Online Estimation of State of Power for Lithium-Ion Batteries in Electric Vehicles Using Genetic Algorithm

Abstract

Online estimation of the state of power (SoP) of lithium-ion batteries is crucial for both battery management system and energy management system in electric vehicles. In this paper, the approach of online estimating the SoP is investigated with a concern of the impact of the imprecise state of charge (SoC). First, the characteristics of lithium batteries under different state of health (SoH) conditions are experimented based on a typical vehicle driving cycle; then the SOP estimation algorithm using genetic algorithm (GA) is proposed to deal with the long time-scale estimation for power management application, on top of that, the sensitivity coefficient ( $\delta$ ) of the SoP estimation to the SoC precision is analyzed and the correlations of $\delta $ with the varying SoH, estimation time-scale are established. Finally, the presented algorithm is evaluated by a simulation study. The proposed GA-based estimation method can improve the SoP estimation accuracy by up to 7.2% in certain cases compared with the traditional Taylor method.