Abstract
The state of energy (SOE) of the lithium-ion battery indicates the driving range of electric vehicles, which is affected by its maximum available energy (MAEgy). Meanwhile, the energy efficiency is of great significance to improve the estimation accuracy. In this paper, a collaborative estimation method of the SOE and MAEgy with optimized time windows considering energy efficiency is proposed. Firstly, based on the Thevenin model of the battery, the forgetting factor recursive least squares is used to identify its parameters. The state space equations of the SOE and MAEgy are established respectively and two extended Kalman filters are leveraged to estimate them with different time windows. To reduce calculations and improve estimation accuracy, the multi-objective genetic algorithm is applied to optimize the time windows for the transient scheme and steady scheme, respectively. Then the optimal time windows of the initial and convergence phase are derived from these two schemes by the entropy weight method. Finally, the instantaneous energy efficiency is deduced, and the influences of four different calculation methods on the estimation performances are discussed. The results show that the proposed method has better transient performances and steady-state accuracy under dynamic driving condition and is robust against incorrect initial values.
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