Research on Multi-Parameter Evaluation of Electric Vehicle Power Battery Consistency Based on Principal Component Analysis

doi:10.1007/s12204-018-1987-9

Abstract

Abstract: 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, multiparameter 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 words: electric vehicle| principal component analysis| battery consistency| multi-parameter evaluation

摘要： 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, multiparameter 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.

关键词: electric vehicle| principal component analysis| battery consistency| multi-parameter evaluation

CLC Number:

TM 912.1

WANG Liye (王立业), WANG Lifang (王丽芳), LIAO Chenglin (廖承林), ZHANG Wenjie (张文杰). Research on Multi-Parameter Evaluation of Electric Vehicle Power Battery Consistency Based on Principal Component Analysis[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(5): 711-720.

References 15

[1]	KIM C H, KIM M Y, MOON G W. A modularizedcharge equalizer using a battery monitoring IC forseries-connected Li-ion battery strings in electric vehicles[J]. IEEE Transactions on Power Electronics,2013, 28(8): 3779-3787.
[2]	CHEN Y, LIU X F, CUI Y Y, et al. A multiwindingtransformer cell-to-cell active equalization method forLithium-ion batteries with reduced number of drivingcircuits [J]. IEEE Transactions on Power Electronics,2016, 31(7): 4916-4929.
[3]	XIONG R, ZHANG Y, HE H, et al. A double-scale,particle-filtering, energy state prediction algorithm forlithium-ion batteries [J]. IEEE Transactions on IndustrialElectronics, 2018, 65(2): 1526-1538.
[4]	JIN Y D, SONG Q, LIU W H. Large scaled cascadedbattery energy storage system with charge/ dischargebalancing [J]. Electric Power Automation Equipment,2011, 31(13): 6-11 (in Chinese).
[5]	SANG B Y, TAO Y B, ZHENG G, et al. Research ontopology and control strategy of the super-capacitorand battery hybrid energy storage [J]. Power SystemProtection and Control, 2014, 42(2): 1-6 (in Chinese).
[6]	DANG J, TANG Y, NING J, et al. A strategy for distributionof electric vehicles charging load based onuser intention and trip rule [J]. Power System Protectionand Control, 2015, 43(16): 8-15 (in Chinese).
[7]	SUN B X, GAO K, JIANG J C, et al. Research ondischarge peak power prediction of battery based onANFIS and subtraction clustering [J]. Transactions ofChina Electrotechnical Society, 2015, 30(4): 272-280(in Chinese).
[8]	LI Y, WANG L F, LIAO C L, et al. Research onsubspace-based identification of battery model for electricvehicles [J]. Advanced Technology of Electrical Engineeringand Energy, 2015, 34(1): 1-6 (in Chinese).
[9]	KALIKMANOV V I, KOUDRIACHOVA M V, DELEEUWSW. Lattice-gas model for intercalation compounds[J]. Solid State Ionics, 2000, 136/137: 1373-1378.
[10]	WAAG W, FLEISCHER C, SAUER D U. On-line estimationof lithium-ion battery impedance parametersusing a novel varied-parameters approach [J]. Journalof Power Sources, 2013, 237: 260-269.
[11]	WAAG W, SAUER D U. Adaptive estimation of theelectromotive force of the lithium-ion battery after currentinterruption for an accurate state-of-charge andcapacity determination [J]. Applied Energy, 2013, 111:416-427.
[12]	HE H W, XIONG R, GUO H Q. Online estimationof model parameters and state-of-charge of LiFePO4batteries in electric vehicles [J]. Applied Energy, 2012,89(1): 413-420.
[13]	LUO S H, HUANG J, LI Z L. Prediction for silicon contentin molten iron using unsupervised optimal fuzzyclustering [C]//Proceedings of the 7th World Congresson Intelligent Control and Automation. Chongqing,China: IEEE, 2008: 6503-6506.
[14]	NISHIJIMA K, SAKAMOTO H, HARADA K. APWM controlled simple and high performance batterybalancing system [C]//Proceedings of the 31st AnnualPower Electronics Specialists Conference. Galway, Ireland:IEEE, 2000: 517-520.
[15]	SMITH K A, RAHN C D, WANG C Y. Modelbasedelectrochemical estimation and constraint managementfor pulse operation of lithium ion batteries[J]. IEEE Transactions on Control Systems Technology,2010, 18(3): 654-663.