Electric Vehicle Fleet Charging Research Articles

An Optimal Hybrid Management of Electric Vehicle Fleet Charging and Load Scheduling in Active Electric Distribution System

An Optimal Hybrid Management of Electric Vehicle Fleet Charging and Load Scheduling in Active Electric Distribution System

Optimal Siting of EV Fleet Charging Station Considering EV Mobility and Microgrid Formation for Enhanced Grid Resilience

Coordinating infrastructure planning for transportation and the power grid is essential for enhanced reliability and resilience during operation and disaster management. This paper presents a two-stage stochastic model to optimize the location of electric vehicle fleet charging stations (FEVCSs) to enhance the resilience of a distribution network. The first stage of this model deals with the decision to place an FEVCS at the most favorable and optimized location, whereas the second stage aims to minimize the weighted sum of the value of lost load in multiple potential scenarios with different faults. Indeed, the second stage is a joint grid restoration scheme with network reconfiguration and microgrid formation using available distributed generators and fleet electric vehicles. The proposed model is tested on a modified IEEE-33 node distribution network and a four-node transportation network. Case studies demonstrate the effectiveness of the proposed model.

A Bilevel Graph Reinforcement Learning Method for Electric Vehicle Fleet Charging Guidance

This letter proposes a bilevel graph reinforcement learning method for electric vehicle (EV) fleet charging guidance, achieving collaborative optimization of the transportation-electrification coupled system. A dual-agent architecture is first constructed, where the upper-level is used for charging and the lower-level is used for routing. The EV traveling and charging behavior is characterized as a graph-structured interaction process. A graph attention network (GAT) is leveraged to extract the topology correlation and feature information. Then the extracted topology embedded with knowledge, as intermediate latent environment states, is fed into the underlying network of deep reinforcement learning (DRL). A DRL-based sequential scheduling pattern is developed to realize the guidance of multiple EVs. Extensive experimental results verify the superiority and adaptability of our proposed methodology.

Combining predictive and prescriptive techniques for optimizing electric vehicle fleet charging

The last decade has witnessed a burgeoning interest in transportation electrification from the academia, government, and industry. A current barrier faced by fleet operators is the charge scheduling, a problem that becomes more pronounced with the fleet size, heterogeneity (in both the vehicle fleet and the charging infrastructure), and uncertainty, which has given rise to the Charging-as-a-Service (CaaS) industry. A CaaS provider intermediates between the fleet owner and the macrogrid, and is key to ease the transition to the future of transportation with electric vehicles. This paper addresses the CaaS providers’ electric vehicle fleet (EVF) charge scheduling problem with time-varying electricity prices. We develop a rolling-horizon online optimization approach reinforced with a predictive model and a heuristic warm-start to solve this emerging multi-stage stochastic optimization problem. A numerical experiment demonstrates that our method outperforms an industry benchmark by 13.24%–18.44% with respect to charging costs under the tested conditions. In addition to cost savings, the energy use profile of resulting schedules consumes less energy in peak hours, which can reduce carbon emissions and improve grid stability. Thus, the proposed approach identifies charging schedules that simultaneously benefit CaaS providers, fleet owners, electric power producers, and the macrogrid in general.

Stochastic Modeling and Analysis of Public Electric Vehicle Fleet Charging Station Operations

The electric vehicle (EV) fleet is gradually growing into a major part of public transportation. Proper planning and operation of EV supply equipment (EVSE) is essential to ensure the efficient and economic operations of the EV fleets. Charging stations (CS) have gained market attention due to their lower cost and versatility. Battery swapping stations (BSS) have also received considerable attention because of their promise to provide fast and sustainable battery replacements. However, their commercial viability is unclear due to their requirement for large capital and infrastructure deployment. In this paper, we develop a stochastic model for interactions between CS/BSS and taxi/bus fleets. The model is based on a realistic abstraction of users’ behavior defined by various stochastic processes. It also considers the dynamic impacts of the road congestion. Analytical revenue boundaries are derived and verified by simulations. These simulation results may prove valuable for future studies of public transit.

Smart Sustainable Freight Transport for a City Multi-Floor Manufacturing Cluster: A Framework of the Energy Efficiency Monitoring of Electric Vehicle Fleet Charging

This study focuses on the problem of the efficient energy management of an independent fleet of freight electric vehicles (EVs) providing service to a city multi-floor manufacturing cluster (CMFMC) within a metropolis while considering the requirements of smart sustainable electromobility and the limitations of the power system. The energy efficiency monitoring system is considered an information support tool for the management process. An object-oriented formalization of monitoring information technology is proposed which has a block structure and contains three categories of classes (information acquisition, calculation algorithms, and control procedures). An example of the implementation of the class “Operation with the electrical grid” of information technology is presented. The planning of the freight EVs charging under power limits of the charging station (CS) was carried out using a situational algorithm based on a Fuzzy expert system. The situational algorithm provides for monitoring the charging of a freight EV at a charging station, taking into account the charge weight index (CWI) assigned to it. The optimization of the CS electrical load is carried out from the standpoint of minimizing electricity costs and ensuring the demand for EV charging without going beyond its limits. A computer simulation of the EV charging mode and the CS load was performed. The results of modeling the electrical grid and CS load using the proposed algorithm were compared with the results of modeling using a controlled charging algorithm with electrical grid limitations and an uncontrolled charging algorithm. The proposed approach provides a reduction in power consumption during peak hours of the electrical grid and charging of connected EVs for an on-demand state of charge (SOC).

A Multiagent Federated Reinforcement Learning Approach for Plug-In Electric Vehicle Fleet Charging Coordination in a Residential Community

The increasing penetration of distributed renewable energy and electric vehicles (EV) in local microgrids/residential-community has brought a great challenge to balancing system stability and economic benefits. This paper proposes a decentralized framework based on an efficient federated deep reinforcement learning method for plug-in electric vehicle (PEV) fleet charging management in a residential community, which is equipped with a photovoltaic and battery energy storage system and connected to a local transformer. Firstly, the framework of PEV charging management is described as a virtual EV charging station coordinating charging tasks through sharing public information with distributed agents. Then, an individual preference model of PEV is developed considering heterogenous PEV charging anxiety, battery degradation, and collective penalty. Subsequently, we propose an attention-weighted federated soft-actor-critic method to efficiently seek the co-ordinational scheduling of the PEV fleet charging in a distributed way, where scalability and privacy protection can be ensured with attention-based information sharing. Finally, a real-world case study is conducted to validate the effectiveness and feasibility of the proposed approach.

Social welfare maximizing fleet charging scheduling through voting-based negotiation

As an alternative to traditional taxi services, Transportation Network Companies (TNCs) such as Uber and Lyft are playing an increasingly important role in the paradigm shifting from car ownership to mobility as a service. We consider an electric vehicle fleet charging scheduling problem in the TNC setting where taxi drivers, as freelancers, have their individual preferences regarding when and where to charge their vehicles. In this setting, obtaining social welfare maximizing schedules is particularly difficult as drivers may behave strategically in competing over shared charging resources to advance their own benefits rather than the system wide social welfare. We propose a negotiation mechanism which allows drivers to collectively evolve an incumbent schedule into a socially beneficial one through an iterative voting process. The proposed mechanism provides a platform which enables multilateral negotiation among a large number of drivers. We prove that, given the design of the proposed mechanism, drivers’ best response strategy is to truthfully vote their best valued candidate schedules according to the acceptance quota prescribed by the scheduler at each voting round. In addition, experiment results show that the mechanism achieves on average 93% efficiency compared with optimal solutions and scales well to larger problem instances.

Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

Autonomous charging of electric vehicle fleets to enhance renewable generation dispatchability

A total 19% of generation capacity in California is offered by PV units and over some months, more than 10% of this energy is curtailed. In this research, a novel approach to reduce renewable generation curtailments and increasing system flexibility by means of electric vehicles' charging coordination is represented. The presented problem is a sequential decision making process, and is solved by fitted Q-iteration algorithm which unlike other reinforcement learning methods, needs fewer episodes of learning. Three case studies are presented to validate the effectiveness of the proposed approach. These cases include aggregator load following, ramp service and utilization of non-deterministic PV generation. The results suggest that through this framework, EVs successfully learn how to adjust their charging schedule in stochastic scenarios where their trip times, as well as solar power generation are unknown beforehand.

Open Source Algorithm for Smart Charging of Electric Vehicle Fleets

Smart charging assigns charging capacities between vehicles in limited charging infrastructures. Smart charging solutions are becoming widespread with increasing numbers of commercial offerings. However, commercial solutions are intransparent regarding algorithm implementation. In contrast, open source solutions are transparent and open for collaborative development and scientific research. This article presents an open source package with a smart charging algorithm. The algorithm schedules heterogeneous fleets of vehicles for charging while ensuring a fair share for each vehicle. We present implementation aspects of the smart charging algorithm including data structures and representational state transfer application programming interfaces. Additionally, the smart charging algorithm was validated in a one-year field test. Experimental results of the field test show elcetronic vehicles (EVs) at six charging stations can be scheduled for charging when the grid connection only allows two EVs to charge concurrently. Runtime measurements demonstrate the smart charging algorithm is applicable in real-time and scales to large fleet sizes.

The Role of Charging Infrastructure in Electric Vehicle Implementation within Smart Grids

In the integration of electric vehicle (EV) fleets into the smart grid context, charging infrastructure serves as the interlinkage between EV fleets and the power grid and, as such, affects the impacts of EV operation on the smart grid. In this study, the impacts of charging infrastructure on the effectiveness of different EV operational modes were simulated using a multi-component modelling approach, which accounts for both stochastic EV fleet charging behaviors as well as optimal energy vector dispatch operation. Moreover, a campus microgrid case study was presented to demonstrate the various design factors and impacts of charging infrastructure implementation affecting EV fleet adoption and operation. Based on results from the study, it was shown that charging infrastructure should be adopted in excess of the minimum required to satisfy EV charging for driving needs. In addressing uncontrolled charging behaviors, additional charging infrastructure improves EV owner convenience and reduces queuing duration. Meanwhile, controlled charging strategies benefit from increased resilience against uncertain charging behavior and operate more optimally in systems subject to time-of-use (TOU) electricity pricing. Lastly, it was demonstrated that successful vehicle-to-grid (V2G) implementation requires charging infrastructure to emulate the availability and fast response characteristics of stationary energy storage systems, which translates to excess charging port availability, long EV plug-in durations, and bi-directional power flow capabilities well beyond the level 2 charging standard.

An Adaptive Learning-Based Approach for Nearly Optimal Dynamic Charging of Electric Vehicle Fleets

Managing grid-connected charging stations for fleets of electric vehicles leads to an optimal control problem where user preferences must be met with minimum energy costs (e.g., by exploiting lower electricity prices through the day, renewable energy production, and stored energy of parked vehicles). Instead of state-of-the-art charging scheduling based on open-loop strategies that explicitly depend on initial operating conditions, this paper proposes an approximate dynamic programming feedback-based optimization method with continuous state space and action space, where the feedback action guarantees uniformity with respect to initial operating conditions, while price variations in the electricity and available solar energy are handled automatically in the optimization. The resulting control action is a multi-modal feedback, which is shown to handle a wide range of operating regimes, via a set of controllers whose action that can be activated or deactivated depending on availability of solar energy and pricing model. Extensive simulations via a charging test case demonstrate the effectiveness of the approach.

A Distributed Control Algorithm for Optimal Charging of Electric Vehicle Fleets with Congestion Management

This paper proposes a novel distributed control strategy for the optimal charging of a fleet of Electric Vehicles (EVs) in case of limited overall capacity of the electrical distribution network. The optimal charging is obtained as the solution of a scheduling problem aiming at a cost-optimal profile of the aggregated energy demand. The resulting optimization problem is formulated as a quadratic programming problem with a coupling of decision variables both in the objective function and in the constraint. We assume a minimal information structure, where users locally communicate only with their neighbors, without relying on a central decision maker. The solution approach relies on an iterative distributed algorithm based on duality, proximity, and consensus theory. A simulated case study demonstrates that the approach allows achieving the global optimum.

Charging Load Aggregation Potential of Multiple Electric Vehicles Based on Road Traffic Census Data

SUMMARYHigh penetration of renewable generation systems will lead to supply–demand imbalances, and will also require output curtailment of renewable power plants. We focused on management of electric vehicle fleet charging which can promote charging during specific time zone for reducing this curtailment. In this study, we evaluated the Charging Promoting Potential (CPP) of passenger electric vehicles (EVs) using optimization model based on the mixed‐integer linear programming. In addition, we also evaluated the CPP of other EVs, such as electric buses and electric trucks. We found that wider zone of the charging promoting time led effective concentration of charging load. Furthermore, we found that the CPP of the electric buses is higher than those of the passenger EVs and electric trucks.

道路交通センサスデータを用いた電気自動車の充電促進ポテンシャルの定量的評価

High penetration of renewable generation systems will lead to supply-demand imbalances, and will also require output curtailment of renewable power plants. We focused on management of electric vehicle fleet charging which can promote charging during specific time zone for reducing this curtailment. In this study, we evaluated the Charging Promoting Potential (CPP) of passenger EVs using optimization model based on the mixed integer linear programming. In addition, we also evaluated the CPP of other EVs, such as electric buses and electric trucks. We found that wider zone of the charging promoting time led effective concentration of charging load. Further, we found that the CPP of the electric buses is higher than those of the passenger EVs and electric trucks.

A bi-level optimisation framework for electric vehicle fleet charging management

The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) fleet charging based on a realistic EV fleet model including a transport demand sub-model. The EV fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery vehicle fleet. The EV fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State-of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent-based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery vehicle fleet transport-energy system.

Dynamic programming-based optimisation of charging an electric vehicle fleet system represented by an aggregate battery model

This paper proposes a DP(dynamic programming)-based optimisation method of charging an EV (electric vehicle) fleet modelled as a single, so-called aggregate battery. The main advantage of the approach is that it provides a globally optimal solution, with a relatively non-excessive computational load owing to a low order of the aggregate battery model. The method is illustrated through a case study of an isolated, hypothetically electrified delivery truck transport system charged from both grid and RES (renewable energy sources). Two scenarios of energy production from RES (with and without excess in RES production), along with several electricity price models are studied. The DP optimisation results are compared with the results obtained by an existing heuristic charging algorithm used in EnergyPLAN software to illustrate the DP algorithm advantages in minimising the charging energy cost and satisfying the aggregate battery charge sustaining conditions. The proposed DP optimisation method can be used in various energy planning studies, as well as a core of the supervisory/aggregator level of hierarchical EV fleet charging strategies.

Using Price-Based Signals to Control Plug-in Electric Vehicle Fleet Charging

We study decentralized plug-in electric vehicle (PEV) charging control, wherein the system operator (SO) sends price-based signals to a load aggregator (LA) that optimizes charging of a PEV fleet. We study a pricing scheme that conveys price and quantity information to the LA and compare it to a simpler price-only scheme. We prove that the price/quantity-based mechanism can yield a socially optimal solution. We also examine several numerical case studies to demonstrate the superior performance of the price/quantity-based scheme. The price/quantity scheme yields nearly identical PEV charging costs compared to the social optima, whereas the price-only scheme is highly sensitive to the choice of a regularization penalty term that is needed to ensure convergence. We also show that the time to compute an equilibrium with the price-only mechanism can be up to two orders of magnitude greater than with the price/quantity scheme and can involve 24 times more information exchange between the SO and LA.