GA-BP network based battery SOC prediction for quasi anti-damage power supply

Dianji yu Kongzhi Xuebao/Electric Machines and Control

Volumn 14, Issue 6, 2010, Pages 61-65

  Wang, Ya Jun ^a   Wang, Xu Dong ^a   Zhou, Yong Qin ^a   Yan, Yi Xin ^a  

^a HARBIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Battery; Error back proragation neural network; Genetic algorithm; State of charge prediction

Indexed keywords

ARBITRARY ACCURACY; BACKPROPAGATION NETWORK; BATTERY; BATTERY SOC; BP NETWORKS; CONVERGENCE VELOCITY; DISCHARGE VOLTAGES; EXTERNAL CHARACTERISTIC; GA ALGORITHM; GA-BP NETWORKS; GLOBAL SEARCHING; LEVENBERG-MARQUARDT ALGORITHM; LOCAL MINIMUMS; NON-LINEAR RELATIONSHIPS; NONLINEAR FUNCTIONS; NONLINEAR MAPPINGS; POWER SUPPLY; PREDICTION ERRORS; PREDICTION METHODS; PREDICTION MODEL; SIMULATION RESULT; STATE OF CHARGE;

COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; FORECASTING; GENETIC ALGORITHMS; LEAD ACID BATTERIES; MATHEMATICAL MODELS; NEURAL NETWORKS; UNCERTAINTY ANALYSIS;

BACKPROPAGATION;

EID: 77956354283     PISSN: 1007449X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (10)

1. LEI Xiao, CHEN Qingquan, LIU Kaipei, et al. Battery state of charge estimation basedon neural network for electric vehicles[J]. Transactions of China Electro-technical Society, 2007, 22(8): 155-160.
2. LEI Xiao, CHEN Qingquan, LIU Kaipei, et al. Radial-Based-Function neural network based SOC estimation for electric vehicles[J]. Transactions of China Electro-technical Society, 2008, 23(5): 81-86.
3. ZHAO Kegang, LUO Yutao, PEI Feng. SOC estimation of battery on neural network[J]. Journal of Center South University: Science and Technology, 2007, 38(S1): 931-936.
4. HU Jianming, CHEN Yuanrui. Forecasting battery surplus capacity in hybrid electric vehicle using grey theory[J]. Telecom Power Technology, 2009, 26(5): 54-58.
5. HAN Jaehyun, KIM Dongchul, SUNWOO Myoungho. State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filte[J]. Journal of Power Sources, 2009, (2): 606-612.
6. 2007.
7. 2005.
8. 2005.
9. HUANG Hanxiong, HE Jianmin, LIU Xuhui, et al. Modeling and prediction of water-assisted injection molding based on GA-LMBP inverse neural network[J]. Journal of South China University of Technology: Natural Science Edition, 2007, 35(12): 23-27.
10. OUYANG Mingsan, YU Shijie. The status quo and development of capacity forecasting technology for VRLA batteries[J]. Chinese LABAT Man, 2004, 2: 59-66.