The electrification of heavy-duty vehicles is a critical pathway toward improved energy efficiency in the freight sector. The current battery electric truck technology poses several challenges to commercial vehicle operations, such as limited driving range, sensitivity to climate conditions, and long recharging times. Estimating the energy consumption of heavy-duty electric trucks is crucial to assessing the feasibility of fleet electrification and its impact on the electric grid. This article focuses on developing a model-based simulation approach to predict and analyze the energy consumption of electric trucks by considering the impact of weather and geographical conditions on vehicle road load and auxiliary components power consumption, as well as the impact these factors have on driving range. Specifically, drayage trucks employed in logistics around maritime ports are used as a case study, with consideration of seasonal climate variations and geographical characteristics at different locations. The article includes results for three major container ports within the United States, providing region-specific insights into the energy requirements and driving range of the electric drayage trucks in these regions, which will inform decision-makers in integrating electric trucks into the existing drayage operations and plan investments for electric grid development.
A full lithium-ion battery (LIB) pack has hundreds to thousands of cells, coolant flow lines and channels, and channel bends to control cell temperature within its operating window and minimize cell internal resistance, aging, and fire risk. A 75 kWh LIB pack has four modules, and each has 23–25 bricks. Two challenges in battery state predictions for hot and subzero temperatures are battery temperature (Tbatt ) and coolant flow within the whole pack. In this work, a 1D 75 kWh full-pack model with its thermal management system is developed using a holistic reverse-engineering method, which can predict Tbatt at any bricks/modules and inlet/outlet coolant flow characteristics. A Tesla Model Y equipped with dual e-motors is tested on an in-house state-of-the-art chassis dynamometer. The test data at V = 60–80 km/h, 100–150 A constant discharge, and Tbatt = −10°C to 40°C are used to develop the model. The 75 kWh pack model features 4000+ cylindrical cells (96S46P, Panasonic 21700-format), 20+ coolant lines (or plates, tubes), and 700+ flow channels. The model considers heat exchange from cells to the ambient air via coolant (water-glycol), coolant channel walls, adhesive bonding, trays, and cases. Four forced convective heat transfer coefficient correlations (α) from the coolant to the walls are used to predict coolant outlet temperature (T cool, out ) and Tbatt at different bricks. Three coolant flow losses correlations (K) due to pipe friction, and pipe bends are used to predict the coolant pressure drop ∆Pcool across the pack. Optimal α and K correlations are identified using the fully validated pack model, and the transient temperatures at any cell in bricks and the inlet/outlet coolant flow characteristics are well predicted with over 90% accuracy. This work provides guidelines for selecting optimal α and K correlations to develop any 1D fully liquid-based battery pack models for all-weather driving.
As global demand for sustainable energy solutions increases, there is a push to develop alternatives to lithium-ion batteries, which face limitations in cost, resource availability, and safety. In particular, multivalent-ion batteries based on magnesium, calcium, zinc, and aluminum have emerged as promising candidates due to their ability to transfer multiple electrons per ion, offering higher volumetric energy density and greater material abundance. This review examines recent advances in electrode and electrolyte development for these systems, highlighting cathode innovations such as cobalt sulfides for magnesium, NASICON-type and redox-coupled materials for calcium, molybdenum trioxide frameworks for zinc, and organic and composite electrodes for aluminum. Electrolyte research has produced improved ionic transport and stability through solvation tuning, hybrid and polymer systems, and deep eutectic solvents. Interfacial engineering is identified as a key enabler for enhancing reversibility, dendrite suppression, and long-term cycling stability. A comparative analysis of the different chemistries found that zinc-ion systems are closest to commercial deployment, aluminum-ion batteries are advancing for grid and flexible devices, and magnesium and calcium-ion batteries hold long-term potential for high-energy applications. The study concludes with future research directions emphasizing solvation control, sustainable materials, and intelligent diagnostics to achieve scalable multivalent battery technologies.
This article surveys the most recent data-driven methods of lithium-ion (Li-ion) battery state of health (SOH) estimation methods and dataset resources utilized in electrified vehicles (EV) and their potential adoption for automotive battery management systems. These include regression-based models, ensemble learners, deep neural networks, and physics-informed hybrid methods. The review describes estimation methods found in articles published between 2023 and 2025, and investigates their differences in terms of estimation accuracy, data requirement, interpretability, and real-time deployment ability. The article traverses the dataset space, focusing on laboratory aging datasets, vehicle field–based datasets, telematics-derived records, and synthetic or augmented datasets, to underline that model performance in the estimation of SOH cannot be disentangled from the quality of the data, the operating coverage, and the transfer conditions. Apart from the model design, this work reviews the large-scale estimation pipeline, which involves preprocessing under sensor noise and irregular timestamps, feature extraction from incremental capacity, differential voltage, relaxation response and impedance-related indicators, and uncertainty handling for diagnostics and safety-based decision support. Practical constraints to the deployment of embedded BMS are covered. Such as ECU memory and computing limits, communication overhead, calibration effort, update approach, and functional-safety requirements. The review determines that the distance between laboratory validation and field robustness is large raising a need for more work in this area and also, that domain adaptation, federated learning, and improving benchmarking practice turn out to be promising directions for improving generalization and reproducibility. The article concludes that future advances in automotive SOH estimation will not only rely on better learning algorithms but also on improvement in the availability of realistic and field representative data, the application of robust evaluation mechanisms, and methods that are developed under real BMS constraints.
This article presents a novel finite element modeling approach to predict the mechanical response of jellyrolls in large-scale explicit crash simulations up to the experimental occurrence of internal short-circuit. The proposed simplified layered model embeds membrane elements within a solid element mesh to improve the prediction in load cases dominated by the buckling and sliding of the jellyroll’s layered structure. The model was validated against experimental results from in-plane, out-of-plane, and bending tests on jellyroll samples extracted from prismatic lithium-ion cells. The experimental results confirmed the jellyroll’s high compressibility under out-of-plane loads and its behavior as a collection of unconnected layers under in-plane and bending loading. Compared to the widely used crushable foam model, the simplified layered model offered additional flexibility, especially for in-plane and bending load cases. Additionally, it meets critical time increment requirements for explicit analysis and requires a limited number of calibration tests. These results highlight the model’s potential to improve the prediction of the jellyroll’s mechanical behavior in large-scale simulations.
In a traditional electric vehicle, managing its battery thermal performance is of prime importance. A well-designed battery thermal management system helps in extending its life and avoids safety-related issues like thermal runaways. A critical part of this thermal management is the battery cooling system (BCS), which can be air- or liquid-cooled. Based on the vehicle battery pack size, location, and its design complexity, the original equipment manufacturer can opt for either of the previous two methods. An air-cooled type of BCS system usually involves an active ventilation fan to dissipate the battery heat in the surroundings, which brings symbiotic noise into the picture. In an air-cooled BCS system, the primary source of noise is the cooling airflow over the heat exchanger caused by the fan. The airflow and noise performance characteristics of this fan are typically measured by the supplier in a standalone condition. These performance parameters deviate greatly when the fan is introduced inside a battery cooling module. In the current work, flow-induced noise simulation of a fan placed inside a confined BCS is performed. The simulation has made use of a statistically based tool due to its inherent low dissipative and dispersion properties. The simulation model included all complex interior parts of the BCS, including the mating gaps higher than 1 mm. The simulation results were correlated with the test, and further iterations were performed in simulations to understand the sensitivity of the condenser core location with respect to the fan. Additionally, the changes in noise performance behavior while moving from a standalone fan toward a fan integrated with the BCS system are also studied. The overall noise correlation between the simulation and test is achieved within a 0.4 dBA level. Further, the presence of flow-induced resonance inside the BCS at a lower frequency than the BPF was identified in simulation.
The increasing demand for quiet and efficient electric vehicles has highlighted the importance of understanding vibration and noise characteristics of motor stators. Previous studies have extensively modeled electromagnetic excitation and laminated structures, but there has been little experimental evidence clarifying how different interlaminate fastening methods affect vibration modes under comparable conditions. This knowledge gap limits the ability to optimize fastening strategies for noise and vibration control in practical motor design. In this study, laminated stator cores were fabricated with different fastening conditions—bolting, clinching, and welding—and subjected to vibration testing and experimental modal analysis. Natural frequencies, damping ratios, and mode shapes were identified for torsional, circumferential, and breathing modes. The results revealed that the in-plane torsional natural frequencies increase with bolt axial force, while clinching provides additional resistance to interlaminate movement but shows only a minor dependence on the number of clinching points. In contrast, the circumferential modes and the breathing-type (0,0) mode remain largely unaffected by these fastening variations. Welding points did not exhibit a consistent trend across the tested conditions, indicating that their influence on the modal properties is less systematic compared with bolting and clinching. The findings contribute not only to fundamental understanding of laminated stack vibration behavior but also to practical guidelines for designing fastening strategies that enhance vibration robustness and acoustic performance in automotive electric motors.
The aim of this study is to develop a methodology to significantly reduce emissions in bus fleet renewal scenarios by investigating both technical and economic aspects. This work presents a case study based on Elba Island, Italy, which investigates optimal solutions for replacing existing Diesel buses through a total cost of ownership analysis. The investigation is carried out for four different potential scenarios: renewing the fleet with Diesel buses, renewing the fleet with electric buses, adopting fuel cell buses, and implementing a hybrid solution. The latter represents a synergistic solution that integrates fuel cell buses with the development of a hydrogen refueling station driven by a proton exchange membrane electrolyzer, unlocking the techno-economic potential of selfproducing green hydrogen for bus refueling. The novelty of this study is its integrated methodology that combines a total cost of ownership analysis with a tailored design of a green hydrogen production network optimized for continuous fleet operation. A constrained optimization algorithm was employed to determine the optimal configuration of key plant components, including the proton exchange membrane electrolyzer system size, the amount of photovoltaic panels and wind turbines, and the capacity of the hydrogen storage tank. The grid-based alternative offers a simple payback period under 4 years and a total cost of ownership of 6 M & euro;, making it more cost-effective than the 6.5 M & euro; electric and 7.5 M & euro; Diesel options. These results provide a scalable, replicable roadmap for accelerating sustainable public transport adoption in similar contexts.
The integration of electric vehicle charging station (EVCS) and renewable distribution generation (RDG) in the grid affects the grid voltage, power losses, and system instability in the distribution system, therefore the article presents an approach for optimal placement and sizing of EVCS and RDG using an optimization approach named as modified particle swarm optimization (MOPSO) radial distribution network (RDN). The efficacy of the optimization approach is demonstrated under both balanced and unbalanced dynamic load conditions in the IEEE 33-bus system. The influence of EVs and RDG on the RDN is analyzed by considering the maximum possible cases, e.g., 13 different scenarios, which replicate real-world scenarios. These results are validated using DIgSILENT Power Factory Software. The proposed research also covers Techno-Economic Assessment using HOMER software, which may enhance visibility of the renewable distribution generation importance in the current scenario.
In the recent years, the use of conventional passenger vehicles has been increasingly discouraged, from European-level policies to local municipal regulations, due to the urgent need to reduce greenhouse gas emissions and urban pollution. In response to these challenges, the PRIN2020 for light and heavy quadricycles to achieve near-zero pollutant emissions, focusing on internal combustion engine hybrid electric vehicles and fuel cell hybrid electric vehicles. Taking all these aspects into consideration, this article proposes an integrated solution for cooling/HVAC circuits, to improve energy efficiency and occupants' comfort, while focusing on proper battery operation, with a recuperator heat exchanger used to recover the available heat at the powertrain output, in order to reduce the HVAC heater energy consumption. The complexity of the circuit requires a specific control logic to be implemented to simultaneously ensure cabin comfort, effective thermal management of the battery, and minimize energy consumption. The study is applied to the HySUM fuel cell/battery hybrid L-class electric vehicle. A thermal and electrical model for predicting the heat generation and the state of charge of the battery under dynamic load profiles is employed to better understand the potential of the thermal integration of the battery cooling with the HVAC system. The simulation results are encouraging and demonstrate the effectiveness of the proposed thermal load management. Significant energy savings are achieved through the use of the recuperator during driving, while battery thermal management is accomplished without the need for a dedicated circuit, by utilizing conditioned air from the HVAC/cabin system. Unlike traditional lightweight electrified vehicles, which often lack efficient HVAC systems, this solution enhances energy efficiency and guarantees reliable component operation in varying environmental conditions.
0D, quasi-3D, and 3D chemistry solvers with varying degrees of complexity are developed to predict the thermal runaway propagation in battery cells. The 0D solver assumes the system as homogeneous and closed. The quasi-3D solver assumes the system as homogeneous on the selection level and the 3D solver accounts all spatial inhomogeneities in the temperature and composition. Both the quasi-3D and 3D solvers are fully integrated into a computational fluid dynamic (CFD) solver and capable of predicting thermal runaway in multiple battery cells with cell-specific kinetic reaction model. As the modeling complexity increases with each solver, respectively, the accuracy and the simulation time increases. With the large amount of heat and rapid transitions from the onset thermal runaway, the CFD solvers usually encounter difficulties in predicting the solution accurately and in extreme heat release cases the solver may diverge. A chemical time scale based adaptive time stepping is developed in this work to address the accuracy, convergence, and stability issues of the CFD solver. The proposed timescale contains in the definition the reaction rate, reaction enthalpy, and total enthalpy content of the system. As the thermal runaway progresses, the CFD solver time step is obtained dynamically from the defined timescale. The developed solvers and the adaptive time-stepping method were quite intensively tested and analyzed by using different reaction mechanisms representing different battery cells and test conditions. The analysis of the timescales and the adaptive time stepping proved quite efficient for solution accuracy, simulation time, and solver stability.
With the wide application of electric vehicles (EVs) around the world, the increase in battery pack energy density and the growing complexity of electrical systems have gradually heightened the risk of vehicle fires. Therefore, achieving efficient and timely fire risk prediction is essential to minimize the probability of fires in EVs. However, the development of EV prediction models requires multidisciplinary integration to address complex safety challenges. This article provides a detailed discussion on the mechanisms and combustion characteristics of EV fires, followed by an investigation into the high-risk factors that trigger such fires. Based on the above content, this article conducts an in-depth analysis of the characteristics of different models for high-risk factors such as batteries, electrical systems, and collision damage, offering insights to bridge the gap between different disciplines. Finally, it explores the future development direction of predictive models for EVs. This review provides the reader a clear, systematic overview of EV combustion characteristics and early warning systems that can offer insights into the development of predictive models for EVs.
This article presents an artificial neural network (ANN)-based hybrid design methodology for motors used in electric vehicle applications. The proposed method uses ANN to achieve a semi-optimized motor geometry, followed by the drive cycle analysis for the desired vehicle. For this, a large pool of motor design data is used as a training set for the ANN. The semi-optimized motor geometry is further processed for power factor improvement, overall motor efficiency, and electromagnetic noise reduction. The proposed method reduces the overall complexity of the iterative motor design and optimization process. The implementation of the method is demonstrated with a case study wherein a 110 kW three-phase induction motor is designed for an electric bus using the NREL drive cycle. The performance of the motor is verified using a finite element analysis motor using The work described in this article was motivated by the complexities of the iterative motor design process, which involves a high level of human resources engagement and time consumption. To address this, the presented work proposes a design approach that bypasses all the complex parts of the work by applying machine learning. The main feature of the approach is that it adopts an ANN-based method that provides a primitive set of motor design parameters for different structures/models of the motor. It eases the work of the motor designer, who has to select the best possible motor structure among these structures and revamp it for further improvement of motor performance. The application of the method is more prolific if the motor is designed for an electric vehicle that exhibits variable loading conditions. The assessment of the proposed model by designing a heavy-duty exhibit shows a significant reduction in the process complexities.
In recent years, the automotive industry has shown growing interest in the vibroacoustic characteristics of electric propulsion motors. Investigation of such characteristics can open avenues for motor design optimization and refined control strategies to mitigate vibration and acoustic noise in an electric motor. This article presents a comprehensive vibroacoustic analysis of a propulsion interior permanent magnet synchronous motor (IPMSM) under various current excitations generated by the power converter in combination with three different modulation schemes. To evaluate the switching effect from the inverter drive on motor noise, different simulations and processes are performed in ANSYS Workbench and MATLAB/Simulink. The multi-physics noise and vibration workflow, and sampling requirements used for the study are also presented. The simulation results, presented as equivalent radiated power (ERP) waterfall diagrams, show diverse acoustic noise signatures for the different types of excitation currents.
In recent years, the powertrains of agricultural tractors have been transitioning toward hybrid electric configurations, paving the way for a greener future agricultural machinery. However, stability challenges arise in hybrid electric tractors due to the relative small capacity to perform powerintensive tasks, such as plowing and harvesting. These operations demand significant power, which are supplied by the electric power take-off system. The substantial disturbances introduced by the electric power take-off system during these tasks render conventional small-signal analysis methods inadequate for ensuring system stability. In this article, we first develop a large-signal model of the onboard power electronic systems, which includes components such as the diesel engine-generator set, batteries, in-wheel motors, and electric power take-off system. By employing mixed potential theory, we conduct a thorough analysis of this model and derive a stability criterion for the onboard power electronic systems under large disturbance conditions. Using this criterion, we estimate the stability boundaries of the onboard power electronic systems and evaluate the influence of various circuit parameters on its performance under large load fluctuations. Finally, case studies are presented to validate the proposed stability criterion and to demonstrate the impact of key circuit parameters on system stability.
Recent policies have set ambitious goals for reducing greenhouse gas (GHG) emissions to mitigate climate change and achieve climate neutrality by 2050. In this context, the feasibility of hydrogen applications is under investigation in various sectors and promoted by government funding. The transport sector is one of the most investigated sectors in terms of emission mitigation strategies, as it contributes to about one-fifth of the total GHG emissions. This study proposes an integrated numerical approach, using a simulation framework, to analyze potential powertrain alternatives in the road transport sector. Non-causal point parametric vehicle models have been developed for various vehicle classes to evaluate key environmental, energy, and economic performance indicators. The modular architecture of the simulation framework allows the analysis of different vehicle classes. The developed framework has been used to compare powertrain alternatives based on hydrogen and electricity energy carriers. Light-, medium-, and heavy-duty applications have been analyzed. Additionally, a vehicle performance indicator has been proposed as a quantitative index to compare alternative architectures. A sensitivity analysis is conducted showing that the optimal powertrain architecture depends on various factors (e.g., vehicle range, fuel costs, powertrain components costs, emission factors, etc.). The results show that the battery electric and fuel cell hybrid electric vehicles are the most promising options for all vehicle classes. Moreover, hydrogen-based powertrains generally perform better in terms of total cost of ownership and GHG emissions for long-range vehicles. In contrast, battery electric vehicles are better suited for short-range applications.
Lithium-ion batteries used in electric vehicles (EVs) are facing issues owing to internal short-circuit (ISC), leading to thermal runaway. In this study, a pseudo-two-dimensional (P2D) model is employed to numerically investigate the effects of charging rate (C-rate) and separator electrical conductivity on the ISC behavior of a lithium-ion cell. The results reveal that as C-rate increases, both the voltage and capacity decrease more rapidly marked by higher solid potential gradient indicating increased internal resistance. These effects further intensified at higher separator conductivity, which facilitates greater ISC current and accelerates cell degradation. Also, the variations in current density and solid-phase lithium concentration become more pronounced at higher C-rates, particularly near the anode-separator interface, indicating increased non-uniformity during ISC conditions. Furthermore, the electrolyte voltage drop intensifies with rising C-rate, contributing to additional polarization. Further, it is observed that the separator conductivity has a significant influence on ISC current, although it shows a minimal effect on the terminal voltage. The value of the ISC current is found to increase with the increase in the value of the conductivity of the separator. Finally, it can be inferred that the lower electrical conductivity of the separator is desirable to prevent ISC of the Li-ion cell. The study highlights that a lower separator conductivity is beneficial in mitigating the severity of ISC events. These findings provide valuable insights for the design of safer lithium-ion cells considering the separator conductivity and operational C-rates.
With current and future regulations continuing to drive reductions in carbon dioxide equivalent (CO2e) emissions in the on-road industry, the off-road industry is also likely to be regulated for fuel and CO2e savings. This work focuses on converting a heavy-duty off-road material handler from a conventional diesel powertrain to a plug-in series hybrid, achieving a 49% fuel reduction and 29% CO2e reduction via simulation. Control strategies were refined for energy savings, including a regenerative braking strategy to increase regenerative braking and a load-following hydraulic strategy to decrease electrical energy consumption. The load-following hydraulic control shuts off the hydraulic electric machine when it is not needed-an approach not previously seen in a load-sensing, pressure-compensated system. These strategies achieved a 24.1% fuel savings, resulting in total savings of 61% in fuel and 41% in CO2e in the plug-in series compared to the conventional machine. Beyond control strategies, this study evaluated battery chemistry and charging strategy refinements for total cost of ownership (TCO) and lifetime CO2e. LFP batteries emerged as the most cost-effective and least emitting due to their longer lifespan, which reduced replacement frequency. Charging comparisons showed that Level 2 charging (L2C) typically resulted in lower TCO but higher lifetime CO2e than DC fast charging (DCFC). DCFC costs were heavily influenced by local demand charges, and DCFC emissions were heavily influenced by local grid emissions.
As the adoption of battery electric vehicles (BEVs) continues to rise, analyzing their performance under varying environmental conditions that affect energy consumption has become increasingly important. A critical factor influencing the efficiency of BEVs is the heat loss from the operation and interaction between the vehicle components, such as the battery and motor, and the surrounding temperature. This study presents a comprehensive analysis of the thermal interaction in BEVs by integrating hub motor vehicle and battery electrochemical model with environmental factors. It explores how ambient temperature variations influence the performance of EV components, particularly the motors and battery systems, in both hot and cold weather conditions. The simulations also consider the passenger comfort inside the cabin as it investigates the effects of operating the air-conditioning system on overall energy consumption, revealing significant energy consumption shifts during extreme ambient temperatures. Results indicate that high ambient temperatures exacerbate energy losses, especially in HVAC systems, while low temperatures significantly affect battery efficiency. By modeling the thermal interactions, this research provides valuable insights into optimizing energy management strategies for EVs under varying environmental conditions, contributing to improved energy efficiency and extended vehicle range.
