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World Electr. Veh. J., Volume 16, Issue 2 (February 2025) – 49 articles

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12 pages, 20754 KiB  
Article
Development of a New Electric Vehicle Post-Crash Fire Safety Test in Korea (Proposed for the Korean New Car Assessment Program)
by Jeongmin In, Jaehong Ma and Hongik Kim
World Electr. Veh. J. 2025, 16(2), 103; https://doi.org/10.3390/wevj16020103 - 13 Feb 2025
Abstract
Recent fire incidents following electric vehicle (EV) collisions have been increasing rapidly in Korea, corresponding to the growing distribution of EVs. While the overall number of EV fires is lower compared to those involving internal combustion engine (ICE) vehicles, EV fires can lead [...] Read more.
Recent fire incidents following electric vehicle (EV) collisions have been increasing rapidly in Korea, corresponding to the growing distribution of EVs. While the overall number of EV fires is lower compared to those involving internal combustion engine (ICE) vehicles, EV fires can lead to more severe outcomes. Current regulations for post-crash fuel system integrity evaluation do not differentiate between EVs and ICE vehicles. However, the causes of fires in these vehicles differ due to variations in the design and construction of their fuel systems. This study analyzed seventeen cases of EV post-crash fires in Korea to derive two representative risk scenarios for EV post-crash fires. The first scenario involves significant intrusion into the EV front-end structure resulting from high-speed frontal collisions, while the second scenario involves direct impacts to the battery pack mounted under the vehicle from road curbs at low speeds (30–40 km/h). Based on these scenarios, we conducted tests to assess battery damage severity under two crash test modes, simulating both high-speed frontal collisions and low-speed curb impacts. The test results led to the development of a draft crash test concept to evaluate EV post-crash fire risks. Furthermore, we assessed the reproducibility of these test modes in relation to actual EV post-crash fires. Our findings indicate that square-shaped impactors provide higher reproducibility in simulating real EV post-crash fire incidents compared to hemisphere-shaped impactors. Additionally, a fire occurred 31 days after the storage of a crash-evaluated battery test specimen, which was determined to be caused by moisture invasion during post-crash storage, accelerating a micro-short circuit. This study aims to contribute to the development of new evaluation methods for the Korean New Car Assessment Program (KNCAP) to enhance EV post-crash fire safety by utilizing these test results to refine collision severity evaluation methods. Full article
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25 pages, 7902 KiB  
Article
Operating Condition Recognition Based Fuzzy Power-Following Control Strategy for Hydrogen Fuel Cell Vehicles (HFCVs)
by Yingxiao Yu, Kun Wang, Yukun Fan, Xiangyu Tang, Minghao Huang and Junjie Bao
World Electr. Veh. J. 2025, 16(2), 102; https://doi.org/10.3390/wevj16020102 - 13 Feb 2025
Abstract
To reduce hydrogen consumption by hydrogen fuel cell vehicles (HFCVs), an adaptive power-following control strategy based on gated recurrent unit (GRU) neural network operating condition recognition was proposed. The future vehicle speed was predicted based on a GRU neural network and a driving [...] Read more.
To reduce hydrogen consumption by hydrogen fuel cell vehicles (HFCVs), an adaptive power-following control strategy based on gated recurrent unit (GRU) neural network operating condition recognition was proposed. The future vehicle speed was predicted based on a GRU neural network and a driving cycle condition recognition model was established based on k-means cluster analysis. By predicting the speed over a specific time horizon, feature parameters were extracted and compared with those of typical operating conditions to determine the categories of the parameters, thus the adjustment of the power-following control strategy was realized. The simulation results indicate that the proposed control strategy reduces hydrogen consumption by hydrogen fuel cell vehicles (HFCVs) by 16.6% with the CLTC-P driving cycle and by 4.7% with the NEDC driving cycle, compared to the conventional power-following control strategy. Additionally, the proposed strategy effectively stabilizes the battery’s state of charge (SOC). Full article
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23 pages, 1942 KiB  
Article
Hybrid Electric Vehicles as a Strategy for Reducing Fuel Consumption and Emissions in Latin America
by Juan C. Castillo, Andrés F. Uribe, Juan E. Tibaquirá, Michael Giraldo and Manuela Idárraga
World Electr. Veh. J. 2025, 16(2), 101; https://doi.org/10.3390/wevj16020101 - 13 Feb 2025
Abstract
The vehicle fleets in Latin America are increasingly incorporating hybrid electric vehicles due to the economic and non-economic incentives provided by governments aiming to reduce energy consumption and emissions in the transportation sector. However, the impacts of implementing hybrid vehicles remain uncertain, especially [...] Read more.
The vehicle fleets in Latin America are increasingly incorporating hybrid electric vehicles due to the economic and non-economic incentives provided by governments aiming to reduce energy consumption and emissions in the transportation sector. However, the impacts of implementing hybrid vehicles remain uncertain, especially in Latin American, which poses a risk to the achievement of environmental objectives in developing countries. The aim of this study is to evaluate the benefits of incorporating hybrid vehicles to replace internal combustion vehicles, considering the improvement in the level of emission standards. This study uses data reported by Colombian vehicle importers during the homologation process in Colombia and the number of vehicles registered in the country between 2010 and 2022. The Gompertz model and logistic growth curves are used to project the total number of vehicles, taking into account the level of hybridization and including conventional natural gas and electric vehicles. In this way, tailpipe emissions and energy efficiency up to 2040 are also projected for different hybrid vehicle penetration scenarios. Results show that the scenario in which the share of hybrid vehicles remains stable (Scenario 1) shows a slight increase in energy consumption compared to the baseline scenario, about 1.72% in 2035 and 2.87% in 2040. The scenario where the share of MHEVs, HEVs, and PHEVs reaches approximately 50% of the vehicle fleet in 2040 (Scenario 2) shows a reduction in energy consumption of 24.64% in 2035 and 33.81% in 2040. Finally, the scenario that accelerates the growth of HEVs and PHEVs while keeping MHEVs at the same level of participation from 2025 (Scenario 3) does not differ from Scenario 2. Results show that the introduction of full hybrids and plug-in hybrid vehicles improve fleet fuel consumption and emissions. Additionally, when the adoption rates of these technologies are relatively low, the benefits may be questionable, but when the market share of hybrid vehicles is high, energy consumption and emissions are significantly reduced. Nevertheless, this study also shows that Mild Hybrid Electric Vehicles (MHEVs) do not provide a significant improvement in terms of fuel consumption and emissions. Full article
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17 pages, 526 KiB  
Article
On-Road Wireless EV Charging Systems as a Complementary to Fast Charging Stations in Smart Grids
by Fawzi Alorifi, Walied Alfraidi and Mohamed Shalaby
World Electr. Veh. J. 2025, 16(2), 99; https://doi.org/10.3390/wevj16020099 - 12 Feb 2025
Abstract
Electric vehicle (EV) users have the flexibility to fulfill their charging needs using either high-speed charging stations or innovative on-road wireless charging systems, ensuring uninterrupted travel to their destinations. These options present a spectrum of benefits, enhancing convenience and efficiency. The adoption of [...] Read more.
Electric vehicle (EV) users have the flexibility to fulfill their charging needs using either high-speed charging stations or innovative on-road wireless charging systems, ensuring uninterrupted travel to their destinations. These options present a spectrum of benefits, enhancing convenience and efficiency. The adoption of on-road wireless charging as a complementary method influences both the timing and extent of demand at fast-charging stations. This study introduces a comprehensive probabilistic framework to analyze EV arrival rates at fast-charging facilities, incorporating the impact of on-road wireless charging availability. The proposed model utilizes transportation data, including patterns from the US National Household Travel Survey (NHTS), to predict the specific times when EVs would need fast charging. To account for uncertainties in EV user decisions concerning charging preferences, a Monte Carlo simulation (MCS) approach is employed, ensuring a comprehensive analysis of charging behaviors and their potential impact on charging stations. A queuing model is developed to estimate the charging demand for numerous electric vehicles at a charging station, considering both scenarios: on-road EV wireless charging and relying exclusively on fast-charging stations. This study includes an analysis of a case and its simulation results based on a 32-bus distribution system and data from the US National Household Travel Survey (NHTS). The results indicate that integrating on-road EV wireless charging as complementary to fast charging significantly reduces the peak load at the charging station. Additionally, considering the on-road EV wireless charging system, the peak load of the station no longer aligns with the peak load of the power grid, resulting in improved power system capacity and deferred system upgrades. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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31 pages, 2386 KiB  
Article
Optimising Ventilation Strategies for Improved Driving Range and Comfort in Electric Vehicles
by Matisse Lesage, David Chalet and Jérôme Migaud
World Electr. Veh. J. 2025, 16(2), 98; https://doi.org/10.3390/wevj16020098 - 12 Feb 2025
Abstract
A car cabin’s small volume makes it vulnerable to discomfort if temperature, humidity, and carbon dioxide levels are poorly regulated. In electric vehicles, the HVAC system draws energy from the car battery, reducing the driving range by several dozen kilometres under extreme conditions. [...] Read more.
A car cabin’s small volume makes it vulnerable to discomfort if temperature, humidity, and carbon dioxide levels are poorly regulated. In electric vehicles, the HVAC system draws energy from the car battery, reducing the driving range by several dozen kilometres under extreme conditions. A 1D simulation model calibrated for the Renault ZOE was used to evaluate the effects of ventilation parameters on thermal comfort, humidity, and power consumption. The results highlighted the interdependence of factors such as the recirculation ratio and blower flow rate, showing that energy-efficient settings depend on ambient conditions and other factors (such as occupancy, vehicle speed, infiltration). Adjustments can reduce heat pump energy use, but no single setting optimally balances power consumption and thermal comfort across all scenarios. The opti-CO2 mode is proposed as a trade-off, offering energy savings while maintaining safety and comfort. This mode quickly achieves the cabin temperature target, limits carbon dioxide concentration at a safe level (1100 ppm), minimises fogging risks, and reduces heat pump power consumption. Compared to fresh air mode, the opti-CO2 mode extends the driving range by 9 km in cold conditions and 26 km in hot conditions, highlighting its potential for improving energy efficiency and occupant comfort in electric vehicles. Full article
19 pages, 5200 KiB  
Article
Research on Anti-Rollover Coordinated Control Strategy of Electric Forklift
by Yuefei Yang, Jingbo Wu and Zhijun Guo
World Electr. Veh. J. 2025, 16(2), 97; https://doi.org/10.3390/wevj16020097 - 12 Feb 2025
Abstract
In order to solve the problem that electric forklifts are prone to rollover when turning, a coordinated control strategy for anti-rollover of electric forklifts is proposed. A forklift dynamics simulation model with integrated centroid position is constructed, the stability of the forklift is [...] Read more.
In order to solve the problem that electric forklifts are prone to rollover when turning, a coordinated control strategy for anti-rollover of electric forklifts is proposed. A forklift dynamics simulation model with integrated centroid position is constructed, the stability of the forklift is judged by the phase plane area division method, the upper controller, including the active steering controller, and the differential brake controller are designed, the control weight coefficient of the active steering controller and the differential brake controller in different control domains is determined through the coordination controller, so as to obtain the required additional rear wheel rotation angle and additional yaw torque, and the braking force distribution controller exerts braking force to the wheel according to the additional yaw torque. A simulation model is built to verify the effectiveness of this control strategy, and the simulation results show that the control strategy can greatly reduce the risk of rollover when the forklift is cornering and further improve the stability of the forklift. Full article
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16 pages, 3277 KiB  
Article
Electric Long-Haul Trucks and High-Power Charging: Modelling and Analysis of the Required Infrastructure in Germany
by Tobias Tietz, Tu-Anh Fay, Tilmann Schlenther and Dietmar Göhlich
World Electr. Veh. J. 2025, 16(2), 96; https://doi.org/10.3390/wevj16020096 - 12 Feb 2025
Abstract
Heavy goods transportation is responsible for around 27% of CO2 emissions from road transport in the EU and for 5% of total CO2 emissions in the EU. The decarbonization of long-distance transport in particular remains a major challenge. The combination of [...] Read more.
Heavy goods transportation is responsible for around 27% of CO2 emissions from road transport in the EU and for 5% of total CO2 emissions in the EU. The decarbonization of long-distance transport in particular remains a major challenge. The combination of battery electric trucks (BETs) with on-route high-power charging (HPC) offers a promising solution. Planning and setting up the required infrastructure is a critical success factor here. We propose a methodology to evaluate the charging infrastructure needed to support the large-scale introduction of heavy-duty BETs in Germany, considering different levels of electrification, taking the European driving and rest time regulations into account. Our analysis employs MATSim, an activity-based multi-agent transport simulation, to assess potential bottlenecks in the charging infrastructure and to simulate the demand-based distribution of charging stations. The MATSim simulation is combined with an extensive pre-processing of transport-related data and a suitable post-processing. This approach allows for a detailed examination of the required charging infrastructure, considering the impacts of depot charging solutions and the dynamic nature of truck movements and charging needs. The results indicate a significant need to augment HPC with substantial low power overnight charging facilities and highlight the importance of strategic infrastructure development to accommodate the growing demand for chargers for BETs. By simulating various scenarios of electrification, we demonstrate the critical role of demand-oriented infrastructure planning in reducing emissions from the road freight sector until 2030. This study contributes to the ongoing discourse on sustainable transportation, offering insights into the infrastructure requirements and planning challenges associated with the transition to battery electric heavy-duty vehicles. Full article
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24 pages, 4110 KiB  
Article
A Comparative Life Cycle Analysis of an Active and a Passive Battery Thermal Management System for an Electric Vehicle: A Cold Plate and a Loop Heat Pipe
by Michele Monticelli, Antonella Accardo, Marco Bernagozzi and Ezio Spessa
World Electr. Veh. J. 2025, 16(2), 100; https://doi.org/10.3390/wevj16020100 - 12 Feb 2025
Abstract
This study extends beyond conventional Battery Thermal Management System (BTMS) research by conducting a Life Cycle Analysis comparing the environmental impacts of two technologies: a traditional active cold plate system and an innovative passive Loop Heat Pipe (LHP) system. While active cold plate [...] Read more.
This study extends beyond conventional Battery Thermal Management System (BTMS) research by conducting a Life Cycle Analysis comparing the environmental impacts of two technologies: a traditional active cold plate system and an innovative passive Loop Heat Pipe (LHP) system. While active cold plate BTMS requires continuous energy input during operation and charging, leading to significant energy consumption and emissions, the passive LHP BTMS operates without external power or moving parts, substantially reducing the climate change impact. This analysis considered two materials for LHP construction: copper and stainless steel. The results demonstrated that the LHP design achieved a 9.9 kg reduction in overall BTMS mass compared to the cold plate system. The implementation of stainless steel effectively addressed the high resource consumption associated with copper while reducing environmental impact by over 50% across most impact categories, compared to the cold plate BTMS. The passive operation of the LHP system leads to substantially lower energy usage and emissions during the use phase compared to the active cold plate. These findings highlight the potential of passive LHP technology to enhance the environmental sustainability of Battery Thermal Management Systems while maintaining effective thermal performance. Full article
(This article belongs to the Special Issue Heat Pipes in Thermal Management Systems for Electric Vehicles)
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18 pages, 1910 KiB  
Article
Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity
by Sheng Li, Xiangyu Yan and Guanhua Wang
World Electr. Veh. J. 2025, 16(2), 95; https://doi.org/10.3390/wevj16020095 - 11 Feb 2025
Abstract
Electric vehicles (EVs) are increasing in number every year, and large-scale uncontrolled EV charging can impose significant load pressure on the power grid (PG), affecting its stability and economy. This paper proposes an EV charging strategy that considers user selectivity. The user’s selection [...] Read more.
Electric vehicles (EVs) are increasing in number every year, and large-scale uncontrolled EV charging can impose significant load pressure on the power grid (PG), affecting its stability and economy. This paper proposes an EV charging strategy that considers user selectivity. The user’s selection strategy includes options for fast and slow charging types, as well as the choice of whether to comply with grid-controlled charging. Charging types are selected based on the ability to reach the desired state of charge (SOC), while compliance with grid-controlled charging is determined by comparing the unit charging cost (CC). An objective function is established to minimize the peak valley load difference (PVLD) rate of PGs and users’ CC. To achieve this, an improved non-dominated sorting whale optimization algorithm (INSWOA) is proposed which initializes the population through logistic mapping, introduces nonlinear convergence factors for position updates, and uses adaptive inertia weights to improve population diversity, enhance global optimization ability, reduce premature convergence, and improve solution accuracy. Finally, simulating distribution networks in a certain region, the results obtained from the INSWOA were compared with those from the non-dominated sorting whale optimization algorithm (NSWOA) and other algorithms. The comparisons demonstrated that the INSWOA significantly reduced the PVLD rate of the PG load and users’ CCs, highlighting its high practical value. Full article
(This article belongs to the Special Issue Impact of Electric Vehicles on Power Systems and Society)
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30 pages, 2867 KiB  
Review
Are We Testing Vehicles the Right Way? Challenges of Electrified and Connected Vehicles for Standard Drive Cycles and On-Road Testing
by Elia Grano, Manfredi Villani, Henrique de Carvalho Pinheiro and Massimiliana Carello
World Electr. Veh. J. 2025, 16(2), 94; https://doi.org/10.3390/wevj16020094 - 11 Feb 2025
Abstract
Standard driving cycles have been the method of choice for testing vehicle performance for decades, both in research and at the regulatory level. These methodologies offer the significant advantage of test reproducibility, allowing for consistent comparisons between vehicles. However, their inability to reflect [...] Read more.
Standard driving cycles have been the method of choice for testing vehicle performance for decades, both in research and at the regulatory level. These methodologies offer the significant advantage of test reproducibility, allowing for consistent comparisons between vehicles. However, their inability to reflect real-world driving conditions has become increasingly evident. This issue was first exacerbated by the advent of hybrid and plug-in hybrid vehicles, which introduced new complexities in powertrain operation. Legislators attempted to adapt testing procedures to account for electric energy usage in emissions assessments, but these efforts have largely failed to address the technical challenges posed by modern vehicles. As a result, the gap between real-world fuel consumption and type-approval values has continued to grow. The introduction of ADAS technologies has further widened this discrepancy, as standard driving cycles are no longer capable of accurately representing modern vehicle performance. In light of these challenges, this paper critically evaluates the limitations of standard drive cycles and on-road testing procedures, explores how hybrid and connected vehicles further complicate performance assessment, and proposes directions for improving these methodologies. Full article
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45 pages, 4198 KiB  
Article
Battery Capacity or Charging Infrastructure? Cost Modeling Study to Evaluate Investments of Electric Motorcycles and Supporting Infrastructure in Malaysia
by Satrio Fachri Chaniago, Wahyudi Sutopo and Azanizawati Ma’aram
World Electr. Veh. J. 2025, 16(2), 93; https://doi.org/10.3390/wevj16020093 - 11 Feb 2025
Abstract
Conventional motorcycles with internal combustion engines have significantly contributed to air pollution in Southeast Asia, posing challenges to achieving the ambitious net-zero emissions targets ratified by ASEAN member countries. In response, ASEAN countries have begun to adopt electric vehicles to achieve this ambitious [...] Read more.
Conventional motorcycles with internal combustion engines have significantly contributed to air pollution in Southeast Asia, posing challenges to achieving the ambitious net-zero emissions targets ratified by ASEAN member countries. In response, ASEAN countries have begun to adopt electric vehicles to achieve this ambitious target, especially electric motorcycles (EMs). However, the implementation of EMs faced several obstacles, notably limited battery range and insufficient charging infrastructure. Addressing these issues requires a huge investment from EM users and infrastructure providers. The government also plays a significant role in improving the investment climate for the EM ecosystem by providing financial incentives. This research aimed to model cost variables to evaluate the cost-effectiveness of government subsidies for EMs and their charging infrastructure in Malaysia using an equivalent annual cost (EAC) model and determine whether increasing battery capacity or increasing charging infrastructure would be more favorable. Data were collected through interviews with EM dealers, government agency, electric vehicle experts, and surveys of EM users in Malaysia, supplemented with secondary data through research articles, government regulations, and current news related to EM policies implemented in Malaysia. Surveys and interviews with relevant stakeholders were conducted to identify cost variables that influenced EM ownership and operation of EM infrastructure. This study found that Scenario 1 (subsidize EM purchases and charging infrastructure while excluding the battery purchase subsidy) was an optimal subsidy strategy for the government. Scenario 1 also reduced the EAC value, which is a cost burden for EM users, by 10.06% (for battery swap system users) and 5.84% (for direct charging system users). Additionally, this study also found that encouraging the use of EMs with battery swap systems was more profitable than EMs with direct charging systems. The findings of this research provide some insights about the most cost-efficient subsidy scenario for overcoming the obstacles, fostering a win–win situation for both EM users and the government. Thus, accelerating EM adoption forms part of the government’s goal to achieve net-zero emissions. Full article
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15 pages, 11075 KiB  
Article
The Development and Characteristics of an In-Wheel Assembly Using a Variable Speed-Reducing Device
by Kyeongho Shin, Kyoungjin Ko and Junha Hwang
World Electr. Veh. J. 2025, 16(2), 92; https://doi.org/10.3390/wevj16020092 - 11 Feb 2025
Abstract
This study proposes an in-wheel assembly with a variable speed-reduction device designed to maximize torque and vehicle speed, enabling high-performance vehicle-level driving characteristics in front-engine, rear-wheel drive (FR), internal combustion engine (ICE) vehicles, where conventional EV motors cannot facilitate e-4WD. The proposed system [...] Read more.
This study proposes an in-wheel assembly with a variable speed-reduction device designed to maximize torque and vehicle speed, enabling high-performance vehicle-level driving characteristics in front-engine, rear-wheel drive (FR), internal combustion engine (ICE) vehicles, where conventional EV motors cannot facilitate e-4WD. The proposed system integrates a motor and speed reducer within the wheel while avoiding interference from braking, steering, and suspension components. Through various innovative approaches, concepts for an integrated wheel-bearing planetary reducer and a variable speed planetary reducer were derived. The developed system achieved twice the maximum torque and a 35% increase in top speed compared to previously developed in-wheel systems, all without altering the front hard points. Multi-body dynamic analysis and component testing revealed wheel lock-up issues during reverse driving, and instability in the one-way clutch at high speeds. To address these issues, the power transmission structure was improved, and the type of one-way clutch was modified. Additionally, deficiencies in lubrication supply to the friction surface of the one-way clutch were identified through flow analysis and visualization tests, leading to design improvements. The findings of this study demonstrate that even in in-wheel systems where the application of large and complex transmission devices is challenging, it is possible to simultaneously enhance both maximum torque and top vehicle speed to achieve high-performance vehicle-level driving dynamics. Consequently, implementing an in-wheel e-4WD system in ICE FR vehicles is expected to improve fuel efficiency, achieve high-performance vehicle capabilities, and enhance market competitiveness. Full article
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20 pages, 2999 KiB  
Article
Development of Integrated Chassis Control of Semi-Active Suspension with Differential Brake for Vehicle Lateral Stability
by Kyungtack Lee and Jinwoo Seol
World Electr. Veh. J. 2025, 16(2), 91; https://doi.org/10.3390/wevj16020091 - 11 Feb 2025
Abstract
This paper describes an integrated control strategy that utilizes semi-active suspension and differential braking to enhance lateral stability while maintaining roll performance. The integrated control architecture adopts a hierarchical structure consisting of an estimator, a supervisor, a controller, and an allocator. In the [...] Read more.
This paper describes an integrated control strategy that utilizes semi-active suspension and differential braking to enhance lateral stability while maintaining roll performance. The integrated control architecture adopts a hierarchical structure consisting of an estimator, a supervisor, a controller, and an allocator. In the estimation layer, an algorithm is proposed to robustly estimate the side slip angle and roll angle in various situations. The control mode is established by the supervision layer based on the state of the vehicle. The maneuverability mode tracks the driver’s intentions, and the lateral stability mode ensures the vehicle’s stability. Reference values such as yaw rate and roll angle are determined by the control mode. In the controller layer, the yaw and roll moments are generated using sliding mode control to achieve the target yaw rate and roll angle. Brake torque and suspension damping force are distributed to each wheel in the allocator layer. In particular, a damping distribution method based on the roll region index is proposed. The proposed method is compared with conventional methods, such as full stiff damping and yaw-assisted damping, through simulation and real-world evaluation. The tests demonstrate that the proposed approach enhances lateral and roll stability, particularly regarding maximum side slip and roll angle values. The roll-region-index-based distribution method reduces the maximum roll angle by about 17.4% and the maximum side slip angle by about 8.7% compared to each conventional method. Compared to conventional methods, the proposed method showed more stable driving performance by ensuring stability in both directions in extreme lane change situations. Full article
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34 pages, 842 KiB  
Article
The Rise and Recent Decline of Tesla’s Share of the U.S. Electric Vehicle Market
by Chang (Charo) Liu, Stella G. Boothman and John D. Graham
World Electr. Veh. J. 2025, 16(2), 90; https://doi.org/10.3390/wevj16020090 - 10 Feb 2025
Abstract
This article examines the rise and recent decline of Tesla in the U.S. electric vehicle market. Using qualitative, semi-quantitative, and statistical methods, the article traces how Tesla acquired a first-mover advantage and how second movers, both established automakers and start-ups, responded to Tesla’s [...] Read more.
This article examines the rise and recent decline of Tesla in the U.S. electric vehicle market. Using qualitative, semi-quantitative, and statistical methods, the article traces how Tesla acquired a first-mover advantage and how second movers, both established automakers and start-ups, responded to Tesla’s rise. The recent decline in Tesla’s share of the U.S. electric vehicle market is linked to several factors: the proliferation of electric vehicle offerings from competitors, changes in public policy, and controversial decisions by Tesla and its CEO. The article concludes with a discussion of promising future strategies for both Tesla and its competitors. Full article
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25 pages, 8585 KiB  
Article
Research on the Performance of Radiators in Hybrid Vehicle Thermal Management Systems
by Jiahui Li, Jintao Su and Shuxian Wang
World Electr. Veh. J. 2025, 16(2), 89; https://doi.org/10.3390/wevj16020089 - 10 Feb 2025
Abstract
The cooling system plays an essential role in regulating the temperature of hybrid vehicle engines. With the contemporary surge in the number of hybrid vehicles, the cooling system’s performance is vital for the safe and stable operation of these cars. The radiator, as [...] Read more.
The cooling system plays an essential role in regulating the temperature of hybrid vehicle engines. With the contemporary surge in the number of hybrid vehicles, the cooling system’s performance is vital for the safe and stable operation of these cars. The radiator, as the core component of the cooling system, has become central to enhancing thermal efficiency through performance optimization. Improvements to existing radiators are especially important in order to meet increasing performance demands. This paper firstly outlines the development of radiator technology for hybrid vehicles both domestically and internationally; it then analyzes the tube and belt radiator, and selects a louvered finned radiator with highly efficient heat dissipation performance as the object of research. It then carries out the detailed design and assessment of the radiator, formulates an accurate design scheme, and creates a three-dimensional model of the radiator and its main parts using the CATIA V5 software. Finally, the simulation and analysis Fluent software (ANSYS 2023 R1) is used to carry out a comparative analysis of the designed radiator and its important parts. The study focuses on how fin angle, inlet and outlet positioning, radiator orientation, and fan speed affect thermal performance. The findings indicate that a 26° fin angle, a same-side inlet and outlet layout, correct radiator orientation, and higher fan speeds enhance cooling efficiency. These optimizations improve radiator performance, ensuring efficient cooling under various operating conditions. Full article
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17 pages, 4710 KiB  
Article
Quantifying the Uncertainty of Electric Vehicle Charging with Probabilistic Load Forecasting
by Yvenn Amara-Ouali, Bachir Hamrouche, Guillaume Principato and Yannig Goude
World Electr. Veh. J. 2025, 16(2), 88; https://doi.org/10.3390/wevj16020088 - 9 Feb 2025
Abstract
The transition to electric vehicles (EVs) presents challenges and opportunities for the management of electrical networks. This paper focuses on developing and evaluating probabilistic forecasting algorithms to understand and predict EV charging behaviours, crucial for optimising grid operations and ensuring a balance between [...] Read more.
The transition to electric vehicles (EVs) presents challenges and opportunities for the management of electrical networks. This paper focuses on developing and evaluating probabilistic forecasting algorithms to understand and predict EV charging behaviours, crucial for optimising grid operations and ensuring a balance between electricity demand and generation. Several forecasting approaches tailored to different time horizons are proposed across diverse model classes, including direct, bottom-up, and adaptive approaches. In all approaches, the target variable can be the load curve quantiles from 0.1 to 0.9 with 0.1 increments or prediction sets with a target coverage of 80%. Direct approaches learn from past load curves using GAMLSS or QGAM methods. Bottom-up approaches predict individual charging session characteristics (arrival time, charging duration, and energy demand) with mixture models before reconstructing the load curve. Adaptive approaches correct in real-time the prediction sets issued by direct or bottom-up approaches with conformal predictions. The experiments, conducted on real-world charging session data from Palo Alto, demonstrate the effectiveness of the proposed methods with regard to different metrics, including pinball loss, empirical coverage, and RPS. Overall, the results highlight the importance of quantifying uncertainty in load forecasts and the potential of probabilistic forecasting for EV load management. Full article
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36 pages, 509 KiB  
Review
Review of State-of-Charge Estimation Methods for Electric Vehicle Applications
by Miguel Antonio Pisani Orta, David García Elvira and Hugo Valderrama Blaví
World Electr. Veh. J. 2025, 16(2), 87; https://doi.org/10.3390/wevj16020087 - 9 Feb 2025
Abstract
Continuous and accurate state-of-charge estimation is essential for optimal reliability and performance in electric vehicle battery management systems. This work reviews state-of-charge estimation strategies, from straightforward methods like lookup tables and ampere-hour counting to advanced mathematical models, such as electrochemical, observer-assisted equivalent circuit, [...] Read more.
Continuous and accurate state-of-charge estimation is essential for optimal reliability and performance in electric vehicle battery management systems. This work reviews state-of-charge estimation strategies, from straightforward methods like lookup tables and ampere-hour counting to advanced mathematical models, such as electrochemical, observer-assisted equivalent circuit, and impedance-based models that capture cell dynamics. Additionally, data-driven models including fuzzy logic, neural networks, and support vector machines are explored for their ability to leverage large datasets. This review highlights the strengths and limitations of each method, emphasizing the specific contexts in which these strategies can be applied to achieve optimal effectiveness. Full article
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17 pages, 2068 KiB  
Article
Requirements and Test Stand Development for ERS Pantographs
by Alexander Prinz, Kil Young Lee, Abhishek Gupta, Dietmar Göhlich and Sangyoung Park
World Electr. Veh. J. 2025, 16(2), 86; https://doi.org/10.3390/wevj16020086 - 8 Feb 2025
Abstract
Electric road systems (ERSs) are a promising solution for electrifying heavy-duty freight transport by providing traction and charging power from the power lines installed along the road. Development of ERSs has been accelerated in the last decade, and several pilot projects have been [...] Read more.
Electric road systems (ERSs) are a promising solution for electrifying heavy-duty freight transport by providing traction and charging power from the power lines installed along the road. Development of ERSs has been accelerated in the last decade, and several pilot projects have been successfully implemented, proving the high level of maturity that the technology has achieved. One crucial step that could be initiated before a rollout is the standardization and certification of ERS infrastructure and system components. For instance, pantographs for overhead ERSs face unique challenges, in that the power transfer should be safe and reliable in the presence of dynamic longitudinal and lateral movements of the vehicle. To tackle this problem, we outline the requirements for overhead ERSs and ERS pantograph testing. Among the key requirements are the rising and lowering times, response to lateral maneuvers, such as lane changes, and high electrical current during stillstand. We introduce our developed test stands capable of testing various aspects of an ERS pantograph. The lateral test stand was developed to test basic functionalities and simulate lateral movements. A second test stand was implemented, to test high currents and the subsequent temperature development. Furthermore, a digital test stand used for planning, design, and modeling is introduced. Full article
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19 pages, 2336 KiB  
Article
Research on the Formation Mechanism of the Purchasing Behavior of Electric Vehicles with a Battery-Swap Mode
by Siyan Xu, Guohua Hu and Hui Han
World Electr. Veh. J. 2025, 16(2), 85; https://doi.org/10.3390/wevj16020085 - 7 Feb 2025
Abstract
The driving range and replenishment problem of electric vehicles have become the main contradictions that interfere with consumers’ purchasing decisions. To alleviate these problems, battery-swap technology has been introduced into the public view. Existing research rarely explores the factors that affect consumers’ decision [...] Read more.
The driving range and replenishment problem of electric vehicles have become the main contradictions that interfere with consumers’ purchasing decisions. To alleviate these problems, battery-swap technology has been introduced into the public view. Existing research rarely explores the factors that affect consumers’ decision of purchasing electric vehicles. This article introduces the Technology Acceptance Model (TAM), as well as the Theory of Planned Behavior (TPB) with its extensions and the perceived risk, to construct the structural equation model (SEM) based on TAM and TPB, and studies the influence mechanism of the purchase intention of electric vehicles with a battery-swap mode. A total of 530 valid questionnaires were collected from participants in Beijing, providing a representative sample for the study. The results show that attitude, technological development, perceived behavior control, environmental awareness, and subjective norm have significant positive influences on the purchase intention, and the influences increase in turn; perceived risk has a significant negative effect; subjective norms and environmental awareness have an indirect positive effect. Full article
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19 pages, 10355 KiB  
Article
Anti-Slip Control System with Self-Oscillation Suppression Function for the Electromechanical Drive of Wheeled Vehicles
by Aleksandr V. Klimov, Akop V. Antonyan, Andrey V. Keller, Sergey S. Shadrin, Daria A. Makarova and Yury M. Furletov
World Electr. Veh. J. 2025, 16(2), 84; https://doi.org/10.3390/wevj16020084 - 6 Feb 2025
Abstract
The movement of a wheeled vehicle is a non-regular dynamic process characterized by a large number of states that depend on the movement conditions. This movement involves a large number of situations where elastic tires skid and slip against the base surface. This [...] Read more.
The movement of a wheeled vehicle is a non-regular dynamic process characterized by a large number of states that depend on the movement conditions. This movement involves a large number of situations where elastic tires skid and slip against the base surface. This reduces the efficiency of movement as useful mechanical energy of the electromechanical drive is spent to overcome the increased skidding and slipping. Complete sliding results in the loss of control over the vehicle, which is unsafe. Processes that take place immediately before such phenomena are of special interest as their parameters can be useful in diagnostics and control. Additionally, such situations involve adverse oscillatory processes that cause additional dynamic mechanical and electrical loading in the electromechanical drive that can result in its failure. The authors provide the results of laboratory road research into the emergence of self-oscillatory phenomena during the rolling of a wheel with increased skidding on the base surface and a low traction factor. This paper reviews the methods of designing an anti-slip control system for wheels with an oscillation damping function and studies the applicability and efficiency of the suggested method using mathematical simulation of the virtual vehicle operation in the Matlab Simulink software package. Using the self-oscillation suppression algorithm in the control system helps reduce the maximum amplitude values by 5 times and average amplitudes by 2.5 times while preventing the moment operator from changing. The maximum values of current oscillation amplitude during algorithm changes were reduced by 2.5 times, while the current change rate was reduced by 3 times. The reduction in the current-change amplitude and rate proves the efficiency of the self-oscillation suppression algorithm. The high change rate of the current consumed by the drive’s inverters may have a negative impact on the remaining operating life of the rechargeable electric power storage system. This impact increases with the proximity of its location due to the low inductance of the connecting lines and the operating parameters, and the useful life of the components of the autonomous voltage inverters. Full article
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13 pages, 2262 KiB  
Article
An Energy-Saving Evaluation Method for Driving Behavior Based on the SHAP Scorecard Model
by Guowen Zheng, Deyang Kong, Chenyang Ding, Jiaming Hu and Zhanping Liu
World Electr. Veh. J. 2025, 16(2), 83; https://doi.org/10.3390/wevj16020083 - 6 Feb 2025
Abstract
In order to fully examine how driving behavior affects vehicle energy consumption and further cut down on excessive energy use while driving, an energy-saving evaluation method for driving behavior based on the SHAP (Shapley Additive exPlanations) scorecard model is proposed. First, to categorize [...] Read more.
In order to fully examine how driving behavior affects vehicle energy consumption and further cut down on excessive energy use while driving, an energy-saving evaluation method for driving behavior based on the SHAP (Shapley Additive exPlanations) scorecard model is proposed. First, to categorize short trip segments, a LightGBM model is built, which performs better and has high interpretability. The SHAP method is then used to statistically assess the categorization findings in order to ascertain how various driving events and driving behavior characteristics affect energy consumption. After that, the data are converted into scores using a scorecard model, which is then used to examine the evaluation outcomes of each sample and individual driver. Lastly, by checking the distribution of scores and the pertinent factors for various scores, the scorecard model’s correctness and reasonableness in evaluating energy-saving driving practices of short-trip segments are confirmed. Full article
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46 pages, 12966 KiB  
Article
VRDeepSafety: A Scalable VR Simulation Platform with V2X Communication for Enhanced Accident Prediction in Autonomous Vehicles
by Mohammad BaniSalman, Mohammad Aljaidi, Najat Elgeberi, Ayoub Alsarhan and Rabia Emhamed Al Mamlook
World Electr. Veh. J. 2025, 16(2), 82; https://doi.org/10.3390/wevj16020082 - 6 Feb 2025
Abstract
Safe real-world navigation for autonomous vehicles (AVs) requires robust perception and decision-making, especially in complex, multi-agent scenarios. Existing AV datasets are limited by their inability to capture diverse V2X communication scenarios, lack of synchronized multi-sensor data, and insufficient coverage of critical edge cases [...] Read more.
Safe real-world navigation for autonomous vehicles (AVs) requires robust perception and decision-making, especially in complex, multi-agent scenarios. Existing AV datasets are limited by their inability to capture diverse V2X communication scenarios, lack of synchronized multi-sensor data, and insufficient coverage of critical edge cases in multi-vehicle interactions. This paper introduces VRDeepSafety, a novel and scalable VR simulation platform that overcomes these limitations by integrating Vehicle-to-Everything (V2X) communication, including realistic latency, packet loss, and signal prioritization, to enhance AV accident prediction and mitigation. VRDeepSafety generates comprehensive datasets featuring synchronized multi-vehicle interactions, coordinated V2X scenarios, and diverse sensor data, including visual, LiDAR, radar, and V2X streams. Evaluated with our novel deep-learning model, VRFormer, which uniquely fuses VR sensor data with V2X using a probabilistic Bayesian inference, as well as a hierarchical Kalman and particle filter structure, VRDeepSafety achieved an 85% accident prediction accuracy (APA) at a 2 s horizon, a 17% increase in 3D object detection precision (mAP), and a 0.3 s reduction in response time, outperforming a single-vehicle baseline. Furthermore, V2X integration increased APA by 15%. Extending the prediction horizon to 3–4 s reduced APA to 70%, highlighting the trade-off between prediction time and accuracy. The VRDeepSafety high-fidelity simulation and integrated V2X provide a valuable and rigorous tool for developing safer and more responsive AVs. Full article
(This article belongs to the Special Issue Vehicular Communications for Cooperative and Automated Mobility)
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29 pages, 7168 KiB  
Review
Research Progress on Thermal Runaway Warning Methods and Fire Extinguishing Technologies for Lithium-Ion Batteries
by Peicheng Shi, Hailong Zhu, Xinlong Dong and Bin Hai
World Electr. Veh. J. 2025, 16(2), 81; https://doi.org/10.3390/wevj16020081 - 6 Feb 2025
Abstract
Lithium-ion batteries (LIBs), valued for their high energy density, long lifespan, and low environmental impact, are widely used in electric vehicles (EVs) and energy storage. However, increased energy density has exacerbated thermal runaway (TR) issues, hindering large-scale applications. This paper systematically analyzes the [...] Read more.
Lithium-ion batteries (LIBs), valued for their high energy density, long lifespan, and low environmental impact, are widely used in electric vehicles (EVs) and energy storage. However, increased energy density has exacerbated thermal runaway (TR) issues, hindering large-scale applications. This paper systematically analyzes the mechanisms of TR and strategies for early warning and prevention to enhance battery safety. It begins by detailing TR mechanisms and their triggers, then reviews various TR early warning technologies, fire prevention methods, and the effectiveness and mechanisms of novel extinguishing agents such as hydrogels, perfluorohexanone, liquid nitrogen (LN), dry powder, and aqueous vermiculite dispersion (AVD). The study also explores advancements in new fire-retardant coatings for batteries. Finally, it summarizes current challenges and forecasts future research directions in battery technology. This review offers readers a clear, systematic overview of TR mechanisms, warning systems, and prevention technologies, providing comprehensive insights into TR management. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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18 pages, 14896 KiB  
Article
Deep Learning-Based Point Cloud Classification of Obstacles for Intelligent Vehicles
by Yiqi Xu, Dengke Wu, Mengfei Zhou and Jiafu Yang
World Electr. Veh. J. 2025, 16(2), 80; https://doi.org/10.3390/wevj16020080 - 5 Feb 2025
Abstract
Intelligent driving research has focused much attention on point cloud obstacles since they are a class of high-dimensional data that can adequately depict the shape and placement of obstacles, unlike picture data. Currently, deep learning technology is primarily employed for vehicle autonomy point [...] Read more.
Intelligent driving research has focused much attention on point cloud obstacles since they are a class of high-dimensional data that can adequately depict the shape and placement of obstacles, unlike picture data. Currently, deep learning technology is primarily employed for vehicle autonomy point cloud obstacle classification tasks. These techniques typically struggle with low classification accuracy, processing efficiency, and model stability. To tackle the abovementioned issues, this paper suggests a novel random forest algorithm that integrates the out-of-bag error theory and can consistently and accurately evaluate the influence of point cloud properties. Then, building on the novel algorithm, this paper suggests a modified PointNet network that incorporates the effects of both global and local features on the classification task, therefore increasing the conventional network’s classification accuracy. To assess the effectiveness of this novel approach in the experimental portion, we set up an evaluation system based on the metrics for average accuracy, overall accuracy, and a confusion matrix. According to the simulation results, the overall accuracy of the proposed network in terms of classification accuracy is 94.4% and the average accuracy is 84.9%, which are then compared to the prototype PointNet and its variants. The classification accuracies for the four types of obstacles are 97.6%, 63.6%, 92.5%, and 86.1%. In addition, the proposed method is effective at improving both the computational complexity and stability of the network. Full article
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)
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27 pages, 1802 KiB  
Article
Optimal Design of Interior Permanent Magnet Synchronous Motor Considering Various Sources of Uncertainty
by Giacomo Guidotti, Dario Barri, Federico Soresini and Massimiliano Gobbi
World Electr. Veh. J. 2025, 16(2), 79; https://doi.org/10.3390/wevj16020079 - 5 Feb 2025
Abstract
The automotive industry is experiencing a period of transition from traditional internal combustion engine (ICE) vehicles to electric vehicles. Although electric machines have always been used in many applications, they are generally designed neglecting the sources of uncertainty, even such uncertainty can lead [...] Read more.
The automotive industry is experiencing a period of transition from traditional internal combustion engine (ICE) vehicles to electric vehicles. Although electric machines have always been used in many applications, they are generally designed neglecting the sources of uncertainty, even such uncertainty can lead to significant deterioration of the motor performance. The aim of this paper is to compare the results obtained from the multi-objective optimization of an interior permanent magnet synchronous motor (IPMSM) using a robust approach versus a deterministic one. Unlike other studies in the literature, this research simultaneously considers different sources of uncertainty, such as geometric parameters, magnet properties, and operating temperature, to assess the variability of electric motor performance. Different designs of a 48 slot–8 pole motor are simulated with finite element analysis, then the outputs are used to train artificial neural networks that are employed to find the optimal design with different approaches. The method incorporates an innovative use of the neural network-based variance estimation (NNVE) technique to efficiently calculate the standard deviation of the objective functions. Finally, the results of the robust optimization are compared with those of the deterministic optimization. Due to the small margin of improvement in robustness, both methods lead to similar results. Full article
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17 pages, 4517 KiB  
Article
Comparative Analysis of Neural Network Models for Predicting Battery Pack Safety in Frontal Collisions
by Jun Wang, Ouyang Chen, Zhenfei Zhan, Zhiwei Zhao and Huanhuan Bao
World Electr. Veh. J. 2025, 16(2), 78; https://doi.org/10.3390/wevj16020078 - 5 Feb 2025
Abstract
Amid concerns about environmental degradation and the consumption of non-renewable energy, the development of electric vehicles (EVs) has accelerated, with increasing focus on safety. On the road, battery packs are exposed to potential risks from unforeseen objects that may collide with or scratch [...] Read more.
Amid concerns about environmental degradation and the consumption of non-renewable energy, the development of electric vehicles (EVs) has accelerated, with increasing focus on safety. On the road, battery packs are exposed to potential risks from unforeseen objects that may collide with or scratch the system, which may lead to damage or even explosions, thus endangering the safety of transportation participants. In this study, several predictive models aimed at assessing the safety performances of battery packs are proposed to provide a basis for data-driven structural optimization by numerically simulating the deformation of the battery base plate. Initially, a finite element model of the battery pack was developed, and the accuracy of the model was verified by performing modal analysis with various commercial software tools. Then, representative samples were collected using optimal Latin hypercube sampling, followed by collision simulations to gather data under different collision conditions. Next, the prediction accuracy of three models—PSO-BP neural network, RIME-BP neural network, and RBF neural network—was compared for predicting battery pack bottom shell deformation. Finally, the prediction accuracy of the models was compared based on error functions. The results indicate that these neural network models can accurately predict deformation under frontal collision conditions within the specified limits, with the RIME-BP model yielding the best performance beyond those limits. The developed neural network prediction model is able to accurately assess the mechanical response of battery packs under frontal collision, providing support for data-driven structural optimization. It also provides an important reference for improving the safety and durability of battery pack design. Full article
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18 pages, 6104 KiB  
Article
Charting the Path to Electrification: Analyzing the Economic and Technological Potential of Advanced Vehicle Powertrains
by Ehsan Sabri Islam, Ram Vijayagopal and Aymeric Rousseau
World Electr. Veh. J. 2025, 16(2), 77; https://doi.org/10.3390/wevj16020077 - 5 Feb 2025
Abstract
The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) is driving advancements in highway transportation by targeting energy efficiency, environmental sustainability, and cost reductions. This study investigates the fuel economy potential and cost implications of advanced powertrain technologies using comprehensive system simulations. Leveraging [...] Read more.
The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) is driving advancements in highway transportation by targeting energy efficiency, environmental sustainability, and cost reductions. This study investigates the fuel economy potential and cost implications of advanced powertrain technologies using comprehensive system simulations. Leveraging tools such as Autonomie and TechScape, developed by Argonne National Laboratory, this study evaluates multiple timeframes (2023–2050) and powertrain types, including conventional internal combustion engines, hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). Simulations conducted across standard regulatory driving cycles provide detailed insights into fuel economy improvements, cost trajectories, and total cost of ownership. The findings highlight key innovations in battery energy density, lightweighting, and powertrain optimization, demonstrating the growing viability of BEVs and their projected economic competitiveness with conventional vehicles by 2050. This work delivers actionable insights for policymakers and industry stakeholders, underscoring the transformative potential of vehicle electrification in achieving sustainable transportation goals. Full article
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32 pages, 5065 KiB  
Article
Decarbonization of Long-Haul Heavy-Duty Truck Transport: Technologies, Life Cycle Emissions, and Costs
by Anne Magdalene Syré and Dietmar Göhlich
World Electr. Veh. J. 2025, 16(2), 76; https://doi.org/10.3390/wevj16020076 - 5 Feb 2025
Abstract
Decarbonizing long-haul, heavy-duty transport in Europe focuses on battery-electric trucks with high-power chargers or electric road systems and fuel-cell-electric vehicles with hydrogen refueling stations. We present a comparative life cycle assessment and total cost of ownership analysis of these technologies for 20% of [...] Read more.
Decarbonizing long-haul, heavy-duty transport in Europe focuses on battery-electric trucks with high-power chargers or electric road systems and fuel-cell-electric vehicles with hydrogen refueling stations. We present a comparative life cycle assessment and total cost of ownership analysis of these technologies for 20% of Germany’s heavy-duty, long-haul transport alongside internal combustion engine vehicles. The results show that fuel cell vehicles with on-site hydrogen have the highest life cycle emissions (65 Mt CO2e), followed by internal combustion engine vehicles (55 Mt CO2e). Battery-electric vehicles using electric road systems achieve the lowest emissions (21 Mt CO2e) and the lowest costs (EUR 45 billion). In contrast, fuel cell vehicles with on-site hydrogen have the highest costs (EUR 69 billion). Operational costs dominate total expenses, making them a compelling target for subsidies. The choice between battery and fuel cell technologies depends on the ratio of vehicles to infrastructure, transport performance, and range. Fuel cell trucks are better suited for remote areas due to their longer range, while integrating electric road systems with high-power charging could offer synergies. Recent advancements in battery and fuel cell durability further highlight the potential of both technologies in heavy-duty transport. This study provides insights for policymakers and industry stakeholders in the shift towards sustainable transport. The greenhouse gas emission savings from adopting battery-electric trucks are 54% in our high-power charging scenario and 62% in the electric road system scenario in comparison to the reference scenario with diesel trucks. Full article
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16 pages, 766 KiB  
Article
Synthetic Data Generation for AI-Informed End-of-Line Testing for Lithium-Ion Battery Production
by Tessa Krause, Daniel Nusko, Johannes Rittmann, Luciana Pitta Bauermann, Moritz Kroll and Carlo Holly
World Electr. Veh. J. 2025, 16(2), 75; https://doi.org/10.3390/wevj16020075 - 4 Feb 2025
Abstract
Lithium-ion batteries are a key technology in supply chains for modern electric vehicles. Their production is complex and can be prone to defects. As such, the detection of defective batteries is critical to ensure performance and consumer safety. Existing end-of-line testing relies heavily [...] Read more.
Lithium-ion batteries are a key technology in supply chains for modern electric vehicles. Their production is complex and can be prone to defects. As such, the detection of defective batteries is critical to ensure performance and consumer safety. Existing end-of-line testing relies heavily on electrical measurements for identifying defective cells. However, it is possible that not all pertinent information is encoded within the electrical measurements alone. Reversible expansion in lithium-ion cells is an indicator of lithiation within the cell, while irreversible expansion is a consequence of the ageing process; unexpected expansion may indicate the presence of undesirable defects. By measuring expansion in addition to electrical measurements, we aim to make better and faster quality predictions during end-of-line testing, thereby facilitating the early detection of potential defects. To make these predictions, we implement artificial intelligence algorithms to extract information from the measurements. Training these networks requires large training datasets, which are expensive to produce. In this paper, we demonstrate a first-order physical modelling approach for generating synthetic data to pre-train artificial intelligence algorithms that perform anomaly detection on lithium-ion battery cells at the end-of-line. The equivalent circuit model used to generate voltage curves could be fit to real data with a mean absolute error of less than 1%, and the expansion model could be fit to a mean absolute error of less than 2% of the measured values. By pretraining the artificial intelligence network using synthetic data, we can leverage existing physical models to reduce the amount of training data required. Full article
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16 pages, 4464 KiB  
Article
Research on Composite Liquid Cooling Technology for the Thermal Management System of Power Batteries
by Lin Zhu, Dianqi Li and Ziyao Wu
World Electr. Veh. J. 2025, 16(2), 74; https://doi.org/10.3390/wevj16020074 - 2 Feb 2025
Abstract
A battery thermal management system is crucial for maintaining battery temperatures within an acceptable range with high uniformity. A new BTMS combining a liquid cooling plate and vapor chamber is proposed and experimentally validated for ternary lithium soft pack batteries. An orthogonal test [...] Read more.
A battery thermal management system is crucial for maintaining battery temperatures within an acceptable range with high uniformity. A new BTMS combining a liquid cooling plate and vapor chamber is proposed and experimentally validated for ternary lithium soft pack batteries. An orthogonal test optimizes the liquid-cooling plate’s structure at a 2C discharge rate. With a vapor chamber, the battery’s temperature consistency improves. Experiments show that, at a 2C discharge rate, with coolant and ambient temperatures at 25 °C, the battery’s maximum temperature is 35.191 °C, and the temperature difference is 3.77 °C. This represents a 2.1% increase in average temperature, and a 4.9% decrease in temperature difference compared to a liquid-cooling plate alone. The results indicate that the combined liquid-cooling and vapor chamber enhance temperature consistency. Full article
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