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We study the problem of eco-routing for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. We propose an algorithm which can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. In order to show the effectiveness of our method in practice, we use a HERE Maps API to apply our algorithms based on traffic data in the city of Boston with more than 110,000 links. Moreover, we validate the performance of our eco-routing algorithm using speed profiles collected from a traffic simulator (SUMO) as input to a high-fidelity energy model to calculate energy consumption costs. Our results show significant energy savings (around 12%) for PHEVs with a near real-time execution time for the algorithm., arXiv admin note: text overlap with arXiv:1810.01443

Reinforcement learning (RL) can be used to design smart driving policies in complex situations where traditional methods cannot. However, they are frequently black-box in nature, and the resulting policy may perform poorly, including in scenarios where few training cases are available. In this paper, we propose a method to use RL under two conditions: (i) RL works together with a baseline rule-based driving policy; and (ii) the RL intervenes only when the rule-based method seems to have difficulty handling and when the confidence of the RL policy is high. Our motivation is to use a not-well trained RL policy to reliably improve AV performance. The confidence of the policy is evaluated by Lindeberg-Levy Theorem using the recorded data distribution in the training process. The overall framework is named ``confidence-aware reinforcement learning'' (CARL). The condition to switch between the RL policy and the baseline policy is analyzed and presented. Driving in a two-lane roundabout scenario is used as the application case study. Simulation results show the proposed method outperforms the pure RL policy and the baseline rule-based policy.

We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures GPS trajectories of vehicles along with their time-series data of fuel, energy, speed, and auxiliary power usage. A diverse fleet consisting of 264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs drove in real-world from Nov, 2017 to Nov, 2018, where the data were collected through onboard OBD-II loggers. Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. In total, VED accumulates approximately 374,000 miles. We discuss participant privacy protection and develop a method to de-identify personally identifiable information while preserving the quality of the data. After the de-identification, we conducted case studies on the dataset to investigate the impacts of factors known to affect fuel economy and identify energy-saving opportunities that hybrid-electric vehicles and eco-driving techniques can provide. The case studies are supplemented with a number of examples to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars. We believe that VED can be an instrumental asset to the development of future automotive technologies. The dataset can be accessed at https://github.com/gsoh/VED., 11 pages, 15 figures

In this article, a new control method is proposed for the stability of Electrical Vehicles (EVs) with 2-motor wheels. This control method provides an optimized limit for the torque levels that produced by the front and rear motors, proportional to the road surface condition, to prevent the vehicle from slipping. In addition, a fuzzy logic-based braking system is proposed to improve vehicle performance during deceleration. The vehicle is described by the model with three degrees of freedom, which provide good accuracy. The tires are modeled according to the magic formula. To evaluate the effectiveness of the proposed method, simulations were performed in the ©MATLAB. The conclusions show that the proposed control method can well maintain the steadiness of the EV on dry and slippery roads when driving straight, accelerating or decelerating, and turning. This will prevent the vehicle from slipping and locking the wheels.