This study presents an optimized multi-source energy management strategy for a series hybrid vehicle integrating an internal combustion engine (ICE), battery storage, photovoltaic panels, and regenerative braking. A multi-objective NSGA-II approach is applied to minimize fuel consumption and CO₂ emissions while enhancing system stability. Under optimization, solar production and regenerative braking curves are smoothed and regularized, indicating improved power integration and energy recovery. The battery state of charge (SOC) remains within safe limits, avoiding deep discharges. Fuel consumption drops 20 % (from 6.5 to 5.2 L/100 km), and CO₂ emissions decrease (160→135 g/km). These gains stem from optimal energy distribution among ICE, photovoltaic (PV) generation, regenerative braking, and battery usage. An optimized proportional–integral (PI) controller and dynamic maximum power point tracking (MPPT) contribute to enhanced stability, especially under highway-like Federal Test Procedure 75 (FTP-75) cycle conditions. Thermal and irradiance analyses underscore the need for adaptive MPPT to manage environmental fluctuations. Overall, the framework yields a 20 % efficiency gain over an urban cycle, meeting propulsion constraints. • Vehicle fuel consumption reduced by ∼20 % via NSGA-II optimization. • CO₂ emissions decreased from 160 to 135 g/km thanks to integrated PV and regen. • SOC remains stable within safe limits, avoiding deep discharges. • MPPT and optimized PI control enhance system stability under real-driving conditions.

Real-time control of plug-in hybrid vehicles under low-temperature heat constraints

Environmental protection is currently receiving significant attention, particularly in the context of transport's impact on the environment. Limited fossil resources, climate change, and global warming are driving the automotive industry toward more efficient and sustainable solutions. These challenges are driving car manufacturers to adopt new technologies and alternative drive systems. Examples of such vehicles include electric vehicles (EVs) and hybrid vehicles (HEVs or PHEVs). The impact of operating these modes of transport on the emission of pollutants other than exhaust gases is crucial. Examples of such emissions include particulate matter generated by brake and tire wear. All vehicles, whether conventionally or alternatively powered, generate such emissions during operation, regardless of their drive type. These particulate matter enter the air and can pose a threat to the environment and human health. While it may seem that electric cars may emit less particulate matter from their braking systems due to the frequent use of recuperation, tires remain a significant source of emissions. The article included measurements of dust emissions during the operation of an electric vehicle and a conventionally driven vehicle, as well as studies of the elemental composition of particles using scanning electron microscopy, analyzing dust collected from the vehicle's surroundings, braking system and tires

Cross-type transfer of fuel cell hybrid vehicles energy management strategies based on deep reinforcement learning

ABSTRACT Deep learning has shown strong potential for diagnosing faults in planetary gearboxes, which are critical components in wind and gas turbines, electric drives, and hybrid vehicle powertrains. However, industrial deployment remains constrained by reliance on initial labels, single-step contrastive objectives, limited modelling of cluster structure, and difficulty detecting previously unseen faults. To address these issues, we propose a stagewise framework, Hierarchical Multi-Granularity Prototype-Contrastive Learning (HMPCL), which maximises the value of limited known-class data by organising representation learning across instance-, subcluster-, and class-level granularities. The framework adopts a multi-stage objective (Stages 0–3), progressing from coarse instance discrimination to refined prototype-guided alignment, while jointly improving intra-cluster compactness and inter-cluster separability under both over-clustered and standard clustering settings. A prototype bank with Sinkhorn-Knopp balanced assignments is introduced to stabilise representation learning, prevent collapse, and strengthen novel fault detection. This hierarchical prototype-based strategy accelerates convergence, improves generalisation, and yields a robust classification system for gearbox fault diagnosis. Experimental results on planetary gearbox datasets across two case studies demonstrate consistently strong diagnostic performance and practical effectiveness.

Research on intelligent control strategy of active grille shutters of hybrid vehicle based on low energy consumption

In order to improve the safety and energy utilization of vehicles, a combination of vehicle stability criterion models and traffic flow models is proposed to plan vehicle paths from the level of path planning to avoid extreme and inefficient working conditions, enabling fast and safe driving under slippery road conditions. The traffic flow model is used to predict the future changes in the traffic environment that the vehicle will face, and then the stability criterion model is used to assess the safety of future traffic in order to plan the fastest and safest path for the hybrid vehicle. Specifically, the generalized Aw–Rascle–Zhang (GARZ) macroscopic traffic flow model is solved using the flux vector splitting format in order to predict the future changes in speed and traffic density that the hybrid vehicle will face. In addition, the front-wheel steering angle responses given by the same driver at different speeds and different distances relative to the vehicle in front were collected using the driver-in-the-loop simulation platform Prescan. Simulink models based on front-wheel drive (FWD) front-wheel steering (FWS) vehicles and all-wheel steering (AWS) distributed drive vehicles (DDVs) give the force saturation factor [Formula: see text] response corresponding to different front-wheel steering angles. The stability criterion model of the vehicle was established by using artificial neural network (ANN) to train [Formula: see text] corresponding to different speeds and traffic densities. The parameters predicted by the traffic flow model (vehicle speed and traffic density) were evaluated for stability using the newly established stability criterion model. The vehicle traveling paths were optimized based on the above methods to ensure the safety of vehicle traveling on slippery road surfaces. Finally, real US-101 traffic flow data were used to verify the predictions of the traffic flow model.

To address the limited accuracy of power demand prediction for hybrid electric vehicles under complex and dynamic driving conditions, this paper proposes a hybrid prediction approach based on the cascade correction of Long Short-Term Memory networks and Least Squares Support Vector Machines (LSTM-LSSVM-CC). The proposed method adopts a stage-wise modeling framework that exploits the least-squares optimality of LSSVM for low-frequency steady-state signals and the dynamic compensation capability of LSTM for high-frequency non-stationary residuals, thereby achieving complementary feature representation in the frequency domain. Specifically, an LSSVM is first used to construct a baseline regression model that captures stationary components, followed by an LSTM network that performs deep temporal modeling of the residual sequence to correct nonlinear prediction errors. Extensive experiments conducted on three standard driving cycles—CLTC-P, WLTP, and UDDS—demonstrate that the proposed model consistently outperforms conventional methods including LSSVM, RNN, ELMAN, and Random Forest in multi-step predictions, achieving an average RMSE reduction of 28–52% and maintaining correlation coefficients (R2) between 0.87 and 0.99. Particularly under highly dynamic and abrupt load conditions, the model exhibits superior real-time performance and stability while significantly mitigating cumulative prediction errors. These results demonstrate that the proposed LSTM-LSSVM-CC model achieves robust modeling performance of non-stationary time series while balancing prediction accuracy and computational efficiency, providing an effective technical foundation for hybrid vehicle energy management optimization and offering a transferable theoretical framework for time-series prediction in complex systems.

The hydrogen fuel cell electric vehicles (FCEVs) are becoming a worldwide recognized eco-friendly choice which produces no tailpipe emissions while providing better energy efficiency than traditional internal combustion engine vehicles. The review delivers an in-depth evaluation of FCEVs through their assessment which focuses on their transportation and power generation functions. The research investigates hydrogen production methods together with storage and distribution systems and vehicle integration practices and performance enhancement techniques. The paper highlights major technical challenges such as high production costs, limited refueling infrastructure, storage inefficiencies, and fuel cell durability. The research uses battery electric and hybrid vehicle comparisons to assess FCEV market competitiveness. The life-cycle environmental impact assessment proves that using clean hydrogen sources and sustainable end-of-life strategies is essential for achieving FCEV operational capabilities. The review examines new electrochemistry materials science and hybridization solutions which have become essential methods for creating better efficiency and durability while decreasing costs. The study shows how policy regulations and collaborative programs fast-track hydrogen adoption through their impact on future hydrogen grid integration and renewable hydrogen production and circular economy methods. The review shows how experts from different fields reached their achievements while still facing challenges to improve FCEVs as fundamental components of environmentally friendly transportation systems and clean energy networks.

Transportation plays a vital role in the economic development, trade and connectivity. The traditional transport system which is based on petrol and diesel vehicles contributes significantly to greenhouse gas emissions and urban air pollution. This transition towards sustainable transportation has become a major policy priority in India due to the rising environmental concerns, increasing fuel consumption and the need to reduce carbon emissions. Electric Vehicles (EVs) are considered as a key solution for promoting clean and energy efficient mobility. To accelerate the adoption of EVs, the Government of India has introduced many schemes among them the Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) India Scheme under the National Electric Mobility Mission Plan plays a key role. This study analyzes the current status and growth trends of electric vehicles adoption, role of government policies and initiatives in promoting electric mobility in India, and to identify the major infrastructural challenges, economic and technological barriers. This study is completely based on secondary data and focuses on EV adoption trends, development of charging infrastructure and growth of domestic EV manufacturing. The findings reveal that the FAME II scheme significantly contributed to the rapid growth of EV adoption particularly in the two wheeler and three wheeler segments while also supporting the expansion of charging infrastructure across major cities and highways. However, challenges such as high initial costs, infrastructure gaps remains. This study concludes that continued policy support and integrated ecosystem development are essential for accelerating mobility in India.

The construction of a simulation model for the Energy Management (EM) of an Electric Vehicle (EV) hybrid traction drive based on fuel cells and lithium batteries in the MATLAB Simulink software environment is the focus of this paper. Battery and hybrid traction drive application topologies are taken into consideration. A traction drive model has been developed, where a fuel cell (FC) with a proton exchange membrane (PEM) serves as the primary energy source and a high-power buffer storage unit (BSU) based on a lithium-ion battery smoothens out the uneven transport load. Reduced to a ton of vehicle weight, the reliance of the necessary BSU capacity on the power of the primary source was found. This led to the determination of the ideal range of hybrid power plant characteristics (FC power from 5 to 11 kW/t, Lithium-Ion battery capacity from 6 to 10 Ah/t) when operating in accordance with the WLTC load cycle. In this instance, the period of included state of FC was 94.53%, and the computed fuel consumption was 0.56 kg/km-t.

Hybrid Vehicle with Supercapacitor

With the growing demand for hybrid and electric vehicles, the accurate prediction of NVH (Noise, Vibration, and Harshness) behavior in Permanent Magnet Synchronous Machines (PMSMs) has become a critical aspect of electric motor design. This paper presents a detailed modeling approach for electromagnetic-induced noise and vibrations in PMSMs, integrating both analytical and numerical methods. The model focuses on quantifying the contributions of radial and tangential electromagnetic forces, which are key drivers of vibro-acoustic responses. The analytical part employs curved beam theory and a simplified acoustic model, offering rapid insights during early design stages. In parallel, a detailed numerical model based on finite element analysis is developed using a physics-based approach that accounts for the actual geometry and material properties of the PMSM prototype. This allows for enhanced accuracy without relying on experimental material parameter identification. Moreover, the detailed model includes the fluid–structure interaction introduced by the channels of the cooling fluid of the electric machine, which, although poorly addressed by the existing literature, was found to play a key role in driving the vibrational behaviour of the structure. By combining analytical speed with numerical precision, the proposed approach enables consistent and physically-based NVH predictions across various design phases, ultimately supporting improved electric machine performance and reducing development time and costs. Validation against experimental data confirms the ability of the model to accurately predict both sound pressure levels and housing surface vibrations. The novelty of this work lies in its integration of fluid–structure interaction and material modeling without the need for empirical parameter tuning, offering a robust tool for NVH design in electric vehicle applications.

This article examines variation in green industrial policies for electrified vehicles (EVs) in Brazil and Mexico. Both are middle-income democracies with significant automotive sectors, yet they have adopted distinct technological pathways under similar global decarbonization pressures. We argue that technological choices are mediated by sectoral developmental alliances whose preferences are primarily structured by the politics of national growth models. Using a descriptive comparative analysis, we show that Brazil’s commodity-driven model and large domestic market have supported an alliance between automakers and biofuel producers, leading to the prioritization of ethanol-compatible hybrid vehicles. By contrast, Mexico’s export-led integration into North American value chains has reinforced alliances aligned with battery electric vehicles (BEVs), consistent with the inherent pressures of its export-led growth model and regulatory dynamics. The comparison advances a plausible hypothesis: In peripheral economies, green technological pathways are politically negotiated outcomes shaped by the politics of developmental alliances, rather than purely efficiency-driven responses to global climate imperatives.

Disaster evacuation of the old city of Nicosia

Deep reinforcement learning (DRL) algorithms have become an essential approach for enabling autonomous evolution in artificial intelligence (AI) models. As the representative form, autonomous vehicles equipped with new energy powertrains are expected to take into account the external dynamic environment, human-centered driving demand, and the internal power supply. This paper aims to integrate fundamental tasks of energy-saving and autonomous driving, realizing end-to-end self-learning-based improvements. Firstly, a Python-Simulink-Unreal Engine/Carla-based co-simulation framework is proposed after customizing 3D driving scenarios and enhancing vehicle modeling. Then, focusing on the vehicle-level embodied intelligence, a DRL-based energy-saving full-body control system is proposed, in which DRL agents are trained to collaboratively learn adaptive cruise control (ACC) and lane-keeping assist (LKA) at the vehicle driving layer, as well as energy management strategy (EMS) and transmission shift strategy (TSS) at the powertrain level. Moreover, to achieve the integration of energy-saving driving and end-to-end autonomous driving, Bird's Eye View (BEV) perception is incorporated into the state space. This not only constructs a dual-modal state space but also facilitates extensions to other end-to-end tasks. Finally, a hardware-in-the-loop (HIL) platform is established. The results show that the DRL-based energy-saving control system maintains a speed of 60 km/h while staying within the lane 3.82 m wide on a 7.041 km circular road. The complete drive lasts 557 seconds and achieves a fuel economy of 7.748 L/100 km.

Fuel cell hybrid vehicles (FCHVs) have drawn tremendous attention due to the advantages of zero emissions. Existing energy management strategies (EMSs) typically fail to adequately address the coupled relationship between power allocation and thermal dynamics in the powertrain system, which is a gap that affects the economic and durability performance of FCHVs. To address this, this research proposes a hierarchical EMS that innovatively integrates: (1) a fuzzy-encode Markov Chain speed predictor for speed forecasting at the upper level, and (2) a multi-objective model predictive control that optimizes system operating cost, durability and thermal safety of battery and fuel cell system at the lower level. In the validation phase, the impacts of different membership functions and state numbers on speed prediction accuracy are explored firstly. Then, the effects of weighting factors in multi-objective function are studied. Furthermore, an effectiveness evaluation method is set up to score each strategy with dynamic programming (DP) as the upper benchmark. The comparison with the other three benchmark strategies proves that the suggested strategy is more cost-effective, thermally safe and life-extended, bringing an overall performance improvement of at least 20.61% and a score improvement of at least 14.02 points in the [0,100] range.

Is plug-in hybrid vehicle a green mode in daily use?

Introduction In order to solve the problems of low accuracy and multi-objective optimization imbalance in traditional hybrid electric vehicle energy management strategies under dynamic conditions. Methods A study was conducted to design an improved reinforcement learning energy management strategy based on dual delay deep deterministic strategy gradient (TD3), aiming to improve fuel economy, extend battery life, and enhance strategy robustness. Firstly, a multi energy system dynamics model was constructed, which includes an engine, power battery, and electric motor. Secondly, in order to solve the problems of slow convergence and easy getting stuck in local optima in traditional reinforcement learning for multi-objective optimization, adaptive reward functions and priority experience replay mechanisms are introduced. Results The results indicate that the initial value of the state of charge for all three strategies is 0.5, and the research strategy maintains it at 0.5. Discussion ITD3 can more accurately control the state of charge, making it close to the initial value and reducing excessive energy consumption; Overall, compared with traditional strategies, this research strategy exhibits better battery state of charge retention ability under two typical operating conditions. This strategy can achieve precise energy management, effectively reduce costs, improve energy utilization efficiency, support environmental sustainability, and provide better solutions for energy management of new energy hybrid vehicles.

Traffic congestion frequently occurs on slope segments of highways, which are typical bottlenecks. With the development of connected and autonomous vehicle (CAV) technology, CAVs and human-driven vehicles (HDVs) are anticipated to coexist on the same roads in the foreseeable future, thereby transforming the traffic environment from predominantly HDVs to a mixture of CAVs and HDVs. To the best of the authors’ knowledge, no studies address slope bottlenecks on highways in mixed traffic flow. Thus, this paper proposes a traffic flow model for slope bottlenecks incorporating CAV platooning based on cellular automata. A novel traffic flow control strategy for slope bottlenecks is proposed, based on variable speed limits (VSL) and vehicle platooning. Firstly, it divides the upstream section of the slope bottleneck into two zones for implementing VSL and vehicle platooning. Via speed restrictions within the VSL zone, the inflow of vehicles into the vehicle platooning zone is effectively mitigated to create low traffic density. In the vehicle platooning zone, a hybrid vehicle platooning method for mixed scenarios is proposed. Extensive experiments are conducted to validate that the proposed strategy increases the traffic flow of the slope bottleneck, improves the average speed, and alleviates traffic congestion.