On‐road estimation of state of charge of lithium‐ion battery by extended and dual extended Kalman filter considering sensor bias. (15th June 2022) - Bhattacharyya, Himadri Sekhar; Choudhury, Amalendu Bikash; Chanda, Chandan Kumar
Summary: The lithium‐ion battery, as an electrochemical energy storage technology, has expanded its application in recent years, owing primarily to electric vehicles (EVs). The battery management system (BMS) is housed within the battery pack and is in charge of calculating one of the most important variables, the state of charge (SOC). The electrical equivalent circuit model (EECM) of the battery has been developed and different model parameters are identified by solving the equations with the help of Levenberg‐Marquardt (LM) method. To calculate the SOC for various load conditions, a precise relationship between the SOC and open‐circuit voltage is required. In this paper, the extended Kalman filter (EKF) and dual extended Kalman filter (DEKF) algorithms are utilised in order to get a fairly good estimate of SOC based on the EECM. The impact of voltage and current sensor bias on SOC is investigated. Three driving cycles, namely the Urban Dynamometer Driving Schedule, New York City Cycle, and Braunschweig City Driving Cycle, are used as a simulated variable load to create a real‐life EV environment at different temperatures to validate the effectiveness of these algorithms. The proposed algorithms give a fairly early indication of the SOC threshold levels from 0.5 to 0.1. Abstract : An application‐specific accurate as well as a generalised battery model is developed in this study. With the use of the EKF and DEKF technique, both voltage and current bias are independently examined to determine SOC and these bias values for various drive cycles at different temperatures.