Next Article in Journal
Monitoring the State of Charge of the Positive Electrolyte in a Vanadium Redox-Flow Battery with a Novel Amperometric Sensor
Previous Article in Journal
Prototype System of Rocking-Chair Zn-Ion Battery Adopting Zinc Chevrel Phase Anode and Rhombohedral Zinc Hexacyanoferrate Cathode
Article Menu

Export Article

Open AccessArticle
Batteries 2019, 5(1), 4; https://doi.org/10.3390/batteries5010004

Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model

1
KPIT Technologies, Inc., Columbus, IN 47201, USA
2
Department of Mechanical and Energy Engineering, Purdue School of Engineering and Technology, IUPUI, Indianapolis, IN 46202, USA
*
Author to whom correspondence should be addressed.
Received: 23 October 2018 / Revised: 19 November 2018 / Accepted: 26 November 2018 / Published: 4 January 2019
Full-Text   |   PDF [3296 KB, uploaded 4 January 2019]   |  

Abstract

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
Keywords: state of charge; particle swarm optimization; real-time estimation; single-cell model; Simulink© state of charge; particle swarm optimization; real-time estimation; single-cell model; Simulink©
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Chandra Shekar, A.; Anwar, S. Real-Time State-of-Charge Estimation via Particle Swarm Optimization on a Lithium-Ion Electrochemical Cell Model. Batteries 2019, 5, 4.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Batteries EISSN 2313-0105 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top