  This paper develops and investigates the impacts of multi-objective Nash
optimum (user equilibrium) traffic assignment on a large-scale network for
battery electric vehicles (BEVs) and internal combustion engine vehicles
(ICEVs) in a microscopic traffic simulation environment. Eco-routing is a
technique that finds the most energy efficient route. ICEV and BEV energy
consumption patterns are significantly different with regard to their
sensitivity to driving cycles. Unlike ICEVs, BEVs are more energy efficient on
low-speed arterial trips compared to highway trips. Different energy
consumption patterns require different eco-routing strategies for ICEVs and
BEVs. This study found that eco-routing could reduce energy consumption for
BEVs but also significantly increases their average travel time. The simulation
study found that multi-objective routing could reduce the energy consumption of
BEVs by 13.5, 14.2, 12.9, and 10.7 percent, as well as the fuel consumption of
ICEVs by 0.1, 4.3, 3.4, and 10.6 percent for "not congested", "slightly
congested", "moderately congested", and "highly congested" conditions,
respectively. The study also found that multi-objective user equilibrium
routing reduced the average vehicle travel time by up to 10.1% compared to the
standard user equilibrium traffic assignment for the highly congested
conditions, producing a solution closer to the system optimum traffic
assignment. The results indicate that the multi-objective eco-routing can
effectively reduce fuel/energy consumption with minimum impacts on travel times
for both BEVs and ICEVs.
