Understanding and mitigating fire risk in hybrid electric vehicles: A numerical and experimental study

Numerical analysis of low-viscosity lubricants in hybrid electric vehicles using validated methodologies: Friction, fuel economy, and wear performance

Deep belief network based reinforcement learning for energy management in hybrid electric vehicles

Particle number and size distribution in hybrid electric vehicles using Low-Level Methanol–Gasoline blends

Model-predictive-control-based hierarchical energy management strategy for fuel cell hybrid electric vehicles considering traffic information

Cooperative multi-agent deep reinforcement learning-based eco-driving strategy for hybrid electric vehicles at multi-intersection scenarios

Battery-aging-aware co-optimization of adaptive cruise control and hybrid electric vehicle energy management: A constrained hybrid-action reinforcement learning approach

A model-data fusion approach utilizing residual subset selection and adaptive thresholding for hybrid electric vehicle fault diagnosis

Research on hierarchical eco-driving strategy for fuel cell hybrid electric vehicles based on traffic flow

Hierarchical model predictive control with multi-agent reinforcement learning for eco-driving of fuel cell hybrid electric vehicles

Hierarchical control of hybrid electric vehicle platoon with slope-adaptive variable spacing and soft actor-critic based energy management

A reservation-based hybrid electric vehicle energy supply system via direct and asynchronous vehicle-to-vehicle charging modes

Adaptive state of energy evaluation for supercapacitor in hybrid electric vehicle applications considering non-integer order impedance

The rapid proliferation of new energy vehicles (NEVs), including battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs), has fundamentally transformed automotive chassis design paradigms. The McPherson strut suspension, renowned for its compact architecture and cost-effectiveness, has emerged as a predominant configuration for NEV front axles. This review systematically examines the adaptation, optimization, and challenges of McPherson suspension systems in the context of electrified powertrains. We analyze the unique requirements imposed by NEV weight distributions, battery integration, and noise vibration harshness (NVH) characteristics, synthesizing recent advances in lightweight design, multi objective optimization algorithms, and active control integration. Key discussion areas include kinematic performance optimization through genetic algorithms and AI-driven methods, material innovations enabling mass reduction, NVH mitigation strategies, and the evolution toward semi active and energy-regenerative variants. Through critical analysis of over 30 representative studies and industrial applications, this review identifies that while McPherson suspension remains viable for NEVs, its successful implementation necessitates sophisticated parameter optimization, advanced materials, and intelligent control systems to address inherent limitations in roll stiffness and camber control. Future trajectories emphasize synergy with autonomous driving architectures and electromagnetic energy-harvesting technologies, positioning McPherson-derived systems as foundational components of next-generation intelligent electric chassis.

As the oil price has skyrocketed, the attraction towards electric vehicles has gone up. This scenario has also increased the demand for charging infrastructure. This paper proposes a novel charging infrastructure for electric vehicles which is energized by a solar photovoltaic unit, integrated with a distribution static compensator. The output of the photovoltaic array is regulated by a DC–DC converter, which uses maximum power point tracking to support optimal solar energy conversion. The compensator is integrated into the grid through a zigzag-star transformer, which helps with neutral current compensation, promoting balanced and distortion-free operation. The control algorithm is designed to ensure superior power quality during grid synchronization and sustainable energy management. This novel architecture ensures bidirectional power flow, enabling the charge–discharge dynamics of the electric vehicles, which can be termed Grid-to-Vehicle and Vehicle-to-Grid modes. Better grid flexibility and resilience are ensured by this dynamic power exchange. The control strategy based on the Linear Kalman Filter provides reactive power balance and maintains steady voltage at the point of common coupling, and it ensures enhanced power quality during power flow, resulting in efficient and reliable grid operations. The effectiveness of the control algorithm is tested and validated under Grid-to-Vehicle, Vehicle-to-Grid, nonlinear, unbalanced, and isolated solar conditions. Analytical tuning of the gains in the controller, by using the conventional methods, is not efficient under dynamic conditions and nonlinear loads. An optimization technique is used to estimate the proportional–integral control gains, which avoids the difficulty of tuning the controllers. Simulation of the system is carried out using MATLAB 2022b/SIMULINK. Simulation results under diverse operating scenarios confirm the system’s capability to sustain superior power quality, maintain grid stability, and support a robust and reliable charging infrastructure. By enabling regulated bidirectional energy exchange and autonomous operation during grid disturbances, the charger operates as a resilient grid-support asset rather than as a passive electrical load.

ABSTRACT Future mobility options, such as electric vehicles (EVs), have been growing in popularity in recent years. EVs may improve urban climates and provide affordable and flexible mobility throughout their lifespan. They benefit society by offering zero tailpipe emissions, superior comfort, low lifespan costs and enhanced connectivity. In this study, after identifying various aspects influencing vehicle purchases, we determined the total cost of ownership (TCO) and studied the average TCO for each vehicle type: EVs, internal combustion engine vehicles (ICEVs) and hybrid electric vehicles (HEVs). Next, we employed a combination of two multi-criteria decision-making methods to rank EVs, ICEVs and HEVs across various pricing groups. The Best-Worst Method (BWM) was used to calculate the weights of each criterion, while the Technique for Order Preference by Similarity to Ideal Solution was employed to compute the ranking of the alternatives based on the BWM results. The ranking indicates that buyers should prioritize purchasing EVs, followed by HEVs, and then ICEVs, to enhance flexibility in their mobility.

Dimensioning the energy storage systems for a heavy-duty fuel cell hybrid electric vehicle is not straightforward. This study proposes a methodology to address this challenge, aiming to maximize efficiency while mitigating the aging effects on the energy storage systems. Various configurations of storage system ratios have been analyzed using the concept of hybridization percentage, which represents the ratio between the supercapacitor weight and the total weight of the energy storage elements. Simulations were conducted using models developed in AVL Cruise MTM. A case study is included to test the methodology, incorporating commercial components, a standard driving cycle, and a rule-based energy management strategy. The conclusions of this application example illustrate the types of results that can be obtained by using this hybrid energy storage system sizing methodology. Findings for this case study suggest that for cycles lacking extreme power peaks, non-hybridized configurations can be the optimal solution, as the battery size reduction outweighs the benefits of hybridization in terms of efficiency, achieving 76.08% without supercapacitors compared to 65.7% with a high hybridization grade of 32.4%, and overall cost. However, sensitivity analysis reveals that if the optimization weights are adjusted to prioritize aging over efficiency, the optimal configuration shifts to a 6.48% hybridization grade at a 0.3C threshold.

ABSTRACT The increasing transportation demands and environmental concerns in India necessitate the selection of optimal battery technologies for hybrid electric vehicles (HEVs). As the fifth‐largest car market globally, India faces rising vehicle demand, while the transportation sector remains a major contributor to air pollution. This study aims to identify the most suitable battery option for HEVs in India by considering performance, cost, and environmental impact. A multicriteria decision‐making framework based on the technique for order of preference by similarity to ideal solution, supported by matrix laboratory validation, is employed to evaluate four widely used battery technologies: lithium‐ion (Li‐ion), nickel–metal hydride (Ni‐MH), lead‐acid, and nickel–cadmium (Ni─Cd). Key performance indicators, including electrical efficiency, specific energy, energy density, nominal cell voltage, energy per cycle, self‐discharge rate, specific power, cost per kWh, and durability, are incorporated into the analysis. The results consistently indicate that Li‐ion and Ni‐MH batteries outperform Pb‐acid and Ni─Cd batteries, with Li‐ion emerging as the most suitable option due to its superior energy density, specific energy, and low self‐discharge rate. The proposed framework provides a robust and reproducible decision‐support tool for HEV battery selection in India, supporting efficient vehicle performance and reduced environmental impact.

Plug-in hybrid electric vehicles (PHEVs) operate using both electricity and liquid fuel, offering emissions reduction while eliminating driving-range concerns. Determining the optimal electric range or battery capacity is crucial for the total cost of ownership, decarbonization potential, and battery material demand. However, the effect of battery degradation has not been incorporated into market-oriented range-optimization studies. This paper extends the existing MOR-PHEV range optimization model by integrating both cycle-based and calendar-based battery degradations. The results show meaningful optimization benefits, reducing consumer ownership cost by approximately $3000–5000. The optimal solution—defined by the minimized lifetime cost and the optimal battery capacity—is robust across the key external parameters. Intertwined with certain factors, battery degradation can have a significant impact on the optimal battery capacity. Particularly, at faster cycle-based degradation, high driving intensity and high CS efficiency can lead to optimization tipping points, where the degradation effect is so significant that the consumer is better off by choosing a small-battery PHEV (or HEV if the choice space expands beyond PHEV) in order to fully degrade the battery faster, totally avoid the charging behavior cost earlier, and maximally benefit from the high CS efficiency earlier. This points to the importance of reducing the cycle-based degradation coefficient and improving the vehicle energy efficiency and charging convenience. One basis point (0.01%) reduction in the cycle-based degradation coefficient is estimated to reduce the optimal battery capacity by 4.9–5.2 kWh and increase consumer value by $275–497, depending on the battery unit cost. These are useful insights into decision-making regarding battery technology R&D, battery chemistry roadmaps, critical material supply risks, and EV product strategies. While the findings in the study scope depend on assumptions of consumer behavior, battery degradation, vehicle efficiency and charging infrastructure, the expanded MOR-PHEV provides a systematic framework for considering different assumptions in support of user-defined decision context and discussing future research.

This paper studies an economic driving control method for plug-in hybrid electric vehicles (PHEVs) on undulating roads. Existing energy management systems often rely on standard driving cycles or historical speed data, neglecting the real-time effects of road gradient and dynamic speed variation. To address this, an economic speed planning algorithm based on dynamic programming is proposed from an energy management perspective. The algorithm optimises the vehicle speed on undulating roads to improve powertrain efficiency and achieve economical driving. Simulation results demonstrate that the proposed method significantly improves energy saving, reduces fuel consumption, and lowers the final battery state of charge compared to other approaches. In summary, this study provides valuable insights for enhancing the energy economy of PHEVs on undulating roads.