Low-complexity state of charge and anode potential prediction for lithium-ion batteries using a simplified electrochemical model-based observer under variable load condition

The accurate knowledge of the physics-based state of charge (SOC) and anode potential for lithium-ion batteries (LIBs) plays an essential role in the driving range prediction and charge strategy optimization of electric vehicles (EVs). However, the SOC estimation based on empirical equivalent circuit models and the lack of anode potential information makes it challenging in developing advanced battery management systems for EVs. For this reason, this paper proposes a low-complexity SOC and anode potential prediction method for LIBs using a simplified electrochemical model (SEM)-based observer under variable load condition. First, based on the Pade approximation and volume average method, a reduced-order SEM is proposed and verified. Then, a low-complexity proportional-integral-differential observer framework incorporating the SEM is developed to obtain the physics-based SOC and anode potential. Finally, the effectiveness of the proposed method under variable load conditions is assessed by combining data collected by experiment and COMOSL simulation. The results show that the maximum absolute errors of SOC estimation are basically maintained within 2% under HPPC test profiles and the root mean squared errors of anode potential can be kept at 4.31 mV under US06 test profiles, which achieves a good balance between accuracy and computation cost and provides a strong support on substantially ensuring safe operation of EVs.

Automated summary

This paper proposes a low-complexity SOC and anode potential prediction method for LIBs using a simplified electrochemical model (SEM)-based observer under variable load condition. The effectiveness of the proposed method is assessed by combining data collected by experiment and COMOSL simulation, showing a good balance between accuracy and computation cost.