TY - JOUR
T1 - Optimizing the Journey
T2 - Dynamic Charging Strategies for Battery Electric Trucks in Long-Haul Transport
AU - Zähringer, Maximilian
AU - Teichert, Olaf
AU - Balke, Georg
AU - Schneider, Jakob
AU - Lienkamp, Markus
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/2
Y1 - 2024/2
N2 - Battery electric trucks (BETs) represent a well-suited option for decarbonizing road freight transport to achieve climate targets in the European Union. However, lower ranges than the daily distance of up to 700 km make charging stops mandatory. This paper presents an online algorithm for optimal dynamic charging strategies for long-haul BET based on a dynamic programming approach. In several case studies, we investigate the advantages optimal strategies can bring compared to driver decisions. We further show which charging infrastructure characteristics in terms of charging power, density, and charging station availability should be achieved for BETs in long-haul applications to keep the additional time required for charging stops low. In doing so, we consider the dynamic handling of occupied charging stations for the first time in the context of BET. Our findings show that, compared to driver decisions, optimal charging strategies can reduce the time loss by half compared to diesel trucks. To keep the time loss compared to a diesel truck below 30 min a day, a BET with a 500 kWh battery would need a charging point every 50 km on average, a distributed charging power between 700 and 1500 kW, and an average charger availability above 75%. The presented method and the case studies’ results’ plausibility are interpreted within a comprehensive sensitivity analysis and subsequently discussed in detail. Finally, we transformed our findings into concrete recommendations for action for the efficient rollout of BETs in long-haul applications.
AB - Battery electric trucks (BETs) represent a well-suited option for decarbonizing road freight transport to achieve climate targets in the European Union. However, lower ranges than the daily distance of up to 700 km make charging stops mandatory. This paper presents an online algorithm for optimal dynamic charging strategies for long-haul BET based on a dynamic programming approach. In several case studies, we investigate the advantages optimal strategies can bring compared to driver decisions. We further show which charging infrastructure characteristics in terms of charging power, density, and charging station availability should be achieved for BETs in long-haul applications to keep the additional time required for charging stops low. In doing so, we consider the dynamic handling of occupied charging stations for the first time in the context of BET. Our findings show that, compared to driver decisions, optimal charging strategies can reduce the time loss by half compared to diesel trucks. To keep the time loss compared to a diesel truck below 30 min a day, a BET with a 500 kWh battery would need a charging point every 50 km on average, a distributed charging power between 700 and 1500 kW, and an average charger availability above 75%. The presented method and the case studies’ results’ plausibility are interpreted within a comprehensive sensitivity analysis and subsequently discussed in detail. Finally, we transformed our findings into concrete recommendations for action for the efficient rollout of BETs in long-haul applications.
KW - charging infrastructure design
KW - charging management
KW - long-haul battery electric trucks
KW - operation strategy
KW - transportation electrification
UR - http://www.scopus.com/inward/record.url?scp=85185725551&partnerID=8YFLogxK
U2 - 10.3390/en17040973
DO - 10.3390/en17040973
M3 - Article
AN - SCOPUS:85185725551
SN - 1996-1073
VL - 17
JO - Energies
JF - Energies
IS - 4
M1 - 973
ER -
