Vehicle Supply Research Articles

Investment and Financing Decisions for Fuel Consumption Reduction in a Vehicle Supply Chain

This paper models the financing and fuel reduction investment decisions of a supply chain under chain and chain competition to analyze firms’ pricing, product, and investment and financing strategies. In two competing supply chains, one of them is willing to invest in fuel consumption reduction to meet its carbon social responsibility and pursue a competitive. It can choose between two financing models, manufacturer financing and retailer financing, to address the lack of investment capital. Consumers are inclined to purchase low-fuel consumption vehicles due to the cost of use and carbon concerns. Results show that under the model of manufacturer financing, the utility of both the manufacturer and the retailer improves. However, in this model, the fuel consumption reduction is lower than that under the model of retailer financing. If it takes the model of retailer financing, the manufacturer can achieve higher utility than under the model of manufacturer financing, but the retailer’s profits fall rather than increase. So the retailer would not take it unless the manufacturer and government compensate it.

Coordinating the energy transition: Electrifying transportation in California and Germany

California and Germany share ambitious emission reduction targets. Yet California is ahead of Germany in electrifying transportation by several metrics, including the number of public charging stations. We show that variation in the politics of coordination in California and Germany explains the different outcomes. Transforming energy systems requires coordination across various complementary technologies and infrastructures—here between the supply of electric vehicles and the buildout of charging stations. In California, a strong electrification coalition emerged across automakers selling electric vehicles as well as utilities and third-party firms providing charging infrastructure. Power market rules made capital investments for charging infrastructure instantly profitable for California monopoly utilities. By contrast, in Germany's liberalized power market, investing in capital-intensive charging infrastructure was not profitable for electric utilities. As a result, utilities did not emerge as a political force in the electrification coalition. Instead, utilities and automakers were in gridlock, failing to coordinate electric vehicle rollout and public charging station buildout. Our findings highlight the limits of business-led coordination, raising the question which institutions help address coordination failures in clean energy transitions.

Impact Of Global Semiconductor Shortage on The Domestic New Energy Vehicle Supply Chain in Australia And Measures to Mitigate Such Risks in The Future

Different industries have been affected by the shortage of semiconductors globally, and one of the most affected industries is the New Energy Vehicle (NEV) industry within Australia. Semiconductors play a massive role in NEV features like battery management and power management. These factors include the COVID-19 pandemic, the rise in electronics demand, natural calamities, and most importantly the shortage which has affected NEV production and sales in Australia. This paper discusses the consequences of the lack of supply of such components on the Australian NEV supply chain: They stop production, low inventory levels, financial losses, and slowed innovation. It brings out vulnerability issues including dependence on limited suppliers from Asia and the absence of chip-making factories within North America. Based on the identified threats, the paper outlines the following solutions: supplier diversification, increasing inventory levels, raising supply chain visibility, investing in domestic semiconductor production, incentives for buyers, and sourcing non-semiconductor material technologies. Such measures are strategic and intended to secure an NEV supply chain in Australia that is both sustainable and environmentally friendly.

Identification of Supply Chain Disruption Risks and Strategies in the Electric Vehicle Industry Based on Fuzzy Analytic Hierarchy Process (FAHP)

In recent years, the electric vehicle industry has witnessed unprecedented development opportunities. However, with rapid technological iterations and fluctuating market demands, the vulnerability and complexity of the electric vehicle supply chain have significantly increased the potential risk of supply chain disruptions. Against this backdrop, this study adopts the Fuzzy Analytic Hierarchy Process (FAHP) to systematically analyze the risks of supply chain disruptions in the electric vehicle industry. By constructing a judgment matrix through industry data surveys and expert scoring, and conducting consistency checks, the weights of various risk factors are determined. The study finds that technological quality risk, demand forecast deviation, and logistics disruption are the primary factors affecting the stability of the electric vehicle supply chain. Additionally, although the probability is relatively low, the potential destructiveness of exogenous risks is equally undeniable. Accordingly, the study will propose targeted management strategies to enhance the resilience of the new energy vehicle supply chain at both the enterprise and industry levels in response to these risks.

Cybersecurity Certification Requirements for Distributed Energy Resources: A Survey of SunSpec Alliance Standards

This survey paper explores the cybersecurity certification requirements defined by the SunSpec Alliance for Distributed Energy Resource (DER) devices, focusing on aspects such as software updates, device communications, authentication mechanisms, device security, logging, and test procedures. The SunSpec cybersecurity standards mandate support for remote and automated software updates, secure communication protocols, stringent authentication practices, and robust logging mechanisms to ensure operational integrity. Furthermore, the paper discusses the implementation of the SAE J3072 standard using the IEEE 2030.5 protocol, emphasizing the secure interactions between electric vehicle supply equipment (EVSE) and plug-in electric vehicles (PEVs) for functionalities like vehicle-to-grid (V2G) capabilities. This research also examines the SunSpec Modbus standard, which enhances the interoperability among DER system components, facilitating compliance with grid interconnection standards. This paper also analyzes the existing SunSpec Device Information Models, which standardize data exchange formats for DER systems across communication interfaces. Finally, this paper concludes with a detailed discussion of the energy storage cybersecurity specification and the blockchain cybersecurity requirements as proposed by SunSpec Alliance.

Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid

The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.

Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling

Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling

Battery recycling and coordination in information leakage prevention under blockchain technology in a new energy vehicles supply chain

Retired battery recycling and information leakage prevention under blockchain introduction intertwine in the new energy vehicle (NEVs) supply chain, but rare literature has jointly investigated them. Therefore, we characterize the vehicle goodwill under recycling and marketing efforts as well as the automobile demand function with and without blockchain introduction, identify the long-run discounted profits of the manufacturer and retailers in the supply chain, and investigate the Stackelberg differential game equilibrium under four scenarios: no blockchain introduction, manufacturer introduction, manufacturer-retailer cooperative introduction, and cooperative introduction plus wholesale price discounts for preventing information leakage. The results show that the manufacturer can introduce blockchain when costs are below a certain threshold, which increases the recycling rate of decommissioned batteries and improves goodwill, consumer demand, and marginal profits for the manufacturer and retailer; the retailer participation in the introduction of blockchain solves the free-rider problem and reduces the thresholds introduced, but does not affect battery recycling, goodwill, consumer demand, and wholesale and retail prices; wholesale price discounts effectively discourage the retailer from leaking information about the manufacturer and lead to improved profits for the retailer, but results in higher earnings for the manufacturer only when discounts are below a certain threshold.

Evolutionary Game-Based New Energy Vehicle Supply Chain Strategies That Consider Carbon Reduction and Consumers’ Low-Carbon Preferences

The rapid development of the new energy industry has intensified the competition among companies. Finding solutions to achieve technological innovation, carbon reduction, and to earn consumers’ confidence has become a pressing challenge. In this research, we aim to develop a four-party evolutionary game model involving government, manufacturers, dealers, and consumers to examine the strategic decisions made by these parties in order to accomplish carbon emission reduction goals. We will perform numerical simulations to analyze the strategic choices of each party and the relevant influencing factors. The results suggest the following: (1) The tax hike on traditional car production is less than the innovation expenses for new energy vehicles, leading manufacturers to lean towards manufacturing traditional vehicles. (2) The rise in taxes resulting from the manufacture of conventional vehicles will influence manufacturers’ strategic decisions, whereas the expenses related to technological advancements will have a more significant effect on manufacturers’ strategic choices. (3) Compared to dealers, manufacturers’ strategic choices are more significantly influenced by consumers’ awareness of low-carbon preferences. (4) In the early stages of technological innovation, the government typically offers incentive subsidies to manufacturers to boost technological innovation activities. Whereas, in the later stages of technological innovation, the government usually provides direct subsidies to consumers to encourage the market acceptance and widespread use of innovative products.

Development of a Cost-Driven, Real-Time Management Strategy for e-Mobility Hubs Including Islanded Operation

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes.

Investment in fuel‐consumption reduction technologies in a vehicle supply chain under different subsidy policies: A dynamic framework

AbstractIt develops a differential game model of a fuel car supply chain in which consumers' low fuel consumption, green preferences, and government subsidies promote manufacturers' investment in fuel‐consumption reduction technologies (FCRT); the price, the fuel consumption per mile, and the retailer's marketing efforts jointly determine the demand for the product. There are four game scenarios constituted by ex ante or ex post government that subsidizes and shares or does not share marketing costs. The results show that ex ante and ex post subsidies can incentivize investment in FCRT. However, the preferable subsidy model depends on the parameter values; the manufacturer‐led marketing cost‐sharing contract results in supply chain coordination.

Electric vehicle supply equipment monitoring and early fault detection through autoencoders

This paper presents a novel approach to detecting anomalies in Electric Vehicle charging unit power profiles using a combination of Autoencoders with LSTM techniques. This study presents a robust methodology, combining the two Machine Learning techniques, for early fault estimation in a real-world case study. The proposed methodology offers significant advantages over existing methods by providing a more comprehensive analysis of anomalous trends. To validate the effectiveness of the proposed methodology, the authors tested it on real Electric Vehicles charging power curves provided by an Italian Distribution System Operator recorded on a historical database and compared the performances with the ones of a traditional anomaly detection technique. The results of the study, tested on Electric Vehicles Supply Equipment or charging stations, demonstrate that the proposed approach is highly effective in detecting anomalous trends in Electric Vehicles charging profiles.

Experimental Investigation of a Distributed Architecture for EV Chargers Performing Frequency Control

The demand for electric vehicle supply equipment (EVSE) is increasing because of the rapid shift toward electric transport. Introducing EVSE on a large scale into the power grid can increase power demand volatility, negatively affecting frequency stability. A viable solution to this challenge is the development of smart charging technologies capable of performing frequency regulation. This paper presents an experimental proof of concept for a new frequency regulation method for EVSE utilizing a distributed control architecture. The architecture dynamically adjusts the contribution of electric vehicles (EVs) to frequency regulation response based on the charging urgency assigned by the EV users. The method is demonstrated with two Renault ZOEs responding to frequency fluctuation with a combined power range of 6 kW in the frequency range of 50.1 to 49.9 Hz. The results confirm consistent power sharing and effective frequency regulation, with the system controlling the engagement of the EVs in frequency regulation based on priority. The delay and accuracy analyses reveal a fast and accurate response, with the cross-correlation indicating an 8.48 s delay and an average undershoot of 0.17 kW. In the conclusions, the paper discusses prospective improvements and outlines future research directions for integrating EVs as service providers.

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Modeling and Transmission Characteristics Study of a Resonant Underwater Wireless Electric Power Transmission System

Compared to the traditional wet-mate underwater power supply method, Magnetic Coupling Resonant Wireless Power Transfer (MCR-WPT) technology boasts advantages such as excellent insulation, high safety, and convenient operation, showing promising application prospects in the field of power supply for underwater vehicles and other mobile underwater devices. In order to explore the transmission characteristics of this technology underwater, this article first establishes a traditional mathematical model, and then modifies the underwater model through analysis of changes in coil self-inductance and mutual inductance, as well as the impact of eddy current losses. Using the modified mathematical model of the underwater MCR-WPT system, the transmission characteristics are analyzed, and simulations and experimental validations are performed using MATLAB R2022a software. In the study of frequency characteristics, it is found that the system operates optimally when both ends of the circuit work at the resonant state; that is, when finput = fresonance = 100 kHz, the output performance is at its best, and the optimal resonant frequency significantly improves power and transmission efficiency. When the input frequency is less than 87.3 kHz or greater than 122.9 kHz, the output power decreases to less than half of the maximum power. In the investigation of load effects, the optimal load for maximizing system output power was identified, but the load that maximizes transmission efficiency is different from this optimal load. This study provides strong theoretical support and guidance for improving the performance of underwater wireless power transmission systems.

Resilience evaluation and enhancing for China’s electric vehicle supply chain in the presence of attacks: A complex network analysis approach

Over the past decade, the electric vehicle (EV) industry has experienced rapid growth, necessitating a deeper understanding of its supply chain network’s structure and resilience. This study presents an analysis into China’s EV supply chain network through a two-tier network, employing complex network approach to explore its topological characteristics and network’s resilience. The results reveal that the nodes within China’s EV network follow a power-law distribution, with shorter average path lengths compared to other automobile networks, indicating a concentration of connections upstream. The resilience assessment shows that while the network is resilient against random attacks, it is significantly vulnerable to targeted attacks. Importantly, the High-Betweenness Addition (HBA) strategy is identified as a better strategy for improving the network’s overall resilience. This strategy of reinforcing the centrality of core nodes within the supply chain is highlighted as a crucial approach for enhancing the network’s resilience. Our findings underscore the importance of developing strategic connections among high-betweenness nodes to distribute these enhancements evenly, aiming to add one or two edges for enhancing network resilience. This study offers a lucid roadmap for enhancing the resilience of China’s EV supply chain network in the face of emerging challenges.

Performance analysis and optimization of a novel S-CO2 closed-Brayton-cycle-based thermal management system for scramjets

The thermal management system (TMS) based on closed-Brayton-cycle is a solution to solve the problems of thermal protection and electricity supply for long-endurance hypersonic vehicles. A novel TMS is proposed in this paper, and the system performance is simulated and analyzed based on a zero-dimensional thermodynamic cycle model and a quasi-one-dimensional heat transfer model. The results indicate that the heat transfer coefficients of the post-mixing method are significantly higher. As the split ratio of the bottom sub-cycle increases, net power first increases to a maximum value and then gradually decreases, but the fuel flow rate for cooling is the opposite. Moreover, the increase in turbine inlet temperature has a positive effect on both fuel flow rate and net power. The optimization results show that the minimum fuel flow rate for cooling is 0.3313 kg/s and net power reaches 162.37 kW when keeps wall temperature under the limit. Furthermore, if the maximum fuel temperature is 723 K, the fuel mass flow rate will further decrease to 0.2907 kg/s. This research provides a potential solution for enhancing the performance of the thermal management system for hypersonic vehicles powered by scramjets.

Shared autonomous vehicle operational decisions with vehicle movement and user travel behaviour

Shared Autonomous Vehicle (SAV) has many impacts on the transport development, such as saving parking space. However, SAV meets a huge challenge in terms of vehicle supply and user demand imbalance. The traditional mathematical optimization method cannot be well used due to the computational burden. Hence, this paper proposes a Reinforcement Learning (RL) based SAV relocation approach. First, two types of RL agents, car-based and zone-based agents, are developed as agents for vehicles and stations, respectively. Then, the RL scheme is trained by using historical demand data to facilitate real-time carsharing relocation. Finally, to compare the proposed two types of RL methods, three scenarios are used: small-scale, middle-scale, and large-scale networks. Solutions indicate that the enhanced zone-based method achieves an additional 146% profit compared to traditional threshold-based relocation strategies. The user travel behaviour impacts are provided by analyzing parking demand and travel movements among residential, industrial and commercial zones.

Fault analysis for DC Bus-integrated energy storage system, electric vehicle supply equipment, and photo voltaic systems

Reliable access to cost-effective electricity is the backbone of the U.S. economy, and energy storage is an integral element of the power system with high penetration of Distributed energy resources (DERs). Investment in energy storage is essential for keeping pace with the increasing demands for electricity arising from continued growth in U.S. productivity, continued expansion of national cultural imperatives (e.g., emergence of the distributed grid and electric vehicles), and the projected increase in DERs. This research is on the forefront of this transition with fault analysis execute for a real system to be implemented in New York City at a Utility substation. The project seeks to pair a grid-connected battery energy storage system (BESS), solar photovoltaic (PV) system, and an electric vehicle charging system (EVCS) on a common DC bus. A transient model has been developed and different fault scenarios (i.e., high-impedance and low-impedance faults) have been simulated, at various points of the system for several durations. The objective of this study is to determine the requirements for electrical protection equipment, i.e., DC Circuit Breakers (DCCBs) in terms of capacity and fault clearing time that can ensure stable operation of the DC bus system while meeting the utility and IEEE requirements for system stability. The results showed that fast-response DCCBs is a key element for the system. Solid-State DC breakers with ultra-high speed must be deployed for prompt detection/isolation of faults. Actual ratings of converters and allied equipment need to be considered before the finalization of ratings and specification of protection equipment.

Optimization of residential energy system configurations considering the bidirectional power supply of electric vehicles and electricity interchange between two residences

Electric vehicles (EVs) combined with bidirectional home chargers (vehicle-to-home (V2H) units) and electricity interchange between residences are promising options for the self-consumption of rooftop photovoltaics (PVs) in residences. Furthermore, considering that EVs have large storage capacities, one EV + V2H might support neighborhood-scale electricity storage by electricity interchange between residences, resulting in more cost-efficient self-consumption systems. Therefore, this paper investigates how permitting electricity interchange between residences affects the economically optimal design of distributed energy systems, such as PVs, EVs and V2H units. For this purpose, a mixed-integer linear programming model is developed to optimize the configurations and operation strategies of distributed energy resources (DERs) considering electricity interchange in neighborhoods. V2H units, which supply electricity from EVs to residences, are considered DER options independent of EVs. Furthermore, the optimization results are compared under two scenarios wherein electricity interchange is and is not permitted between two residences. The results indicate that permitting electricity interchange improves the self-consumption rate of PV power while reducing investment in storage facilities. The one influencing factor is sharing one EV + V2H between two residences and effectively utilizing the available storage capacity.