State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles

Taimoor Zahid, Kun Xu, Weimin Li, Chenming Li and Hongzhe Li

Energy, 2018, vol. 162, issue C, 871-882

Abstract: State of charge estimation is one of the most critical factors to solve the key issues of monitoring and safety concerns of an electric vehicle power battery. In this paper, a state of charge estimation approach using subtractive clustering based neuro-fuzzy system is presented and evaluated by the simulation experiments using advanced vehicle simulator in comparison with back propagation neural network and Elman neural networks. Input parameters to model the state of charge estimation approach using subtractive clustering based neuro-fuzzy system are current, temperature, actual power loss, available and requested power, cooling air temperature and battery thermal factor. Data collected from 10 different drive cycles are utilized for the training and testing stages of the state of charge estimation model. Experimental results illustrated that the proposed model exhibits sufficient accuracy and outperforms both neural network and Elman neural network based models. Thus, the proposed model under different drive cycles show remarkable advancement in state of charge estimation with high potential to overcome the drawbacks in traditional methods and therefore provides an alternative approach in state of charge estimation. In addition, a sensitivity analysis is also performed to determine the importance of each input parameter on output i.e. state of charge.

Keywords: Battery management system; Electric vehicle; Energy storage; Battery state estimation; State of charge; Machine learning (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:162:y:2018:i:c:p:871-882

DOI: 10.1016/j.energy.2018.08.071

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