# A Formal Approach for Efficient Navigation Management of Hybrid Electric Vehicles on Long Trips

## TL;DR

This paper introduces a formal model for optimizing route planning and recharging schedules for PHEVs on long trips, balancing fuel costs and trip duration to improve efficiency and traffic load management.

## Contribution

It presents a novel formal approach for integrated navigation and recharging management of PHEVs, addressing long-trip constraints and traffic load balancing.

## Key findings

- The model efficiently computes optimal routes with recharge points.
- It effectively balances fuel costs and trip time constraints.
- The approach scales well to large road networks.

## Abstract

Plug-in Hybrid Electric Vehicles (PHEVs) are gaining popularity due to their economic efficiency as well as their contribution to green management. PHEVs allow the driver to use electric power exclusively for driving and then switch to gasoline as needed. The more gasoline a vehicle uses, the higher cost is required for the trip. However, a PHEV cannot last for a long period on stored electricity without being recharged. Thus, it needs frequent recharging compared to traditional gasoline-powered vehicles. Moreover, the battery recharging time is usually long, which leads to longer delays on a trip. Therefore, it is necessary to provide a flexible navigation management scheme along with an efficient recharging schedule, which allows the driver to choose an optimal route based on the fuel-cost and time-to-destination constraints. In this paper, we present a formal model to solve this PHEV navigation management problem. The model is solved to provide a driver with a comprehensive routing plan including the potential recharging and refueling points that satisfy the given requirements, particularly the maximum fuel cost and the maximum trip time. In addition, we propose a price-based navigation control technique to achieve better load balance for the traffic system. Evaluation results show that the proposed formal models can be solved efficiently even with large road networks.