State of charge estimation based on evolutionary neural network

Author:

Bo Cheng,Zhifeng Bai,Binggang Cao

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference20 articles.

1. Prediction of residual capacity of MH/Ni batteries based on neural network;Deng;J Harbin Inst Technol,2003

2. Chenghui Ca, Dong Du, Zhiyu Liu, Jingtian Ge. State of charge (SOC) estimation of high power Ni–MH rechargeable battery with artificial neural network, In: Proceedings of the 9th international conference on neural information processing, 2002, ICONIP’02, vol. 2; 2002. p. 824–8.

3. Grewal S, Grant DA. A novel technique for modeling the state-of-charge of lithium ion batteries using artificial neural networks. In: Telecommunications energy conference, INTELEC; 2001. p. 174–9.

4. Ruochen Liu, Haifeng Du, Licheng Jiao. Immunity clonal strategy. In: Fifth international conference on computational intelligence and multimedia applications (ICCIMA’03); 2003. p. 290–5.

5. Learning and optimization using the clonal selection principle;De Castro;IEEE Trans Evolut Comput,2002

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