Online estimation of state of power for lithium-ion battery considering the battery aging

Precise Estimation of battery state of power (SoP) is crucial for designing the energy management strategy of power system in electric vehicles (EVs) and hybrid electric vehicles (HEVs). In this paper, a novel online model-based estimation algorithm of SoP is proposed for the lithium-ion battery considering the monotonicity of output power and the influence of battery state of health (SoH). Genetic algorithm (GA) is employed to identify the parameters of battery for this algorithm. The performance of the algorithm is experimentally validated by batteries of different aging states with UDDS (Urban Dynamometer Driving Schedule) profile. Based on the results, the rationality of the algorithm is analyzed and the relationship between SoP and SoH is investigated. It is noted that SoP has close relationship with the internal resistance during the aging of the battery.