Charging Service Research Articles

Impact of downstream emissions cap-and-trade policy on electric vehicle and clean utility adoption

In this research, we incorporate emissions cap-and-trade policy into a decision making model for the adoption of electric vehicles (EVs). We consider a fleet environment that includes a taxicab company (TC, a downstream tailpipe emitter) and an infrastructure-service provider (SP, an upstream energy user) that provides fast charging services. Both SP and TC must have allowances to account for their own emissions. We examine several scenarios including (i) an equilibrium case, where the selling and buying prices of carbon are equal, (ii) a “buy-low-sell-high” case with financial derivatives, and (iii) a “transient” case based on market conditions. We combine both analytical and computational approaches to assess the impact of various emission cap values and carbon pricing on TC’s adoption of EVs, SP’s utility provider selection, total emissions, and the profitability of both entities. For instance, in both buy-low-sell-high and transient scenarios, penalizing upstream utility emissions more than tailpipe emissions is preferred if electric vehicles are likely to be adopted; otherwise, tighter tailpipe emissions restrictions should be preferred. In the equilibrium case, we show that policymakers will require additional mandates or market mechanisms to influence TC and SP choices, as both entities prefer dirty technologies. Additionally, we demonstrate that providing intermediate utility options – less expensive, but dirtier or more expensive, but cleaner – did not result in reduced emissions. This indicates that expanding utility options may not be a necessary or high-priority policy.

The construction of Bert fusion model of speech recognition and sensing for South China electricity charge service scenario

Electric charge service and management is an important part of electric power work. The effective recovery of the electric charge relates to the smooth development of daily work and continuous improvement of the operation and management of power supply enterprises. With the large-scale implementation of the card prepayment system, the problem of electricity customers defaulting on electricity charges has been solved to a large extent, but some large electricity users still fail to pay electricity charges on time. Therefore, under the current situation of power grid development, it is still necessary to strengthen the service and management of electricity charges to promote efficient recovery of electricity charges. Speech recognition technology has increasingly become the focus of research institutions at home and abroad. People are committed to enabling machines to understand human speech instructions and hope to control the machine through speech. The research and development of speech recognition will greatly facilitate people's lives shortly. The development of 5G technology and the proposal of 6G technology make the interconnection of all things not only a hope but also a reality. To realize the interconnection of all things, one of the key technical breakthroughs is the development of a new human–computer interaction sensing system. Under the guidance of relevant theories and methods, this paper systematically analyzes the user structure, electricity charge recovery management and service system, existing problems and causes in South China, and clarifies the necessity of design and application of electricity charge service system in South China power supply companies. The experimental data and empirical analysis results show that the optimized Bert fusion model can provide more digital support for the power supply companies in South China in terms of electricity charge recovery efficiency, management level system improvement, and electricity charge service.

A secure system for electric vehicle battery swap stations using blockchain

As electric vehicles become more popular, battery swap stations are gaining attention as a new type of charging facility. However, the charging process for electric vehicles involves privacy information such as user location and charging mode, which can be easily stolen or leaked, posing security risks and personal privacy concerns for users. Therefore, protecting the privacy of electric vehicle battery swap station users has become an important issue. This paper aims to study a privacy protection system for electric vehicle battery swap stations using blockchain technology. First, the basic principles and application scenarios of blockchain technology are introduced. Second, potential privacy leaks in electric vehicle battery swap stations are analysed, and a privacy protection scheme based on blockchain is proposed, including anonymous identity authentication, zero-knowledge proof, and encrypted communication. Third, a blockchain-based privacy protection system for electric vehicle battery swap stations is designed and implemented, and its performance is experimentally evaluated and compared with traditional privacy protection schemes in terms of security and efficiency. This paper demonstrates that the blockchain-based privacy protection scheme for electric vehicle battery swap stations possesses high levels of security and reliability, effectively safeguarding users' privacy information. Furthermore, this scheme exhibits promising application prospects and potential for widespread adoption. With the continuous development and utilization of blockchain technology, the privacy protection scheme for electric vehicle battery swap stations using blockchain is expected to provide users with more secure, reliable, and convenient charging services.

Research on multi-sensor-based online inspection technology for electric vehicle charging pile transfer type equipment

Abstract This paper performs data fusion based on a multi-sensor fusion algorithm, which measures the sensors and calculates the distance between the data to obtain the measurement weights when participating in the fusion. The sensor observation model is created using an extended Kalman filter, and the nonlinear state transition function is linearized using the covariance matrix. Accurately estimating sensor inter-cluster data is necessary to achieve the scalability of online detection technology for charging piles. The results show that the disconnection time of the contactor of the charging pile transfer type equipment is 1.153s after the simulated charging pile output over-voltage in the disconnection time detection. The online detection efficiency can be improved by using multiple sensors, the method analysis can be intuitive, and the charging service capability of the electric vehicle charging pile can be effectively improved.

Robust Operation Scheme of EV Charging Facility With Uncertain User Behavior

With the widespread of electric vehicles (EV), the necessity of installing EV charging facility has rapidly increased. Since operating EV charging facility are costly and complex, choosing a proper power operation scheme is critical in implementing a cost-effective charging service. Therefore, this paper proposes an efficient power operation scheme for EV charging facility by jointly analyzing not only monetary issues with various power rate policies and battery wear-out costs of the energy storage system (ESS) but also the uncertainty issues caused by EV users' behavior under different charging requirements. By considering the issues, this paper analyzes and formulates the problem with robust optimization techniques. By using the real-world EV operation datasets, the effectiveness of the proposed scheme is analyzed in terms of daily peak power and total cost. In addition, by applying the proposed scheme in an actual power system, it is shown that the proposed scheme is able to reduce the overall cost by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.5\%$</tex-math></inline-formula> and reduce peak power by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$17.3\%$</tex-math></inline-formula> compared to other benchmark models.

An Enhanced Backtracking Search Algorithm for the Flight Planning of a Multi-Drones-Assisted Commercial Parcel Delivery System

Using drones to carry out commercial parcel delivery can significantly promote the transformation and upgrading of the logistics industry thanks to the saving of human labor source, which is becoming a new component of intelligent transportation systems. However, the flight distance of drones is often constrained due to the limited battery capacity. To address this challenge, this paper designs a multi-drones-assisted commercial parcel delivery system, which supports long-distance delivery by a generalized service network (GSN). Each node of the GSN is equipped with charging piles to provide a charging service for drones. Given the limited number of charging piles at each node and the limited battery capacity of a drone, to ensure the efficient operation of the system, the flight planning problem of drones is converted into a large-scale optimization problem by a priority-based encoding mechanism. To solve this problem, an enhanced backtracking search algorithm (EBSA) is reported, which is inspired by the characteristics of the considered flight planning problem and the weak ability of the backtracking search algorithm to escape from a local optimum. The core components of EBSA are the designed comprehensive learning mechanism and local escape operator. Experimental results prove the validity of the improved strategies and the excellent performance of EBSA on the considered flight planning problem.

Communication-efficient distributed electricity and carbon allowance management for community microgrids with hybrid energy supply stations

With the rapid development of plug-in electric vehicles and hydrogen fuel vehicles, the introduction of hybrid energy supply stations into modern energy systems has attracted much attention in recent years. The hybrid energy supply stations are promising energy facilities that can provide charging services for plug-in electric vehicles and refilling services for hydrogen fuel vehicles. This paper studies electricity and carbon allowance sharing among community microgrids integrated with hybrid energy supply stations from a new perspective. The main objective of this work is to reduce the communication overheads when implementing distributed energy and carbon allowance management for microgrids, rather than merely protecting the privacy of microgrids as other studies have done. Thus, a communication-censored alternating direction method of multipliers-based communication-efficient distributed algorithm is proposed for solving the electricity and carbon allowance sharing problem in a distributed manner. Moreover, an adaptive penalty coefficient selection method is introduced and integrated into the communication-censored alternating direction method of multipliers for improving the convergence performance of the distributed algorithm. Simulation results demonstrate that the proposed mechanisms for sharing electricity and carbon allowance among microgrids can result in a 50.18% reduction in the total operation cost and a 13.70% reduction in the total carbon emissions, compared to the scenario where such sharing is not implemented. In comparison to the alternating direction method of multipliers, the proposed distributed algorithm can reduce the total communication overhead by 46.47%. Furthermore, the adaptive penalty coefficient selection method can improve the convergence performance of the communication-censored alternating direction method of multipliers by 21.26%.

Need of Integrated Regional Planning Approach for the Decentralisation and Optimisation of Renewable Energy Based Electric Vehicle Infrastructure: A Comprehensive Visualisation

The existing research narrates avenues in the regional and thematic area preconditions set via the framework and institutional mechanism towards energy decentralisation (ED). Moreover, with the entrant stakeholders and inclusive energy policy, the researchers find potential in local resources and capacity towards the decarbonisation of the economy. Consequently, governments around the globe emphasise multi-level sectoral economic support packages to buffer the immediate to medium-term gap and subsequently investigate their implementation in infrastructure and technology. The present review attempts to focus on the conditions of energy transformation in enabling a policy and regulatory framework. As such an integrated regional spatial framework focuses to identify support packages across urban and rural planning, transportation, renewable energy, infrastructure, environment and climate change and sustainability. Moreover, the present review revolves its thematic focus around the transport sector to strategize the energy decentralisation of the local potential in the ongoing electric vehicle (EV) transportation. The presented review toils on EV users’ struggle to orient to the need of the hour in the inadequacies of refill/charging infrastructure. Thus, it is imperative to review parameters such as availability and access to charge, depending on the source of energy in the charging infrastructure, facilities and services at the regional level. The presented review investigates under categoric and key search across conceptual regional settlement hierarchy to strategize the development of the Renewable Energy based Electric Vehicle Infrastructure (REEVI) package under system, support and services (SSS). Conclusively, the review study brings out a tentative regional settlement hierarchical integrated methodology for REEVI-SSS in the missing linkages and gaps of spatial planning to ease medium and long trip EV transportation challenges faced at the inter-city–rural, i.e., regional level.

A Multi-Period Charging Service Pricing Game for Public Charging Network Operators Considering the Dynamics of Coupled Traffic-Power Systems

The proliferation of electric vehicles (EVs) couples the operations of traffic systems and power systems, necessitating the interdependent traffic-power modeling to optimize the on-road EV charging decisions. In this paper, the multi-period charging service pricing interactions between multiple charging network operators (CNOs) are discussed considering the dynamics of traffic-power systems. A multi-period user equilibrium model considering mixed vehicle types is formulated to describe the possible vehicle trip transitions between different periods in urban transportation network. For the power distribution network, a convexified multi-period AC optimal power flow model is adopted for local electricity market clearing. With the dynamics of traffic-power systems, the pricing interaction between multiple CNOs is a non-cooperative game. We analyze the existence of Nash Equilibrium with fixed-point theory, and propose an iterative method based on the best response strategy to find <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon$</tex-math> </inline-formula> -Nash Equilibrium. Numerical studies based on an interdependent power-traffic system consisting of two 18-node power distribution networks and one revised Nguyen-Dupuis transportation network demonstrate that with the proposed CNOs’ charging pricing strategy, the CNOs’ service profits can increase by 1%-3% while the PDNs’ operation costs decrease about 1%-2% for the interdependent traffic-power system.

Mobile Charging Services for the Internet of Electric Vehicles: Concepts, Scenarios, and Challenges

Mobile Charging Services for the Internet of Electric Vehicles: Concepts, Scenarios, and Challenges

Electric Vehicle Charging Service System

The energy crisis is that new energy vehicles have become popular. Electric vehicle charging infrastructure investment is expanding to balance the supply and demand of infrastructure. The project is divided into two parts: research and design results. I have conducted in-depth research on user groups, markets, and technology, and the research results guide my design. After the team's field visit, it was found that the problem of parking and charging electric vehicles has brought difficulties. I designed a service design that balances the basic structure, improving people's congestion in peak periods. Balance the contradiction between supply and demand between electric vehicles and infrastructure and realize the service model of resource rationalization with open community as the core.

Integrated optimization of electric vehicles charging location and allocation for valet charging service

Integrated optimization of electric vehicles charging location and allocation for valet charging service

Design and Development of IOT based Telematics System for Electric vehicle

This paper functionally designs an efficient electric vehicle telematics framework for smart transportation, aiming at providing an EV-related advertisement via digital multimedia broadcasting. The electric vehicle charge service develops a reservation protocol between drivers and stations, station-side scheduling, and path adaptation according to a new charge plan. Taking advantage of information technology and wireless communication, the telematics system can support electric vehicle tracking, vehicle sharing, charging station selection, and location data analysis. In addition, as a promising business model, electric vehicle sharing needs station placement and relocation schemes, to which a previous pick-up point analysis result can give a helpful guide. The telematics framework enriches the related applications with diverse basic service building blocks and thus accelerates the penetration of electric vehicles into our daily life. Keywords: Electric Vehicle Road Network, Smart Grid, Path Adoption, Malware

Comprehensive Cost Optimization for Charger Deployment in Multi-hop Wireless Charging

The multi-hop wireless charging technology can largely extend the charging service range of chargers, thus has promising prospect in sustainable energy replenishment for wireless rechargeable sensor network. This paper proposes a new cost criterion, termed comprehensive cost consisting of energy cost and deployment cost, to measure the actual expenditure of wireless charging. We present a multi-hop wireless charging model and formulate the problem of minimizing the comprehensive cost such that the energy demand of all sensor nodes can be fulfilled by the energy capacitated chargers. We propose a (ln n+1)-approximation algorithm for the optimization problem, where n is the number of sensor nodes. Then, we propose a straightforward cost sharing mechanism, which ensures that no subset of sensor nodes can benefit by breaking away from the current charging tree for any fixed charger position, to realize the paid charging service of multi-hop wireless charging. Furthermore, to keep the magnetic fields of transmitters from the interfering, the conflict avoidance schemes are proposed in both central and distributed situations. Finally, we discuss the distributed scheme for minimizing the comprehensive cost without support of central server. Through extensive simulations, we demonstrate the significant superiority of the proposed algorithms in terms of comprehensive cost.

Analysis of the Impact of Electric Charging Infrastructure Provision Rules on the Calculation of Fast and Ultrafast Charging Service Costs at Time Base and Energy Base Charging Stations

This study aims to analyze the impact of the issuance of MEMR Regulation Number 1 of 2023 concerning the Provision of Electric Charging Infrastructure for Battery-Based Electric Motor Vehicles, especially focusing on provisions related to electricity tariffs on surcharge fees for KBLBB fast charging and ultrafast charging consumers/customers. The calculation of surcharge fees can be economically affected by investment costs, operational costs, electric vehicle volume, and charging energy per electric vehicle (EV), so this study on surcharge fees is calculated using several scenarios and simulations carried out. The scenario is based on investment costs and operational costs using high and low cost financing calculations, and the simulation in question is on the volume of energy and electric vehicles over the next 15 years with pessimistic, moderate and optimistic simulations. This study uses quantitative descriptive methods that aim to describe or descriptive about a situation objectively using numbers, starting from data collection, interpretation through analysis of the data and appearance and results. The results of calculations from scenarios and simulations that have been carried out, calculations that are close to economics and based on current conditions that low cost scenarios with moderate simulations are appropriate recommendations to be applied at this time. Of course, the amount of application of the service fee must be evaluated by the Government every year, because the calculation component greatly affects the amount of the service fee.

Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review

Planning public electric vehicle (EV) charging infrastructure has gradually become a key factor in the electrification of mobility and decarbonization of the transport sector. In order to achieve a high level of electrification in mobility, in recent years, different studies have been presented, proposing novel practices and methodologies for the planning and operation of electric vehicles charging infrastructure. In this paper, the authors present an up-to-date analysis of the existing literature in this research field, organized by considering the perspectives and objectives of the principal actors/operators of the EV public charging infrastructure value chain. Among these actors, the electric vehicle, the charging operators and service providers, and the power system infrastructure (transmission and distribution system) are analyzed in depth. By classifying the reviewed literature based on this manifold viewpoints approach, this paper aims to facilitate researchers and technology developers in exploring the state-of-the-art methodologies for each actor’s perspective, and identify conflicting interests and synergies in charging infrastructure operation and planning.

Optimum Scenarios of EV Charging Infrastructure: A Case Study for the Saudi Arabia Market

The lack of an EV charging infrastructure is of the top five barriers preventing the adoption of EVs on a large scale. A long charging time is also one of the five barriers, according to the latest survey published by the IEA in 2021. The estimated increase in demand for EVs is a big challenge in many countries all around the world. This challenge exists in many EU and Middle East countries. The main reason for this problem is the requirement of huge funds to install enough public charging points that result in satisfactory charging services. Hence, the phase-out plans of internal combustion engine (ICE) vehicles can be carried out successfully and smoothly. Unfortunately, there is a trade-off between the cost of installing charging points and EV charging time. Therefore, it is important to optimize both factors simultaneously. This way, the charging services can be provided at the minimum possible cost and at a satisfactory level of quality. This study determines the optimum ratio of the number of chargers to the number of EVs in a certain province. The optimal number of chargers that are necessary to optimally serve a certain number of EVs has been determined. Two well-known evolutionary search techniques have solved the optimization problem: particle swarm optimization (PSO) and genetic algorithms (GA). Both algorithms have succeeded in providing many optimal charging infrastructure scenarios. Hence, the decision maker can select the most convenient scenario from several alternatives based on the available budgets and the most convenient charging time.

Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations

The rapid development of electric vehicles (EVs) has increased the demand for public fast-charging stations (FCSs). Multiple self-interested charging service operators (CSOs) in an urban transportation network compete with each other through pricing signals to maximize their respective profits, where the information about customer demands and heterogeneous preferences work as important decision criteria. With the prevailing data transaction, the information superiority differences between CSOs may significantly affect the competitive pattern in the charging market. In this study, a Nash-Stackelberg-Nash game framework is established to investigate the strategic pricing of CSOs under different information distribution scenarios. The customer response pattern, namely, the routing and charging choices of EV drivers to pricing signals, is described via a computationally efficient extended stochastic user equilibrium model comprehensively considering heterogeneous customer preferences and decision stochasticity. Then, aiming at different data forms, both parameter-driven and sample-driven CSO pricing models are proposed. The pricing models are reformulated as a tractable single-level mixed-integer linear program, and the game equilibrium is solved alternately via Gauss-Seidel iteration. Numerical simulations are conducted to analyze the impact of single CSO information superiority and double CSO information distribution on CSO profitability and customer experience in both data forms. The influences of endogenous factors like CSO data amount and processing ability, as well as exogenous scenario parameters like customer preference distribution and perception error distribution, are discussed.

Reducing abnormal expenses in national health insurance based on a control chart and decision tree-driven define, measure, analyze, improve and control process.

This study examined the cost of medical insurance for "sepsis" treatment in Taiwan. We applied statistical tests, cost control charts, and C5.0 decision trees using the define, measure, analyze, improve and control (DMAIC) process to mine data on Diagnosis-Related Groups (DRGs) and clinics that reported expense anomalies and disposal costs. Analyzing 353 valid samples (application fees) from four DRGs, 70 clinics, and 15 input variables, abnormalities in application fees for adults (age ≧18years old) with comorbidities or complications was significant (95% confidence interval) in one DRG and nine clinics. Four input variables (ward charge, treatment fee, laboratory fee, and pharmaceutical service charge) had a significant impact. Improvements or controls should be prioritized for three clinics (Nos. 49, 44, and 14) and two input variables (treatment and laboratory fees). This model can be replicated to ascertain excess medical expenditures and improve the efficiency of medical resource use.

An Efficient and Secure Trading Framework for Shared Charging Service Based on Multiple Consortium Blockchains

While electric vehicles are proliferating rapidly, the number of charging piles is only 1/16 of the number of electric vehicles, showing big tension between small supply of public charging piles and intense charging demand of electric vehicles. To alleviate the tension, it is highly desired to incorporate existing idle private charging piles into a shared charging service. However, existing solutions do not well implement the system due to some non-negligible issues, like poor consensus performance or lack of verification for outputs from blockchain networks. In the paper, we propose an efficient and secure trading framework built atop multiple consortium blockchains. It devises a novel node voting mechanism so as to improve consensus efficiency. Meanwhile, it employs a publicly verifiable design based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(t,n)$</tex-math></inline-formula> Boneh-Lynn-Shacham (BLS) threshold signature technique, so as to enable entities outside the blockchain to verify the consistency between the output results with the valid consensus. Besides these novel system-level designs, it also introduces a fair and robust trading strategy running by smart contracts on the platform. Furthermore, we implemented a proof-of-concept system and conducted extensive experiments. The results show that the overall latency across chains is reduced by about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$26\%$</tex-math></inline-formula> with the node voting mechanism and the transactions fees are comparable to recent popular cross-chain applications.