Simultaneous and rapid estimation of state of health and state of charge for lithium-ion battery based on response characteristics of load surges

Abstract

The state of charge (SOC) and the state of health (SOH) are two crucial parameters for monitoring the battery status because SOH determines whether the battery can continue to operate safely and stably, and the SOC determines the battery's endurance. This paper proposes an online synthesis method based on the response characteristics of load surges and an improved fuzzy cerebellar model neural network (IFCMNN) to co-estimate SOH and SOC. Firstly, multiple features are extracted from the voltage response signals. Then, the grey relational analysis is utilized to verify the rationality of the extracted features and to fuse key features for SOH and SOC estimation. Secondly, IFCMNN models aiming at estimating SOH and SOC are proposed to estimate the SOH and SOC simultaneously and quickly. Finally, experimental results on ten batteries with different aging levels show that the proposed method can achieve fast estimation of SOH and SOC at 1.64 % and 2 % resolution accuracy respectively, regardless of the temperature and in rush currents variations. In addition, the proposed model has higher estimation accuracy compared with other traditional methods. Thus, the proposed method has a high generalization ability.