Hardware Implementation Optimization of Extended Kalman Filter for the Estimation of State of Charge of Li-Ion Battery

Extended Kalman Filter (EKF) is widely studied in the field of State of Charge (SOC) estimation of Li-ion batteries, however, in applications like Electric Vehicles (EV), there are usually a large number of individual battery cells. In order to meet the demand of real-time computation, MCU of high performance is essential. In this paper, we proposed a hardware structure to implement EKF which is economical in area and power consumption and could be easily integrated in a larger design and at the same time could satisfy the real-time restriction.

[1]  Sergio M. Savaresi,et al.  Kalman Filter SoC estimation for Li-Ion batteries , 2011, 2011 IEEE International Conference on Control Applications (CCA).

[2]  G. Plett.  Kalman-Filter SOC Estimation for LiPB HEV Cells , 2002 .

[3]  Mo-Yuen Chow,et al.  Adaptive parameter identification and State-of-Charge estimation of lithium-ion batteries , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[4]  Andrew F. Burke,et al.  Batteries and Ultracapacitors for Electric, Hybrid, and Fuel Cell Vehicles , 2007, Proceedings of the IEEE.

[5]  Hen-Geul Yeh.  Kalman filtering and systolic processors , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.