Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter

Venu Sangwan; Venkata R. Vakacharla; Rajesh Kumar; Akshay K. Rathore

doi:10.1109/ICPES.2017.8387385

Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter

Venu Sangwan
, Venkata R. Vakacharla
, Rajesh Kumar
, Akshay K. Rathore

Malaviya National Institute of Technology
Concordia University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

An meticulous estimation of the state of charge (SOC) is of great significance in a battery management system (BMS) due to the requirement of ensuring safe and reliable operations for a Li-ion battery in battery electric vehicles (BEVs). Firstly, an equivalent circuit using one resistance-capacitor for describing transient behavior of the battery has been developed. The parameters of this equivalent model of battery, depends on temperature, that have been determined using Ageist Spider Monkey Optimization (ASMO). The objective of using optimization is to produce voltage curve using developed model that optimally fits the voltage curve obtained from experimental results for Driving Stress Test (DST) profile. Then, a model-based online iterative estimation, Extended Kalman Filter (EKF) has been implemented for battery SOC estimation. The estimation has an absolute root-mean-square error (RMSE) of less than 2% and an absolute maximum error of 6% in case of 0°C. In the other case (25°C and 50°C) it is less than 2%.

Original language	English
Title of host publication	2017 7th International Conference on Power Systems, ICPS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	726-731
Number of pages	6
ISBN (Electronic)	9781538617892
DOIs	https://doi.org/10.1109/ICPES.2017.8387385
Publication status	Published - 15 Jun 2018
Externally published	Yes
Event	7th International Conference on Power Systems, ICPS 2017 - Pune, India Duration: 21 Dec 2017 → 23 Dec 2017

Publication series

Name	2017 7th International Conference on Power Systems, ICPS 2017

Conference

Conference	7th International Conference on Power Systems, ICPS 2017
Country/Territory	India
City	Pune
Period	21/12/17 → 23/12/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Battery Electric Vehicle
Battery Management System
Extended Kalman Filter
Li-ion batteries
State of Charge estimation

ASJC Scopus subject areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Control and Optimization

Access to Document

10.1109/ICPES.2017.8387385

Cite this

Sangwan, V., Vakacharla, V. R., Kumar, R., & Rathore, A. K. (2018). Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter. In 2017 7th International Conference on Power Systems, ICPS 2017 (pp. 726-731). (2017 7th International Conference on Power Systems, ICPS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPES.2017.8387385

@inproceedings{df9e51a139084be9a72467613e7de924,

title = "Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter",

abstract = "An meticulous estimation of the state of charge (SOC) is of great significance in a battery management system (BMS) due to the requirement of ensuring safe and reliable operations for a Li-ion battery in battery electric vehicles (BEVs). Firstly, an equivalent circuit using one resistance-capacitor for describing transient behavior of the battery has been developed. The parameters of this equivalent model of battery, depends on temperature, that have been determined using Ageist Spider Monkey Optimization (ASMO). The objective of using optimization is to produce voltage curve using developed model that optimally fits the voltage curve obtained from experimental results for Driving Stress Test (DST) profile. Then, a model-based online iterative estimation, Extended Kalman Filter (EKF) has been implemented for battery SOC estimation. The estimation has an absolute root-mean-square error (RMSE) of less than 2\% and an absolute maximum error of 6\% in case of 0°C. In the other case (25°C and 50°C) it is less than 2\%.",

keywords = "Battery Electric Vehicle, Battery Management System, Extended Kalman Filter, Li-ion batteries, State of Charge estimation",

author = "Venu Sangwan and Vakacharla, \{Venkata R.\} and Rajesh Kumar and Rathore, \{Akshay K.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th International Conference on Power Systems, ICPS 2017 ; Conference date: 21-12-2017 Through 23-12-2017",

year = "2018",

month = jun,

day = "15",

doi = "10.1109/ICPES.2017.8387385",

language = "English",

series = "2017 7th International Conference on Power Systems, ICPS 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2017 7th International Conference on Power Systems, ICPS 2017",

address = "United States",

}

Sangwan, V, Vakacharla, VR, Kumar, R & Rathore, AK 2018, Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter. in 2017 7th International Conference on Power Systems, ICPS 2017. 2017 7th International Conference on Power Systems, ICPS 2017, Institute of Electrical and Electronics Engineers Inc., pp. 726-731, 7th International Conference on Power Systems, ICPS 2017, Pune, India, 21/12/17. https://doi.org/10.1109/ICPES.2017.8387385

Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter. / Sangwan, Venu; Vakacharla, Venkata R.; Kumar, Rajesh et al.
2017 7th International Conference on Power Systems, ICPS 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 726-731 (2017 7th International Conference on Power Systems, ICPS 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter

AU - Sangwan, Venu

AU - Vakacharla, Venkata R.

AU - Kumar, Rajesh

AU - Rathore, Akshay K.

PY - 2018/6/15

Y1 - 2018/6/15

N2 - An meticulous estimation of the state of charge (SOC) is of great significance in a battery management system (BMS) due to the requirement of ensuring safe and reliable operations for a Li-ion battery in battery electric vehicles (BEVs). Firstly, an equivalent circuit using one resistance-capacitor for describing transient behavior of the battery has been developed. The parameters of this equivalent model of battery, depends on temperature, that have been determined using Ageist Spider Monkey Optimization (ASMO). The objective of using optimization is to produce voltage curve using developed model that optimally fits the voltage curve obtained from experimental results for Driving Stress Test (DST) profile. Then, a model-based online iterative estimation, Extended Kalman Filter (EKF) has been implemented for battery SOC estimation. The estimation has an absolute root-mean-square error (RMSE) of less than 2% and an absolute maximum error of 6% in case of 0°C. In the other case (25°C and 50°C) it is less than 2%.

AB - An meticulous estimation of the state of charge (SOC) is of great significance in a battery management system (BMS) due to the requirement of ensuring safe and reliable operations for a Li-ion battery in battery electric vehicles (BEVs). Firstly, an equivalent circuit using one resistance-capacitor for describing transient behavior of the battery has been developed. The parameters of this equivalent model of battery, depends on temperature, that have been determined using Ageist Spider Monkey Optimization (ASMO). The objective of using optimization is to produce voltage curve using developed model that optimally fits the voltage curve obtained from experimental results for Driving Stress Test (DST) profile. Then, a model-based online iterative estimation, Extended Kalman Filter (EKF) has been implemented for battery SOC estimation. The estimation has an absolute root-mean-square error (RMSE) of less than 2% and an absolute maximum error of 6% in case of 0°C. In the other case (25°C and 50°C) it is less than 2%.

KW - Battery Electric Vehicle

KW - Battery Management System

KW - Extended Kalman Filter

KW - Li-ion batteries

KW - State of Charge estimation

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Y2 - 21 December 2017 through 23 December 2017

ER -

Sangwan V, Vakacharla VR, Kumar R, Rathore AK. Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter. In 2017 7th International Conference on Power Systems, ICPS 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 726-731. (2017 7th International Conference on Power Systems, ICPS 2017). doi: 10.1109/ICPES.2017.8387385

Estimation of state of charge for Li-ion battery using model adaptive extended Kalman filter

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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