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Applying individual eco-driving methods within a group of electric vehicles (EVs) is usually restricted by a preceding vehicle because the optimal velocity is different by type of EV. Driving with the same velocity for different types of vehicles is not a fundamental solution because of inefficiency. Overtaking a preceding vehicle allows enhancement of the driving efficiency, though it should be used carefully because energy consumption by the overtaking depends greatly on given conditions: time constraint for overtaking, the distance between intersections, vehicle type, and overtaking scheme. This paper introduces a system-level framework for vehicle overtaking velocity planning and scheduling to maximize the total driving efficiency of all vehicles. The framework starts with an accurate EV powertrain modeling of various vehicle types and derives the optimal velocity and acceleration for overtaking. Simulation results show the energy-efficient velocity planning for overtaking that reduces a driving cost function related to energy and delay. The proposed overtaking framework achieves 15% driving cost savings compared with the conventional group driving.

Traditional IoT devices operate generally with rechargeable batteries, which limit the weight, size, and cost of the device as well as the maintenance burden. To overcome these limitations, energy harvesting is a promising option for achieving the small form-factor and maintenance-free. In this paper, we introduce a novel and practical storage-less energy harvesting and power management technique performing maximum power point tracking (MPPT), and its on-chip implementation. The proposed power management unit is based on a duty-controlled method which achieves higher energy efficiency and longer operation time than the traditional energy harvesting system. The efficiency and feasibility of the proposed energy harvesting and power management techniques are verified by implementing them using off-the-shelf components first, and then on-chip fabrication with an example application as the final.

According to recent works, a coordinated delivery strategy in which terrestrial and aerial electric vehicles work together effectively improves delivery throughput and energy efficiency. However, most research on logistics and transportation focuses on delivery performance and does not care about energy efficiency, with three main limitations: 1. Most of these works ignore geographic information along the delivery route, while road slope is one of the most critical energy consumption components. 2. Vehicle and drone power consumption models are simplified as driving mileage, while the delivery time is a significant concern. 3. The battery model is simplified as a linear model even though practical batteries have non-linearity properties. This work proposes a framework to provide energy- and time-efficient delivery schedules with a hybrid delivery service with terrestrial and aerial electric vehicles. We first implement accurate electric van and drone power models and a battery model based on manufacturers' system specifications and experimental data. Then, we propose a heuristic delivery scheduling algorithm to determine the electric van and drone delivery schedule. We also introduce various cost functions to evaluate the delivery scheduling results regarding time, energy, the weighted sum of time and energy, and the economic model. The proposed framework is validated on randomly implemented delivery missions and delivery scenarios in existing cities. Results indicate that the coordinated delivery saves delivery costs up to 27.25% in terms of the economic model compared with the electric van-only delivery schedule.

The ever-faster transformation of road vehicles from traditional fuel engines to electric motors, is leading to increasingly widespread research on and development of electric vehicles and related infrastructures. In this context, this article addresses the cost aspect of batteries from the owner’s perspective. Specifically, it proposes an analysis of the optimal usage cost of batteries in order to maximize the benefit-cost ratio and battery replacement intervals. In order to analyze battery degradation, various tests were utilized for both a full-battery electric vehicle (BEV) and a plug-in hybrid electric vehicle (PHEV). The results demonstrate greater wear of the PHEV battery when the vehicle is under charge-sustaining mode, that is, when using the combustion engine, while driving with frequent starts and stops. On the other hand, the degradation costs of the BEV battery are generally close to optimal in every scenario, in which the main parameter affecting battery wear is average daily mileage.