Presentation of the New Method for Simultaneous Estimation of the Parameters and the Charge Situation of Used Batteries in Hybrid Vehicles

Year 2022, Volume: 2 Issue: 2, 103 - 114, 31.10.2022

Monireh Ahmadi

https://doi.org/10.5152/tepes.2022.21006

https://izlik.org/JA23RM79KZ

Abstract

Estimating the state of charge (SOC) of a battery in the lithium-ion batteries used in hybrid vehicles is of high significance to ensure safe operation and to pre- vent overmuch charging and discharging. Despite the great importance of the parameter SOC, this parameter cannot be directly measured from the battery terminals. Thus, there is the need to estimate it. So far, various methods for estimating the SOC of lithium-ion batteries have been introduced. In this paper, by using the estimator of recursive least squares, firstly, the battery parameters are estimated and calculated and also the modified particle filter has been used for estimating the SOC of the battery. The standard particle filter has the problem of particle degeneracy phenomenon that decreases the estimation accuracy. Thus, in modified filter based PSO , the difference evolutionary algorithm (DEA) and Markov chain Monte Carlo (MCMC) applied to the standard PF that causes the estimation of SOC more accurate and consistent. To obtain an accurate and reliable method for estimating SOC, the desired battery must be modeled that in this paper, the first order resistor capacitor (RC) model has been used to have the high accuracy and low calculation complexity. This model is able to model the dynamic behavior of battery and for this reason it is suitable for hybrid vehicles application. To evaluate the performance of the proposed method, this method is compared with other methods that results represent the effective performance of the proposed method compared to other methods.

Keywords

Hybrid vehicle , state of charge , particle filter , recursive least squares , lithium-ion battery

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