Optimizing Energy and Time for Electric Vehicle Charging Routes

This article investigates energy-efficient, charging-aware route planning for battery electric vehicles (BEVs) while accounting for uncertainties in charging station availability and time-varying traffic conditions. The study focuses on integrating recharge scheduling into route planning, addressing challenges such as limited charging infrastructure, intermittent access, and temporal factors like charger waiting times based on availability, charging durations, and fluctuating traffic conditions. A time-aware charging selection algorithm is introduced to adjust schedules based on operating costs and time components, such as traffic-dependent travel times, waiting times, and charging times. The case study shows that time-adaptive recharging planning increases energy consumption by 1.6%, while reducing travel time by 12% and costs by 14% compared to the baseline, which does not account for temporal impacts. Additionally, it provides insights into the preferences and timing factors in charging station selection that contribute to these differences. The findings highlight the importance of considering time factors such as waiting and recharging times in the planning of electric vehicle (EV) operations.

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Publication Details

Journal
IEEE Transactions on Transportation Electrification
Published
2025-01-22
DOI
https://doi.org/10.1109/tte.2025.3532826
Citations
6
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
4.38
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article

Optimizing Energy and Time for Electric Vehicle Charging Routes

Qadeer Ahmed, Hamza Anwar, Joon Moon, Muhammad Qaisar Fahim
6 citations
IEEE Transactions on Transportation Electrification
Electric Vehicles and Infrastructure
4.38
article

Optimizing Energy and Time for Electric Vehicle Charging Routes

Qadeer Ahmed, Hamza Anwar, Joon Moon, Muhammad Qaisar Fahim
article en
6 citations

Abstract

This article investigates energy-efficient, charging-aware route planning for battery electric vehicles (BEVs) while accounting for uncertainties in charging station availability and time-varying traffic conditions. The study focuses on integrating recharge scheduling into route planning, addressing challenges such as limited charging infrastructure, intermittent access, and temporal factors like charger waiting times based on availability, charging durations, and fluctuating traffic conditions. A time-aware charging selection algorithm is introduced to adjust schedules based on operating costs and time components, such as traffic-dependent travel times, waiting times, and charging times. The case study shows that time-adaptive recharging planning increases energy consumption by 1.6%, while reducing travel time by 12% and costs by 14% compared to the baseline, which does not account for temporal impacts. Additionally, it provides insights into the preferences and timing factors in charging station selection that contribute to these differences. The findings highlight the importance of considering time factors such as waiting and recharging times in the planning of electric vehicle (EV) operations.

IEEE Transactions on Transportation ElectrificationVol. 11(3)
Cummins (United States) (US), The Ohio State University (US)
Affordable and clean energy
Openalex Percentile: Top 6%
Electric Vehicles and Infrastructure
4.38
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Optimizing Energy and Time for Electric Vehicle Charging Routes — Qadeer Ahmed, Hamza Anwar, et al. · IEEE Transactions on Transportation Electrification (2025) | TGRS Research Map | TGRS