The recent acceleration of the battery technology, due to emergence of smart grid and electric vehicle (EV) applications, has increased the demand for advanced battery management systems (BMS). State of charge (SOC) and State of health (SOH) estimation are two of the main features of the BMS. Although there have been several attempts to estimate the battery states, the results do not have enough accuracy to satisfy sensitivity analysis criteria for reliability assessment and energy efficiency purposes. The goal of this project is to develop algorithms to estimate the State of Charge (SOC) and State of Health (SOH) of the battery accurately in these applications.
Considering an RC equivalent circuit to model the battery dynamics, we design an adaptive on-line parameters/SOC/SOH co-estimation algorithm that identifies different parameters of the battery model at various temperature, ageing, SOC and charging/discharging rate conditions. This model with updating parameters is used to estimate the SOC with compensating the initial SOC error, accumulative error and noise effect. The estimated SOC and the updating model are also utilized to estimate the full capacity and internal resistance of the battery. This information along with the statistical data regarding the application and the battery performance degradation are deployed to predict the remaining useful life (RUL) and end of life (EOL) as indicators of the battery SOH.
Publication
- Rahimi-Eichi and M.-Y. Chow, “Batteries,” in Handbook of Energy, G. M. Crawley, Ed., ed USA, World Scientific Publishing Company and Imperial College Press, 2012.
- Rahimi-Eichi, F. Baronti, and M. Y. Chow, “Online Adaptive Parameter Identification and State-of-Charge Coestimation for Lithium-Polymer Battery Cells,” Industrial Electronics, IEEE Transactions on, vol. 61, pp. 2053-2061, 2014.
- Rahimi-Eichi, U. Ojha, F. Baronti, and M. Chow, “Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles,” Industrial Electronics Magazine, IEEE, vol. 7, pp. 4-16, 2013 (Best Paper Award).
- Otero, H. Rahimi-Eichi, J. J. Rodriguez-Andina, M.-Y. Chow, “FPGA Implementation of an Observer for State-of-Charge Estimation in Lithium-Polymer Batteries”, presented at IEEE International Conference on Mechatronics and Control (ICMC), Jinchou, China, 2014 (Best paper award).
- Rahimi-Eichi, B. Balagopal, M.-Y. Chow, and T.-J. Yeo, “Sensitivity Analysis of Lithium-Ion Battery Model to Battery Parameters,” presented at the 39th Annual Conference of the IEEE Industrial Electronics Society (IECON2013), IEEE, Vienna, Austria, 2013.
- Baronti, W. Zamboni, N. Femia, H. Rahimi-Eichi, R. Roncella, S. Rosi, et al., “Parameter identification of Li-Po batteries in electric vehicles: A comparative study,” presented at the IEEE International Symposium on Industrial Electronics (ISIE2013), pp. 1-7, Taipei, Taiwan, 2013.
- Rahimi-Eichi and M.-Y. Chow, “Adaptive online battery parameters/SOC/capacity co-estimation,” presented at the Transportation Electrification Conference and Expo (ITEC2013), IEEE, pp. 1 – 6, Metro Detroit, Michigan, USA, 2013.
- Rahimi-Eichi and M.-Y. Chow, “Adaptive parameter identification and State-of-Charge estimation of lithium-ion batteries,” presented at the 38th Annual Conference on IEEE Industrial Electronics Society (IECON 2012), IEEE, pp. 4012 – 4017, Montreal, QC, Canada, 2012.
- Rahimi Eichi and M.-Y. Chow, “Modeling and analysis of battery hysteresis effects,” presented at the Energy Conversion Congress and Exposition (ECCE2012), IEEE, pp. 4479-4486, Raleigh, NC, USA, 2012.
- Rahimi-Eichi, F. Baronti, and M. Y. Chow, “Modeling and online parameter identification of Li-Polymer battery cells for SOC estimation,” presented at the International Symposium on Industrial Electronics (ISIE2012), IEEE, pp. 1336-1341, Hangzhou, Zhejiang, China, 2012.
Links
- FREEDM System Center
- Advanced Transportation Energy Center
- Energy Storage Technical Committee at IEEE IES
