Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles

Today’s market leading electric vehicles, driven on typical UK motorways, have real-world range estimation inaccuracy of up to 27%, at around 10 °C outside temperature. The inaccuracy worsens for city driving or lower outside temperature. The reliability of range estimation largely depends on the ac...

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Main Authors: Yashraj Tripathy, Andrew McGordon, Anup Barai
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/13/8/2021
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spelling doaj-e7e12b89008244a787a501a0cf59d5652020-11-25T02:28:11ZengMDPI AGEnergies1996-10732020-04-01132021202110.3390/en13082021Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric VehiclesYashraj Tripathy0Andrew McGordon1Anup Barai2Energy Innovation Centre (EIC), WMG, University of Warwick, Coventry CV4 7AL, UKEnergy Innovation Centre (EIC), WMG, University of Warwick, Coventry CV4 7AL, UKEnergy Innovation Centre (EIC), WMG, University of Warwick, Coventry CV4 7AL, UKToday’s market leading electric vehicles, driven on typical UK motorways, have real-world range estimation inaccuracy of up to 27%, at around 10 °C outside temperature. The inaccuracy worsens for city driving or lower outside temperature. The reliability of range estimation largely depends on the accuracy of the battery’s underlying state estimators, e.g., state-of-charge and state-of-energy. This is affected by accuracy of the models embedded in the battery management system. The performance of these models fundamentally depends on experimentally obtained parameterisation and validation data. These experiments are mostly performed within thermal chambers, which maintain pre-set temperatures using forced air convection. Although these setups claim to maintain isothermal test conditions, they rarely do so. In this paper, we show that this is potentially the root-cause for deterioration of range estimation at low temperatures. This is because, while such setups produce results comparable to isothermal conditions at higher temperatures (25 °C), they fail to achieve isothermal conditions at sub-zero temperatures. Employing an immersed oil-cooled experimental setup, which can create close-to isothermal conditions, we show battery state estimation can be improved by reducing error from 49.3% to 11.7% at −15 °C. These findings provide a way forward towards improving range estimation in cold weather conditions.https://www.mdpi.com/1996-1073/13/8/2021electric vehiclelow temperaturerange anxietymodel parameterisationisothermal parameterisationrange estimation
collection DOAJ
language English
format Article
sources DOAJ
author Yashraj Tripathy
Andrew McGordon
Anup Barai
spellingShingle Yashraj Tripathy
Andrew McGordon
Anup Barai
Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles
Energies
electric vehicle
low temperature
range anxiety
model parameterisation
isothermal parameterisation
range estimation
author_facet Yashraj Tripathy
Andrew McGordon
Anup Barai
author_sort Yashraj Tripathy
title Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles
title_short Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles
title_full Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles
title_fullStr Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles
title_full_unstemmed Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles
title_sort improving accessible capacity tracking at low ambient temperatures for range estimation of battery electric vehicles
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2020-04-01
description Today’s market leading electric vehicles, driven on typical UK motorways, have real-world range estimation inaccuracy of up to 27%, at around 10 °C outside temperature. The inaccuracy worsens for city driving or lower outside temperature. The reliability of range estimation largely depends on the accuracy of the battery’s underlying state estimators, e.g., state-of-charge and state-of-energy. This is affected by accuracy of the models embedded in the battery management system. The performance of these models fundamentally depends on experimentally obtained parameterisation and validation data. These experiments are mostly performed within thermal chambers, which maintain pre-set temperatures using forced air convection. Although these setups claim to maintain isothermal test conditions, they rarely do so. In this paper, we show that this is potentially the root-cause for deterioration of range estimation at low temperatures. This is because, while such setups produce results comparable to isothermal conditions at higher temperatures (25 °C), they fail to achieve isothermal conditions at sub-zero temperatures. Employing an immersed oil-cooled experimental setup, which can create close-to isothermal conditions, we show battery state estimation can be improved by reducing error from 49.3% to 11.7% at −15 °C. These findings provide a way forward towards improving range estimation in cold weather conditions.
topic electric vehicle
low temperature
range anxiety
model parameterisation
isothermal parameterisation
range estimation
url https://www.mdpi.com/1996-1073/13/8/2021
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