The impact of energy estimation inaccuracy on the optimality of electric vehicle routing decisions

Accurate estimation of on-road energy consumption is often assumed to be critical in Electric Vehicle Routing Problems with Time Windows (EVRPTW). Yet the actual impact of estimation inaccuracies on routing feasibility and optimality remains largely unexplored. This paper focuses on assessing and tackling this impact through realistic testing and the development of an intuitive solution methodology based on a bi-objective ɛ-constraint optimisation framework. The proposed methodology relies only on the basic consumption rate reported by the vehicle manufacturer and builds on new intuitive results that suggest that despite the general tendency towards overestimating methods to remain conservative and risk-averse, the incorporation of an underestimating method might be more beneficial in implicitly ensuring the on-road optimality of routing decisions. Our results show that ignoring instantaneous speed variations and acceleration/deceleration rates can lead to significant inaccuracies, but the impact of disregarding factors such as slope, weather, or variable rolling resistance individually and collectively is rather negligible. Regardless of the size of estimation inaccuracy, however, we find inaccuracies to have very little impact on route-level outcomes as these rarely translate into infeasibility or meaningful optimality loss. Our analyses of the Pareto-optimal solutions on the generated efficient frontier of the considered EVRPTW instances, on the other hand, indicate that while the optimal solution of an instance is highly likely to also be on-road optimal, with a marginal sacrifice in instance optimality, significant allowance for inaccuracy is achievable to suit a strictly risk-averse decision maker.