Abstract
In order to properly manage lithium-ion batteries of electric vehicles (EVs), it is essential to build the battery model and estimate the state of charge (SOC). In this paper, the fractional order forms of Thevenin and partnership for a new generation of vehicles (PNGV) models are built, of which the model parameters including the fractional orders and the corresponding resistance and capacitance values are simultaneously identified based on genetic algorithm (GA). The relationships between different model parameters and SOC are established and analyzed. The calculation precisions of the fractional order model (FOM) and integral order model (IOM) are validated and compared under hybrid test cycles. Finally, extended Kalman filter (EKF) is employed to estimate the SOC based on different models. The results prove that the FOMs can simulate the output voltage more accurately and the fractional order EKF (FOEKF) can estimate the SOC more precisely under dynamic conditions.
Highlights
Lithium-ion batteries have attained substantial attention due to their high safety, long life, and high energy density [1,2]
The widely adopted modeling method of lithium-ion battery is based on the centralized integral order calculus (IOC)
Grünwald–Letnikov defined it with a discrete form, which is widely applied in numerical solution for fractional order calculus (FOC)
Summary
Lithium-ion batteries have attained substantial attention due to their high safety, long life, and high energy density [1,2]. The widely adopted modeling method of lithium-ion battery is based on the centralized integral order calculus (IOC) This method is relatively simple, the battery inner parameters including capacitance, resistance, which are with diffused and decentralized characteristics [6,14,15], may vary during battery operation. This estimation method is independent of initial SOC value, which is an essential and difficult task for the coulomb counting method Another merit is that the recursive formulations can be applied in the embedded computer system, and have been substantially employed in real application [35,36]. Genetic algorithm (GA) is applied to simultaneously identify the orders of FOM and resistor and capacitor parameters for the lithium-ion battery The precisions of these two models are compared and analyzed. EKF is applied to estimate the SOC, and the results based on different FOM and IOM are compared and summarized
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