Abstract
This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.
Highlights
Due to energy crisis and environmental issues, energy storage systems have been applied more and more widely in electric vehicles (EVs), power grids, consumer electronics equipment, etc
This paper focuses on state of charge (SOC) estimation for the battery packs of EVs
The AEKFbased algorithm is cell applied to estimate the cell SOC first, and a novel pack algorithm is proposed on the estimation
Summary
Due to energy crisis and environmental issues, energy storage systems have been applied more and more widely in electric vehicles (EVs), power grids, consumer electronics equipment, etc. Most vehicle original equipment manufacturers (OEMs) and BMS manufacturers adopt this method, along with a variety of look-up tables and threshold protections [4]; there exist three obvious defects with this method It needs precise estimation of the initial SOC values, which usually relies on memorial values of the last operation or the help of the initially built relationship with battery open circuit voltage (OCV). It relies on each cell’s SOC calculation, which requires considerable labor and storage memory, especially when hundreds of cells constitute a battery pack It can induce oscillations when the battery is transitioned between the charge process and discharge process, leading to an unsatisfactory user experience.
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