity Building for Sci-Tech Innovationd-Fundamental Scientifific Research Funds [025185305000e187]
- Youth Innovative Research Team of Capital Normal University
- Beijing Youth Talent Support Program [CITTCD201804036]
This research proposes a new method to predict the remaining useful life of lithium-ion batteries with variable discharge current, through experimental design and model establishment for adaptive updates, achieving more accurate and robust results.
Lithium-ion batteries are widely used in many electronic and electrical devices, and accurately predicting their remaining useful life is essential to ensure the safe and reliable operation of the systems. The discharge current of lithium-ion batteries in the actual environment changes randomly during one charge and discharge cycle, and the randomly changing current has a greater impact on battery life. Existing prediction methods rarely take this into account. Therefore, this paper proposes a new method for predicting the remaining useful life of lithium-ion batteries with variable discharge current. First, the battery aging experiment under variable discharge current is designed by simulating the operation state of batteries and capacity data is collected. Secondly, a novel two-stage Wiener process model is established to describe the differences in the degradation characteristics of lithium-ion batteries at different stages. Finally, the unscented particle filtering algorithm is introduced so that all parameters in the model and the remaining useful life distribution of lithium-ion batteries are adaptively updated with the latest on-line measurements. The experimental results demonstrate that the proposed method can achieve more accurate and robust results compared with the two previous methods, which verifies the effectiveness and robustness of the proposed method. (C) 2020 Elsevier Ltd. All rights reserved.
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