Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles
This paper presents an efficient method for estimating capacity-fade uncertainty in lithium-ion batteries (LIBs) in order to integrate them into the battery-management system (BMS) of electric vehicles, which requires simple and inexpensive computation for successful application. The study uses the...
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doaj-6624ad4b341f497aaed9923429b24e8d2020-11-24T22:09:16ZengMDPI AGEnergies1996-10732015-06-01865538555410.3390/en8065538en8065538Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric VehiclesJaewook Lee0Woosuk Sung1Joo-Ho Choi2School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 412-791, KoreaResearch and Development Division, Hyundai Motor Company, Hwaseong 445-706, KoreaSchool of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 412-791, KoreaThis paper presents an efficient method for estimating capacity-fade uncertainty in lithium-ion batteries (LIBs) in order to integrate them into the battery-management system (BMS) of electric vehicles, which requires simple and inexpensive computation for successful application. The study uses the pseudo-two-dimensional (P2D) electrochemical model, which simulates the battery state by solving a system of coupled nonlinear partial differential equations (PDEs). The model parameters that are responsible for electrode degradation are identified and estimated, based on battery data obtained from the charge cycles. The Bayesian approach, with parameters estimated by probability distributions, is employed to account for uncertainties arising in the model and battery data. The Markov Chain Monte Carlo (MCMC) technique is used to draw samples from the distributions. The complex computations that solve a PDE system for each sample are avoided by employing a polynomial-based metamodel. As a result, the computational cost is reduced from 5.5 h to a few seconds, enabling the integration of the method into the vehicle BMS. Using this approach, the conservative bound of capacity fade can be determined for the vehicle in service, which represents the safety margin reflecting the uncertainty.http://www.mdpi.com/1996-1073/8/6/5538lithium-ion batterycapacity fadeelectrochemical modelbattery management systemelectric vehiclesuncertainty estimationMarkov Chain Monte Carlometamodel |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jaewook Lee Woosuk Sung Joo-Ho Choi |
spellingShingle |
Jaewook Lee Woosuk Sung Joo-Ho Choi Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles Energies lithium-ion battery capacity fade electrochemical model battery management system electric vehicles uncertainty estimation Markov Chain Monte Carlo metamodel |
author_facet |
Jaewook Lee Woosuk Sung Joo-Ho Choi |
author_sort |
Jaewook Lee |
title |
Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles |
title_short |
Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles |
title_full |
Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles |
title_fullStr |
Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles |
title_full_unstemmed |
Metamodel for Efficient Estimation of Capacity-Fade Uncertainty in Li-Ion Batteries for Electric Vehicles |
title_sort |
metamodel for efficient estimation of capacity-fade uncertainty in li-ion batteries for electric vehicles |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2015-06-01 |
description |
This paper presents an efficient method for estimating capacity-fade uncertainty in lithium-ion batteries (LIBs) in order to integrate them into the battery-management system (BMS) of electric vehicles, which requires simple and inexpensive computation for successful application. The study uses the pseudo-two-dimensional (P2D) electrochemical model, which simulates the battery state by solving a system of coupled nonlinear partial differential equations (PDEs). The model parameters that are responsible for electrode degradation are identified and estimated, based on battery data obtained from the charge cycles. The Bayesian approach, with parameters estimated by probability distributions, is employed to account for uncertainties arising in the model and battery data. The Markov Chain Monte Carlo (MCMC) technique is used to draw samples from the distributions. The complex computations that solve a PDE system for each sample are avoided by employing a polynomial-based metamodel. As a result, the computational cost is reduced from 5.5 h to a few seconds, enabling the integration of the method into the vehicle BMS. Using this approach, the conservative bound of capacity fade can be determined for the vehicle in service, which represents the safety margin reflecting the uncertainty. |
topic |
lithium-ion battery capacity fade electrochemical model battery management system electric vehicles uncertainty estimation Markov Chain Monte Carlo metamodel |
url |
http://www.mdpi.com/1996-1073/8/6/5538 |
work_keys_str_mv |
AT jaewooklee metamodelforefficientestimationofcapacityfadeuncertaintyinliionbatteriesforelectricvehicles AT woosuksung metamodelforefficientestimationofcapacityfadeuncertaintyinliionbatteriesforelectricvehicles AT joohochoi metamodelforefficientestimationofcapacityfadeuncertaintyinliionbatteriesforelectricvehicles |
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