Abstract：To solve the problems that only single factor was considered for battery peak power estimation and the battery model was simple, a new estimation method was proposed. Taking the ternary lithium power lithium batteries as research object, considering the comprehensive effects of battery temperature, charging state and ohm resistance on the power state, a neural network battery model on simulated annealing and back propagation（SA+BP）hybrid algorithm was eatablished based on data statistics and machine learning by neural network toolbox and Matlab programming. Hybrid pulse power characteristic (HPPC) method was used to conduct the experiments. 245 groups valid experimental data were obtained with selected 200 groups experimental data as training samples, and the rest experimental data were used as test samples. The simulation results of the training model by single BP algorithm were compared with those by SA+BP hybrid algorithm. The results show that the trained model based on SA+BP hybrid algorithm has better estimation accuracy, which can more accurately describe the peak power of battery.
朱浩, 张文博, 邓元望, 李梦, 吉祥. 基于SA+BP混合算法的动力电池放电峰值功率估算[J]. 江苏大学学报（自然科学版）, 2020, 41(2): 192-198.
ZHU Hao, ZHANG Wenbo, DENG Yuanwang, LI Meng, JI Xiang. Peak power estimation of power battery discharge based on SA+BP hybrid algorithm[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2020, 41(2): 192-198.
CHUNG C T, HUNG Y H. Performance and energy management of a novel full hybrid electric powertrain system［J］. Energy, 2015, 89:626-636.
［2］
XIONG R, HE H W, SUN F C, et al. Online estimation of peak power capability of Li-ion batteries in electric vehicles by a hardware-in-loop approach［J］. Energies, 2012, 5(12):1455-1469.
［3］
ZHANG P, YAN F W, DU C Q. A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics［J］. Renewable and Sustainable Energy Reviews, 2015, 48:88-104.
［4］
JIANG W J, ZHANG N, LI P C, et al. A temperature-based peak power capability estimation method for li-thium-ion batteries［J］. Procedia Engineering, 2017, 187:249-256.
［5］
PEI L, ZHU C B, WANG T S, et al. Online peak po-wer prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles［J］. Energy, 2014, 66(1):766-778.
［6］
FENG T H, YANG L, ZHAO X W, et al. Online identification of lithium-ion battery parameters based on an improved equivalent circuit model and its implementation on battery state-of-power prediction［J］. Journal of Po-wer Sources, 2015, 281:192-203.
［7］
WANG S Q, VERBRUGGE M, WANG J S, et al. Po-wer prediction from a battery state estimator that incorporates diffusion resistance［J］. Journal of Power Sources, 2012, 214(1): 399-406.
［8］
DONG G Z, ZHANG X, ZHANG C B, et al. A method for state of energy estimation of lithium-ion batteries based on neural network model［J］. Energy, 2015, 90: 879-888.
［9］
HE W, WILLIARD N, CHEN C C, et al. State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation［J］. Electrical Power ＆ Energy Systems, 2014, 62:783-791.
［10］
YANG D, WANG Y J, PAN R, et al. A neural network based state-of-health estimation of lithium-ion battery in electric vehicles［J］. Energy Procedia, 2017, 105:2059-2064.
ZHENG F D, JIANG J C, CHEN K L, et al. Peak po-wer prediction model for batteries based on data statistical characteristic and GS-SVM［J］. Electric Power Automation Equipment, 2017,37(9):56-61. (in Chinese)
［12］
GUO Y F, ZHAO Z S, HUANG L M. SoC estimation of lithium battery based on improved BP neural network［J］. Energy Procedia, 2017,105:4153-4158.
HUANG M H, YAN Y G, ZHU L M. SOC estimation of lithium iron phosphate battery based on improved BP neural network［J］. Journal of WUT(Information ＆ Management Engineering), 2014, 36(6): 790-793.(in Chinese)
2020, Vol. 41 
