Browsing by Author "Chandra Shekar, Arun"

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    Real-time estimation of state-of-charge using particle swarm optimization on the electro-chemical model of a single cell
    (2017-05) Chandra Shekar, Arun; Anwar, Sohel
    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.
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    Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model
    (MDPI, 2019-01) Chandra Shekar, Arun; Anwar, Sohel; Mechanical and Energy Engineering, School of Engineering and Technology
    With the ever-increasing usage of lithium-ion batteries, especially in transportation applications, accurate estimation of battery state of charge (SOC) is of paramount importance. A majority of the current SOC estimation methods rely on data collected and calibrated offline, which could lead to inaccuracies in SOC estimation under different operating conditions or when the battery ages. This paper presents a novel real-time SOC estimation of a lithium-ion battery by applying the particle swarm optimization (PSO) method to a detailed electrochemical model of a single cell. This work also optimizes both the single-cell model and PSO algorithm so that the developed algorithm can run on an embedded hardware with reasonable utilization of central processing unit (CPU) and memory resources while estimating the SOC with reasonable accuracy. A modular single-cell electrochemical model, as well as the proposed constrained PSO-based SOC estimation algorithm, was developed in Simulink©, and its performance was theoretically verified in simulation. Experimental data were collected for healthy and aged Li-ion battery cells in order to validate the proposed algorithm. Both simulation and experimental results demonstrate that the developed algorithm is able to accurately estimate the battery SOC for 1C charge and 1C discharge operations for both healthy and aged cells. View Full-Text