Real-time estimation of state-of-charge using particle swarm optimization on the electro-chemical model of a single cell

Abstract

Accurate estimation of State of Charge (SOC) is crucial. With the ever-increasing usage of batteries, especially in safety critical applications, the requirement of accurate estimation of SOC is paramount. Most current methods of SOC estimation rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation as the battery ages or under different operating conditions. This work aims at exploring the real-time estimation and optimization of SOC by applying Particle Swarm Optimization (PSO) to a detailed electrochemical model of a single cell. The goal is to develop a single cell model and PSO algorithm which can run on an embedded device with reasonable utilization of CPU and memory resources and still be able to estimate SOC with acceptable accuracy. The scope is to demonstrate the accurate estimation of SOC for 1C charge and discharge for both healthy and aged cell.