Are electric vehicles greener than hybrid electric vehicles in carsharing? Insights from large-scale multi-objective simulation-optimization

Yan Li, Lu Hu, Haobin Li, Ek Peng Chew, Hao Li and Juanxiu Zhu

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 198, issue C

Abstract: Hybrid electric vehicles (HEVs) are perceived as transitional products bridging the gap between fueled vehicles and electric vehicles (EVs) because people intuitively believe that EVs are more environmentally friendly than HEVs. But is this perception true in the context of carsharing services (CSSs)? This paper pioneers a general large-scale multi-objective simulation–optimization (MOSO) method to explore the values of deploying HEVs in CSSs. We firstly develop a physically logical simulation model, emulating operations of CSSs and capturing mesoscopic dynamics of shared vehicles in a link-based traffic network. This model adopts an event-driven discrete-event mechanism, enhancing efficiency while maintaining high fidelity. Subsequently, we design a simulation–optimization framework aimed at achieving Pareto optimality by jointly optimizing station capacity, fleet size, and trip pricing. The goal is twofold: to maximize operational profits and to minimize carbon emissions, thereby quantitatively analyzing the potential of shared HEVs (SHEVs). To tackle the high-dimensional MOSO problem, we introduce the multi-objective optimization into stochastic approximation field by proposing a general algorithm that incorporates the multiple gradient descent algorithm with the simultaneous perturbation stochastic approximation algorithm. Furthermore, we derive its analytical expression for bi-objective optimization problems. We theoretically prove and practically demonstrate its strong global convergence. The efficiency of this method was validated through large-scale computational experiments conducted in Chengdu, Sichuan Province, involving 66,710 decision variables. These experiments showcased the method’s superiority over existing MOSO algorithms. Several groups of sensitivity experiments focusing on vehicle types and traffic scenarios reveal some interesting findings. (1) Regardless of the increase in travel distances, SHEVs, which can be viewed as shared EVs (SEVs) without range anxiety (RA), continue to primarily rely on electricity rather than fuel for their operational mileages. This high utilization of electricity results in lower carbon emissions compared to SEVs. (2) Under any traffic condition, the dual-engine feature of SHEVs significantly reduces the number of failed pickups. (3) As travel demand increases, the state of charge for SEVs may rapidly fall below the threshold that triggers RA, whereas SHEVs maintain a more reliable power supply.

Keywords: Carsharing services; Carbon emissions; Hybrid electric vehicles; Multiple-objective simulation–optimization; Pareto optimality; MGD-SPSA (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104098

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