A novel approach to state of charge estimation using extended Kalman filtering for lithium-ion batteries in electric vehicles

Abstract

This paper proposed a novel approach to state-ofcharge (SoC) estimation of the lithium-ion batteries (LiBs) used in electric vehicles (EVs) based on the extended Kalman filtering (EKF). An improved lumped parameter model was developed for describing the dynamic behavior of the LiBs with an optimized open circuit voltage. This improved approach can reduces model error effectively. Other model parameters were identified via the genetic algorithm (GA) to optimizes the polarization time constant. Experimental and simulation results with two kinds of dynamic cycles show that, compared to the commonly used coulomb counting method, the EFK based SoC estimation algorithm is more precise. The proposed methodology can resolve the deficiency of coulomb counting method. The coulomb counting method fails to correct the erroneous initial SoC and is prone to cause greater accumulated error. In contrast, the proposed novel SoC estimation approach can accurately project the SoC trajectory. It employs real-time measurements of battery current and voltage. This approach then can be applied conveniently to battery management system in commercial electric vehicles. Index Terms-Lithium-ion battery, battery modeling, state of charge, extended Kalman filter, electric vehicle.I.

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

“…The formula using identified diffusion capacitance was derived to determine the SOH of a Li-ion battery. The GA fused with some other model-based estimation methods was used to estimate the SOC [155,[167][168][169][170][171]. The GA was also utilized in tuning of the fading KF to further improve the estimation of the SOC for a Li-ion battery [126].…”

A Temperature-Dependent State of Charge Estimation Method Including Hysteresis for Lithium-Ion Batteries in Hybrid Electric Vehicles

“…Figure 4 shows the 2 curve fitting models: 1RC network and 2RC network. In Figure 4, the 1RC network and the 2RC network indicate the one resistance plus first-order RC [9] and the one resistance plus second-order RC [10]- [12], respectively. In this paper, we select the 2RC network for the cell modeling of the lithiumion battery as a tradeoff between model complexity and RMSE performance.…”

“…In addition, some practical ECMs are based on the resistance-capacitance (RC) networks. Such ECM types include one resistance plus first-order RC [9], one resistance plus second-order RC [10]- [12], and one resistance plus third-order RC [13], [14]. For the lithium-ion battery in the electrified vehicle, a temperature-dependent ECM is required.…”

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

“…The approaches presented in [59,146] use a GA to identify time constant and other parameters of the battery model (fractional order and EIS-based model [59], and Thevenin ECM [146]). Despite these proposals utilizing a UKF-based SoC and EKF-based SoC estimation, respectively, the model parameter estimation using the GA is crucial, and accounts for the high accuracy of the methods.…”

“…In [112], OCV is calculated based on a Thevenin ECM, and then, by using it, SoC is estimated through a fading KF where the fading factors that optimize the KF are calculated by the means of a GA. GA is used as an optimization technique to estimate different states of the battery. The approaches presented in [59,146] use a GA to identify time constant and other parameters of the battery model (fractional order and EIS-based model [59], and Thevenin ECM [146]). Despite these proposals utilizing a UKF-based SoC and EKF-based SoC estimation, respectively, the model parameter estimation using the GA is crucial, and accounts for the high accuracy of the methods.…”