© The Institution of Engineering and Technology
State of charge (SOC) estimation plays a critical role in the operation of an electric vehicle (EV) power battery. In this Letter, the authors propose machine learning (ML) algorithms as alternate to the existing filtering algorithms used for SOC estimation of an EV battery. The SOC estimation approach is evaluated by the simulation experiments in advanced vehicle simulator (ADVISOR). For the modelling of ML algorithms, the input parameters that affect the SOC estimation are battery current, battery module temperature, power out of the battery (available and requested), battery power loss and heat removed from the battery. Training and testing stages of the models are carried out using the data collected from ADVISOR. As the drive cycle conditions provided by ADVISOR are universal therefore present method is applicable to all kinds of batteries used in EVs including lithium ion, nickel metal hydride and lead acid batteries. Thus, the proposed models for SOC estimation provide an alternative approach in SOC estimation.
References
-
-
1)
-
7. Zahid, T., Xu, G., Li, W., et al: ‘Performance analysis of particle filter for SOC estimation of LiFePO4 battery pack for electric vehicles’. 2014 IEEE Int. Conf. Information and Automation (ICIA), Hailar, Hulun Buir, China, July 2014, pp. 1061–1065.
-
2)
-
6. Zahid, T., Li, W.: ‘A comparative study based on the least square parameter identification method for state of charge estimation of a LiFePO4 battery pack using three model-based algorithms for electric vehicles’, Energies, 2016, 9, p. 720 (doi: 10.3390/en9090720).
-
3)
-
12. He, H., Xiong, R., Guo, H., Li, S.: ‘Comparison study on the battery models used for the energy management of batteries in electric vehicles’, Energy Convers. Manage., 2012, 64, pp. 113–121 (doi: 10.1016/j.enconman.2012.04.014).
-
4)
-
3. Schmidt, A.P.: ‘A novel electrochemical battery model for state of charge and state of health estimation’, , ETH Zurich, 2010.
-
5)
-
5. Bartlett, A., Marcicki, J., Onori, S., et al: ‘Electrochemical model-based state of charge and capacity estimation for a composite electrode lithium-ion battery’, Trans. Control Syst. Technol., 2016, 24, pp. 384–399.
-
6)
-
1. Yan, F., Wong, J.: ‘China electric vehicles to hit 1 million by 2020: report’, 2010. .
-
7)
-
4. Rahimian, S.K., Rayman, S., White, R.E.: ‘State of charge and loss of active material estimation of a lithium ion cell under low earth orbit condition using Kalman filtering approaches’, J. Electrochem. Soc., 2012, 159, pp. A860–A872 (doi: 10.1149/2.098206jes).
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.2677
Related content
content/journals/10.1049/el.2017.2677
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Machine learning an alternate technique to estimate the state of charge of energy storage devices