Intelligent emission-sensitive routing for plugin hybrid electric vehicles

Area of Science:

• Environmental Science
• Transportation Engineering
• Computer Science

Background:

• The transportation sector significantly contributes to climate change, necessitating emission reduction strategies.
• Electric vehicles (EVs) are crucial, with plug-in hybrid electric vehicles (PHEVs) offering extended range via combustion engines.
• PHEV hybridization results in higher emissions than all-electric vehicles, highlighting the need for optimized routing.

Purpose of the Study:

• To develop an algorithm for minimizing emissions in plug-in hybrid electric vehicle (PHEV) routing.
• To address unique challenges in PHEV routing, including detours for recharging, terrain-dependent emissions, and regenerative braking.

Main Methods:

• Proposes a Green Navigation Algorithm (GNA) to determine optimal routes and charging strategies for PHEVs.
• Discretizes the state of charge (SOC) to apply the principle of optimality.
• Utilizes dynamic programming to solve the PHEV routing problem.

Main Results:

• GNA achieves over 10% energy savings and 10% emission reductions compared to standard shortest path algorithms.
• PHEV routing behavior varies with battery capacity: 10-15 kWh batteries show more detours, while >30 kWh batteries exhibit minimal detours.
• The algorithm effectively manages complex routing scenarios involving recharging and energy harvesting.

Conclusions:

• GNA provides an effective solution for optimizing PHEV routes to minimize environmental impact.
• The study offers insights into PHEV design and usage based on observed routing behaviors related to battery capacity.
• Optimized routing is essential for maximizing the environmental benefits of PHEVs.