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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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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DOI: https://doi.org/10.1007/s12239-021-0116-1