On-Line Electric Vehicle Research Articles

Harmonics mitigation in distribution networks comprising smart online electric vehicles chargers based on equal sharing algorithm

This study focuses on analyzing the distribution network's performance due to the scheduled integration of electric vehicles (EVs). In this context, an online equal sharing scheduled algorithm is utilized to monitor and manage the charging process of EVs inside the charging stations (CSs). By this way, the planned charging demand may aid in minimizing the power stresses at the point of common coupling (PCC). Afterwards, the penetration of CSs into practical distribution networks is investigated and resolved from power quality perspectives. Meanwhile, voltage and current harmonic distortions of 3.3 % and 8.3 %, respectively resulted at the PCC due to installing these harmonic injection sources. It is worth mentioning that diverse topologies of passive filters are interrogated in this research for harmonics mitigation purposes. Single tuned passive filters demonstrate their superiority over other architectures in harmonics suppression with the simplest and most cost-effective solution. Additionally, the overloading stresses over the installed package substation of about 7 % are also alleviated permitting the CS to charge with full capacity. Finally, this work presents a forward step in the fields of EVs smart charging and harmonics mitigation in practical distribution gids.

A Novel Long Short-Term Memory Approach for Online State-of-Health Identification in Lithium-Ion Battery Cells

Revolutionary and cost-effective state estimation techniques are crucial for advancing lithium-ion battery technology, especially in mobile applications. Accurate prediction of battery state-of-health (SoH) enhances state-of-charge estimation while providing valuable insights into performance, second-life utility, and safety. While recent machine learning developments show promise in SoH estimation, this paper addresses two challenges. First, many existing approaches depend on predefined charge/discharge cycles with constant current/constant voltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery’s lifespan in order to formulate predictions within the time series. Our novel hybrid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing current pulses filtered from authentic drive cycles. Our innovative solution employs a Long Short-Term Memory-based neural network for SoH prediction based on residual capacity, making it well suited for online electric vehicle applications. By overcoming these challenges, our hybrid approach emerges as a reliable alternative for precise SoH estimation in electric vehicle batteries, marking a significant advancement in machine learning-based SoH estimation.

How do changing institutional logics affect innovation? The cases of Online Electric Vehicle and Fuel Cell Electric Vehicle in Korea

ABSTRACT The coevolution of technological capabilities and institutional arrangements is critical for the success of innovations. Yet, it remains unclear how actors’ shifting relations during institutional change affect innovation. The paper examines the dynamics in how actors shape the institution by integrating the institutional logics perspective with policy mix literature. Insights are drawn from the Online Electric Vehicle and Fuel Cell Electric Vehicle cases in Korea, where involved stakeholders revealed contrasting identities and practices throughout the innovation stages. While both cases witnessed shifts in institutional logics, the repercussions on innovation differed depending on the characteristics of actors’ identity mix. Findings contribute to institutional theory by expanding the traditional dyadic perspective on incumbent and emerging institutional logics. We also add to the policy mix literature by introducing a neo-institutional perspective. The paper concludes by underlining how the overarching characteristics of the institutional mix trigger different coordination failures.

A model for the optimization of battery capacity and power transmitters of on-line electric vehicles for closed/open environments

A model for the optimization of battery capacity and power transmitters of on-line electric vehicles for closed/open environments

Repercussions of innovation actors’ pursuit of institutional transition: evidence from emerging innovations in Korea

ABSTRACT This research investigates why late-industrialised economies with sufficient technology capability face challenges in transitioning into industrial leaders in frontier technology sectors by examining the role of innovation actors in creating new institutions. Specifically, we investigate how industrial actors’ works to change the institution affect the performance of innovations. Data collected from the cases of Online Electric Vehicle, Fuel Cell Electric Vehicle and Energy Storage System in Korea are analysed through the partial least squares structural equation modelling technique. The results suggest that while the institutional agency is constrained by control-based institutional works, it holds a significant negative effect on innovation performance. This provides an alternative perspective to literature that has emphasised the need for institutional experimentations to take place for innovations to succeed. The paper contributes to transition and institution studies by discussing possible repercussions of actors’ works to change the institution during the development and deployment of frontier technologies.

Energy Logistics Cost Study for Wireless Charging Transportation Networks

Many cities around the world encourage the transition to battery-powered vehicles to minimize the carbon footprint of the transportation sector. Deploying large-scale wireless charging infrastructures to charge electric transit buses when loading and unloading passengers have become an effective way to reduce emissions. The standard plug-in electric vehicles have a limited amount of power stored in the battery, resulting in frequent stops to refill the energy. Optimal siting of wireless charging bus stops is essential to reducing these inconveniences and enhancing the sustainability performance of a wireless charging bus fleet. Wireless charging is an innovation of transmitting power through electromagnetic induction to portable electrical devices for energy renewal. Online Electric Vehicle (OLEV) is a new technology that allows the vehicle to be charged while it is in motion, thus removing the need to stop at a charging station. Developed by the Korea Advanced Institute of Science and Technology (KAIST), OLEV picks up electricity from power transmitters buried underground. This paper aims to investigate the cost of the energy logistics for the three types of wireless charging networks: stationary wireless charging (SWC), quasi-dynamic wireless charging (QWC), and dynamic wireless charging (DWC), deployed at stops and size of battery capacity for electric buses, using OLEV technology for a bus service transit in the borough of Manhattan (MN) in New York City (NYC).

Systematic Design of Wireless Charging Transportation Network

Many cities around the world encourage the transition to battery-powered vehicles to minimize the emissions of greenhouse gases. The standard plug-in electric vehicles have a limited amount of power stored in the battery resulting in frequent stops to refill the power. Wireless charging is an innovation of transmitting power through electromagnetic induction to portable electrical devices for energy renewal. Online Electric Vehicle (OLEV) is a new technology that allows the vehicle to be charged while it is in motion, thus removing the need to stop at a charging station. Developed by the Korea Advanced Institute of Science and Technology (KAIST), OLEV picks up electricity from power transmitters buried underground. This paper investigating bus routes to determine the optimum study area for planning out the costs of deploying a pilot service network by comparing the cost of initial investment for the three types of wireless charging: Stationary Wireless Charging (SWC), Quasi-Dynamic Wireless Charging (QWC), and Dynamic Wireless Charging (DWC), using OLEV technology for a bus service transit in the borough of Manhattan (MN) in New York City (NYC).

Online electric vehicle charging with discrete charging rates

Abstract Due to the increasing penetration of electric vehicles (EVs) in the distribution grid, coordinated control of their charging is required to maintain a proper grid operation. Many EV charging strategies assume that the EV can charge at any rate up to a maximum value. Furthermore, many strategies use detailed predictions of uncertain data such as uncontrollable loads as input. However, in practice, charging can often be done only at a few discrete charging rates and obtaining detailed predictions of the uncertain data is difficult. Therefore, this paper presents an online EV scheduling approach based on discrete charging rates that does not require detailed predictions of this uncertain data. Instead, the approach requires only a prediction of a single value that characterizes an optimal offline EV schedule. Simulation results show that this approach is robust against prediction errors in this characterizing value and that this value can be easily predicted. Moreover, the results indicate that incorporating practical limitations such as discrete charging rates and uncertainty in uncontrollable loads can be done in an efficient and effective way.

Enabling the Wireless Charging via Bus Network: Route Scheduling for Electric Vehicles

The development of Electric Vehicle (EV) helps to ease energy crises and deduce vehicle exhaust emissions. However, it also brings a great impact on both transportation networks and power grids. There are some serious impediments in terms of energy charging to the popularization of EV, such as high deployment cost of charging stations, low charging efficiency, and voltage deviation of power grid. To address these issues, we design a new EV charging system, which levers the bus network in urban areas through the integration of OnLine Electric Vehicle (OLEV) system and Microwave Power Transfer (MPT) system. We formulate the EV route scheduling problem based on this new charging system to maximize the total residual energy subject to all EVs can arrive to their destinations before deadlines. Then, we propose an approximation algorithm, RSA , to solve the route scheduling problem. To relieve the traffic congestion, we further formulate the conflict-free EV route scheduling problem, and use the matching based algorithm, FRSA , to find the EV route schedules with the maximal residual energy. Through the extensive simulations, we demonstrate that RSA and FRSA can increase the average residual energy by 67.66% and 50.36% compared with the solution without the designed wireless charging system, respectively. Moreover, RSA reduces 22.22% of travel time and outputs 77.23% of residual energy, and FRSA can obtain 83.51% residual energy with 3.62% of extra travel time of the corresponding optimal solutions on average, respectively.

Integrated transit route network design and infrastructure planning for on-line electric vehicles

The emergence of electromobility along with recent developments in wireless power transfer (WPT) technology offer potentials to improve the carbon footprint of bus transport, while offering quality services. Indeed, the deployment of fast charging stations and dynamic charging roadway segments (lanes) can ensure fast energy transmission to electricity-powered buses, mitigating existing energy-related concerns and limitations. Existing models for public transport network design cannot adequately capture the dependence between electric vehicle charging infrastructure requirements and route operational characteristics. In this context, this paper investigates the combined Transit Route Network Design and Charging Infrastructure Location Problem and proposes a bi-level formulation to handle both planning stages. At the upper level, candidate route sets are generated and evaluated, while at the lower-level wireless charging infrastructures are optimally deployed. A multi-objective Particle Swarm Optimization (MO-PSO) algorithm embedded with an integer programming solver is employed to handle the complexity of the problem and the conflicting design objectives related to passengers and operators. The resulting model is applied to an established benchmark network to assess the tradeoffs arising between user-oriented and operator-oriented solutions and highlight the complex decision process associated with the deployment of electric public transport networks.

Offline and Online Electric Vehicle Charging Scheduling With V2V Energy Transfer

We propose offline and online scheduling algorithms for the charging of electric vehicles (EVs) in a single charging station (CS). The station has available cheaper, but limited, energy from renewable energy sources (RES). The EVs are capable of and willing to participate in vehicle-to-vehicle (V2V) energy transfers that are used to reduce the charging cost and increase the RES utilization. The algorithms are centralized and aim to minimize the total charging cost for the EVs. We formulate the problem as a mixed integer programming (MIP) one and we solve it optimally assuming full knowledge of the EV demand and energy generation. Later, we propose an online algorithm that iteratively calls the offline one and copes with unknown future interruptions by arriving the EVs and with the inability to predict accurately RES production. In addition, a novel technique called virtual demand is developed that increases the demand of already existing EVs, in order to store renewable energy and later transfer it via V2V to EVs that will arrive at the CS in the future. This technique is used for mitigating the inefficiency due to the uncertainty about future actions that real-time scheduling entails. In a setting with up to 150 EVs and using real data regarding the RES production, our algorithms are shown to have low execution times, while the use of virtual demand increases RES utilization by 12% and reduces cost by 3.3%.

Optimal Design of Wireless Charging Electric Bus System Based on Reinforcement Learning

The design of conventional electric vehicles (EVs) is affected by numerous limitations, such as a short travel distance and long charging time. As one of the first wireless charging systems, the Online Electric Vehicle (OLEV) was developed to overcome the limitations of the current generation of EVs. Using wireless charging, an electric vehicle can be charged by power cables embedded in the road. In this paper, a model and algorithm for the optimal design of a wireless charging electric bus system is proposed. The model is built using a Markov decision process and is used to verify the optimal number of power cables, as well as optimal pickup capacity and battery capacity. Using reinforcement learning, the optimization problem of a wireless charging electric bus system in a diverse traffic environment is then solved. The numerical results show that the proposed algorithm maximizes average reward and minimizes total cost. We show the effectiveness of the proposed algorithm compared with obtaining the exact solution via mixed integer programming (MIP).

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.

State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks

This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( $\text{LiFePO}_4$ ) and lithium titanate ( $\text{LTO}$ ) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).

PBF: A New Privacy-Aware Billing Framework for Online Electric Vehicles with Bidirectional Auditability

Recently an online electric vehicle (OLEV) concept has been introduced, where vehicles are propelled by the wirelessly transmitted electrical power from the infrastructure installed under the road while moving. The absence of secure-and-fair billing is one of the main hurdles to widely adopt this promising technology. This paper introduces a new secure and privacy-aware fair billing framework for OLEV on the move through the charging plates installed under the road. We first propose two extreme lightweight mutual authentication mechanisms, a direct authentication and a hash chain-based authentication between vehicles and the charging plates that can be used for different vehicular speeds on the road. Second, we propose a secure and privacy-aware wireless power transfer on move for the vehicles with bidirectional auditability guarantee by leveraging game theoretic approach. Each charging plate transfers a fixed amount of energy to the vehicle and bills the vehicle in a privacy-aware way accordingly. Our protocol guarantees secure, privacy-aware, and fair billing mechanism for the OLEVs while receiving electric power from the infrastructure installed under the road. Moreover, our proposed framework can play a vital role in eliminating the security and privacy challenges in the deployment of power transfer technology to the OLEVs.

Privacy-Preserving Billing Scheme against Free-Riders for Wireless Charging Electric Vehicles

Recently, scientists in South Korea developed on-line electric vehicle (OLEV), which is a kind of electric vehicle that can be charged wirelessly while it is moving on the road. The battery in the vehicle can absorb electric energy from the power transmitters buried under the road without any contact with them. Several billing schemes have been presented to offer privacy-preserving billing for OLEV owners. However, they did not consider the existence of free-riders. When some vehicles are being charged after showing the tokens, vehicles that are running ahead or behind can switch on their systems and drive closely for a free charging. We describe a billing scheme against free-riders by using several cryptographic tools. Each vehicle should authenticate with a compensation-prepaid token before it can drive on the wireless-charging-enabled road. The service provider can obtain compensation if it can prove that certain vehicle is a free-rider. Our scheme is privacy-preserving so the charging will not disclose the locations and routine routes of each vehicle. In fact, our scheme is a fast authentication scheme that anonymously authenticates each user on accessing a sequence of services. Thus, it can be applied to sequential data delivering services in future 5G systems.

Modern Advances in Wireless Power Transfer Systems for Roadway Powered Electric Vehicles

Wireless power transfer system (WPTS)-based wireless electric vehicles, classified into roadway-powered electric vehicles (RPEVs) and stationary charging electric vehicles (SCEVs), are in the spotlight as future mainstream transportations. RPEVs are free from serious battery problems such as large, heavy, and expensive battery packs and long charging time because they get power directly from the road while moving. The power transfer capacity, efficiency, lateral tolerance, electromagnetic field, air-gap, size, weight, and cost of the WPTSs have been improved by virtues of innovative semiconductor switches, better coil designs, roadway construction techniques, and higher operating frequency. Recent advances in WPTSs for RPEVs are summarized in this review paper. The fifth- and sixth-generation online electric vehicles, which reduce infrastructure cost for commercialization, and the interoperability between RPEVs and SCEVs are addressed in detail in this paper. Major milestones of the developments of other RPEVs are also summarized. The rest of this paper deals with a few important technical issues such as coil structures, power supply schemes, and segmentation switching techniques of a lumped inductive power transfer system for RPEVs.

System Architecture and Mathematical Models of Electric Transit Bus System Utilizing Wireless Power Transfer Technology

We introduce a new type of electric transit bus (ETB) system that uses the innovative wireless power transfer technology developed by the Korea Advanced Institute of Technology (KAIST), which is called on-line electric vehicle (OLEV). In the ETB system, the wireless-charging infrastructure installed under the road charges the fleet of electric buses that are operative over that road. The technology is innovative in that the battery in the bus is charged while it is moving over the charging infrastructure. Unlike conventional electric vehicles, the OLEV-based ETB system is a road–vehicle integrated system. Since charging occurs while the vehicle is operational, the performance of the operation depends on the system integration of the vehicle and the road in which the charging infrastructure is embedded. In this paper, we qualitatively analyze the benefits of the OLEV-based ETB system from the energy logistics perspective. We then present two analytical economic design optimization models. The first model is for an ETB system operating in a “closed environment” with no traffic and no heavy vehicle interactions. The OLEV-based shuttle bus currently operating on the KAIST campus constitutes such a case. The second model is the “open environment model” and considers an ETB system operating in normal traffic conditions. We also present the result of numerical case studies for the optimization models. The goal of this paper is to present an innovative ETB system and a logical design framework for commercializing and deploying that system.

Uniform Power I-Type Inductive Power Transfer System With <italic>DQ</italic>-Power Supply Rails for On-Line Electric Vehicles

A narrow-width power-invariant inductive power transfer system (IPTS) along the driving direction is newly proposed in this paper. The conventional I-type power supply rail for on-line electric vehicles (OLEVs) has a very narrow power supply rail with 10-cm width and exposes pedestrians to a very low electromagnetic field due to its alternatively arranged magnetic poles along the driving direction of electric vehicles; however, it has a major drawback: Sinusoidal variation of the induced pick-up voltage depending on pick-up positions on the power supply rail along driving direction. To overcome this disadvantage, a dq -power supply rail fed by two high-frequency ac currents of the d -phase and q -phase is introduced in this paper. The d -phase and q -phase magnetic poles are alternatively arranged in a line; hence, the induced voltage of a pickup becomes spatially uniform. The power invariant characteristic of the proposed IPTS for OLEV has been verified by analysis, simulations, and experiments. A practical winding method is suggested as well.

A Three-Phase Wireless-Power-Transfer System for Online Electric Vehicles With Reduction of Leakage Magnetic Fields

Wireless charging of electric vehicles based on wireless power transfer (WPT) has become increasingly popular in recent years. However, a leakage electromagnetic field (EMF) that affects other electronic devices or the human body is inevitably generated from the coils of a WPT system, and requirements regarding the suppression of the leakage EMF are increasing. In this paper, we propose a novel three-phase power line in a WPT system for the reduction of the leakage EMF. The proposed structure employs six power lines to reduce the leakage magnetic flux. This structure is compared with conventional power line structures with respect to induced voltage and magnetic field distribution. We also present analytical solutions of the EMF for the proposed and conventional power line structures. These solutions are then compared with numerical solutions using 3-D finite-element analysis and good agreement is demonstrated. The results of the numerical analysis indicate that the proposed power line can significantly reduce the leakage magnetic field from a WPT system. For verification, the proposed and conventional power lines are implemented and the EMFs are measured. A strong correlation between the numerical and experimental results is exhibited.