The allocation problem of electric car-sharing system: A data-driven approach

Car-sharing is an emerging transportation mode with increasing applications of electric vehicles (EVs). One of the important issues for one-way electric car-sharing systems (ECS) is unbalanced vehicle distributions and high relocation costs. To improve its efficiency and overall profit, this research proposes a data-driven optimization model with the consideration of demand uncertainty. Firstly, a large amount of historical order data from an ECS company are analyzed to characterize the dynamics of the vehicles and the behavioral features of the users. An important observation is that the daily demand by users, i.e., pick-ups, follows Poisson distribution; and the arrival rates vary across time exhibiting four major temporal stages. Based on this observation, this research constructs the ECS reallocation problem as a data-driven optimization model which is a combination of a probability expectation model and a linear programming problem with real-time data as input. More importantly, different from existing research, this research formulates the profit as the mathematical expectation of a discrete random variable with uncertain consumer demands. This allows for a comprehensive consideration of all possible future demands. Furthermore, driving range constraint has been considered in the proposed model as EV is the focus of this paper. A linear solution method is proposed to obtain the global optimal. At the end, the model is validated using real data from 30 ECS stations. The results indicate the daily improvement of profit could be as high as 19.05% with an average of 10.16%.     

Energy recovery based on pedal situation for regenerative braking system of electric vehicle

ABSTRACT

Energy recovery is a key technology to improve energy efficiency and extend driving range of electric vehicle. It is still a challenging issue to maximise energy recovery. We present an energy recovery mode (mode A) which recovers braking energy under all situations that accelerator pedal (AP) is lifted, brake pedal (BP) is depressed, as well as AP and BP are released completely; and propose a control strategy of regenerative braking based on driver's intention identified by a fuzzy recognition method. Other two modes: (1) recovery braking energy only the BP is depressed (mode B), (2) no energy recovery, have been studied to compare with mode A. Simulations are carried out on different adhesion conditions. Recovered energy and driving range are also obtained under FTP75 driving cycle. Road test is implemented to validate simulation results. Results show that mode A can improve energy recovery by almost 15.8% compared with mode B, and extend driving range by almost 8.81% compared with mode B and 20.39% with the mode of no energy recovery; the control strategy of regenerative braking can balance energy recovery and braking stability. The proposed energy recovery mode provides a possibility to achieve a single-pedal design of the electric vehicle.     

双转子电机驱动系统对电动汽车操纵稳定性的影响研究

对电动汽车用双转子电机驱动系统进行简化建模,利用8自由度整车模型,通过仿真高速加速工况和低速加速工况,针对前驱和后驱车型双转子电机中内、外转子转动惯量不均衡对整车操纵稳定性的影响进行了分析。提出利用交叉四驱方式减少此种转动惯量不均衡造成的不良影响。仿真分析表明,四驱车型较前驱和后驱车型侧向稳定性显著提高。     

电动轮驱动的轿车差速性能试验

电动轮驱动的汽车取消了机械式差速器后,在转向行驶、路面不平及车轮半径不等3种工况下,会出现差速问题。文章进行了实车转向行驶试验和车轮半径不等时的差速试验,验证了对电动轮电机控制按转矩模式控制而转速随动以实现自适应差速的控制策略。电动轮控制器可以实现很好的差速性能,说明采用转矩控制和转速随动的策略是解决汽车电子差速问题的前提和关键。     

积木式工艺技术在电动汽车上的应用探讨

本文通过对＂积木式工艺技术＂的研究和应用探讨,结合电动汽车生产过程中的各技术要素,将电动汽车非最优的零部件组合,经组装—分解—再组装和优化匹配,解决电动汽车生产过程中不确定性需求与确定性生产能力之间、个性化需求与标准化生产之间的矛盾。     

安装电涡流缓速器的驱动桥设计要素

本文介绍了电涡流缓速器的工作原理,分析了电涡流缓速器对驱动桥的利弊,说明了电涡流缓速器在驱动桥上的安装方法,总结了安装电涡流缓速器对驱动桥的总体要求。     

迎接船舶推进技术的变革

本文分别从市场现状和技术发展趋势等角度,分析了船舶电力推进系统相对于传统机械推进系统的巨大优势,提出了随着环保观念、电力电子技术以及高温超导技术的飞速发展,在未来十五到二十年后,吊舱式全电力推进系统将会成为船舶动力系统的主流模式,并且市场份额有望占据船舶推进系统的半壁江山.文章还分析了中国船舶重工集团公司在发展国产化船舶电力推进系统上的有利条件,提出了预研、技术引进、跨行业整合等加快产业化的建议.     

How much should gasoline be taxed when electric vehicles conquer the market? An analysis of the mismatch between efficient and existing gasoline taxes under emerging electric mobility

The taxation of gasoline is characterized by large variability across countries and recent research has analyzed existing gasoline tax levels from an economic efficiency point of view focusing on conventional internal combustion engine vehicles. Most studies find that existing fuel tax rates do not coincide with economically efficient levels. As long as policymakers do not take action to reduce the resulting efficiency gap, there will be an ongoing welfare loss to the economy. However, the composition of passenger car fleets will probably be subject to fundamental changes in the (near) future due to the emergence of electric mobility. This raises the question of whether the mismatch between current and efficient fuel taxation will persist, shrink, or even exacerbate under emerging electric mobility. This paper aims at answering this question by determining the structure and level of optimal gasoline taxes in the presence of electric vehicles (EVs). First, the optimal (nationwide) gasoline tax is analytically derived employing a general equilibrium approach. It is shown that differences in traffic related marginal external costs among fuel powered cars and EVs affect the corrective Pigouvian component of the optimal gasoline tax while a differential tax treatment influences the fiscal rational of the tax. Second, the model is applied to Germany using differentiated data on e.g. external costs and behavioral responses. Under a wide range of scenarios, the present analyses indicate a strong relationship between optimal gasoline taxes and electric mobility, calling for a downward adjustment of efficient gasoline taxes. The effect is mainly driven by financial incentives for purchasing and using EVs. Since fuel is likely to be undertaxed in many countries, the emergence of electric mobility will therefore close the gap between gasoline taxes in place and economically efficient taxes. On the other side, it will increase the efficiency gap in those countries where gasoline is overtaxed. This also has important implications for policy concerned with environmental objectives. Pushing electric mobility seriously and at the same time taxing gasoline efficiently could actually prevent sufficient CO₂ emission savings. However, at least in the case of Germany, even a downward adjusted optimal gasoline tax under electric mobility is likely to be higher than the current (non-optimal) tax.     

Comparison of energy demands of drone-based and ground-based parcel delivery services

Drones are one of the most intensively studied technologies in logistics in recent years. They combine technological features matching current trends in transport industry and society like autonomy, flexibility, and agility. Among the various concepts for using drones in logistics, parcel delivery is one of the most popular application scenarios. Companies like Amazon test drones particularly for last-mile delivery intending to achieve both reducing total cost and increasing customer satisfaction by fast deliveries. As drones are electric vehicles, they are also often claimed to be an eco-friendly mean of transportation.In this paper an energy consumption model for drones is proposed to describe the energy demand for drone deliveries depending on environmental conditions and the flight pattern. The model is used to simulate the energy demand of a stationary parcel delivery system which serves a set customers from a depot. The energy consumed by drones is compared to the energy demand of Diesel trucks and electric trucks serving the same customers from the same depot.The results indicate that switching to a solely drone-based parcel delivery system is not worthwhile from an energetic perspective in most scenarios. A stationary drone-based parcel delivery system requires more energy than a truck-based parcel delivery system particularly in urban areas where customer density is high and truck tours are comparatively short. In rather rural settings with long distances between customers, a drone-based parcel delivery system creates an energy demand comparable to a parcel delivery system with electric trucks provided environmental conditions are moderate.     

Modelling energy consumption of electric freight vehicles in urban pickup/delivery operations: analysis and estimation on a real-world dataset

Electric Freight Vehicles (EFVs) are a promising and increasingly popular alternative to conventional trucks in urban pickup/delivery operations. A key concerned research topic is to develop trip-based Tank-to-Wheel (TTW) analyses/models for EFVs energy consumption: notably, there are just a few studies in this area. Leveraging an earlier research on passenger electric vehicles, this paper aims at filling this gap by proposing a microscopic backward highly-resolved power-based EFVs energy consumption model (EFVs-ECM). The model is estimated and validated against real-world data, collected on a fleet of five EFVs in the city centre of Rome, for a total of 144 observed trips between subsequent pickup/delivery stops. Different model specifications are tested and contrasted, with promising results, in line with previous findings on electric passenger vehicles.