Optimal Eco-Driving for Electric and Conventional Refuse Trucks: a Comparative Analysis of Actual Speed, Energy Efficiency, and Travel Time.

Achieving significant reductions in energy consumption and CO₂ emissions in the transportation sector is a key challenge for sustainable mobility, particularly for vocational trucks operating under demanding driving and duty cycles. Beyond technological advancements in powertrain design, energy efficiency can be improved through optimized driving strategies. In this context, eco driving has emerged as an effective approach to reduce energy consumption by optimizing the speed profile under given operational constraints. Eco driving optimization strategies are indeed particularly well suited to predefined and repetitive driving cycles, such as those typically encountered in waste collection. This work presents a comparative analysis between electric and internal combustion engine powertrain configurations applied to refuse trucks and vehicles, highlighting the impact of intrinsic differences on optimal speed profiles, energy consumption, and travel time. Both configurations are required to follow identical routes characterized by speed limits, mandatory stops, and comfort-related constraints. To address the optimization problem, the conventional longitudinal dynamics equations are reformulated from a time speed domain to a spatial speed domain through spatial discretization. The spatial domain is subsequently discretized, enabling the formulation of an optimization problem solved using dynamic programming to determine the optimal speed profile along real-world routes. The optimization framework is based on the minimization of a cost function composed of two main terms, normalized energy consumption and travel time variation, and a shape factor introduced to balance the tradeoff between energy efficiency and travel time. The objective of the study is to compare the two powertrain configurations under identical routes and constraints, analyzing energy consumption and travel time, and to identify the optimal compromise between time and energy consumption for each case. Optimal results are compared to real-world measurements. The results provide insights into the effectiveness of eco driving strategies and underline the influence of powertrain architecture on optimal driving behavior.