State-of-Charge estimation of Li-ion battery at different temperatures using particle filter

Abstract

State-of-Charge (SOC) estimation is one of the fundamental functions undertaken by Battery Management System (BMS) in an Electric Vehicle (EV) to assess the residual service time of the battery during operation. Thus, an accurate model of the battery that efficiently describes its dynamic characteristics is necessary for precise SOC estimation. The variation in temperature effects battery parameters, and consequently, the estimation of SOC is subject to change in temperature. In this paper, the identification of parameters of battery model is considered as an optimisation problem and solved using meta-heuristic Ageist Spider Monkey Algorithm (ASMO) under the influence of varying temperature. The developed model is used for SOC estimation using three Recursive Bayesian filtering based adaptive filter algorithms. Further, the efficiency of the implemented adaptive filter algorithms is compared in terms of solution quality and computation time required for evaluation of SOC.

Keywords

• secondary cells
• adaptive filters
• particle filtering (numerical methods)
• bayes methods
• battery powered vehicles
• battery management systems
• lithium compounds
• optimisation
• temperature variation
• recursive bayesian filtering based adaptive filter algorithms
• asmo
• optimisation problem
• bms
• li-ion battery state-of-charge estimation
• battery parameters
• soc estimation
• electric vehicle
• battery management system
• particle filter
• meta-heuristic ageist spider monkey algorithm
• residual service time