Charging Network Research Articles

Green Wheels, Greener Wallets: Economic Viability of Last-Mile Delivery Fleet Electrification in Case of Latvia

The analysis of internal combustion engine vehicle (ICEV) usage habits in the DPD Latvija company and the study of the specifics of its last-mile delivery transport business will allow to understand whether electromobility solutions can make the company more efficient. The question addressed in the research is whether the electrification of the delivery vehicle fleet can reduce transport expenses for the company and under what conditions. For estimation of direct and indirect costs of transport acquisition and usage Total Cost of Ownership (TCO) is used. The company’s daily operation parameters and technical data of the used vehicles VW e-Crafter and VW Crafter 35 Kasten/long base are considered in the study. Net Present Value (NPV) and Internal Rate of Return (IRR) calculation methods are used for analyzing the investment project. The expense of the creation of electrical vehicle charging infrastructure is not considered, because the company’s daily routine allows to cover all charging processes overnight by using domestic sockets together with Mode 2 chargers. All technical data and calculations were made for deliveries in a city cycle. In financial terms of owning delivery transport, the company is using a 5-year operational lease with 0% first payment. The obtained results in terms of TCO comparison showed a slight advantage by using EV after 5 years of last-mile delivery service per 1 vehicle – 706.98 Euro at 31200km a year or 100km in the working day of 6-day working week (156000km in 5-year is under 160000 warranty condition). The calculation is performed for DPD Latvija 213 ICE vans of a total 240-vehicle fleet. Results of NPV, NPVI, and IRR with a comparison rate of 10.6% for EV case clearly show a sufficiently good return. The Net Present Value of the solution is 89271.92 Euro and the Internal rate of return is 48.05%. The study proves that the electrification of the last-mile delivery company's fleet is economically justified and within 5 years. In the process of electrification, the company will reduce its expenses – by 1.45%, which increases the net annual profit by 0.13%. The main conclusion is that the company will not incur losses by switching to an environmentally friendly vehicle, the capital investment in a realistic scenario pays off already from the 3rd year. Nevertheless, the authors of the study recommend evaluating delivery vehicles with higher driving range parameters, because during peak delivery and seasons with cold weather, the company may experience unplanned downtime and expenses due to the need for additional charging time at public charging networks on the road, that could lead to financial losses and need for TCO calculation corrections.

Joint Path Selection, Energy Trading, and Task Offloading in Electric Vehicle Charging and Computing Network

Joint Path Selection, Energy Trading, and Task Offloading in Electric Vehicle Charging and Computing Network

Reconfigurable-Intelligent-Surface-Enhanced Dynamic Resource Allocation for the Social Internet of Electric Vehicle Charging Networks with Causal-Structure-Based Reinforcement Learning

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.

A comprehensive framework for electric vehicle charging station siting along highways using weighted sum method

The proliferation of electric vehicles (EVs) has led to an increased demand for strategically located EV charging stations (EVCSs) to ensure a balanced and accessible charging network. The siting of EVCS involves a multifaceted process that includes technological, economic, social, geographical, and environmental factors. An EVCS network should deliver high-quality service, alleviate drivers' range anxiety, and be compatible with renewable energy system integration, while also accounting for technical infrastructure and future expansion expectations. Resting areas along highways, equipped with amenities and grid connections, emerge as potential sites for EVCS installation. Accordingly, this study presents an EVCS siting framework along highways, integrating expert opinions from diverse disciplines. The proposed method employs expert opinions to weigh site selection criteria and then uses a clustering-based approach to identify suitable locations for EVCS siting, utilizing the weighted sum method. According to the experts, the most important criterion is determined as the service level of the candidate point with a weight of 0.375, followed by traffic density (0.218), and proximity to connection roads (0.215). The method is tested both on a test highway and the Edirne-Ankara highway, which is the most heavily used in Türkiye. The results demonstrate that the test highway identifies 13 out of 18 optimal locations using the clustering strategy. Similarly, the siting method identifies 18 out of 31 optimal EVCS locations along the Edirne-Ankara highway ensuring the distance constraints. This approach is scalable and adaptable for application on highways in various countries where EVCS infrastructure is still developing.

Analysis on Tesla's Marketing Strategy

With the seriousness of energy consumption and environmental pollution as well as the world's attention to environmental protection, the global attention to new energy vehicles is getting higher and higher. This article aims to analyze Tesla's three marketing strategies (direct sales strategy, building charging network, and word-of-mouth marketing) to analyze its competitive advantages and defects in the market through SWOT analysis, PEST analysis and qualitative research. The study found that Tesla has been effective in all three of these marketing approaches, but that certain changes and enhancements need to be made as the market evolves. And this thesis also gives some suggestions for improvement on the basis and background of this research to help Tesla better generate profits and increase customer satisfaction and loyalty. Overall, Tesla has been very successful and effective in its marketing strategy and will have room to improve even better after going through the improvement process.

Low-carbon planning of urban charging stations considering carbon emission evolution characteristics and dynamic demand

As a new generation of transportation, electric vehicles play an important role in carbon-peak targets. The development of electric vehicles needs the support of a charging network, and improper planning of charging stations will result in a waste of resources. In order to expand the charging network of electric vehicles and give full play to the low-carbon and efficient characteristics of electric vehicles, this paper proposed a charging station planning method that considers the characteristics of carbon emission trends. This paper combined the long short-term memory (LSTM) network with the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to predict the carbon emission trend and quantified the correlation between the construction speed of a charging station and the evolution characteristics of carbon emission by Pearson’s correlation coefficient. A multi-stage charging station planning model was established, which captures the dynamic characteristics of the charging demand of the transportation network and determines the station deployment scheme with economic and low-carbon benefits on the spatiotemporal scale. The Pareto frontier was solved by using the elitist non-dominated sorting genetic algorithm. The model and solution algorithm were verified by the actual road network in a certain area of Shanghai. The results showed that the proposed scheme can meet the charging demand of regional electric vehicles in the future, improve the utilization rate of charging facilities, and reduce the carbon emission of transportation networks.

An intelligent partial charging navigation strategy for electric vehicles

ABSTRACT The rise of electric vehicles (EVs) has highlighted the issue of insufficient charging infrastructure, a major barrier to their widespread adoption. This problem is due to the limited capacity of existing charging networks and the performance constraints of EV batteries. Intelligent route planning, which optimizes travel time and energy use, could be a solution. Traditional studies on electric vehicle route planning oversimplify the charging process, assuming a linear relationship between charging level and time, neglecting its actual nonlinear nature. Additionally, these studies underestimate the impact of dynamically changing traffic conditions and charging station queue times on travel time. Integrating these factors into route planning can significantly reduce energy consumption and travel time. Our work proposes an intelligent partial charging strategy, relaxing full recharging restrictions, and fully considering the nonlinear battery characteristics, dynamic traffic conditions, and charging station queue times. Employing an improved genetic algorithm for route planning, our method demonstrates a dual advantage – reducing computational complexity and saving an average of 15% in travel time. Experimental results over a 400 km2 traffic road network validate the efficiency of our approach, outperforming existing methods facing similar challenges.

A Hybrid Deep Learning Model Utilizing Cross-Structural Multi-Behavioral Comparative Recommendation for Sustainable Electric Transportation Infrastructure

Ensuring the sustainability of transportation infrastructure for electric vehicles (e-trans) is increasingly imperative in the pursuit of decarbonization goals and addressing the pressing energy shortage. By prioritizing the development and maintenance of resilient e-trans platforms through the optimization of the public charging network, electric vehicle businesses can effectively meet the needs of users, thereby contributing to efforts aimed at improving environmental quality. To achieve this goal, researching the dynamics of vehicle user behaviors plays a crucial role. In this paper, we propose cross-structure multi-behavior contrastive learning for recommendation (C-MBR), which takes into account the dynamic preferences of users, and develops model profiles from the global structure module, local structure module, cross-behavior contrastive learning module, cross-structure contrastive learning module, and model prediction and optimization. C-MBR is mainly designed to learn user preferences from the diversity of users’ behaviors in the process of interacting with the project, so as to grasp the different behavioral intentions of users. The experimental and analytical research is further conducted and validated for dealing with cold start problems. The results indicate that C-MBR has a strong ability to deal with the problem of sparse data. Compared with the ablation experiment, the model performance of C-MBR is significantly enhanced, showing that the C-MBR model can fully apply the information of a global structure and local structure in cross-structure comparative learning and multi-behavioral comparative learning to further alleviate the problem of data sparsity. As a result, the e-trans infrastructure will be significantly enhanced by addressing the issue of data-driven disruption.

Towards a multimodal charging network: Joint planning of charging stations and battery swapping stations for electrified ride-hailing fleets

This paper considers a multimodal charging network in which charging stations and battery swapping stations are jointly built to support an electric ride-hailing fleet synergistically. Our argument is based on the observation that charging an EV is a time-consuming burden, and battery swapping faces scaling issues due to its deployment costs. However, charging stations are cost-effective, making them ideal for scaling up EV fleets, while battery swapping stations offer quick turnaround and can be deployed in tandem with charging stations to improve fleet utilization and reduce operational costs. To fulfill this vision, we consider a ride-hailing platform that jointly builds charging and battery swapping stations to support an EV fleet. An optimization model is proposed to capture the platform’s planning and operational decisions. In particular, the model incorporates essential components such as elastic passenger demand, spatial charging equilibrium, charging and swapping congestion, etc. The overall problem is formulated as a nonconcave program. Instead of pursuing the globally optimal solution, we establish a tight upper bound through relaxation and decomposition, allowing us to evaluate the solution optimality even in the absence of concavity. Through case studies for Manhattan, New York City, we find that joint planning of charging and battery swapping stations outperforms deploying only one of them, yielding a total profit that is 11.7% higher than swapping-only deployment under a limited budget, and 17.5% higher than charging-only deployment under a sufficient budget. These results underscore the complementary benefit between charging and battery swapping facilities.

EV Charging Scheduling Under Demand Charge: A Block Model Predictive Control Approach

This paper studies the online scheduling of electric vehicle charging by a service provider subject to a demand charge in a distribution system. Demand charge imposes a penalty on the peak power consumption over each billing period, representing a substantial cost for the service provider with a large number of clients. Because the demand charge is calculated at the end of the billing period, it poses challenges in real-time scheduling when energy demand forecasts are inaccurate, resulting in either overly conservative power consumption or substantial demand charge. We propose a block model predictive control approach that decomposes the demand charge into a sequence of stage costs. Optimality conditions on demand patterns are also presented and analyzed. Numerical simulations demonstrate the efficacy of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper addresses a significant practical problem of minimizing the demand charge on the real-time scheduling of deferrable demands. In particular, we consider a setting where a commercial electric vehicle (EV) charging service provider has to manage the online scheduling of a large number of arriving EVs at a charging facility subject to a maximum charging power constraint and a tariff with the demand charge. A major practical challenge is to balance the tradeoff between maximizing profit in scheduling as much EV charging as possible and the need to minimize penalty on the peak charging power. We propose a model predictive control strategy that decomposes the overall demand charge into a sequence of terminal costs. Also addressed is the practical constraint arising from the mismatched EV charging decision period and the power measurement period used to compute the demand charge. Using real data collected at the Adaptive Charging Network (ACN) testbed in simulations, the proposed approach yields 8-12% improvement in operational profit over existing benchmarks, while it has yet been tested in actual charging systems. In the future research, we will address the charging scheduling under demand charge over multiple charging stations.

Electric vehicle charging stations: Model, algorithm, simulation, location, and capacity planning

The transition to sustainable transportation is imperative in mitigating environmental impacts, with electric vehicles (EVs) at the forefront of this shift. Despite their environmental benefits, the global adoption of EVs is curtailed by challenges such as nascent battery technology, high costs, and insufficient charging infrastructure. This study addresses the optimizing electric vehicle charging station (EVCS) locations as a critical step toward enhancing EV adoption rates. Thus, establishing efficient charging stations is critical to meet the increasing demand. By integrating location modeling with demand forecasts and market penetration, we propose a comprehensive approach to determine optimal locations and capacities for EVCS. Firstly, review existing literature, highlighting the significance of facility location models in optimizing EV charging infrastructure and identifying gaps in addressing demand and market penetration. Our methodology uses a genetic algorithm to solve the p-median problem for location selection and Arena 14 simulation software to model station traffic and optimize charging unit types and quantities. The model prioritizes public areas, considering potential demand points and station locations to propose optimal charging areas. Results indicate that our model minimizes travel distances and waiting times, offering a scalable solution adaptable to future EV market growth. This study contributes to the field by presenting a sustainable and economical model for EVCS placement and capacity planning, underlining the importance of a robust charging network in the broader adoption of electric transportation. The findings suggest that proactive infrastructure development, guided by accurate demand predictions and optimized location strategies, can significantly enhance the feasibility and attractiveness of EVs, supporting global efforts towards a cleaner, more sustainable transportation system.

The Willingness to Purchase Electric Vehicles: A Comprehensive Review

This paper aims to comprehensively investigate the multifaceted factors influencing consumers’ willingness to purchase electric vehicles (EVs), providing a deeper understanding of the drivers behind EV adoption. The study draws on various scholarly sources to examine the intricate relationships among multiple determinants, including environmental concern, performance expectations, effort expectancy, charging point networks, charging time, price, operational costs, incentives, and attitude toward EVs. These factors are analyzed within a conceptual framework, highlighting the pivotal role of attitude as a mediating variable. The review reveals that environmental concern, driven by awareness of the ecological impact of conventional vehicles, motivates individuals to opt for EVs as a more sustainable mode of transportation. Performance expectations, such as energy efficiency and emission reduction, significantly influence consumer preferences. Effort expectancy, indicating the ease of using EV technology, plays a critical role in adoption decisions. Charging infrastructure, both availability and accessibility, is a crucial facilitator of EV adoption, emphasizing the importance of robust charging networks. Concerns about charging time underscore the need for faster and more convenient recharging methods. Price remains a barrier, with high initial costs, although operational cost advantages make EVs economically appealing. Depending on the region, government incentives have varying effectiveness in reducing financial barriers to EV adoption. Attitude toward EVs, acting as a mediating variable, highlights the importance of positive perceptions in shaping consumer intentions. This study offers a comprehensive review of the interplay of factors influencing EV adoption, providing a holistic view of consumer decision-making in the EV market. It emphasizes the need for further empirical research to validate and extend the proposed model. It offers valuable insights for policymakers, manufacturers, and researchers in promoting sustainable transportation solutions and accelerating EV adoption in response to environmental concerns.

Analyzing the urban-rural divide: Understanding geographic variations in charging behavior for a user-centered EVSE infrastructure

Understanding charging behavior in relation to its geographic locale is crucial for fostering a user-centered and equitable electric vehicle supply equipment (EVSE) infrastructure. This study employs a comprehensive and multivariate approach to investigate the utilization of public Level 2 electric vehicle chargers, revealing notable differences in charging behavior across provinces and varying urbanity levels in Canada. The findings showcase a clear urban-rural divide in charging behavior, characterized by distinct patterns across different provinces. While a weekly repetitive charging demand pattern is evident, high-demand periods diverge among charging networks. Furthermore, the study identifies charging demand trends within these networks, which display variations in direction and shape. The results of this study indicate that charging behavior is inconsistent between different charging networks, indicating that stakeholders should recognize the necessity of developing individualized and area-specific charging networks to bolster utilization and enhance usability. As the first study, to the best of the authors' knowledge, to explore geographic variations in charging behavioral patterns, this study serves as a crucial stepping stone towards establishing an inclusive, user-centric, and accessible EVSE infrastructure for all.

Rush to Charge, Dead to Drive: Application of Deadline Rush Model to Electric Vehicle User's Charging Behavior.

This work aims to estimate the portion of electric vehicle (EV) users who exhibit procrastination-like behavior, almost equivalent to an "empty" battery, before they decide to charge their vehicles. There is a human tendency to procrastinate when a deadline approaches. Human behavior in the presence of deadlines has been studied in different fields to evaluate individuals' performance or organizational efficiency and effectiveness. However, this phenomenon has not been investigated among EV users. This study explores users' procrastination-like behavior among 69 Rhode Island public charging stations' data representing 70,611 charging events. The Deadline Rush Model is incorporated to model frequent users' charging profiles. To conduct a robust estimation, the Bayesian Mixture Model is implemented. With the selection of an informative prior, the Bayesian Mixture Model estimated that almost one-third of frequent users procrastinate charging. The majority of procrastination-like users have small battery sizes. Although procrastination-like users need to charge when they arrive at a location, that might not necessarily be true for a plug-in hybrid; thus, systematically, they can clog the system for other users whose needs are more pressing. Understanding unique and unexplored charging behaviors among EV users is beneficial to EV infrastructure stakeholders in reducing the adoption threshold by providing a reliable and ubiquitous charging network. The findings identify a different kind of demand on the EV infrastructure than previously modeled and can directly influence future decision-making criteria in terms of planning to optimize to accommodate EV drivers with different charging behaviors.

Optimal Pricing and Energy Sharing of EV Charging Stations With an Augmented User Equilibrium Model

With the proliferation of electrical vehicles (EVs), tariff-based monetary incentives (e.g., charging service fees, electricity prices) have been widey adopted by network operators to efficiently manage EV charging demands. The imposed tariff would enforce drivers to bear an extra travel expense for satisfying the charging demand distributions favored by network operators. Accurately modeling users' responsive behavior to price signals is a prerequisite for properly designing the tariff and achieving the desired goal. The present work bridges the gap of existing studies by proposing an augmented user equilibrium (AUE) model that captures EV users' equity-aware decision makings and closed-loop commuting behavior. The model enables network operators to consider 1) the acceptability of EV travelers on regulation policies and 2) the elasticity of EV charging demand as well its rebound effect. Based on the AUE model, we further investigate the optimal pricing and energy sharing strategy of the charging network operator, who aims to maximize his/her profit by tapping the flexibility of EVs and scheduling energy transactions among charging stations while respecting the operating constraints of power and transportation networks. Finally, a relaxation based iterative algorithm is devised to efficiently solve the mathematical problem with complementarity conditions. Numerical results on two test systems validate the effectiveness and merits of the proposed models and methods.

CHALLENGES AND OBJECTIVES IN THE IMPLEMENTATION OF ELECTRIC AUTOMOTIVE MODELS IN BRAZIL

The article addresses the implementation of electric cars in Brazil, highlighting fundamental challenges and objectives. Amid the search for solutions, the technological transition presents complex challenges, such as the need for efficient charging infrastructure, economic considerations and overcoming barriers in the primary energy sector. The main objective is to understand and address obstacles, from expanding the charging network to issues related to costs and the energy matrix, in order to guide public and social policies. Furthermore, it highlights the importance of partnerships, public incentives and comprehensive strategies to make electric cars not only a viable option, but also economically competitive compared to fossil fuel-powered vehicles, highlighting the need for sustainable energy solutions to support the projected increase in electrical energy consumption.

Distribution-Driven Optimization for Heat Pump and EV Hosting Capacity in LV Networks

The decarburization of heat and transport using electric vehicles (EVs) and heat pumps (HPs) needs an important investment in low-voltage (LV) networks. This article proposes an optimization approach to estimate the availability of headroom for domestic EV charging and optimization on LV networks under various penetrations of heat pumps. The proposed optimization approach is called dynamic differential annealed optimization (DDAO). The objective of the proposed approach is to maximize the hosting capability of low-voltage networks for EVs and HPs by optimizing the flexibility of EV charging. The DDAO is used to optimize different aspects of EV charging and LV networks. The DDAO approach is implemented in three steps: (a) HP headroom assessment, (b) EV optimization, and (c) validation. The performance of the proposed method is validated on a MATLAB platform and compared with the existing Binary Spring Search Algorithm (BSSA), Manta Ray Foraging Optimization Algorithm (MRFO), and Chaos Game Optimization methods. In comparison to the total prices of 3.5£ in BSSA, 4.5£ in MRFO, and 5.5£ in CGO, the proposed DDAO method achieves a lower total price of 3.2£. This suggests that the DDAO method is more cost-effective than the existing techniques.

Modular Design of DC-DC Converters for EV battery fast-charging

Electric vehicles (EV) are emerging as a viable and environment-friendly solution for the daily commute. However, for a broader utilization of the EV, their autonomy range has to be increased. A well dispersed charging network and a fast and efficient battery charger system becomes one important factor for the success of the EV. This paper addresses the challenges of developing such High Power chargers and proposes a modular, easily scalable and efficient implementation of a switch-mode DC-DC converter for performing the constant-current/ constant voltage battery charging process.

Connecting the Dots: A Comprehensive Modeling and Evaluation Approach to Assess the Performance and Robustness of Charging Networks for Battery Electric Trucks and Its Application to Germany

The successful introduction of battery electric trucks heavily depends on public charging infrastructure. But even as the first trucks capable of long-haul transportation are being built, no coherent fast-charging networks are yet available. This paper presents a methodology for assessing fast charging networks for electric trucks in Germany from the literature. It aims to establish a quantitative understanding of the networks’ performance and robustness to deviations from idealized system parameters and identify crucial charging sites from a transportation planning perspective. Additionally, the study explores the quantification of adaptation effects displayed by agents in response to charging site outages. To achieve these objectives, a comprehensive methodology incorporating infrastructure, vehicle and operational strategy modeling, simulation, and subsequent evaluation is presented. Factors such as charging station locations, C-rates, mandatory rest periods, and vehicle parameters are taken into account, along with the distribution of traffic according to publicly available data. The study aims to offer a comprehensive understanding of charging networks’ performance and resilience. This will be applied in a case study on two proposed networks and newly created derivatives. The proposed network offers over 99% coverage for long-haul transport but leads to a time loss of approximately 7% under reference conditions. This study advances the understanding of the performance and resilience of proposed charging networks, providing a solid foundation for the design and implementation of robust and efficient charging infrastructure for electric trucks.

Optimal pricing strategies for distribution system operator in coupled power-transportation system

The power grid and transportation network are coupled by the charging behavior of electric vehicles. Based on the coupled power-transportation network model, this paper first analyzes the effect of the distribution system operator’s (DSO) electricity selling price on guiding the charging behavior of electric vehicles in the transportation network and then builds the DSO’s optimal pricing formulation. Considering the competition between multiple charging network operators (CNOs), this paper establishes a game model between CNOs and solves it iteratively through the best response dynamic method. An approximation method using the elasticity matrix is proposed to speed up the solution by reducing the multi-layer optimization to a single layer one in each iteration, with its effectiveness validated through numerical tests. Furthermore, the paper discusses the issue of the prisoner’s dilemma that arises among CNOs and explores the potential impact of their cooperative strategies on the overall system.