most citedOptimal Thermal Management and Charging of Battery Electric Vehicles over Long Trips

4 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

eess.SY2022

Computationally Efficient Approach for Preheating of Battery Electric Vehicles before Fast Charging in Cold Climates

Ahad Hamednia, Jimmy Forsman, Nikolce Murgovski +2

This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate. To prevent the battery from performance degradation…

eess.SY2022

Conflict-free Charging and Real-time Control for an Electric Bus Network

Rémi Lacombe, Nikolce Murgovski, Sébastien Gros +1

The rapid adoption of electric buses by transit agencies around the world is leading to new challenges in the planning and operation of bus networks. In particular, the limited dri…

eess.SY20224 cited

Optimal Thermal Management and Charging of Battery Electric Vehicles over Long Trips

Ahad Hamednia, Victor Hanson, Jiaming Zhao +5

This paper studies optimal thermal management and charging of a battery electric vehicle driving over long distance trips. The focus is on the potential benefits of including a hea…

eess.SY2022

Optimal Thermal Management, Charging, and Eco-driving of Battery Electric Vehicles

Ahad Hamednia, Nikolce Murgovski, Jonas Fredriksson +3

This paper addresses optimal battery thermal management (BTM), charging, and eco-driving of a battery electric vehicle (BEV) with the goal of improving its grid-to-meter energy eff…

eess.SY2020

Design and comparative analyses of optimal feedback controllers for hybrid electric vehicles

Maryam Razi, Nikolce Murgovski, Tomas McKelvey +1

This paper presents an adaptive equivalent consumption minimization strategy (ECMS) and a linear quadratic tracking (LQT) method for optimal power-split control of combustion engin…

eess.SY2020

Computationally efficient algorithm for eco-driving over long look-ahead horizons

Ahad Hamednia, Nalin Kumar Sharma, Nikolce Murgovski +1

This paper presents a computationally efficient algorithm for eco-driving over long prediction horizons. The eco-driving problem is formulated as a bi-level program, where the bott…