SOC estimation with thermal and charging rate consideration using dual filter approach for lithium-ion battery

Electric vehicles are subjected to various environmental conditions in which ambient temperature variations and high power requirements are common in the duty cycle. These factors greatly impact the state of charge of the batteries. The battery management system in the electric vehicles enables batteries to operate within a safe operating window by monitoring their states and controlling the environment. One of the important states is the state of charge, and accurate information of it is necessary to extract maximum performance from batteries with safety. An equivalent circuit model was used for representing the battery dynamics, and core temperature of the battery was modeled using the algebraic equation. Ambient temperature variation was captured through internal resistance, and a current requirement variation was captured through open circuit voltage. The fading Kalman filter was used for online estimation of the state of charge. The present technique estimates the state of charge at different C rates like 0.5, 1, and 2 C at ambient temperatures like 298, 313, and 333K. It was found that the proposed method estimates SOC with an average root mean square error of 0.358 and a maximum percentage error of 3% SOC for all the considered charging/discharging rates and temperatures. Published by AIP Publishing.