Vehicle Supply Research Articles

Sustainable energy supply of electric vehicle charging parks and hydrogen refueling stations integrated in local energy systems under a risk-averse optimization strategy

This paper proposes a day-ahead optimization framework for the sustainable energy supply of an electric vehicle (EV) charging park and hydrogen refueling station (HRS) outfitted with the power-to‑hydrogen (P2H) conversion facility in a local multi-energy system (LMES). A novel integrated demand response (IDR) program with an incentive mechanism is used for power and heat demands, EV charging park, and HRS to further improve system flexibility and operational costs. A hybrid multi-objective information-gap decision theory/robust optimization (HMIRO) framework is also applied as an effective modeling technique for dealing with existing uncertainties with no need for a probability density function and scenario creation. The suggested HMIRO model is formulated as a tri-level mixed-integer linear programming (MILP) problem and converted into a single-level MILP problem using duality theory that enables the LMES operator to utilize two risk-averse strategies simultaneously due to the information-gap on uncertain parameters. The numerical results indicate that the proposed hybrid risk-averse strategy enables the LMES operator to guarantee a risk reduction of wind generation and HRS demand up to 20.6 % and 14.3 %, respectively, in day-ahead optimization while leading to a 5 % increase in the daily operation cost. Also, the daily energy cost can be reduced by up to 3.52 %, 2.88 %, 7.03 %, and 1.15 % with optimal IDR implementation for power load, heat load, EV charging park, and HRS in LMES, respectively.

An Integrated Two-Dimension Linguistic Intuitionistic Fuzzy Decision-Making Approach for Unmanned Aerial Vehicle Supplier Selection

With the rapid development of unmanned aerial vehicles (UAVs) and their applications in problems such as power line inspection, selecting the appropriate UAV supplier according to several sustainable attributes has attracted many interests. In this regard, an integrated multiattribute group decision-making (MAGDM) method based on the best-worst method (BWM) and MULTIMOORA method with two-dimension linguistic intuitionistic fuzzy variables (2DLIFVs) is proposed in this paper for the selection of UAV suppliers. First, the 2DLIFV is utilized to represent the uncertain, fuzzy, and linguistic evaluations of the experts on the evaluation attributes. Second, the two-dimension linguistic intuitionistic fuzzy BWM (2DLIF-BWM) is introduced to compute the weights of the attributes. Then, a novel expert weight calculation method that combines the uncertainty degree and consensus degree of the experts is introduced. Next, the 2DLIF-MULTIMOORA method is proposed, where the aggregation operators and distance measures of the 2DLIFVs are used to determine the ranking results of different alternatives. Finally, a real case of selecting a sustainable UAV supplier for power line inspection is presented to illustrate the process of the proposed method. The experimental results are further analyzed through sensitivity and comparative analyses to show the feasibility and effectiveness of the proposed method. From the results, it can be found that the proposed method can more flexibly represent the uncertain assessments while providing reasonable and reliable results.

A multi-criteria decision-making framework for electric vehicle supplier selection of government agencies and public bodies in China.

Electric vehicle deployment shows promising potentials in promoting cleaner energy utilization and reducing carbon emission. Due to increasing carbon neutral pressure and market competition from transportation sector, government agencies and public bodies (GAPBs) have emphasized the significance of electric vehicle adoption through supplier selection. Consequently, GAPBs must consider a reasonable criteria system and a comprehensive supplier selection framework and rationally select the electric vehicle supplier that matches their practical needs in terms of economic, social, environmental, and technical factors. This paper provides insights into electric vehicle supplier selection (EVSS) from the perspective of GAPBs using an integrated multi-criteria decision-making (MCDM) framework based on best–worst method (BWM) and fuzzy ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Initially, 14 critical factors from economic, social, environmental, and technical dimensions are identified as the criteria by literature review and experts’ opinions. Then, a comprehensive decision framework using the integrated MCDM approach is proposed. To validate the applicability and feasibility of the proposed framework, a case study is launched and analyzed. It emerges that bad environmental record, cost, quality, service, and environmental initiatives are the most important criteria in EVSS for GAPBs with the weight values of 0.1995, 0.1172, 0.1219, 0.0708, and 0.2553. The comparative analysis and the sensitivity analysis are performed for verifying the reliability of the proposed framework. The work helps to understand the electric vehicle supplier selection criteria and makes methodological decision-making support for GAPBs.

Analysis of displacement damage mechanism and simulation proton irradiation on GaAs

GaAs is an important material for preparing power supply for space vehicles, satellites, and space systems. To study the displacement damage of GaAs material caused by protons in the space environment, in this article, the Monte Carlo software Geant4 was used to simulate the transport process of space protons in GaAs; calculate the non-ionizing energy loss (NIEL) of protons with different energies; and obtain the species, energy range, and proportion of primary knock-on atom produced by proton irradiation with different energies. The results show that with the increase in the incident proton energy, the probability of elastic collision decreases and the probability of inelastic collision increases. The main source of displacement damage changes from elastic collision to inelastic collision. The radiation damage of low energy protons is serious at the end of the range, while that of high energy protons is serious at the beginning and end of the range. With the increase in the proton energy, the number of displaced atoms increases and the NIEL changes slowly. This provides a theoretical reference for the reliable operation of spacecraft.

Optimization of Waiting Time for Electric Vehicles Using a Fuzzy Inference System

Electric vehicles (EVs) need to be recharged at intermediate locations, such as shopping malls, restaurants, and parking lots, to meet the daily commute requirements of their users. Currently, public electric vehicle supply equipment (EVSE) serve EVs by conventional methods, which can result in long waiting time for users. This issue reduces the travel efficiency of EVs and thus affects user comfort. Most previous research has studied energy consumption and charging cost optimization; however, comparatively less work has focused on waiting time optimization despite its great importance from the EV user’s perspective. In this paper, we formulate the waiting time optimization as a fuzzy integer linear programming problem and propose a novel heuristic fuzzy inference system-based algorithm (FISA) that resolves the objective function and minimizes the waiting time of EVs at public EVSE installations. We developed the underlying fuzzy inference system by defining the membership functions, expert rules, and formulation for obtaining the optimal solution. The novel FISA automates the correlations of the uncertain and independent input parameters into weighted control variables and resolves the objective function in each sampling period to optimize the waiting time for EVs with the most urgent service requirements. A java language-based simulator is developed for a parking lot to evaluate the effectiveness of the proposed FISA. The simulation results indicate higher efficiency of the proposed FISA compared with state-of-art scheduling algorithms.

Exploring the circular economy paradigm: A natural resource-based view on supplier selection criteria

The paradigm of circular economy and the transfer of its principles to supply chain management has recently received much attention from researchers and practitioners. Limited natural resources, governmental legislation, and social responsibility for environmental preservation are the main factors for the development of circular supply chains. Especially in the automotive industry, critical materials are used to produce electric vehicles, reinforcing the importance of circularity for the industry. As the first stage of the supply chain, suppliers have a considerable influence on creating self-sufficient production systems, and in the automotive sector, a low level of vertical integration is important. Therefore, selecting suppliers is an enabler for circular supply chains, but this decision is a complex process due to multiple, partly conflicting criteria. To contribute to the knowledge in this research area, this study applies a fuzzy decision-making trial and evaluation laboratory approach for supplier selection in a circular supply chain in a case study of electric vehicles. First, a set of criteria for supplier selection in circular supply chains was identified based on a literature review and was systematically categorized based on the natural resource-based view. Afterward, the criteria were assessed and refined by experts with a strong procurement background in the automotive industry. Second, the experts were interviewed for value collection, and finally, the mutual influence of the criteria was determined. The results show that the most important criteria for circular supplier selection in electric vehicle supply chains are environmental standards, environmental-related certifications, resource consumption, and waste generation. Regarding the natural resource-based view, short-term focused criteria addressing the capability of pollution prevention have a higher importance compared to long-term criteria that drive sustainable development. In addition, this study classifies the criteria into the categories of causes and effects, discusses the prominence of the criteria, and presents a strategic map showing the mutual influences of the criteria. The results contribute to the theoretical and practical discussion on circular supply chains by identifying the key criteria for circular supplier selection and providing decision-making support for procurement managers.

Application of blockchain and smart contracts in autonomous vehicle supply chains: An experimental design

With the rise of digital sustainable business models in the Autonomous Vehicles (AV) industry, the traditional automakers are undergoing a major restructuring in their key performance areas and associated supply chains processes. Focusing on an innovative AV design (AD) concept, this paper investigates how Artificial Intelligence (AI) and Blockchain-based Smart Contracts can enhance sustainable supply chain operations. A novel design element, Margin Indicator (MI), is developed to obtain reliable predictive analytics results from the mainstream machine learning algorithms. The proposed approach supports a robust control of costs and energy, while maintaining a high level of transparency in managing decentralized AV supply chain processes, monetary impacts, and environmental sustainability. Testing the developed concept through a preliminary case study, we observed a reduction in energy wastage and hidden financial transactions by 12.48% and 11.58%, respectively. Supported by the rapid advancement in the blockchain and AI technologies, the developed framework is expected to improve product traceability, transaction transparency, and sustainable economic growth for the AV supply chains.

Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging

In this article, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an electrical vehicle (EV) charging facility for human-driven connected vehicles (CVs) and AVs. The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and autonomous vehicles (AVs). To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data-informed manner. We propose a novel risk adversarial multiagent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. The experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE’s active charging time, and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.

A novel ensembling of deep learning based intrusion detection system and scroll chaotic countermeasures for electric vehicle charging system

The usage of Electric vehicle (EVs) has been exponentially growing due to its focus on eco-friendly means of transport, distributed charging platform and user dictated supporting infrastructures. The EVs are charged by the charging stations which equipped with Electric Vehicle Supply Equipment (EVSE) that contains Internet enabled computers. These systems are considered to be more important for controlling the function such as charging electric vehicles, authorization and smart connection to the local power grid using different wireless technologies such as green WIFI, Bluetooth and even 5 G. The cyber-attacks such as DoS and DDoS attacks can violate integrity, confidentiality and availability of the EVSE resources. Hence the intelligent Intrusion Detection System (IDS) is required to ensure the system for the robust and trustworthy deployment of EVSE resources. To meet the above challenge, this paper proposes new composite and intelligent system which contains the deep learning based IDS and high random chaotic generators to safeguard the data against the different cyber-attacks. The proposed IDS has been modelled based on Gated Recurrent Units (GRU) and counter measures are performed by adopting the Enhanced Chaotic Scroll attractor keys (ECSA). The contribution of this research paper is as follows: Novel Dataset Preparation for EVSE under different attack scenarios, Implementation of high accurate multi-objective accurate GRU based IDSs, Design of Enhanced Chaotic Countermeasure Encryption Schemes for the counterfeiting the attacks in Internet Enabled EVSE system. The extensive experimentation has been carried out into two important phases. In first phase algorithm centric metrics such as prediction accuracy, time of detection, whereas in second phase key centric metrics such as Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), Key sensitivity and entropy are calculated and compared with the other existing methodologies. Results demonstrates that the proposed ensemble system has outperformed than the other methodologies and proves its strong place in designing the more secured Internet Enabled EVSE systems.

Vehicle Redistribution in Ride-Sourcing Markets Using Convex Minimum Cost Flows

Ride-sourcing platforms often face imbalances in the demand and supply of rides across areas in their operating road-networks. As such, dynamic pricing methods have been used to mediate these demand asymmetries through surge price multipliers, thus incentivising higher driver participation in the market. However, the anticipated commercialisation of autonomous vehicles could transform the current ride-sourcing platforms to fleet operators. The absence of human drivers fosters the need for empty vehicle management to address any vehicle supply deficiencies. Proactive redistribution using integer programming and demand predictive models have been proposed in research to address this problem. A shortcoming of existing models, however, is that they ignore the market structure and underlying customer choice behaviour. As such, current models do not capture the real value of redistribution. To resolve this, we formulate the vehicle redistribution problem as a non-linear minimum cost flow problem which accounts for the relationship of supply and demand of rides, by assuming a customer discrete choice model and a market structure. We demonstrate that this model can have a convex domain, and we introduce an edge splitting algorithm to solve a transformed convex minimum cost flow problem for vehicle redistribution. By testing our model using simulation, we show that our redistribution algorithm can decrease wait times by more than 50%, increase profit up to 10% with less than 20% increase in vehicle mileage. Our findings outline that the value of redistribution is contingent on localised market structure and customer behaviour.

Steady-state analyses of an smDFR with coupled Serpent-OpenFOAM calculation

The small modular dual fluid reactor (smDFR) adopts a mixture of liquid uranium and plutonium chloride as fuel, with lead as a coolant. With its smaller size, lower weight, and inherent safety, the smDFR can be used as a mobile energy supply for vehicles or ships. Analyses of the smDFR are still in the early stage based on a single or separated physical field, which can hardly capture the impact of the flowing fuel on the neutron field. The coupled neutronic-fluid dynamic calculation is needed for reliable multidimensional, multi-field distribution of the steady-state smDFR core property. In this work, the Monte Carlo code Serpent 2 and the CFD code OpenFOAM are coupled via a Python interface, whereby the neutron field information can be exchanged with the fluid field in a region-average manner. The correctness and reliability of the coupled code are verified with two cases in the pressurized water reactor background, and the results show good agreement. The coupled code is applied to the steady-state analyses of an smDFR, a single-channel case, and a 1/6-core case. The results show that the temperature distribution of the fuel and coolant caused a lower drift of the axial power peak compared to the uncoupled cosine shape power distribution. The 1/6-core results show that the peak temperature of the fuel reaches 980 K at the top-center of the reactor core. Moreover, the influence of fuel thermal properties and inlet velocity are studied with rated power. Through the comparison of mapping schemes, the results can identify the dependence of specific parameters and help to reduce mapping errors. This work shows that through a coupled neutronic-CFD code, the multidimensional and multi-physical fields of the smDFR can be better represented, providing important references for the future design and analyses of smDFR systems.

An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand

The expansion of the battery electric vehicle (BEV) market requires considerable changes in the supply of electricity to fulfill the charging demand. To this end, understanding the spatio-temporal distribution of BEV charging demand at a micro level is crucial for optimal electric vehicle supply equipment (EVSE) planning and electricity load management. This research proposes an integrated activity-based BEV charging demand simulation model, which considers both realistic travel and charging behaviors and provides high-resolution spatio-temporal demand in real-world applications. Moreover, a novel charging choice model is proposed which provides more realistic demand modeling by allowing critical non-linearities in random utility to better describe observed charging behaviors. The results of a case study for the Atlanta metropolitan area imply that work/public charging has a substantial potential market, which can comprise up to 64.5% of the total demand. Out of multiple charging modes, demand for direct-current fast charging (DCFC) is prominent at work/public locations, and it makes up the largest portion of the non-residential demand in all simulation scenarios. Moreover, charging behaviors have significant impacts on the demand distribution. Peak power demand for use of level-2 chargers is 49% to 91% higher among high-risk-sensitive users than among risk-neutral users. Users’ preferences for fast charging rates can change the DCFC demand from 36.4% of the total demand to 53.7% of the total. This study helps to qualitatively analyze the factors that figure in charging demand and their impacts on the demand distribution. The results can be directly used in EVSE planning and electricity load prediction.

Development of a Fault-Diagnosis System through the Power Conversion Module of an Electric Vehicle Fast Charger

The supply of electric vehicles (EVs), charging infrastructure, and the demand for chargers are rapidly increasing owing to global low-carbon and eco-friendly policies. As the maintenance of charging infrastructure varies depending on the manufacturer, fault detection and maintenance cannot be conducted promptly. Consequently, user inconvenience increases and becomes an obstacle to EV distribution. Recognizing charger failure after occurrence is a management method that is not economically effective in terms of follow-up. In this study, a data collection system was developed to diagnose EV fast-charger failure remotely in advance. The power module failure-prediction and management system consists of an AC sensor, DC sensor, temperature and humidity sensor, communication board, and data processing device. Furthermore, it was installed inside the fast charger. Four AC inputs, four DC outputs, and temperature and humidity data were collected for 12 months. Using the collected data, the power conversion efficiency was calculated and the power module status was diagnosed. In addition, a multilayer perceptron neural network was used as an algorithm for training the classification model. Charging patterns according to normal and failure were trained and verified. Based on results, the pre-failure diagnosis system demonstrated an accuracy of 97.2%.

Optimal models for sustainable supply chain finance: evidence from electric vehicle industry

ABSTRACT Prior research studies sustainable supply chain (SC) management for the electric vehicle (EV) industry in which SC members have sufficient funds. However, many EV firms always face the problems of financial constraints, which lead to the importance and urgency for the research of sustainable SC finance. To address this new problem, we consider the members of the electric vehicle supply chain (EVSC) can be risk averse because of the financial risks from uncertain demands. The EV retailer has limited liability and can obtain loans through a bank or an EV manufacturer. We first address a base model in which the EV manufacturer invests in the green research and development (R&D) effort and investigate operational decisions and sustainable SC finance strategies for the EVSC and members. Using the real data from a top-four EV manufacturer in the world, we do a case study to verify the outcomes of the base model and find that the EVSC and members can derive higher profits with MF strategy for the most cases. Furthermore, we extend the base model to consider government subsidies and the EV retailer's service levels, and find that government subsidies and the EV retailer's service are beneficial to the EVSC.

Optimization and Benefit Analysis of Intelligent Networked Vehicle Supply Chain Based on Stackelberg Algorithms

With the rapid development of intelligent technology, the construction of smart cities with the goal of creating a harmonious human life has become a new hot spot in the world. A new round of information technology revolution, represented by the Internet, big data, cloud computing, artificial intelligence (AI), and fifth-generation mobile communications (5G), is driving profound changes in the automotive industry. Smart vehicles (also known as intelligent connected vehicles) that integrate many high-tech technologies to provide safer, more convenient, and low-carbon comprehensive travel solutions have become an inevitable form of future vehicles. This paper constructs a three-level supply chain consisting of high-performance chip suppliers, smart car manufacturers, and retailers. On the basis of considering the level of product innovation and sales effort, Stackelberg game method is used to study the influence of each player in the supply chain on each parameter and the profit of each player under three scenarios: centralized decision making, nonsharing of innovation cost, and sharing of innovation cost. The results show that: when the level of sales effort has nothing to do with the level of innovation, the level of product innovation and the total profit of the supply chain increase with the improvement of the retailer’s sales ability; when the level of sales effort is related to the level of innovation, the level of product innovation under the cost-sharing decision model is greater than the case of no cost sharing, but the total profit of the supply chain is less than the case of no cost sharing.

Construction of Artificial Intelligence Application Model for Supply Chain Financial Risk Assessment

An artificial intelligence integrated application model of supply chain financial risk assessment is constructed. Based on the financial data and supply chain data of listed companies in China’s new energy electric vehicle industry, the supply chain financial credit risk evaluation index system is constructed. The data samples are preprocessed by PCA as the input data of the support vector machine, which effectively solves the problem of high-dimensional data in supply chain finance. By improving the inertia weight of particle swarm optimization and introducing mutation operation, a dynamic mutation particle swarm optimization algorithm is proposed to avoid the problem of particles falling into a local minimum in the process of optimization. Finally, the improved optimization algorithm is used to optimize the parameters of SVM and input AdaBoost integration as a weak classifier to build an integrated model with good performance in many aspects. The model has been successfully applied to the credit risk assessment of China’s new energy vehicle supply chain finance. The comparison with other models shows that the constructed model has certain advantages in performance.

Estimating Potential Employment Impact of the Charging Infrastructure used to Support Transportation Electrification in the United States

Increased concern over greenhouse gas (GHG) emissions and climate change is encouraging many states, companies, and consumers to focus on zero emission vehicle technologies such as electric vehicles (EVs). A major barrier to widespread EV adoption, however, is range anxiety as there is currently insufficient electric vehicle supply equipment (EVSE) available. Although not the primary goal primary of EVSE installations, one of their side effects and a goal of President Biden’s infrastructure plans is their impact on employment, both initially as stations are developed and activated and over time and as they continue to provide charging to a growing population of EVs. This study estimates the potential employment effects of the deployment and operation of President Biden’s goal of installing 500,000 charger plugs. To do this, we develop an input-output (IO) based model called JOBS EV. Unlike existing analyses, JOBS EV includes both the employment effects caused by the front-of-meter EVSE equipment needed at a particular site and the back-of-meter or upstream equipment used to obtain power from the existing utility’s distribution network. The model results indicate that approximately 1.1 million new jobs will be created over a 10-year period. The modeling process outlined in this paper, in addition to the results presented, may be useful to stakeholders involved in transportation decarbonization efforts as another means of evaluating the costs and benefits of pursuing electrification. Further work is needed to improve the underlying IO model to better account for nascent industries, to accurately calculate local share percentages, and to capture the employment effects of the complete EVSE life cycle.

Designing a Crowd-Based Relocation System—The Case of Car-Sharing

Car-sharing services promise environmentally sustainable and cost-efficient alternatives to private car ownership, contributing to more environmentally sustainable mobility. However, the challenge of balancing vehicle supply and demand needs to be addressed for further improvement of the service. Currently, employees must relocate vehicles from low-demand to high-demand areas, which generates extra personnel costs, driven kilometers, and emissions. This study takes a Design Science Research (DSR) approach to develop a new way of balancing the supply and demand of vehicles in car-sharing, namely crowd-based relocation. We base our approach on crowdsourcing, a concept by which customers are requested to perform vehicle relocations. This paper reports on our comprehensive DSR project on designing and instantiating a crowd-based relocation information system (CRIS). We assessed the resulting artifact in a car-sharing simulation and conducted a real-world car-sharing service system field test. The evaluation reveals that CRIS has the potential for improving vehicle availability, increasing environmental sustainability, and reducing operational costs. Further, the prescriptive knowledge derived in our DSR project can be used as a starting point to improve individual parts of the CRIS and to extend its application beyond car-sharing into other sharing services, such as power bank- or e-scooter-sharing.

Engineering Poster E10: Carbon capture clusters

Poster E10 Net zero is an endeavour that will impact every corner of the world. We need global communication and collaboration. To move fast, the transition must become more efficient and spread the best solutions far and wide. For difficult to decarbonise industries, collaboration is essential. The other essential ingredient is policy. The UK banned diesel and petrol car sales from 2030. This is driving electric vehicle manufacturing and supply chains. The EU banned single-use plastics from 2021. Consequently, Coca Cola Europe announced 100% of their bottles would be based on recycled plastic. Norway introduced a carbon tax in 1991 to encourage research into low-carbon solutions. It became the first country to geologically sequester CO2 and the first to do it from an LNG facility. Ten years after the tax was introduced, Norway’s carbon emissions had dropped by 14%. Policies influence emissions. They drive not only environmental outcomes but also sustainable growth and the ability to future-proof their economies. In 2007 and 2012, the UK announced funding for a commercial-scale carbon capture and storage project, both times the programs were aborted. A third attempt, this time focussing on decarbonising four industrial clusters by 2030, was announced in 2018 along with the first ‘net-zero’ carbon cluster by 2040, with the support of a number of UK policies it is expected to progress to construction. This paper discusses the journey from policy through partnerships to the development of Carbon Capture and Hydrogen Clusters in the UK and looks at lessons learnt for Australia. To access the poster click the link on the right. To read the full paper click here

Experimental research of an ocean thermal engine with phase-change material

The long-term mission capacity of battery-powered underwater vehicles is quite limited, which necessitates the development of underwater vehicles that utilise ocean thermal engines (OTEng) to capture renewable energy. The literature indicates that OTEng based on solid–liquid phase-change materials (PCM) is the optimum solution; however, the energy capture performance of OTEng strongly affects the motion and power supply of the thermal underwater vehicle, and thus, improving their energy capture performance is critical. In this study, a new OTEng with an enhanced heat transfer structure based on PCM was designed by employing copper foam and filling fluid. An experimental platform for the performance study of OTEng was established, and a corresponding evaluation method was introduced. Comparative experiments on its performance, including the phase-transition time, volume of oil input and output, and amount of energy captured under various scenarios, were carried out. The results were analysed, and it was concluded that (1) the designed OTEng enhanced the heat transfer ability up to 58.3% in the melting process and 21.4% in the solidification process when the temperature difference was 10 °C; (2) filling liquid with 0.3 MPa system pressure was the optimal choice during the solidification process; (3) increasing the system pressure or ambient temperature during the melting process could improve the captured energy or average power in one cycle, respectively.