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Range-extended electric vehicles (REEVs) face more intricate noise, vibration, and harshness (NVH) challenges compared to traditional fuel and pure electric vehicles, primarily due to the complexities introduced by range extender control strategies. Poorly designed control logic can result in issues such as excessive noise during acceleration and engine roar, which are not only costly to rectify but also time-consuming. This article simplifies the theoretical model of range extender systems by treating the range extender as a single excitation source. By monitoring the vehicle's overall status, the system adjusts the output torque and crankshaft speed to evaluate the vibration and noise at critical points within the vehicle. This proactive approach helps to preemptively optimize NVH performance under various operating conditions, thereby minimizing the need for subsequent adjustments to the suspension, transmission, intake, and exhaust systems. As a result, it significantly reduces costs and accelerates the project's development timeline. [ABSTRACT FROM AUTHOR]

The fuel economy of electric vehicles (EVs) is an important factor in determining the competitiveness of EVs. Since the fuel economy is affected by the efficiency of an e-powertrain composed of a motor and inverter, it is necessary to select a high-efficiency topology for the e-powertrain. In this paper, a novel topology of e-powertrains to improve the fuel economy of EVs is proposed. The proposed topology aims to improve the system efficiency by integrating open-end winding (OEW) and winding changeover (WC). The proposed OEW-PMSM with WC enables to drive a permanent magnet synchronous motor (PMSM) in four different modes. Each mode can increase inverter efficiency and motor efficiency by changing motor parameters and maximum modulation index. In this paper, the system efficiency of the proposed topology was evaluated using electromagnetic finite element analysis and a loss model of power semiconductors. In addition, the vehicle simulations were performed to evaluate the fuel economy of the proposed topology, thereby proving the superiority of the proposed topology compared with the conventional PMSM. [ABSTRACT FROM AUTHOR]

The country's first on-site hydrogen refueling station (HRS) is installed at the Hydrogen Energy Laboratory, Bangladesh Council of Scientific and Industrial Research (BCSIR), and its performance is studied to penetrate and prepare the hydrogen refueling network for the future. The study fills the power conditioning and integration information gap to HRS and refueling performances of on-site HRS in the particular conditions of the country. Strategic integration of grid power to the HRS is materialized and its adaptation with on-site HRS. Low-cost frequency & voltage conversion (conversion of 50 Hz, 420 V, or 220 V to stable 60 Hz, 220 V, or 110 V) and its stabilization are successfully made. An initial pressure of 5 MPa and 10 MPa of the vehicle tank (two-time refueling conditions) at 25 °C fills with the hydrogen at the APRR of 5.07 & 6.39 by 5.91& 7.5 min accordingly whereas the target pressure is 35 MPa. Hydrogen transfer to the vehicle tank at the pressure established by SAE J2799 according to the APRR during refueling time. The system consumes 52.71 kW and 3.73 kW of power to produce and compress 1 kg of hydrogen. The temperature profile shows that at the maximum hydrogen flow rate of 20 g/s, the temperature rose to 56 °C maintaining the fueling protocol. The levelized cost of hydrogen (LCOH P) of on-site hydrogen production is $6.68 and the LCOH S&D of storage & dispensing of hydrogen is $1.07 using the grid electricity whereas it gets down to $3.74 and $0.76 for the case of solar PV. Hydrogen vehicles reduce 43% fuel cost (using grid power) as compared to diesel engines. The study also depicts that a solar-powered hydrogen fleet is a better option for replacing fossil fuels and in shaping a sustainable transport system. The economic analyses provide the net present value (NPV 20) and benefit and cost ratio (BCR) of the on-site HRS are 0.09 million and 1.18 at the discount rate of 9.5% which corresponds to the investment to the on-site HRS is feasible and free of risk. • Strategic conversion and integration of power with on-site hydrogen refueling station. • LCOH of hydrogen at on-site HRS $7.5 & $3.50 using grid and Solar PV accordingly. • SAE J2799 nozzle and slow filling are used to refuel the vehicle without precooling. • Hydrogen vehicle reduces 43% of fuel cost as compared to gasoline-burned vehicles. [ABSTRACT FROM AUTHOR]

Intelligent energy management of new energy vehicles is an important topic of advanced automotive technology research, and is critical to further improve the fuel economy performance of the entire vehicle. Aiming at the challenge of considering both real-time and optimal capabilities for global energy management and control of plug-in hybrid electric vehicle (PHEV), this paper studies full trip adaptive energy management based on energy consumption prediction. An adaptive equivalent fuel consumption minimum control algorithm for full-trip PHEV is proposed. Finally, MATLAB/Simulink modeling and simulations are conducted, and the results show that the proposed control strategy has good followability and adaptability to the changes of actual driving conditions, driving mileage and automobiles’ energy states. The approximate global optimization control has good effects on the full path, and the fuel economy of the vehicle is significantly improved. [ABSTRACT FROM AUTHOR]

The investigation demonstrates the potential of nano zinc oxide as a substitute for conventional rubber grade zinc oxide in the bead filler compound of passenger car tires, aimed at reducing heat generation while preserving mechanical and reinforcement properties. Experimentation showed that introducing nano zinc oxide increased compound viscosity, indicating enhanced filler networking. Cure rates accelerated due to the higher surface area of nano zinc oxides, while the Payne effect indicated improved dispersion levels in experimental compounds. Notably, the variant with four parts per hundred rubber of nano zinc oxide exhibited mechanical properties akin to the control, alongside a 28% reduction in tan δ and improvements in hysteresis loss ratio and specific heat values. Morphological analysis confirmed superior dispersion in experimental compounds. Finite element analysis demonstrated decreased temperature generation and a reduced percentage contribution of the bead filler to overall heat energy in tires, leading to a 3% drop in rolling resistance. These findings suggest that nano zinc oxide can effectively replace conventional zinc oxide, enhancing fuel economy in tires, thereby offering a green initiative to reduce fuel consumption in passenger car radial tires through improved hysteresis at the bead area via bead filler compound modification. Highlights: Nano zinc oxide replaces rubber grade zinc oxide reducing heat generation.Nano zinc oxide improves compound viscosity, enhancing filler networking.Variant with four parts per hundred rubber of nano zinc oxide matches control's mechanics, with 28% lower tan δ.Morphological analysis confirms dispersion, coupled with aggregation.Simulation shows lower temperature generation and 3% less rolling resistance. [ABSTRACT FROM AUTHOR]

The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine's operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy. [ABSTRACT FROM AUTHOR]

Tracked vehicles are integral for maneuvering diverse terrains, with hybrid propulsion systems offering potential benefits in terms of fuel efficiency and performance. However, research in hybrid electric tracked vehicles remains limited, thus necessitating a comprehensive analysis to maximize their advantages. This study presents a numerical analysis focusing on optimizing hybridization in speed-coupled parallel hybrid electric powertrains for tracked vehicles. A dynamic programming algorithm and custom drive cycle are utilized to determine optimal hybridization factors and assess parameter sensitivities. The study reveals that a hybridization factor of 0.48 is optimal for speed-coupled parallel configurations. Furthermore, the sensitivity analysis underscores the substantial impact of factors such as the engine displacement and bore-to-stroke ratio on the fuel economy, with a 10% change in these parameters potentially influencing the fuel economy by up to 2%, thus emphasizing the importance of thorough consideration during powertrain sizing. Parallel hybrid configurations exhibit considerable potential for tracked vehicles, thus highlighting the viability of choosing them over series configurations. [ABSTRACT FROM AUTHOR]

In this study, fuel consumption test cycles and commonly used SORT cycles for buses were discussed. An exemplary bus model was extensively modeled in terms of power systems. In addition to main components such as the engine, transmission, torque converter, and axle, the drags of engine-powered components such as alternators, engine cooling fan, and air conditioning compressor were integrated into the powertrain model to ensure that the calculated consumption comes as close as possible to the real-life values. The tire model, which determines the quality standard in vehicle simulation, was also discussed in detail. During the modeling of these components, necessary parameters were obtained through analyses and tests conducted on the sample vehicle using the CAN bus and added sensors. After the completion of the model, real-life tests were conducted to validate the virtual analysis results. Once the model was validated, virtual studies continued to reduce the vehicle's consumption. Particularly during these studies, reducing consumption and enhancing performance through automatic transmission optimization were emphasized. When virtual models validated with SORT simulations are optimized with route-specific virtual analysis, public vehicles will minimize environmental impacts by reducing carbon emissions and at the same time perform more efficiently. [ABSTRACT FROM AUTHOR]

This study investigates the energy consumption of electric vehicles (EVs) in real-world driving conditions, using in-vehicle sensor data and location tracking. The energy consumption of EVs is compared to that of an internal combustion engine vehicle (ICEV) under similar driving tests. Furthermore, an energy-centric life cycle assessment was conducted to assess the environmental impact of the transition to electrified transportation by reporting the carbon emissions of the EVs and ICEV. Since a large volume of driving data was acquired in the experiment, machine learning (ML) techniques are employed to interpret the obtained data and identify a key feature. ML algorithms can help develop predictive models that estimate energy consumption and explain the major factors that impact such consumption. The findings also offer valuable insights into energy consumption, emission, and the underlying factors that can inform policy discussion related to transport electrification and enhance energy efficiency. This research is significant in comprehending the potential of current EVs and their superiority over traditional vehicles. It is vital to facilitate the shift toward electric transportation, which can be driven by the results.

This study explores how reducing lubricant viscosity affects fuel economy in passenger cars through experimental characterization and semi-predictive modelling. Thus, three engine oils (0W-20, 5W-30, and 5W-40) were tested for their rheological and tribological properties. Data were introduced into a detailed GT-Suite model of a turbocharged gasoline SUV. Simulations over six different driving cycles, including European homologation and real-driving conditions, used a fast-running model (FRM) to assess engine friction and wear protection. Results showed that lower viscosity oils, especially 0W-20, significantly reduced frictional losses, enhancing fuel economy. However, lower viscosity also increased contact friction under high power demand, raising durability concerns. A comprehensive evaluation of lubricants performance, offering insights into the trade-offs between fuel economy improvements and potential durability risks.

Bu çalışmada, ağır ticari araç sınıfında yer alan bir çekicinin performansı dizel ve sıvılaştırılmış doğal gaz (LNG) yakıtlı bir motora sahip olması durumunda deneysel olarak incelenmiştir. Testler sırasında her iki çekicide Euro VI normlarına sahip 338 kW gücünde motor kullanılmıştır. Araçlar toplam 41000 kg ağırlıkta eğimsiz düz bir yolda kullanılarak 85 km/h hızdan 20 km/h hıza serbest yavaşlama ve kalkıştan 85 km/h hıza ulaşana kadar hızlanma testlerine tabi tutulmuştur. Ayrıca 164,3 km’si otoyol, 44,4 km’si kırsal yol ve 54,7 km’si şehir içi yol olmak üzere toplam 263,4 km’den oluşan bir güzergâhta kullanılan araçların ortalama yakıt tüketim maliyetleri hesaplanmıştır. Sonuçlara göre, LNG yakıtlı aracın 100 km için yakıt maliyeti 1003,58 TL dizel aracın ise 1359,32 TL olarak hesaplanmıştır. LNG yakıtlı aracın ilk satış maliyetinin %30 daha pahalı olacağı öngörüldüğünde bile elde edilmiş olan %27 oranındaki yakıt tasarrufunun uzun yol kullanılan bir araç için kayda değer bir sonuç olduğu anlaşılmaktadır.

The demand for clean and efficient new vehicle models is steadily increasing. Methanol-hydrogen engines use methanol as the main fuel and can recover the heat from engine exhaust to dissociate part of methanol into hydrogen-rich syngas. The syngas is recirculated back into the engine cylinder to improve combustion. Therefore, methanol-hydrogen engines have a higher energy utilization efficiency. This paper designed a methanol-hydrogen engine system based on a Miller cycle methanol engine. Bench tests showed that combusting methanol and dissociated methanol gas together could accelerate the fuel burn rate and improve the engine's fuel economy. At a fixed engine speed of 2500 r/min, the BSFC of the methanol-hydrogen engine was reduced up to 5.55% compared to the original methanol engine. Further, a methanol-hydrogen extended-range electric vehicle powertrain model was built in Simulink. Simulation results indicated that, under the WTLC cycle, the methanol-hydrogen engine achieved a 22.60% reduction in fuel consumption per 100 km and saved 58.89% in fuel costs compared to the conventional gasoline engine. The methanol-hydrogen engine presented potential application in the extended-range electric vehicle. [ABSTRACT FROM AUTHOR]

Current developments in vehicles have generated great interest in the research and optimization of heating, ventilation, and air conditioning (HVAC) systems as a factor to reduce fuel consumption. One of the key trends for finding solutions is the intensive development of electric transport and, consequently, additional requirements for reducing energy consumption and modifying climate systems. Of particular interest is the optimal functioning of comfort and life support systems during air recirculation, i.e., when there is a complete or partial absence of outside air supply, in particular to reduce energy consumption or when the environment is polluted. This work examines numerical models of airfields (temperature, speed, and humidity) and also focuses on the concentration of carbon dioxide and oxygen in the cabin, which is a critical factor for ensuring the health of the driver and passengers. To build a mathematical model, the Navier–Stokes equations with energy, continuity, and diffusion equations are used to simulate the diffusion of gases and air humidity. In the Ansys Fluent finite volume analysis package, the model is solved numerically using averaged RANS equations and k-ω turbulence models. The cabin of a mainline locomotive with two drivers, taking into account their breathing, is used as a transport model. The problem was solved in a nonstationary formulation for the design scenario of summer and winter, the time of stabilization of the fields was found, and graphs were constructed for different points in time. A comparative analysis of the uniformity of fields along the height of the cabin was carried out with different locations of deflectors, and optimal configurations were found. Energy efficiency values of the climate system operation in recirculation operating modes were obtained. A qualitative assessment of the driver's blowing directions under different circulation and recirculation modes is given from the point of view of the concentration of carbon dioxide in the breathing area. The proposed solution makes it possible to reduce electricity consumption from 3.1 kW to 0.6 kW and in winter mode from 11.6 kW to 3.9 kW and save up to 1.5 L/h of fuel. The conducted research can be used to develop modern energy-efficient and safe systems for providing comfortable climate conditions for drivers and passengers of various types of transport. [ABSTRACT FROM AUTHOR]

Aimed to more truly and stably measure the fuel economy of a vehicle under different driving cycles, simultaneously reduce prime cost and short experimental period. An engine fuel economy test method and bench which could adapt to multiple cycle conditions was developed. The fuel economy test of a certain SUV under NEDC/WLTC was carried out, and the transient operating parameters of the engine were collected. The engine fuel economy test bench was established and accurately controlled according to transient parameters. The FEI of five different formulations of engine oils was tested. The results indicate that the actual bench parameters are in good agreement with the target transient parameters. Multiple FC measurements of baseline oil and multiple FEI measurements of high reference oil show that the engine bench has good repeatability. The data fluctuation degree of bench test is obviously smaller than that of vehicle test. The correlation coefficients of comprehensive FEI between bench test and vehicle test are 95.25% (NEDC) and 96.39% (WLTC). It means that the bench test can completely replace the vehicle test for fuel economy analysis. The FEI of No.3 oil which has the best fuel improvement are 1.888% (NEDC) and 1.248% (WLTC) respectively. Compared with the vehicle test, the engine bench testing can save 75% of the time. The fuel economy bench and test methods are also suitable for the verification of fuel economy improvement of other engine optimization measures under different driving cycles. The development period of engine technologies can be shortened, and the test cost can be reduced. [ABSTRACT FROM AUTHOR]

Poor clearances, misalignments, and carbon deposits in a turbocharger due to high running hours can decrease engine efficiency. This study analyzes the effects of turbocharger reconditioning on the performance and emissions formation of a marine diesel generator engine. The engine test experiments were performed on a university training ship's auxiliary diesel engine with 4309 running hours before and after the TC overhaul. As a result, cylinder peak pressure was increased by a maximum of 3.57 % while fuel saving of 5–8 g/kWh was realized. Lower exhaust gas temperatures and improved charge air pressure by up to 7.7 % were recorded. A significant CO emission reduction of 21.6 % was recorded at idle load whereas NOx and CO2 emissions were reduced to a maximum of 4.86 % and 7.30 % at 50 % engine load. The results indicate that scheduled TC overhaul and maintenance are very useful to improve the engine’s fuel economy, and performance and reduce exhaust emissions.

This paper presents a control strategy to optimize both the vehicle speed and gearbox position on curved roads with the aim of maximizing fuel economy. Previous studies have focused only on the vehicle speed optimization during cornering. Combining vehicle speed and gearbox position optimization can significantly maximize the energy-saving for an Internal combustion engine (ICE) vehicle. The problem is formulated as two successive optimization problems. In the first one, based on the road map and traffic data, the vehicle’s optimal speed profile is calculated by minimizing the energy spent for the entire curve. In the second one, the gearbox position is optimized for fuel consumption minimization using realistic engine map. The presented control structure’s effectiveness was evaluated through co-simulation of Matlab/Simulink and CarSim in various driving scenarios. The results show that approximately 5.25% to 11.44 % of fuel savings can be achieved compared with a typical driver model.

The fuel economy of electric vehicles (EVs) is an important factor in determining the competitiveness of EVs. Since the fuel economy is affected by the efficiency of an e-powertrain composed of a motor and inverter, it is necessary to select a high-efficiency topology for the e-powertrain. In this paper, a novel topology of e-powertrains to improve the fuel economy of EVs is proposed. The proposed topology aims to improve the system efficiency by integrating open-end winding (OEW) and winding changeover (WC). The proposed OEW-PMSM with WC enables to drive a permanent magnet synchronous motor (PMSM) in four different modes. Each mode can increase inverter efficiency and motor efficiency by changing motor parameters and maximum modulation index. In this paper, the system efficiency of the proposed topology was evaluated using electromagnetic finite element analysis and a loss model of power semiconductors. In addition, the vehicle simulations were performed to evaluate the fuel economy of the proposed topology, thereby proving the superiority of the proposed topology compared with the conventional PMSM.

Precise temperature control in the stack is essential for optimal performance of proton exchange membrane fuel cells, and effective heat dissipation is required to manage temperature fluctuations during power generation. While traditional PID control of cooling systems can yield satisfactory outcomes, there may be better choices for vehicle applications, where the excessive robustness against the performance can result in cost and efficiency concerns. This study involved the design of a vehicle model to assess the cooling system's efficiency and performance using fuzzy control, which adapts actions to specific conditions, enhancing system efficiency. Fuzzy control was applied to regulate the cooling system's valve opening ratio, considering power demand, including parasitic power. In order to observe the improvement by applying fuzzy control, the evaluation protocol used to calculate the fuel economy of actual hydrogen vehicles was adopted, and fuel economy was measured through nine simulations with different initial SOC settings. As a result of simulations of four cases combining PI control and fuzzy control, the case where fuzzy control was applied showed an improvement in fuel economy of about 3.83%, at 18.527 km/kg, compared to 17.844 km/kg, which was the result when only PI control was applied. In addition to the quantitative evaluation of fuel economy, the impact of fuzzy control on improving fuel economy was qualitatively evaluated through analysis of dynamic data such as parasitic power of pumps and fans and valve opening rates. • The component models of the hydrogen vehicle were constructed and integrated into a system. • A power distribution strategy was implemented, and the reliability of the vehicle system was assessed. • Fuzzy logic aimed at reducing fuel efficiency was applied to bypass valves in fuel cell system. • The vehicle's fuel economy was evaluated through simulations under various initial SOC. [ABSTRACT FROM AUTHOR]

Vehicle emission standards (VES) regularly undergo changes and updates in various countries, including the United States and Canada. To help inform the design of VES policy, we simulate the additive impacts of various VES stringencies on greenhouse gas (GHG) emissions, zero-emissions vehicle (ZEV) sales, and cost-effectiveness ($/tonne) out to 2030, using the case of Canada. The VES policy stringencies include four stringencies: Trump era (1.5 percent annual efficiency improvements per year to 2026), California style (3.7 percent improvements to 2026), Obama era (5 percent annual improvements), Biden era (10 percent annual improvement in 2023 and 5 percent subsequently until 2026), and EU style (6.5 percent annual improvements until 2030). We apply the AUtomaker–consumer Model, which endogenously simulates consumer and automaker decisions and technological change. Results show that although the Biden-era VES is more effective than the other designs under consideration in North America, only the EU-style policy makes substantial progress toward GHG emissions reduction and ZEV sales goals. Interestingly, defining cost-effectiveness as dollar per tonne of carbon dioxide abated, we find that the EU-style VES (the most stringent version) is 13 percent more cost-effective than the other VES versions in the median case. Results support the case for further strengthening of a VES toward EU levels. [ABSTRACT FROM AUTHOR]

This study presents a reinforcement-learning-based approach for energy management in hybrid electric vehicles (HEVs). Traditional energy management methods often fall short in simultaneously optimizing fuel economy, passenger comfort, and engine efficiency under diverse driving conditions. To address this, we employed a Q-learning-based algorithm to optimize the activation and torque variation of the internal combustion engine (ICE). In addition, the algorithm underwent a rigorous parameter optimization process, ensuring its robustness and efficiency in varying driving scenarios. Following this, we proposed a comparative analysis of the algorithm's performance against a traditional offline control strategy, namely dynamic programming. The results in the testing phase performed over ARTEMIS driving cycles demonstrate that our approach not only maintains effective charge-sustaining operations but achieves an average 5% increase in fuel economy compared to the benchmark algorithm. Moreover, our method effectively manages ICE activations, maintaining them at less than two per minute. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering & Architectural Faculty of Eskisehir Osmangazi University / Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi is the property of Eskisehir Osmangazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The measurement of consumed hydrogen weight in the off-board gas cylinder is a common approach to measure the hydrogen consumptions of light duty vehicles. Concerning hydrogen consumption measurement of heavy-duty vehicles, this approach is not realistic because the amount of hydrogen consumption is larger than that of a light duty vehicle. Therefore, an approach which directly measures the amount of mass flow of hydrogen from the cylinder to the powertrain system is studied. In this paper, the accuracy of measured results of the mass flow of hydrogen and the challenges to improve the accuracy will be discussed. [ABSTRACT FROM AUTHOR]

Waste Heat Recovery (WHR) System which converts exhaust heat into power was evaluated on actual vehicles. as a result, WHR system tuned exhaust heat into power and the fuel consumption was improved 1.6 % in highway mode. The fuel consumption improvement effect by mounting a retrofit system of exhaust heat recovery and power regeneration was confirmed. This report describes the process of the examination and the results and problems of the system. [ABSTRACT FROM AUTHOR]

Dynamic programming (DP) is frequently used to obtain the optimal solution to the hybrid electric vehicle (HEV) energy management. The trade-off between the accuracy and the computational effort is the biggest problem for the DP method. The closed-form solution to the DP is proposed to solve this problem. Firstly, the affine linear model of the engine fuel rate is obtained based on engine test data. The piecewise linear approximation of the motor power demand is obtained considering the different energy flows in the charging and discharging stages of the battery. Then, the second-order Taylor expansion for the cost matrix at each time and state grid point is introduced to get the closed-form solution of the optimal torque split. The results show that this method can greatly reduce the computing burden by 93% while ensuring near-optimal fuel economy compared with the conventional DP method. [ABSTRACT FROM AUTHOR]

In this study, a vehicle model was developed to examine the performance of a gasoline-powered vehicle and a vehicle with a serial-hybrid-drive system. An extra-downsized gasoline hybrid engine was used as a range extender in the vehicle model. Utilizing OBD II output to collect data, engine data for a sample vehicle was established with a neural network. Vehicle models were subsequently built using Matlab Simulink to complete the study. A comparison was made between the vehicle equipped with a serial-hybrid-drive system and the one with a gasoline engine-powered system regarding their fuel consumption and CO2 emissions for NEDC (New European Drive Cycle) and WLTC (Worldwide Harmonized Light Vehicles Test Cycle) Class 2 cruise cycles. Based on two driving cycles with different speed-time profiles, the results demonstrate the significant impact that powertrains can have on a vehicle's efficiency and performance, particularly during the transition from NEDC to WLTC which takes into account higher speeds, dynamic driving cycles, and factors such as air conditioning usage. Based on the findings, there was a 3.3% rise in CO2 emissions during an NEDC driving cycle with an additional downsized serial-hybrid-drive system. However, the opposite occurred during the WLTC driving cycle, resulting in a 1.7% decline in the serial-hybrid vehicle's fuel consumption and emissions performance.

Efficient control of automotive engine idle speed is crucial for achieving better fuel economy and smoother engine running. This paper presents a comparison between proportional-integral-derivative (PID) control and Reinforcement Learning (RL) using the Deep Q-Network (DQN) algorithm as a high-level control method for minimizing idle speed fluctuations caused by changes in engine irregularities, and the response time and accuracy of the throttle control mechanism. In addition to low-level PID control for the throttle valve position, MATLAB/Simulink was employed to build the simulation environment, incorporating an engine model and an electronic throttle body model, and observing the engine's current speed. The results demonstrated the superiority of RL-based control over PID in reducing idle speed fluctuations and enhancing engine performance in simulations and real-world experiments. This study advances automotive engine control strategies.

To improve vehicle performance and energy utilization, a novel planetary gear set based flywheel hybrid electric powertrain (PGS-FHEP) is proposed. The PGS-FHEP involves an internal combustion engine, a planetary gear set that integrated a control motor and an energy storage flywheel, which combines the high efficiency of the mechanical flywheel energy storage system with the flexible and controllable characteristics of the electric motor. The powertrain is analyzed and modeled using lever analogy method, and a rule-based control strategy is designed and verified under different test cycles. The simulation results indicate that compared with the traditional manual transmission vehicle, the fuel economy of the vehicle equipped with PGS-FHEP can be improved by more than 50%, and the acceleration performance can be increased by 28.01%. Up to 60.61% of vehicle kinetic energy can be recovered by PGS-FHEP, among which 37.85% can be directly captured by the energy storage flywheel. In addition, the battery charging power is reduced, which is beneficial to prolong the battery life.

The results presented in this report are part of Phase II of a two-phase study. Based on the results from mechanistic models of additional fuel consumption in vehicles due to the structural response of the pavement structure, Phase I of this study concluded that pavement has a small but important enough effect on vehicle fuel consumption to warrant field investigation. The goal of the Phase II study was to measure vehicle fuel consumption in the field on different pavement types in winter and summer and at different speeds, and to use the data collected to develop empirical models for this fuel-consumption effect. The field investigation presented in this report included 21 California pavement sections with different pavement types: flexible, semi-rigid, jointed plain concrete, continuously reinforced concrete, and composite structures. The vehicles selected and instrumented for the fuel economy measurements included a five-axle semi-trailer tractor, a diesel truck, a sports utility vehicle (SUV), a gasoline-fueled car, and a diesel-fueled car. Vehicles were run on cruise control and data were recorded at 45 and 55 mph on state roads and at 35 and 45 mph on local roads. The data from the field investigation were analyzed and used to develop an empirical modeling framework considering road geometry, wind, temperature, and pavement structural and surface (roughness and texture) effects on vehicle fuel consumption. Based on the final framework, a final empirical model was developed for each section. The report presents results of a factorial analysis of the effects of each variable using the final model for each vehicle type on each pavement type and in different California climate regions. The within-section variability is almost always greater than the variability between sections for a given pavement type and efficiency condition (tailwind, speed, and climate region) and the within-section variability is also usually larger than the variability between pavement types. Only the data for the heavy heavy-duty truck (HHDT) showed any meaningful difference in results between sections, but that variability is not tied to pavement type and is only present under certain conditions of speed, tailwind, and air temperature (tied to climate region). These results indicate that missing variables (or errors in the existing variables) need to be reduced in further experiments to observe measurable effects of pavements on fuel consumption in real-world driving. While air temperature interacted with cruise control speed for the HHDT, there was a lack of clear evidence that asphalt roads cause more fuel consumption for the HHDT even under the conditions where the most possible effect of pavement type was found. This suggests that pavement type is not the correct explanation for that variation. Instead, the variation in the effect of air temperature by cruise control speed for the HHDT likely has to do with differences in engine efficiency under different conditions.

The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine’s operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy.

Tracked vehicles are integral for maneuvering diverse terrains, with hybrid propulsion systems offering potential benefits in terms of fuel efficiency and performance. However, research in hybrid electric tracked vehicles remains limited, thus necessitating a comprehensive analysis to maximize their advantages. This study presents a numerical analysis focusing on optimizing hybridization in speed-coupled parallel hybrid electric powertrains for tracked vehicles. A dynamic programming algorithm and custom drive cycle are utilized to determine optimal hybridization factors and assess parameter sensitivities. The study reveals that a hybridization factor of 0.48 is optimal for speed-coupled parallel configurations. Furthermore, the sensitivity analysis underscores the substantial impact of factors such as the engine displacement and bore-to-stroke ratio on the fuel economy, with a 10% change in these parameters potentially influencing the fuel economy by up to 2%, thus emphasizing the importance of thorough consideration during powertrain sizing. Parallel hybrid configurations exhibit considerable potential for tracked vehicles, thus highlighting the viability of choosing them over series configurations.

Abstract In recent years, eco‐driving for hybrid electric vehicles (HEVs) has been studied with the emerging connected and automated vehicle (CAV) technologies to improve the mobility, fuel economy and safety of HEVs. This paper develops a hierarchical eco‐driving control strategy for HEV platoons consisting of CAVs and human‐driven vehicles (HDVs) to improve fuel economy at signalized intersections. For each platoon, the speed trajectories of CAVs are optimized in the upper layer controller using model predictive control (MPC) to minimize the total fuel consumption of the whole platoon. For each HEV, the optimal power split between the engine and the battery is obtained in the lower layer controller using adaptive equivalent consumption minimization strategy (A‐ECMS). The time‐varying powertrain efficiencies of HEVs are explicitly considered in the speed trajectory optimization. At last, simulation studies are conducted using MATLAB and VISSIM to evaluate the performances of the strategy in mixed traffic scenarios and different CAV penetration rates. Simulation results indicate that compared with the single vehicle control strategy, the proposed strategy can improve the average fuel economy by up to 8.34% and considering the time‐varying powertrain efficiencies of HEVs in the optimization can further reduce the fuel consumption by up to 1.23%.

Heavy-duty mining trucks are essential for open-pit mining and are significant energy consumers, stressing the need for the mining industry to improve the fuel economy of mining trucks. However, there is a limited discussion on this topic in the specialized literature, mainly focusing on light-duty vehicles. This article discusses the energy and exergy balances of heavy-duty mining trucks operating in an open pit mine in Colombia. Results show saving opportunities by either using batteries or producing hydrogen with the power from regenerative brakes, reducing heat losses in the engine, recovering heat losses with combustion gases using thermoelectric generators, and replacing mechanical pumps with electrical pumps. The assessment shows that reducing engine heat losses by coating the cylinder, cylinder head, and piston crown can reduce fuel consumption between 1.8 % and 9.1 %. Moreover, the production of hydrogen, while economically feasible, needs to assess the implementation of electrolyzers in mining trucks. Other measures are not economically viable. Using batteries, which requires adding 12 t of weight to the truck, reduces truck productivity. Finally, using thermoelectric generators and replacing mechanical pumps shows marginal opportunities to reduce fuel consumption.

In order to simultaneously improve the fuel economy and overall performance of plug-in hybrid electric vehicles (PHEVs), this study selected the P1 + P3 configuration as its research object. Through a configuration analysis of hybrid vehicles, it confirmed the feasibility of P1 + P3 configuration-PHEV operating modes. Based on this, a rule-based control strategy was developed, and simulation models for the entire vehicle and control strategy were constructed in both Cruise and MATLAB/Simulink software. The study conducted simulation analysis by combining three sets of Worldwide Harmonized Light vehicles Test Cycle (WLTC) driving cycles to assess the fuel-saving potential of the dual-motor P1 + P3 configuration. The simulation results showed that the vehicle model was reasonably constructed and the proposed control strategy had good control effects on the entire vehicle. Compared to conventional gasoline vehicles, the P1 + P3 configuration PHEV achieved a 67.4% fuel economy improvement, demonstrating a significant enhancement in fuel efficiency with the introduction of electric motors. [ABSTRACT FROM AUTHOR]

The paper considers the use of exhaust gas heat in modular power plants. In this regard, fuel consumption at power plants will decrease, efficiency of power plant will increase the emission of harmful gases into the atmosphere and environmental burden will reduce. [ABSTRACT FROM AUTHOR]

The world is facing a limited supply of fossil fuel resources and stringent environmental constraints. Therefore, it is very important to develop advanced technologies to improve vehicle fuel economy, especially for construction vehicles, which have large engine displacements and poor emission characteristics. The majority of these vehicles use hydraulic mechanical transmission in the power train in order to improve maneuverability. However, a key issue on the hydraulic mechanical transmission is the low-efficiency torque converter. Focusing on this issue, we proposed the power reflux hydraulic transmission system (PRHTS), which is a new continuously variable transmission system. The PRHTS can improve the overall transmission efficiency of the power train by splitting the engine power into mechanical and hydraulic power. Therefore, the PRHTS is a valid solution to reduce the fuel consumption and subsequently decrease emissions from construction vehicles. In order to quantitatively study the effect of using the PRHTS on improving the fuel economy for construction vehicles, a wheel loader coupled with the PRHTS is modeled, and numerical simulation is conducted under the wheel loader driving condition. The simulation results show that the total fuel consumption of the wheel loader coupled with PRHTS is reduced by 3.39 % compared with that of the original wheel loader. [ABSTRACT FROM AUTHOR]

The driving cycle is a series of driving behaviours, such as acceleration, braking, and cruising, that occur over a set length of time. Predicting the driving cycle can help to improve vehicle performance or anticipate the range of an electric car. Based on prior data, long short-term memory (LSTM) networks can be used to forecast a vehicle's driving cycle. This paper studies a driving cycle prediction based on LSTM by recurrent neural network (RNN) using developed driving cycle data. The objectives of this paper are; to develop an Ipoh driving cycle (IDC), to develop a prediction of future IDC and to analyze the prediction of IDC. Firstly, the driving data is collected in three different routes in Ipoh city at back-from-work times. Then the data is divided into micro-trips and the driving features are extracted. The features are used to develop a driving cycle using k-means clustering approach. The prediction is developed after the training of neural networks by using LSTM network approach with root mean square error (RSME) of 6.2252%. [ABSTRACT FROM AUTHOR]

The target velocity jump has caused the low-velocity stability and fuel economy in the whole process of autonomous agricultural machinery operation. In this study, optimal velocity planning was proposed for the field operation of agricultural machinery in the constantly variable transmission (CVT) tractor. The global and local planning objectives and constraints were collected from the autonomous agricultural machinery. The minimum-jerk polynomial velocity planning was then designed using the Bellman optimality principle and optimal control theory. The velocity-following control system was also developed to realize the stable velocity cruise of autonomous agricultural machinery in the field. The velocity planning was modelled as multi-stage decision-making, according to the time series. The optimal decision was achieved in the jerk optimal control at each stage to realize the state transition. Bellman optimality principle and optimal control theory were selected to design the minimum-jerk polynomial velocity planning. The minimum jerk cost function was subject to hard constraints, such as the maximum and minimum velocity, acceleration, and jerk. The soft constraints were utilized to realize the driving task in the shortest possible time. Numerical solutions were used to reduce the difficulty of the model. Polynomial piecework fitting was generated to fully meet the smooth and continuous optimal speed reference curve under the hard constraints. The performance of the control system was significantly improved to prevent the velocity jump and varying conditions. The velocity stability and fuel economy were realized in the CVT tractor. The feasible solution space was obtained in the numerical solution. The end states of tractor motion were taken to heuristically search for the optimal solution under hard and soft constraints. The numerical solution was obtained after sampling. The unexecuted portion of the previous cycle’s solution in each planning cycle was utilized to achieve the time consistency of the unmanned tractor. The executive layer controller of the unmanned tractor usually tracked the reference curve generated by the planning algorithm with high accuracy and responsiveness. However, the replanning was performed to discard the remaining part of the previous solution, when the motion state deviated significantly from the target trajectory under external disturbances. The U-turn field experiment showed that the mean absolute error (MAE) and the root mean square error (RMSE) of speed decreased by 42.31%, and 50.75%, respectively, compared with the control group. The average absolute value and the variance of acceleration decreased by 8.26%, and 16.36%, respectively, while their jerk decreased by 7.65% and 14.23%, respectively. The variance of engine speed, torque percentage, instantaneous fuel consumption, and total fuel consumption decreased by 63.36%, 60.26%, 71.25%, and 2.37%, respectively. The straight-line navigation velocity adjustment experiment showed a similar optimization trend. The MAE and the RMSE of speed decreased by 9.45%, and 11.14%, while the average absolute value and variance of acceleration decreased by 6.03% and 13.68%, respectively. The average absolute value and variance of jerk decreased by 1.55% and 3.59%, respectively. The variance of engine speed decreased by 31.78%, the variance of engine torque percentage decreased by 25.13%, and the variance of instantaneous fuel consumption decreased by 31.82%. The total fuel consumption decreased by 2.48%. The stability of speed regulation was significantly improved to reduce fuel consumption during operation. The smooth control of velocity switching can fully meet the requirement for autonomous CVT tractors. [ABSTRACT FROM AUTHOR]