Charging Autonomous Electric Vehicle Fleet for Mobility-on-Demand Services: Plug in or Swap out?

This paper compares two prevalent charging strategies for electric vehicles, plug-in charging and battery swapping, to investigate which charging strategy is superior for electric autonomous mobility-on-demand (AMoD) systems. To this end, we use a queueing-theoretic model to characterize the vehicle waiting time at charging stations and battery swapping stations, respectively. The model is integrated into an economic analysis of the electric AMoD system operated by a transportation network company (TNC), where the incentives of passengers, the charging/operating shift of TNC vehicles, the operational decisions of the platform, and the planning decisions of the government are captured. Overall, a bi-level optimization framework is proposed for charging infrastructure planning of the electric AMoD system. Based on the proposed framework, we compare the socio-economic performance of plug-in charging and battery swapping, and investigate how this comparison depends on the evolving charging technologies (such as charging speed, battery capacity, and infrastructure cost). At the planning level, we find that when choosing plug-in charging, increased charging speed leads to a transformation of infrastructure from sparsely distributed large stations to densely distributed small stations, while enlarged battery capacity transforms the infrastructure from densely distributed small stations to sparsely distributed large stations. On the other hand, when choosing battery swapping, both increased charging speed and enlarged battery capacity will lead to a smaller number of battery swapping stations. At the operational level, we find that improved charging speed leads to increased TNC profit when choosing plug-in charging, whereas improved charging speed may lead to smaller TNC profit under battery swapping. The above insights are validated through realistic numerical studies

Joint location and inventory models and algorithms for deployment of hybrid electric vehicle charging stations.

This thesis describes a study of a novel concept of hybrid electric vehicle charging stations in which two types of services are offered: battery swapping and fast level-3 DC charging. The battery swapping and fast-charging service are modeled by using the M/G/s/s model and the M/G/s/$\infty$ model, respectively. In particular, we focus on the operations of joint battery swapping and fast charging services, develop four joint locations and inventory models: two for the deployment of battery swapping service, two for the deployment of hybrid electric vehicle charging service. The first model for each deployment system considers a service-level constraint for battery swapping and hybrid charging service, whereas the second for each deployment system considers total sojourn time in stations. The objective of all four models is to minimize total facility setup cost plus battery and supercharger purchasing cost. The service level, which is calculated by the Erlang loss function, depends on the stockout probability for batteries with enough state of charge (SOC) for the battery swapping service and the risk of running out of superchargers for the quick charging service. The total sojourn time is defined as the sum of the service time and the waiting time in the station. Metaheuristic algorithms using a Tabu search are developed to tackle the proposed nonlinear mixed-integer optimization model. Computational results on randomly generated instances and on a real-world case comprised of 714,000 households show the efficacy of proposed models and algorithms

Evaluating battery charging and swapping strategies in a robotic mobile fulfilment system

Robotic mobile fulfillment systems (RMFS) have seen many implementations in recent years, due to their high flexibility and low operational cost. Such a system stores goods in movable shelves and uses movable robots to transport the shelves. The robot is battery powered and the battery depletes during operations, which can seriously affect the performance of the system. This study focuses on battery management problem in an RMFS, considering a battery swapping and a battery charging strategy with plug-in or inductive charging. We build a semi-open queueing network (SOQN) to estimate system performance, modeling the battery charging process as a single queue and the battery swapping process as a nested SOQN. We develop a decomposition method to solve the analytical models and validate them through simulation. Our models can be used to optimize battery recovery strategies and compare their cost and throughput time performance. The results show that throughput time performance can be significantly affected by the battery recovery policy, that inductive charging performs best, and that battery swapping outperforms plug-in charging by as large as 4.88%, in terms of retrieval transaction throughput time. However, the annual cost of the RMFS using the battery swapping strategy is generally higher than that of the RMFS using the plug-in charging strategy. In the RMFS that uses the inductive charging strategy, a critical price of a robot can be found, for a lower robot price and a small required retrieval transaction throughput time, inductive charging outperforms both plug-in charging and battery swapping strategies in terms of annual cost. We also find that ignoring the battery recovery will underestimate the number of robots required and the system cost for more than 15%

Scheduling of EV Battery Swapping, I: Centralized Solution

We formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best battery station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of EVs’ travel distance and electricity generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints, and ac power flow equations. To deal with the nonconvexity of power flow equations and the binary nature of station assignments, we propose a solution based on second-order cone programming (SOCP) relaxation of optimal power flow and generalized Benders decomposition. When the SOCP relaxation is exact, this approach computes a global optimum. We evaluate the performance of the proposed algorithm through simulations. The algorithm requires global information and is suitable for cases where the distribution grid, battery stations, and EVs are managed centrally by the same operator. In Part II of this paper, we develop distributed solutions for cases where they are operated by different organizations that do not share private information

Smart charging strategies for electric vehicle charging stations

Although the concept of transportation electrification holds enormous prospects in addressing the global environmental pollution problem, consumer concerns over the limited availability of charging stations and long charging/waiting times are major contributors to the slow uptake of plug-in electric vehicles (PEVs) in many countries. To address the consumer concerns, many countries have undertaken projects to deploy a network of both fast and slow charging stations, commonly known as electric vehicle charging networks. While a large electric vehicle charging network will certainly be helpful in addressing PEV owners\u27 concerns, the full potential of this network cannot be realised without the implementation of smart charging strategies. For example, the charging load distribution in an EV charging network would be expected to be skewed towards stations located in hotspot areas, instigating longer queues and waiting times in these areas, particularly during afternoon peak traffic hours. This can also lead to a major challenge for the utilities in the form of an extended PEV charging load period, which could overlap with residential evening peak load hours, increasing peak demand and causing serious issues including network instability and power outages. This thesis presents a smart charging strategy for EV charging networks. The proposed smart charging strategy finds the optimum charging station for a PEV owner to ensure minimum charging time, travel time and charging cost. The problem is modelled as a multi-objective optimisation problem. A metaheuristic solution in the form of ant colony optimisation (ACO) is applied to solve the problem. Considering the influence of pricing on PEV owners\u27 behaviour, the smart charging strategy is then extended to address the charging load imbalance problem in the EV network. A coordinated dynamic pricing model is presented to reduce the load imbalance, which contributes to a reduction in overlaps between residential and charging loads. A constraint optimization problem is formulated and a heuristic solution is introduced to minimize the overlap between the PEV and residential peak load periods. In the last part of this thesis, a smart management strategy for portable charging stations (PCSs) is introduced. It is shown that when smartly managed, PCSs can play an important role in the reduction of waiting times in an EV charging network. A new strategy is proposed for dispatching/allocating PCSs during various hours of the day to reduce waiting times at public charging stations. This also helps to decrease the overlap between the total PEV demand and peak residential load

A Stochastic Resource-Sharing Network for Electric Vehicle Charging

We consider a distribution grid used to charge electric vehicles such that voltage drops stay bounded. We model this as a class of resource-sharing networks, known as bandwidth-sharing networks in the communication network literature. We focus on resource-sharing networks that are driven by a class of greedy control rules that can be implemented in a decentralized fashion. For a large number of such control rules, we can characterize the performance of the system by a fluid approximation. This leads to a set of dynamic equations that take into account the stochastic behavior of EVs. We show that the invariant point of these equations is unique and can be computed by solving a specific ACOPF problem, which admits an exact convex relaxation. We illustrate our findings with a case study using the SCE 47-bus network and several special cases that allow for explicit computations.Comment: 13 pages, 8 figure

Online Station Assignment for Electric Vehicle Battery Swapping

This paper investigates the online station assignment for (commercial) electric vehicles (EVs) that request battery swapping from a central operator, i.e., in the absence of future information a battery swapping service station has to be assigned instantly to each EV upon its request. Based on EVs' locations, the availability of fully-charged batteries at service stations in the system, as well as traffic conditions, the assignment aims to minimize cost to EVs and congestion at service stations. Inspired by a polynomial-time offline solution via a bipartite matching approach, we develop an efficient and implementable online station assignment algorithm that provably achieves the tight (optimal) competitive ratio under mild conditions. Monte Carlo experiments on a real transportation network by Baidu Maps show that our algorithm performs reasonably well on realistic inputs, even with a certain amount of estimation error in parameters