TY - JOUR
T1 - The benefit of assisted and unassisted eco-driving for electrified powertrains
AU - Yan, Xingda
AU - Allison, Craig
AU - Fleming, James
AU - Stanton, Neville
AU - Lot, Roberto
PY - 2021/6/22
Y1 - 2021/6/22
N2 - Eco-driving assistance systems that encourage drivers to engage in fuel-saving behaviour are effective at improving energy-efficiency, with recent research directed towards incorporating predictive models of energy losses in these systems to optimise recommendations. In this paper we evaluate a predictive eco-driving assistance system on three powertrains: a combustion engine-driven vehicle, a parallel hybrid electric vehicle, and a battery electric vehicle. In each case, energy consumption is found by applying a quasi-static model to driving simulator data for a simulated route including urban, rural, and highway sections. We find that both assisted and unassisted eco-driving has a beneficial effect in all cases, with the assistance system leading to reductions in energy usage of 6.1%, 15.9% and 16.6% for the combustion engine, hybrid electric, and battery electric powertrains respectively
AB - Eco-driving assistance systems that encourage drivers to engage in fuel-saving behaviour are effective at improving energy-efficiency, with recent research directed towards incorporating predictive models of energy losses in these systems to optimise recommendations. In this paper we evaluate a predictive eco-driving assistance system on three powertrains: a combustion engine-driven vehicle, a parallel hybrid electric vehicle, and a battery electric vehicle. In each case, energy consumption is found by applying a quasi-static model to driving simulator data for a simulated route including urban, rural, and highway sections. We find that both assisted and unassisted eco-driving has a beneficial effect in all cases, with the assistance system leading to reductions in energy usage of 6.1%, 15.9% and 16.6% for the combustion engine, hybrid electric, and battery electric powertrains respectively
UR - https://www.mendeley.com/catalogue/273e4ebb-dffa-3711-8f75-f68656052bce/
U2 - 10.1109/THMS.2021.3086057
DO - 10.1109/THMS.2021.3086057
M3 - Article
SN - 2168-2291
VL - 51
SP - 403
EP - 407
JO - IEEE Transactions on Human-Machine Systems
JF - IEEE Transactions on Human-Machine Systems
IS - 4
ER -