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Electric vehicle (EV) has been applied as the main transportation tool recently. However, EVs still require a long charging time and, thus, inevitably cause charging congestion. The traditional plug-in charging mode is limited by fixed location and peak hours. Therefore, a flexible vehicle-to-vehicle (V2V) charging mode is considered in this article. Here, parking lots (PLs) widely dispersed in cities are reused as a common place for V2V charging. EVs are divided into EVs as energy consumers and EVs as energy providers to form as vehicle-to-vehicle charging pairs (V2V-Pairs). In this article, we propose a V2V charging management scheme, which includes a distance-based V2V-Pair matching algorithm and a PL-selection scheme. As the occupation status at PLs is difficult to predict, to achieve high PL utilization and evenly PL selection, V2V charging reservation is introduced. Meanwhile, since EV drivers usually park at PLs within a limited duration, our proposed V2V charging scheme introduces the parking duration to optimize V2V charging under a temporal constraint. We simulate this V2V charging scheme under the Helsinki city scenario. The results prove that our proposed V2V charging scheme achieves great charging efficiency (minimized charging waiting time and maximized fully charging times).

Autonomous vehicles (AVs) transform traditional commuting by decreasing congestion, improving road safety, and naturally integrate better with electric controls for flexible implementation of autonomous driving technologies. Indeed, electric-powered AVs or autonomous electric vehicles (AEVs) are benefiting each other in many aspects. While autonomy brings great efficiency in driving as well as battery use, EVs require less maintenance and drastically cut fuel costs. With AVs, a pivotal concern is within the realm of long-range Autonomous Valet Parking (LAVP), such as diverse customer demands on parking (or drop-off / pick-up) for various journey planning. On the other hand, electric-powered AVs are typically with limited cruising range, and locating convenient charging services are also among the major impediments. As of yet, recent studies have started to investigate EV charging and LAVP in isolation as they rarely consider a joint optimization on user trip and energy refueling. Rather, we target in this work the integration of vehicle charging with autonomy in the sense of a systemic approach. Specifically, we propose an integrated AEV charging and LAVP management scheme, to resolve critical decision-making on convenient charging and parking management upon customer requirements during their journeys. The proposed scheme jointly considers charging reservations as well as parking duration at car parks (CPs), aiming to enable accurate predictions on future charging (and parking) states at CPs. Results show the advantage of our proposal over benchmarks, in terms of enhanced customer experiences in traveling period, as well as charging performances at both AEV and CP sides. Particularly, effective load balancing can be achieved across the network regarding the amount of charged as well as parked vehicles.

The service management based on battery heterogeneity has become an increasingly important research problem in battery swapping technology. In this paper, with the method of bipartite matching, we first theoretically analyse the offline optimization problem of battery swapping service under battery heterogeneity. Nevertheless, the information of global view used in offline optimization solution cannot be known in advance during real-time operation. To address the disadvantage, an online framework comprising several sub-procedures is proposed for heterogeneous battery implementation. Firstly, by incorporating battery swapping station (BSS) local status such as charging and waiting queue of heterogeneous batteries, a charging slot allocation mechanism is designed. Utilizing the proposed allocation method, the charging priority is determined by the proportion of heterogeneous batteries demand, so as to guarantee charging fairness. Secondly, with the help of reservation information, the proposed allocation method can further be improved by predicting the future arrival distribution of heterogeneous types of electric vehicles. Thirdly, according to the service demand prediction based on long short-term memory neural network, joint optimization of BSS-selection and charging cost can be achieved by charging power adjustment. Simulation results indicate the desirable performance of proposed scheme in balancing the demands of multi-party participators.

Compared with traditional internal combustion engine vehicles, Electric Vehicles (EVs) have the advantage of eliminating harmful gases in the environment, with great development potential in recent years. However, because the battery capacity of EVs is limited at the current stage, where to charge (to select charging station) and when/whether to charge (order the charging priority of EVs) still limit the large-scale popularity of EVs. In this paper, we develop an Urgency First Charging (UFC) charging scheduling policy, which takes the remaining parking time and charging time of EVs as the standard of charging priority. With this, the CS benefits to the shortest trip duration (summation of travelling time through CS, and charging service time at CS) is selected as optimal solution. We have conducted simulations through Helsinki’s traffic scenarios. The results have shown that our proposed CS-Selection scheme effectively improves the charging comfort (in terms of waiting time and trip time) and charging efficiency (in terms of not-fully charged service due to limited parking duration).

While previous studies focused on managing charging demand for private electric vehicles (EVs), we investigate ways of supporting the upgrade of an entire public urban electric taxi (ET) system. Concerning the coexistence of plugin charging stations (CSs) and battery swap stations (BSSs) in practice, it thus requires further efforts to design a holistic charging management especially for ETs. By jointly considering the combination of plug-in charging and battery swapping, a hybrid charging management framework is proposed in this paper. The proposed scheme is capable of guiding ETs to appropriate stations with time-varying requirements depending on how emergent the demand will be. Through the selection of battery charging/swap, the optimization goal is to reduce the trip delay of ET. Results under a Helsinki city scenario with realistic ETs and charging stations show the effectiveness of our enabling technology, in terms of minimized drivers’ trip duration, as well as charging performance gains at the ET and station sides.

Electric Vehicles (EVs) are environmental friendly comparing with traditional internal combustion vehicles (ICVs), and have great application potential to achieve green transportation. However, due to the battery technology under development, the charging time of EVs is still longer than refuelling time of ICVs. Importantly, CS-Selection scheme (which/where to charge) and charging scheduling (when/whether to charge) are key solutions, for coping with long charging time and uneven distribution of Charging Stations (CSs) in urban city. In this paper, we propose an Urgency First Charging (UFC) scheduling policy, which orders EVs via their charging urgency (calculated by their charging demand and remaining parking duration). With the underlying UFC policy, we further propose a reservation-based CS-Selection scheme that selects the optimal CS with the minimum trip duration (summation of travelling time through CS, and the charging time spent at CS), where the EVs would further report their reservations to help anticipate the service congestion status of CSs in future. We have conducted simulations through Helsinki’s city traffic scenarios. The simulation results show that our proposed CS-Selection scheme has advantages in improving users quality of experience, which shortens the overall trip duration of EVs and fully charges more EVs before departure deadline.

IAI 2019, 1st International Conference on Industrial Artificial Intelligence, Shenyang, CHINE, 22-/07/2019 - 26/07/2019; Electric Vehicles (EV) are environment-friendly with lower CO2 emissions, and financial affordability (in term of battery based refuel) benefits. Here, when and where to recharge are sensitive factors significantly impacting the environmental and financial gains, these are still challenges to be tackled. In this paper, we propose a sustainable and smart EV charging scheme enables the preemptive charging functions for heterogeneous EVs equipped with various charging capabilities and brands. Our scheme intents to address the problems when EVs are with various ownerships and priority, in related to the services agreed with charging infrastructure operators. Particularly, the anticipated EVs' charging reservations information with heterogeneity (are multiscale) including their EV type, expected arrival time and charging waiting time at the charging stations (CSs), have been considered for design, planning and optimal decision making on the selection (i.e., where to charge) among the candidature CSs. We have conducted extensive simulation studies, by taking the realistic Helsinki city geographical and traffic scenarios as an example. The numerical results have confirmed that our proposed preemptive approach is better than the First-Come-First-Serve (FCFS) based system, associated with its significant improvement on the reservation feature in EV charging.