Lithium ion (Li-ion) is the current leading battery technology. Because of their complex behavior, Li-ion batteries require advanced battery management systems (BMSs). One of the most critical tasks of a BMS is state of charge (SoC) estimation. In this paper, an efficient electrochemical model-based SoC estimation algorithm is presented. The use of electrochemical models enables an accurate estimation of the SoC as well during high current events. However, this often due to the cost of a high computational complexity. In this paper, it is shown that by writing the model as a linearly spatially interconnected system and by exploiting the resulting semi-separable structure an efficient extended Kalman filter (EKF) can be implemented. The proposed EKF is compared with another electrochemical-based estimation and shown to deliver an estimation error of less than 5% also during high current peak.

Electrochemical Model-Based State of Charge Estimation for Li-Ion Cells

CORNO, MATTEO;SAVARESI, SERGIO MATTEO;
2015-01-01

Abstract

Lithium ion (Li-ion) is the current leading battery technology. Because of their complex behavior, Li-ion batteries require advanced battery management systems (BMSs). One of the most critical tasks of a BMS is state of charge (SoC) estimation. In this paper, an efficient electrochemical model-based SoC estimation algorithm is presented. The use of electrochemical models enables an accurate estimation of the SoC as well during high current events. However, this often due to the cost of a high computational complexity. In this paper, it is shown that by writing the model as a linearly spatially interconnected system and by exploiting the resulting semi-separable structure an efficient extended Kalman filter (EKF) can be implemented. The proposed EKF is compared with another electrochemical-based estimation and shown to deliver an estimation error of less than 5% also during high current peak.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/865137
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