Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle

Bambang Wahono, Kristian Ismail, Harutoshi Ogai


This paper presents the construction of a battery state of charge (SOC) prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO) succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.


SOC, stepwise method, multicollinearity, electric vehicle, particle swarm optimization

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Haiying Wang, et al., "Estimation of state of charge of batteries for alectric vehicles", International Journal of Control and Automation, Vol. 6, No. 2, pp. 185-194, April 2013.

Wen-Yeau Chang, “The state of charge estimating methods for battery: A review,� ISRN Applied Mathematics, Vol. 2013, Article ID 953792, 7 pages, 2013. crossref

J. Chiasson and B. Vairamohan, “Estimating the state of charge of a battery�, IEEE Transactions on Control Systems Technology, Vol. 13, No. 3, pp. 465–470, 2005. crossref

H. Anbuky and P. E. Pascoe, “VRLA battery state-of charge estimation in telecommunication power systems�, IEEE Transactions on Industrial Electronics, Vol. 47, No. 3, pp. 565–573, 2000. crossref

Bingjun Xiao, et al.,�A universal state-of charge algorithm for batteries�, Design Automation Conference (DAC), 2010 47th ACM/IEEE, pp. 687-692, 2010. crossref

Ning Zhou, et al.,"A modified stepwise linear regression method for estimating modal sensitivity", IEEE Power and Energy Society General Meeting, pp. 1-7, 24-29 July 2011. crossref

A. C. Rencher, �Methods of Multivariate Analysis�, New York: Wiley, 2002.

J. O. Rawlings, et al., �Applied Regression Analysis: A Research Tool�, 2nd ed., New York: Springer, 1998.

J. Kennedy and R. Eberhart, “Particle swarm optimization�, Proceedings of the IEEE International Conference on Neural Networks, Vol. 4, pp. 1942–1948, 1995. crossref

R. Eberhart, and Yuhui Shi, “Particle swarm optimization: developments, applications and resources�, Proceedings Congress on Evolutionary Computation, Vol. 1, pp. 81-86, 2001. crossref

M. Ogawa et al., “Development of Method for Construction of a Response Surface Model and Control Parameter Optimization Method for Automobile Engine�, Transactions of the Society of Instrument and Control Engineers, Vol. 47 No. 10, pp. 501-510, 2011. crossref

J. J. Xu and Z.H. Xin, “An extended particle swarm optimizer�, Proceeding of the 19th IEEE International Parallel and Distributed Processing Symposium, 2005. crossref

S. He, et al., “A particle swarm optimizer with passive congregation�, Biosystems, Vol. 78, pp. 135-147, 2004. crossref

J. Kennedy and R. Eberhart. “Swarm Intelligence�, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001.

Kristian Ismail et al., "Desain Test Vehicle untuk Sistem Manajemen Energi Kendaraan Hibrida Seri", Prosiding seminar nasional SMART, pp. D84-D89, 2010.

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