Abstract
Integration of Coulomb Counting Method in Battery Management System for Electric Vehicle
Highlights
To cut fossil energy consumption and mitigate the greenhouse effect, electric vehicle (EV) has been widely concerned
BATTERY MODEL The nth-order Thevenin model is widely used for state of charge (SOC) estimation because of its relatively high accuracy and computation efficiency compared with other models [1]. dynamic voltage of the where, k denotes the present step, k-1 denotes the previous step
2) DENTIFICATION OF R-OCV CURVE In the paper, the R-OCV curve will be utilized in the numerical iteration approach, which will be Generally, the lumped resistance R defined in (5) can be calculated through the identification ofro,rp[1] and rp[2], respectively. where, I(k) and Uo(k) are measured based on accurate measuring instrument
Summary
B. OFF-LINE PARAMETERS IDENTIFICATION To verify the performance of the proposed algorithm, an 18650 Li-ion battery with the nominal capacity of 3Ah and rated voltage of 3.6V was modeled. REAL-TIME STATE OF CHARGE ESTIMATION USING THE IMPROVED COULOMB COUNTING APPROACH. C. COMPENSATION COEFICIENT TO PREVENT ERROR ACCUMULATION IN CC APPROACH Assuming that numeral iteration happens at ki. DESIGN OF ALGORITHMS USED: Algorithms used for SOC and SOH Estimation are: 1. Enhanced Coulomb Counting Algorithm:In order to beat the shortcomings of the coulomb counting method and to enhance its estimation accuracy, an enhanced coulomb counting algorithm has been proposed for estimating the SOC and SOH parameters of Li-ion batteries. Simple calculation and the uncomplicated hardware requirements, the improved coulomb counting algorithm are often implemented altogether portable devices, as electric vehicles. The estimation error can be reduced to 1% at the operating cycle next to the reevaluation of the SOH
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