Research on Multi-Parameter Evaluation of Electric Vehicle Power Battery Consistency Based on Principal Component Analysis
- 19 Downloads
Electric vehicle power battery consistency is the key factor affecting the performance of power batteries. it is not scientific to evaluate the consistency of the battery depending on voltage or capacity. In this paper, multi-parameter evaluation method for battery consistency based on principal component analysis is proposed. Firstly, the characteristic parameters of battery consistency are analyzed, the principal component score can be used as the basis for evaluating the consistency of the battery. Then, the function that multi-parameter evaluation of battery consistency is established. Finally, battery balancing strategy based on fuzzy control is developed. The basic principle of fuzzy control is to fuzzy the input quantity based on expert knowledge, and the fuzzy control quantity is obtained by fuzzy control rule. The results are verified by test.
Key wordselectric vehicle principal component analysis battery consistency multi-parameter evaluation
CLC numberTM 912.1
Unable to display preview. Download preview PDF.
- CHEN Y, LIU X F, CUI Y Y, et al. A multiwinding transformer cell-to-cell active equalization method for Lithium-ion batteries with reduced number of driving circuits [J]. IEEE Transactions on Power Electronics, 2016, 31(7): 4916–4929.Google Scholar
- JIN Y D, SONG Q, LIU W H. Large scaled cascaded battery energy storage system with charge/discharge balancing [J]. Electric Power Automation Equipment, 2011, 31(13): 6–11 (in Chinese).Google Scholar
- SANG B Y, TAO Y B, ZHENG G, et al. Research on topology and control strategy of the super-capacitor and battery hybrid energy storage [J]. Power System Protection and Control, 2014, 42(2): 1–6 (in Chinese).Google Scholar
- DANG J, TANG Y, NING J, et al. A strategy for distribution of electric vehicles charging load based on user intention and trip rule [J]. Power System Protection and Control, 2015, 43(16): 8–15 (in Chinese).Google Scholar
- SUN B X, GAO K, JIANG J C, et al. Research on discharge peak power prediction of battery based on ANFIS and subtraction clustering [J]. Transactions of China Electrotechnical Society, 2015, 30(4): 272–280 (in Chinese).Google Scholar
- LI Y, WANG L F, LIAO C L, et al. Research on subspace-based identification of battery model for electric vehicles [J]. Advanced Technology of Electrical Engineering and Energy, 2015, 34(1): 1–6 (in Chinese).Google Scholar
- LUO S H, HUANG J, LI Z L. Prediction for silicon content in molten iron using unsupervised optimal fuzzy clustering [C]//Proceedings of the 7th World Congress on Intelligent Control and Automation. Chongqing, China: IEEE, 2008: 6503–6506.Google Scholar
- NISHIJIMA K, SAKAMOTO H, HARADA K. A PWM controlled simple and high performance battery balancing system [C]//Proceedings of the 31st Annual Power Electronics Specialists Conference. Galway, Ireland: IEEE, 2000: 517–520.Google Scholar