Search Results (140)

The emergence of electric vehicles (EVs) has brought specific risks, including the possibility of fires or explosions resulting from mechanical, thermal, or electrical failures, which can lead to thermal runaway (TR). There is a great lack of knowledge about how to act safely in this type of fire. This study carried out two full-scale fire experiments on electric vehicles to investigate response strategies to electric vehicle fires caused by thermal runaway. Centro Zaragoza provided technical advice for these tests, so that they could be carried out safely, controlling the risks. This advice has allowed Centro Zaragoza to analyze different response strategies to the fires in electric vehicles caused by thermal runaway. On the other hand, the propagation patterns of thermal runaway fires in electric vehicles were investigated. The early-phase effectiveness of fire blankets and other extinguishing measures was tested, and the temperature distributions inside the vehicle and the type of fire generated were measured. The results showed that fire blankets successfully extinguished flames by cutting off the oxygen supply. These findings contribute to the development of effective strategies for responding to electric vehicle fires, enabling the establishment of good practice for fire suppression in electric vehicles and their batteries. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

In this paper, a detailed review of electric vehicle (EV) charging station architectures is first presented, and then an optimal architecture suitable for a large MW-scale EV fast-charging station (EVFS) with multiple fast chargers is proposed and evaluated. The study examines various EVFS architectures, including those currently deployed in commercial sites. Most EVFS implementations use either a common AC-bus or a common DC-bus configuration, with DC-bus architectures being slightly more predominant. The paper analyzes the EV charging and battery energy storage system (BESS) requirements for future large-scale EVFSs and identifies key implementation challenges associated with the full adoption of the common DC-bus approach. To overcome these limitations, a novel multi-zone EVFS architecture is proposed that employs an optimal combination of isolated and non-isolated DC-DC converter topologies while maintaining galvanic isolation for EVs. The system efficiency and total power converter capacity requirements of the proposed architecture are evaluated and compared with those of other EVFS models. A major feature of the proposed design is its multi-zone division and zonal isolation capabilities, which are not present in conventional EVFS architectures. These advantages are demonstrated through a scaled-up model consisting of 156 EV fast chargers. The analysis highlights the superior performance of the proposed multi-zone EVFS architecture in terms of efficiency, total power converter requirements, fault tolerance, and reduced grid impacts, making it the best solution for reliable and scalable MW-scale commercial EVFS systems of the future. Full article

California’s SB 1014 (Clean Miles Standard) mandates ridehailing fleet electrification to reduce emissions from vehicle miles traveled, posing financial and infrastructure challenges for drivers. This study employs a mixed-methods approach, including expert interviews (n = 10), group discussions (n = 8), and a survey of full- and part-time drivers (n = 436), to examine electric vehicle (EV) adoption attitudes and policy preferences. Access to home charging and prior EV experience emerged as the most statistically significant predictors of EV acquisition. Socio-demographic variables, particularly income and age, could also influence the EV choice and sensitivity to policy design. Full-time drivers, though confident in the EV range, were concerned about income loss from the charging downtime and access to urban fast chargers. They showed a greater interest in EVs than part-time drivers and favored an income-based instant rebate at the point of sale. In contrast, part-time drivers showed greater hesitancy and were more responsive to vehicle purchase discounts (price reductions or instant rebates at the point of sale available to all customers) and charging credits (monetary incentive or prepaid allowance to offset the cost of EV charging equipment). Policymakers might target low-income full-time drivers with greater price reductions and offer charging credits (USD 500 to USD 1500) to part-time drivers needing operational and infrastructure support. Full article

This paper presents a non-fragile observer-based dissipative control strategy for the suspension systems of electric vehicles equipped with in-wheel motors, accounting for input delays, actuator faults, and observer gain uncertainty. Traditional control approaches—such as $H_{\infty }$, passive control, and robust feedback schemes, often address these challenges in isolation and with increased conservatism. In contrast, this work introduces a unified framework that integrates fault-tolerant control, delay compensation, and robust state estimation within a dissipativity-based setting. A novel dissipativity analysis tailored to Electric Vehicle Active Suspension Systems (EV-ASSs) is developed, with nonzero delay bounds explicitly incorporated into the stability conditions. The observer is designed to ensure accurate state estimation under gain perturbations, enabling robust full-state feedback control. Stability and performance criteria are formulated via Linear Matrix Inequalities (LMIs) using advanced integral inequalities to reduce conservatism. Numerical simulations validate the proposed method, demonstrating effective fault-tolerant performance, disturbance rejection, and precise state reconstruction, thereby extending beyond the capabilities of traditional control frameworks. Full article

Carsharing has long supported trip purposes typically made by private vehicles, with grocery shopping especially benefiting from the carrying capacity of a personal vehicle. BlueLA is a one-way, station-based electric vehicle (EV) carsharing service in Los Angeles aimed at improving access in low-income neighborhoods. We hypothesize that BlueLA improves grocery access for underserved households by increasing their spatial-temporal reach to diverse grocery store types. We test two hypotheses: (1) accessibility from BlueLA stations to grocery stores varies by store type, traffic conditions, and departure times; and (2) Standard (general population) and Community (low-income) members differ in perceived grocery access and station usage. Using a mixed-methods approach, we integrate walking and driving isochrones, store data (n = 5888), trip activity data (n = 59,112), and survey responses (n = 215). Grocery shopping was a key trip purpose, with 69% of Community and 61% of Standard members reporting this use. Late-night grocery access is mostly limited to convenience stores, while roundtrips to full-service stores range from 55 to 100 min and cost USD 12 to USD 20. Survey data show that 84% of Community and 71% of Standard members reported improved grocery access. The findings highlight the importance of trip timing and the potential for carsharing and retail strategies to improve food access. Full article

Enhanced Battery Management Systems (BMS) are essential for improving operational efficacy and safety within Electric Vehicles (EVs), especially in tropical climates where traditional systems encounter considerable performance constraints. This research introduces a novel two-tiered deep learning framework that utilizes a two-stage Long Short-Term Memory (LSTM) framework for precise prediction of battery voltage and SoC. The first tier employs LSTM-1 forecasts individual cell voltages across a full-scale 120-cell Lithium Iron Phosphate (LFP) battery pack using multivariate time-series data, including voltage history, vehicle speed, current, temperature, and load metrics, derived from dynamometer testing. Experiments simulate real-world urban driving, with speeds from 6 km/h to 40 km/h and load variations of 0, 10, and 20%. The second tier uses LSTM-2 for SoC estimation, designed to handle temperature-dependent voltage fluctuations in high-temperature environments. This cascade design allows the system to capture complex temporal and inter-cell dependencies, making it especially effective under high-temperature and variable-load environments. Empirical validation demonstrates a 15% improvement in SoC estimation accuracy over traditional methods under real-world driving conditions. This study marks the first deep learning-based BMS optimization validated in tropical climates, setting a new benchmark for EV battery management in similar regions. The framework’s performance enhances EV reliability, supporting the growing electric mobility sector. Full article

The proliferation of electric vehicle (EV) racing competitions, such as Formula electric vehicle (FEV) competitions, has intensified the quest for high-performance electric propulsion systems. High-speed permanent magnet synchronous motors (PMSMs) for FEVs necessitate an optimized control strategy that adeptly manages the complex interplay between electromagnetic torque production and minimal power loss, ensuring peak operational efficiency and performance stability across the full speed range. This paper delves into the optimization of high-speed PMSM, pivotal for its application in FEVs. It begins with a thorough overview of the FEV motor’s basic principles, followed by the derivation of a detailed mathematical model that lays the groundwork for subsequent analyses. Utilizing MATLAB/Simulink, a simulation model of the motor drive system was constructed. The proposed strategy synergizes the principles of maximum torque per ampere (MTPA) with the flux weakening control technique instead of conventional zero direct axis current (ZDAC), aiming to push the boundaries of motor performance while navigating the inherent limitations of high-speed operation. Covariance matrix adaptation evolution strategy (CMA-ES) was deployed to determine the optimal d-q axis current ratio achieving maximum operating torque without overdesign problems. The implementation of the optimized control strategy was rigorously tested on the simulation model, with subsequent validation conducted on a real test bench setup. The outcomes of the proposed technique reveal that the tailored control strategy significantly elevates motor torque performance by almost 22%, marking a pivotal advancement in the domain of high-speed PMSM. Full article

The integration of Distributed Energy Resources (DERs) such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) introduces new challenges to distribution networks despite offering opportunities for decarbonization and grid flexibility. This paper proposes a scalable simulation-based framework that combines deterministic nodal hosting capacity analysis with probabilistic Monte Carlo simulations to evaluate and optimize DER integration in diverse feeder types. The methodology is demonstrated using the IEEE 13-bus and 123-bus test systems under full-year time-series simulations. Deterministic hosting capacity analysis shows that individual nodes can accommodate up to 76% of base load from PV sources, while Monte Carlo analysis reveals a network-wide average hosting capacity of 62%. Uncoordinated DER deployment leads to increased system losses, overvoltages, and thermal overloads. In contrast, coordinated integration achieves up to 38.7% reduction in power losses, 25% peak demand shaving, and voltage improvements from 0.928 p.u. to 0.971 p.u. Additionally, congestion-centric optimization reduces thermal overload indices by up to 65%. This framework aids utilities and policymakers in making informed decisions on DER planning by capturing both spatial and stochastic constraints. Its modular design allows for flexible adaptation across feeder scales and configurations. The results highlight the need for node-specific deployment strategies and probabilistic validation to ensure reliable, efficient DER integration. Future work will incorporate optimization-driven control and hardware-in-the-loop testing to support real-time implementation. Full article

This paper provides a comprehensive review of Partial Power Converter (PPC) topologies and control methods for fast electric vehicle (EV) charging applications. Partial Power Converters are gaining traction to enhance converter efficiency, reduce power losses, and minimize component sizes by processing only a portion of the total power. This review covers key PPC topologies, including different partial power converters, and highlights their advantages and limitations in the context of EV charging. Various control methods that optimize the performance of these converters are also discussed. The paper presents a comparative analysis between partial power and full power converters. Finally, this review synthesizes the main findings and proposes guidelines for selecting appropriate PPC architectures for future fast EV charging stations. Full article

As electric vehicle (EV) adoption continues to surge globally, the question of what to do with retired EV batteries looms large. While these batteries may no longer meet the rigorous demands of automotive use, they often retain a significant portion of their capacity and functionality. This has led to growing interest in exploring second-life applications for retired EV batteries, ranging from stationary energy storage to grid stabilization and beyond. However, numerous challenges must be addressed to unlock the full potential of this emerging sector. This paper delves into the key challenges and prospects associated with the second life of retired EV batteries. It examines technical hurdles, such as battery degradation, safety concerns, and the development of efficient repurposing methods, along with regulatory and economic barriers, including standards for battery reuse, recycling infrastructure, and market dynamics. Additionally, it highlights the potential environmental benefits, including reduced carbon emissions and resource conservation. In conclusion, the second life of retired EV batteries presents both challenges and opportunities. Addressing technical, regulatory, and economic barriers will be essential for realizing the full potential of this growing sector. However, with continued innovation and collaboration across industries, the future looks bright for leveraging retired EV batteries to create a more sustainable energy ecosystem. Full article

The rapid integration of renewable energy and electric vehicles is challenging modern distribution networks with increased demand volatility and overload risks. To address these issues, we propose an integrated, multi-timescale battery dispatch framework that unifies long-term capacity planning, day-ahead/intra-day scheduling, and sub-minute real-time control. The framework combines HOMER Pro-based capacity sizing, a MISOCP model for economic scheduling, and an agent-based simulation for immediate overload mitigation. In a case study of a Danish distribution network projected to reach full EV penetration by 2034, our approach reduced moderate-to-severe overload incidents by 82.7%. Furthermore, a price-sensitive variant achieved a 27.4% reduction in operational costs, with only a 12.5% increase in minor overload events. These quantitative improvements, alongside qualitative enhancements in grid stability and battery longevity, provide actionable insights for distribution system operators. Full article

Electric vehicle (xEV) battery durability significantly impacts the long-term operation, consumer satisfaction, and market adoption of xEVs. As driving range diminishes over time, it affects vehicle service life and lifecycle GHG emissions. Measuring the full service life of xEV batteries in laboratory tests presents technical and logistical challenges, necessitating representative measurements for parameterizing numerical models. These models are crucial for predicting long-term performance and rely on high-quality experimental data. While performance and aging trends under extreme temperatures are documented, cell thermal contact conditions suitable for direct model input are not well characterized. This study investigates lithium-ion cells from three xEV types, cycled at constant currents from C/40 to 1C, at temperatures between −15 °C and +45 °C, over 1000 cycles in a multi-year campaign. Stable isothermal cell temperatures were achieved using custom-built liquid immersion baths with forced convection, highlighting fundamental electrochemical behaviors by decoupling complex self-heating not typically monitored in air environments. The data inform and validate physics-based models on temperature-dependent performance and durability, providing operational limits to enhance cell and battery thermal management design and educate xEV consumers about conditions affecting performance, range, and durability. Full article

This paper proposes a novel receiver coil configuration referred to as the Vertical Coil (VC), which substantially improves energy transfer efficiency in Dynamic Wireless Power Transfer (DWPT) systems for electric vehicles (EVs). Unlike the commonly used horizontally oriented coil layout (DDQ), the VC design employs multiple coils wound vertically around a ferrite core, thereby capturing magnetic flux more effectively over an extended spatial range. Through comprehensive simulations on full-scale models and corroborating small bench model experiments, we demonstrate that the VC configuration not only achieves a higher peak efficiency exceeding 90% but also widens the efficient power transfer zone by approximately 50% compared to DDQ. This broader coverage translates into an overall energy capture increase of more than 50% during a full pass of the vehicle over the transmitter coils, a significant step toward facilitating the implementation of DWPT technology. While mutual coupling among the VC coils can alter the system’s resonance frequency, our results confirm that this effect does not impede the VC’s superior energy transfer performance. Consequently, the new VC receiver design offers a promising pathway for advancing DWPT technology for reducing EV battery size and extending driving range. Full article

This paper presents a novel sorting algorithm for modular multilevel inverters (MMCs) with integrated batteries, designed to ensure the uninterrupted operation of electric vehicles (EVs) under post-fault conditions. The proposed system structure consists of an MMC with four full-bridge modules per phase, where one module acts as a spinning reserve during normal operation. The algorithm addresses a single switch fault per phase by operating the faulted module in half-bridge mode, ensuring all batteries remain operational and maintaining full power output and battery capacity without any noticeable changes for the vehicle operator. Unlike conventional fault-tolerant strategies that often reduce power output or disable affected modules, the proposed algorithm isolates the faulty switch while preserving system output. This approach avoids derating and eliminates the need for immediate maintenance, enabling the EV to continue operating under fault conditions. Simulation and experimental results validate the effectiveness of the algorithm under a single switch fault scenario, demonstrating its ability to maintain autonomy and consistent power delivery. This work demonstrates a fault-tolerant MMC principle, offering a robust and scalable solution for enhancing reliability and user satisfaction in EV power systems. Full article