Electric Transportation Systems Research Articles

Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging

This paper investigates the application of hybrid reinforcement learning (RL) models to optimize lithium-ion batteries’ charging and discharging processes in electric vehicles (EVs). By integrating two advanced RL algorithms—deep Q-learning (DQL) and active-critic learning—within the framework of battery management systems (BMSs), this study aims to harness the combined strengths of these techniques to improve battery efficiency, performance, and lifespan. The hybrid models are put through their paces via simulation and experimental validation, demonstrating their capability to devise optimal battery management strategies. These strategies effectively adapt to variations in battery state of health (SOH) and state of charge (SOC) relative error, combat battery voltage aging, and adhere to complex operational constraints, including charging/discharging schedules. The results underscore the potential of RL-based hybrid models to enhance BMSs in EVs, offering tangible contributions towards more sustainable and reliable electric transportation systems.

Bi-Level Continuous-Time Model for the Coordinated Operation of Coupled Electric Power Transmission and Energy Storage Transportation Systems

Bi-Level Continuous-Time Model for the Coordinated Operation of Coupled Electric Power Transmission and Energy Storage Transportation Systems

Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data

A surrogate model that predicts thermal runaway (TR) of lithium-ion batteries (LIBs) fast and accurately is essential, yet complex phenomena of TR present significant challenges to achieving adequate performance in both aspects, particularly as traditional finite element models (FEMs) incur significant time and cost. This study proposes a multiphysics-informed deep operator network (MPI-DeepONet) with encoders to address these issues. This proposed neural network aims to predict TR under various thermal abuse conditions, offering a fast and accurate TR prediction surrogate model. In this study, MPI-DeepONet with encoders is trained with virtual data from a multiphysics FEM to overcome the scarcity of actual TR data. The architecture of DeepONet solves interpolation and extrapolation problems, allowing predictions across multiple thermal abuse conditions once trained. The neural network is further enhanced by the supervision of energy balance and chemical reaction equations, ensuring accurate and robust predictions despite limited data by effectively capturing the complex phenomena of TR. Quantitative analysis, compared against actual experiments and ablation studies, confirms the effectiveness of the proposed neural network. Notably, MPI-DeepONet achieves a mean RMSE of 13.2 °C for temperature predictions in the test set, significantly outperforming the 25.4 °C RMSE of purely data-driven DeepONet. This improvement highlights the importance of integrating multiphysics constraints into the neural network. The generality of the proposed neural network is further evidenced by accurate TR prediction in both LFP and NMC cells. The features deployed on the proposed neural network enable real-time quantification of internal temperature distribution and dimensionless concentration of the key components in LIBs, which are challenging to measure directly, achieving speeds at least 10,000 times faster than FEM. The proposed neural network provides comprehensive information for advanced battery management systems to ensure safety and reliability in LIBs, accelerating the digital twin of electric transportation systems through artificial intelligence transformation.

Review of cryogenic power conversion and its potential in future all electric transportation systems: from silicon age to WBG era

Cryogenic power electronic conversion (< 123 K) is expected to offer higher efficiencies for future all-electric transportation platforms. Low temperature operation of converters together with integration of wide bandgap devices (WBG) can enable higher switching frequencies with reduced filtering components. This paper briefly reviews the passive components involved in the development of a cryogenic converter. The paper also reviews in detail the converter development with WBG devices by far, and provides a discussion on benefits achieved, and challenges in terms of auxiliary components and measurements. The last part of the paper gives a discussion on envisioned challenges, and different research directions for future superconducting power conversion systems.

Sliding Mode Control of an Electric Vehicle Driven by a New Powertrain Technology Based on a Dual-Star Induction Machine

This article examines a new powertrain system for electric vehicles based on the dual-star induction machine, presented as a promising option due to its significant advantages in terms of performance, energy efficiency, and reliability. This system could play a key role in the evolution of electro-mobility technology. The dual-star induction machine reduces electromagnetic torque fluctuations, limits current harmonics, improves power factor, and enables half-speed operation. Our study focuses on the control strategy and operation of the traction chain for electric vehicles propelled by the dual-star induction machine (DSIM) using Matlab software with version 2017. We integrate the battery as the main energy source, along with three-level static converters for energy conversion in the vehicle’s four operating quadrants. We have opted for sliding mode control, which has proven to be feasible and robust against external disturbances. Although we have modeled driver behavior, we consider it as an aspect of control, to which we add the driving profile to guide our evaluation of the control to be used for vehicle operation. The results of our study demonstrate the reliability and robustness of DSIM for electric vehicle motorization and speed control. Promoting this technology is essential to improve the overall performance and efficiency of electric vehicles, especially in traction and braking modes for energy recovery. This underscores the importance of DSIM in the sustainable development of the electric transportation system.

Infrastructure System Obstacles and Technology Adoption by Firms in Transition Countries

Abstract This study uses business survey data to analyze firm perceptions of infrastructure systems in transition countries as obstacles to conducting business. Factors that contribute to reported perceptions are modeled, including country macroeconomic conditions, firm characteristics, and measures of overall reforms and infrastructure system reforms. Identified obstacles arise from binding constraints that limit economic growth, reflect implicit shadow costs, and signal areas in need of policy reform or infrastructure investment. The analysis covers electric power, telecommunications, transportation, and water and wastewater infrastructure systems. Also examined is whether transition indicators of reforms, intended to reflect improvements, are associated with perceptions of obstacles. Beyond identifying infrastructure obstacles and the effects of reforms during the transition process, we also explore firm adoptions of new electronic technologies, essentially a new infrastructure system, as they use email, websites, cell phones, and high-speed Internet in their business activity.

The Future of Permanent-Magnet-Based Electric Motors: How Will Rare Earths Affect Electrification?

In this review article, we focus on the relationship between permanent magnets and the electric motor, as this relationship has not been covered in a review paper before. With the increasing focus on battery research, other parts of the electric system have been neglected. To make electrification a smooth transition, as has been promised by governing bodies, we need to understand and improve the electric motor and its main component, the magnet. Today's review papers cover only the engineering perspective of the electric motor or the material-science perspective of the magnetic material, but not both together, which is a crucial part of understanding the needs of electric-motor design and the possibilities that a magnet can give them. We review the road that leads to today's state-of-the-art in electric motors and magnet design and give possible future roads to tackle the obstacles ahead and reach the goals of a fully electric transportation system. With new technologies now available, like additive manufacturing and artificial intelligence, electric motor designers have not yet exploited the possibilities the new freedom of design brings. New out-of-the-box designs will have to emerge to realize the full potential of the new technology. We also focus on the rare-earth crisis and how future price fluctuations can be avoided. Recycling plays a huge role in this, and developing a self-sustained circular economy will be critical, but the road to it is still very steep, as ongoing projects show.

Blockchain-Enabled Cybersecurity Framework for Electric Transportation Systems

This article investigates the integration of blockchain technology into cybersecurity frameworks in electric transportation systems, evaluating the implications and advancements achieved in constructing a safe and resilient infrastructure. An analysis of electric vehicle (EV) specifications reveals a diverse range of battery capacity and driving ranges. As an example, the Tesla Model S has a battery capacity of 100 kWh, enabling it to go a distance of up to 320 miles. In contrast, the Nissan Leaf has a battery capacity of 40 kWh and a range of 150 miles. An examination of charging station data uncovers inconsistencies in power generation and transaction expenses. Charging stations with a higher power output of 100 kW have a price of up to $7, whilst stations with a lower power output of 50 kW charge $5. Moreover, the evaluation of cybersecurity metrics demonstrates significant improvements in data encryption and access control in high-security systems, demonstrating a 26% increase compared to low-security settings. An analysis of blockchain transaction records uncovers discrepancies in energy transfers between electric vehicles (EVs) and charging stations. It has been observed that some electric vehicles (EVs) transmit 50 kilowatt-hours (kWh) of energy, while others only transfer 25 kWh, leading to a notable 100% difference. These findings underscore the need of providing consumers with a diverse selection of electric vehicle options, the impact of location-based choices on charging infrastructure, the imperative of robust cybersecurity measures, and the many methods of energy transmission in electric transportation networks. This report offers comprehensive and crucial data that is necessary for stakeholders and policymakers seeking to enhance infrastructure and security systems to establish secure and efficient electric transportation ecosystems.

Results of a statistical study of types of single-phase faults in low-voltage ship electrical networks

The increase in the power supply of ships is manifested in growth of ship power plants' capacity, the total length of cable lines, and the number of electrified mechanisms. With the development of marine technology, the severity of the consequences of damage to ship electrical equipment and the costs of its maintenance and repair increase. The search for ways to improve the reliability and safety of electrical equipment should be based on design and technological solutions at the stages of its development and production, as well as an analysis of various factors that may appear during the operation of marine equipment and affect the condition of ship electrical systems. During studying these factors, it has been revealed that one of the common types of damage to electrical equipment of marine equipment is single-phase short circuits, the causes of which are varied. Preventing short circuits is labour intensive and in some cases impracticable. However, understanding the characteristics of this type of damage to low-voltage equipment is necessary to improve the reliability and safety of ship electrical systems. The conducted research is based on a survey of ship electrical mechanics. The results of the surveys have made it possible to analyze the frequency of occurrence of single-phase insulation faults, the duration of their existence, to identify the most likely places of occurrence of short circuits and equipment that is more susceptible to such damage compared to other elements of the electrical power system.

Improving critical buildings energy resilience via shared autonomous electric vehicles — A sequential optimization framework

The interdependence between electric power systems and transportation systems is rapidly increasing due to the high adoption of Electric Vehicles (EVs) and their charging infrastructures. Electric vehicles can represent additional load for the power system, but can also bring new opportunities for contributing to the efficient and resilient operations of the power grid. This is mainly because of their ability to provide back power to the system when it is not used for transportation, essentially serving as a moving battery source for the power grid. This so-called Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B), or, generally, Vehicle-to-X (V2X) capability of EVs has been extensively studied in the literature. However, the upcoming development of autonomous driving systems and their integration within sharing mobility services can significantly add to the possibilities of interaction between the transportation system and the power grid. This paradigm is studied to a much lesser extent in the existing literature. Shared Autonomous Electric Vehicles (SAEVs) could allow for more control of the actions of the fleet allowing for large-scale coordinated responses both to mobility and energy demands. This coordinated response can be particularly useful for providing emergency power services in case of power loss while maintaining a high level of transportation service. Thus, improving the overall resilience of the system. In this work, we develop a dynamic optimization framework to evaluate the potential contribution of the SAEV fleet for improving critical buildings’ energy resilience via V2B services. The model considers passengers’ pick-up and transportation, relocation of vehicles, and battery charging and discharging. Power outage scenarios for critical buildings are considered and the potential of the SAEV fleet to fully or partially respond to the emergency power outage is studied. In addition, sensitivity analysis for key parameters such as the outage parameters is introduced. The results of the case study for the Ile-de-France region in France shows that the SAEV fleet has the potential to provide V2B service for critical building at an acceptable loss of passenger total waiting time on the transportation side. Furthermore, it is shown that it is capable of satisfying the emergency power load at a lower cost compared to investing in extra backup generators unless the outage occurs at significantly high frequencies.

A Multidimensional Readiness Index for the Electrification of the Transportation System in China, Norway, and Sweden

The main objective of this paper is to develop a readiness index model that can serve as an analytical tool for exploring the achievements of the electrification of transportation systems. We have applied this readiness index model to evaluate the readiness positioning of China, Norway, and Sweden towards transportation electrification. We have chosen these three countries as they represent diversity among countries adopting electric transportation system solutions. Our developed readiness index model has four key dimensions: technological readiness, political readiness, societal readiness, and economic readiness. The embeddedness of all four dimensions in one model provides a multi-perspective way of analyzing and evaluating the readiness levels of countries moving towards transforming their transportation system. Therefore, we named the model a “multidimensional readiness index”. Our main conclusions are that political processes and decisiveness are the most important factors, followed by societal needs and economic ability, with the current technology as the fourth. Without the participation of dedicated and determined political decision makers, the other three factors are challenging to obtain. Political decision makers need to facilitate economic means to support the transformation in society and affected industries to balance the economic disadvantages of the electrically powered vehicle systems until they pass the cost disadvantage turning point. The development of relevant technology is no longer the significant barrier it was at the beginning of this transformation about 20 years ago. The technology for electrically powered transportation systems and devices is widely available now, although it is continuously evolving and being improved. Associated industries cannot be expected to initiate, finance, take risks, and take the lead in this global societal transformation without clear and strong political support.

Analyzing electric vehicle battery health performance using supervised machine learning

Lithium-ion batteries having high energy and power densities, fast depleting cost, and multifaceted technological improvement lead to the first choice for electric transportation systems. A noble supervised K nearest neighbors (KNN), support vector regressor (SVR), decision tree (DT), and random forest (RF) regressors machine learning algorithm are developed with different accuracy and usefulness for the state of health estimation from direct measurable indices of voltage, current, and temperature without inherited electrochemical characteristics like voltage hysteresis, aging, degradation level, operational, and environmental effect. Based on the battery dataset from Sandia National Laboratories, the proposed technique is validated. The comparison characteristics are provided with the help of mean absolute percent error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) techniques. The MAPE error at 15 °C for different regression algorithms SVR, DT, KNN, and RF are 2.791E-02, 1.607E-03, 9.957E-04, and 4.323E-03 respectively. The MAE error at 25 °C for different regression algorithms SVR, DT, KNN, and RF are 5.593E-02, 2.379E-03, 2.429E-03, and 5.073E-03 respectively. The RMSE error at 35 °C for different regression algorithms SVR, DT, KNN, and RF are 5.062E-02, 6.862E-03, 6.333E-03, and 1.275E-02 respectively. The MSE error percentages for the testing sample at 35 °C for different regression algorithms are 0.37 %, 0.01 %, 0.01 %, and 0.02 % respectively. The results indicate that KNN and DT exhibit higher precision when applied to lithium-ion batteries for electric vehicles. Collaborating with hardware manufacturers to exchange data has the potential to foster the creation of innovative battery health monitoring systems.

A Shunt DC Electric Spring Control Strategy for MVDC Bus Voltage Stability Onboard AES

Background: The recent trend in the all-electric ship (AES) electrical systems, especially in military vessels, is to move towards medium voltage direct current (MVDC) distribution. Bus voltage instability is a major issue in direct current (DC) distribution systems. Nowadays, direct current electric springs (DCES) are extensively used in low-voltage direct current (LVDC) microgrids to address voltage instability issues. This paper extends the use of a shunt DCES to stabilize the bus voltage in an MVDC grid. The work proposes an addition to the MVDC onboard ship distribution system architecture, described in IEEE 1709, by integrating a shunt DCES with a novel control strategy to stabilize the bus voltage under various loading conditions, including propulsion motor (PM) and online pulsed power load (PPL). Method: The shunt DCES is designed to provide current into the MVDC bus, which reduces the bus current ripple to attain a stable bus voltage with reduced ripple. A dual loop control with a battery management system (BMS) is proposed for the shunt DCES and simulated in MATLAB/Simulink. BMS is designed based on the state of charge (SOC) of the battery and bus current ripple extracted from the system's source and load side currents. The current supplied by the shunt DCES and the extracted ripple current validate the effectiveness of the proposed control. Total harmonic distortions (THDs) as a measure of voltage ripple of the MVDC bus voltage at different intervals are measured and compared for both systems, with and without shunt DCES. Result: It was observed that the shunt DCES could reduce the voltage ripple well below the permissible limit, which is 5 % as per IEEE 1709. Conclusion: The proposed control strategy could attain a reduction of 68-85 % in THD under peak to off-peak loading conditions with the addition of shunt DCES.

Empirical patterns of interdependencies among critical infrastructures in cascading disasters: Evidence from a comprehensive multi-case analysis

Critical infrastructures (CIs), such as electric power, transportation, and energy systems, offer essential goods and services to maintain societal health and well-being. Any interruptions in their functioning will result in catastrophic consequences. Besides, due to their growing interdependencies, a local failure can spread across CI networks, exacerbating the vulnerability of the whole system. This study aims to investigate the interdependency patterns of CIs from a comprehensive and holistic perspective. The interdependency natures, including cascading paths, interdependency types, and degrees were analyzed through processing data from 12 typical cascading disaster cases covering various disaster categories. Furthermore, the criticality of the recovery time for CIs was also discussed. A comparative analysis with an existing database was conducted to validate the applicability of the data used in this research. The results reveal that first, almost all cascading effects across CIs propagate within the first and second-order ranges, accounting for 80.4% and 17.9%, respectively. In a few cases, positive feedback loop events with a self-reinforcing effect may also occur. Second, physical interdependency is the most common type among CIs, followed by geographic, and more than 79% of interaction behaviors among CIs are highly interdependent. The same CI pairs can have different interdependency types and degrees depending on specific disruption scenarios and temporal or spatial locations of the CI components being affected. Third, CI failure durations are the key factor in determining interdependency impacts. Maintaining moderate redundancy in CIs and improving their recovery performances can significantly mitigate these impacts. This study extends existing research and enriches the knowledge system of CI interdependencies, its findings can help inform decision-makers on improving the preparedness level of CIs and mitigating interdependency impacts.

A nonlinear optimal control approach for voltage source inverter-fed three-phase PMSMs

A nonlinear optimal control approach for voltage source inverter-fed three-phase PMSMs

The Influence of Service Quality and Station Facilities on Mass Rapid Transit Jakarta Passengers' Satisfaction

Mass Rapid Transit (MRT) is an effective and convenient electric rail-based transportation system which has proven to have positive results since it is applied in large cities in various countries. Various facilities at MRT stations are provided to facilitate customer service. This study aimed to determine the effect of service quality and station facilities on user satisfaction when using the Jakarta MRT. The research method used was quantitative research using a questionnaire. The sample was 100 respondents from MRT user data using the Slovin Formula. The results indicated that the quality of service and station facilities has an influence of 52.7% and strongly relates to user satisfaction.&#x0D; Keywords: Service Quality, Facilities, Passenger Satisfaction

EFFECT OF THE LEARNING OUTCOMES OF THE TERRESTIAL NAVIGATION COURSE IN COMPETENCE PASSAGE PLANNING FOR CADETS WHO ARE CARRYING OUT SEA PROJECT ON BOARD

Terrestial Navigation is a harmonious union between science and art that teaches us how to sail a ship from one place to another safely, quickly, efficiently, and economically and safely to the destination. Passage Planning is the planning of ship voyages from one place to another safely, quickly and economically, and safely to the destination. Shipping from one port to another must be coordinated by all crew members. Politeknik Pelayaran Malahayati, which is a Technical Implementation Unit under the Ministry of Transportation, the Transportation HR Development Agency, which has the task of implementing Vocational Education, research, and Community Service in the field of Shipping, has organized a Diploma III Nautical Studies, Ship Engineering Studies, and Ship Electrical System Studies. This study aims to evaluate the influence of understanding Passage Planning Terrestial Navigation on the competence of cadets when doing Sea Project on board. The research method used is quantitative research with a sampling technique using purposive sampling on cadets at Voyage schools. Data was collected through questionnaires and regression analysis was used to test the hypothesis. The results of the study show that the understanding of Passage Planning in Terrestial Navigation has a significant effect on the competence of cadets when carrying out maritime practices on board. Therefore, it is suggested for Voyage schools improve the learning of Terrestial Navigation Passage Planning to increase the competence of cadets in carrying out maritime practices on board. The results of the study simultaneously obtained the value of Fcount &gt; Ftable [29.008 &gt; 2.513] at the significance level∝ = 5%, this states that simultaneously the variables Catalog &amp; Nautical Chart (x1), Plotting Position (x2), Distance and Course Voyage Determination (x3), Voyage Planning Tabulation (x4), affect competence cadets during Sea Project on board at Politeknik Pelayaran Malahayati.

Optimal allocation of FCSs and PV units in smart grids considering traffic and generation dispatch

Worldwide, transportation electrification and photovoltaics (PVs) have become vital components of modern and future planning problems. Therefore, the large-scale deployment of electric vehicles (EVs) requires reliable fast-charging stations (FCSs). However, random allocation of FCSs and PV adversely impacts the performance and operation of the traffic flow and power networks. Thus, this paper proposes a new planning approach for the optimal allocation of FCSs and PVs in large-scale smart grids and transportation networks. Unlike existing approaches, the well-known Dynamic Network Assignment Simulation for Road Telematics (DYNASMART) is updated and employed in the proposed planning approach to accurately simulate the EVs within the traffic stream. The average waiting time of the EVs at the charging stations (including charging time) and the total annual costs are utilized as conflicting objective functions to be minimized. To effectively solve this comprehensive model with conflicting objectives, a bi-layer multiobjective optimization based on metaheuristic (NSGA-II) and deterministic (GAMS) algorithms is established. The outer layer optimizes the allocation of FCSs and PV accurately, while the inner one optimally dispatches the generated power of the conventional generation units. The proposed planning approach is tested on a 26-bus electrical transmission network and 92-node transportation system. The simulation results demonstrate the efficiency of the proposed approach. The optimal allocation of FCSs and PVs with 80% EVs penetration increases the net profit by 85.36% while maintaining a maximum of 30 minutes for both charging and waiting time.

Optimization and Analysis of Leakage Reactance for a Converter Transformer of the Electric Transport System

Optimization and Analysis of Leakage Reactance for a Converter Transformer of the Electric Transport System

A Medium Voltage Input Multiport Isolated Output DC Transformer With Power Self-Balancing and Output Fault Isolation

Medium voltage dc (MVDC) converters with multiple output ports have the challenges of series input module power balancing control and fault isolation between output ports. This paper proposes a new medium voltage input multiport isolated output dc transformer topology. The proposed topology can be extended to up to 2k-1 output ports of different voltages with k modules input connected in series. Moreover, the proposed topology has the voltage self-balancing feature of input modules since they are coupled via the balancing capacitors. Current self-balancing of all MOSFETs is also achieved, as the equivalent impedance of multiple voltage sources discharging to multiple loads is the same under the influence of the split resonant inductor. Besides, the proposed topology can limit the shorted current in the output port automatically without affecting other ports, due to the impedance of the resonant tank of the faulty port being clamped. A 2kV dc input with three output ports of 410V, 380V and 40V prototype is built to verify the feasibility of the proposed topology. The results show that the proposed topology has multiple module power self-balancing feature, and can improve the reliability of the multiport power supply systems. Therefore, it has good prospects for subsea dc power distribution systems and ship electric power distribution systems.