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Effects of Battery Model on the Accuracy of Battery SOC Estimation Using Extended Kalman Filter under Practical Vehicle Conditions Including Parasitic Current Leakage and Diffusion Of Voltage

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Abstract

Accurate estimation of battery State of Charge (SOC) is a crucial factor for the safe and efficient usage of the batteries in hybrid electric vehicles. The combined method of Coulomb counting and Open circuit Voltage (OCV) is already under practical usage for the estimation of battery SOC, but the methods have significant error when there is parasitic current leakage (dark current) or short rest period. Thus, Extended Kalman Filter (EKF) is one of the battery SOC estimation methods used to overcome such drawbacks. And, most importantly, due to structural dependency of EKF upon battery model, the battery model used for the EKF contributes significantly to the accuracy of EKF. Thus, in this paper, 3 types of battery Equivalent Circuit Models (ECMs) including second order RC model, first order RC model, and R model are compared under practical vehicle driving conditions. To simulate the vehicle driving condition, a micro Hybrid Electric Vehicle (micro-HEV) is modeled and simulation is conducted under NEDC condition.

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Abbreviations

C :

charge, As

dt :

time step, s

f(x) :

state function of dynamic system

h(x) :

predicted measurement function of dynamic system

H:

∂h/∂x

I :

current, A

K :

kalman gain

OCV :

open circuit voltage, V

P(+) :

a priori covariance of state error

P(-) :

a posteriori covariance of state error

Q :

covariance of process noise

R :

covariance of measurement noise

R i :

internal resistance, ohm

SOC :

State of Charge, %

V :

voltage, V

V :

measurement noise, V

w :

process noise

x :

a priori state

\({\bf{\hat x}}\) :

a posteriori state

z :

a priori measurement, V

ẑ:

a posteriori measurement, V

Φ:

∂f/∂x

k:

iteration

1:

first RC region

2:

second RC region

AGM:

absorbent glass mat

BMS:

battery management system

DC:

direct current

EIS:

electrochemical impedance spectroscopy

EKF:

extended kalman filter

EMS:

energy management strategy

HEV:

hybrid electric vehicle

HILS:

hardware-in-the-loop simulation

HPPC:

hybrid pulse power characterization

ICE:

internal combustion engine

ISS:

idle start stop

KF:

Kalman filter

NEDC:

new European driving cycle

OCV:

open circuit voltage

PF:

particle filter

SOC:

state of charge

SOH:

state of health

SPKF:

sigma point Kalman filter

UKF:

unscented Kalman filter

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Acknowledgement

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (NO.R0006240, “Development about Li-Ion Dual Battery Based High Efficiency Lead-Acid Battery for Micro-hybrid Vehicle”) and “3rd Generation xEV industry development project for market independence” funded by the Korea government (MOTIE) (No. 20011629)

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  1. Mechanical and System Design Engineering, Hongik University, Seoul, 04066, Korea

    Hyunjong Oh, Jeehwan Jeon & Sungjin Park

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  1. Hyunjong Oh
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Oh, H., Jeon, J. & Park, S. Effects of Battery Model on the Accuracy of Battery SOC Estimation Using Extended Kalman Filter under Practical Vehicle Conditions Including Parasitic Current Leakage and Diffusion Of Voltage. Int.J Automot. Technol. 22, 1337–1346 (2021). https://doi.org/10.1007/s12239-021-0116-1

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