100 pieces of 26650-type Lithium iron phosphate（LiFePO4） batteries cycled with a fixed charge and discharge rate are tested, and the influence of the battery internal resistance and the instantaneous voltage drop at...100 pieces of 26650-type Lithium iron phosphate（LiFePO4） batteries cycled with a fixed charge and discharge rate are tested, and the influence of the battery internal resistance and the instantaneous voltage drop at the start of discharge on the state of health（SOH） is discussed. A back propagation（BP） neural network model using additional momentum is built up to estimate the state of health of Li-ion batteries. The additional 10 pieces are used to verify the feasibility of the proposed method. The results show that the neural network prediction model have a higher accuracy and can be embedded into battery management system（BMS） to estimate SOH of LiFePO4 Li-ion batteries.展开更多
基金Supported by Special Topic of the Ministry of Education about Humanities and Social Sciences of China(No.12JDGC007)International Scientific and Technological Cooperation Projects of China(No.2012DFB10060)
文摘100 pieces of 26650-type Lithium iron phosphate（LiFePO4） batteries cycled with a fixed charge and discharge rate are tested, and the influence of the battery internal resistance and the instantaneous voltage drop at the start of discharge on the state of health（SOH） is discussed. A back propagation（BP） neural network model using additional momentum is built up to estimate the state of health of Li-ion batteries. The additional 10 pieces are used to verify the feasibility of the proposed method. The results show that the neural network prediction model have a higher accuracy and can be embedded into battery management system（BMS） to estimate SOH of LiFePO4 Li-ion batteries.
