Based On Adaptive Extended Kalman Filter Battery Pack SOC Estimation

Lithium-ion batteries dominate a considerable percentage of these applications in view of its high energy density,high life cycle,and lack of memory effect.In EVs or plug-in hybrid electric vehicles(PHEVs),a battery pack is usually comprised of tens to thousands of battery cells connected in series,parallel,or an even more complex topology.Hence,it is imperative to employ a so-called battery management system(BMS)to safeguard,manage,and oversee the pack to ensure its proper operation.An important task for the BMS is to estimate the battery’s current statuses,which usually include state of charge(SOC)and state of health(SOH).This paper focuses on SOC estimation for the battery packs of EVs.An accurate SOC can present the battery available capacity status,prevent a battery pack from overcharge or over-discharge,and can effectively protect the battery,thereby potentially prolong the battery life.The definition of SOC of a battery pack is much more complex than a single battery cell.This is because in a battery pack,there exists a variety of cells,of which the voltage and capacity cannot be guaranteed to be consistent due to differences in production engineering,materials.as well as the electric connection and internal temperature field distribution.Hence,it is imperative to build a battery pack SOC estimation algorithm which can firstly determine the overall battery pack SOC value,and secondly achieve a satisfactory user experience.In this paper.based on AEKF for cell SOC estimation,a reasonable estimation algorithm is proposed for the battery pack SOC.In order to properly evaluate the SOC estimation,a powerful and effective battery model,which has a fast calculation speed,enough precision,and limited storage size,is necessary to quantify the battery electrochemical reaction.In this paper,the ECM is employed to model a single cell,from which the battery pack model is built and validated.Based on the ECM.genetic algorithm(GA)is applied to optimally identify the model parameters.Then.AEKF is applied to estimate the single cell SOC.By setting a series of rules and threshold values.the pack SOC is determined based on the highest cell SOC and the lowest cell SOC.Finally,experiments examine and justify the effectiveness of the proposed method.