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Different Approaches to Improve Energy Consumption of Battery Electric Buses
Transportation electrification is increasing and recently more focus has been directed on heavy vehicles and especially on city buses. Battery electric buses are inherently more energy efficient than diesel buses and the efficiency can be further increased by different methods. This paper evaluates the key factors that influence on the energy consumption of battery electric buses. A simulation model of a generic electric bus was developed in the Autonomie software. Simulations were carried out in different conditions including typical driving cycles and different climate conditions. Specific focus was given for minimizing the energy consumption by predictive driving. The simulation results were thoroughly analyzed in order to understand better the most efficient methods to improve energy consumption. According to the simulation results, the high level of auxiliary power (cold and hot climate conditions) increases the energy consumption significantly. There is a substantial variation of consumption between different driving cycles. This variation is mainly caused by the auxiliary power, transmission losses and mechanical braking. It was also observed that there is a strong linear correlation between driving aggressiveness and energy consumption.
- Aalto University Finland
- École Polytechnique Fédérale de Lausanne EPFL Switzerland
Resistance, Driving cycle, driving cycle, Batteries, Aerodynamics, energy consumption, driving aggressiveness, predictive driving, electric bus, model-predictive control, Urban areas, EMISSIONS, hybrid, ta213, emissions, Driving aggressiveness, Drag, Energy consumption, Predictive driving, Electric bus, MODEL-PREDICTIVE CONTROL, HYBRID, Simulation
Resistance, Driving cycle, driving cycle, Batteries, Aerodynamics, energy consumption, driving aggressiveness, predictive driving, electric bus, model-predictive control, Urban areas, EMISSIONS, hybrid, ta213, emissions, Driving aggressiveness, Drag, Energy consumption, Predictive driving, Electric bus, MODEL-PREDICTIVE CONTROL, HYBRID, Simulation
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).16 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
