Open AccessState‐of‐Charge estimation of Li‐ion battery at different temperatures using particle filter
Author(s) -
Sangwan Venu,
Kumar Rajesh,
Kumar Rathore Akshay
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.9234
Subject(s) - battery (electricity) , state of charge , particle filter , residual , computer science , heuristic , transient (computer programming) , algorithm , filter (signal processing) , control theory (sociology) , computation , power (physics) , artificial intelligence , computer vision , operating system , physics , quantum mechanics , control (management)
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.