Energy Efficiency Of Vehicles Research Articles

Electro-mobility – an Opportunity to Develop a More Sustainable Transport System

The future of transport is electro-mobility, a technology that ensures environmentally friendly and energy efficient vehicles on the road. But to exploit the full benefits of electric propulsion there are still many challenges to overcome. These challenges are related to the cost and residual value (aftermarket) of the vehicles, battery life and cost, charging and range. This article discusses these challenges as well as the state of the electric vehicle market. The aim is to present to what extent the development of electric mobility in Europe and in Bulgaria will outline a more sustainable transport system and to outline what the opportunities for its development are. Decarburization of transport is key to achieving sustainable mobility ensuring transport connectivity across the continent.

Towards sustainable high-speed cruising: Optimizing energy efficiency of plug-in hybrid electric vehicle via intelligent pulse-and-glide strategy

Conventional pulse-and-glide (PnG) strategies expose issues including poor coordination with energy management systems (EMSs), discomfort and low levels of automation, this paper innovatively addresses these challenges by the comprehensive considerations of energy economy, dynamic performance, and ride comfort, proposing an intelligent pulse-and-glide (I-PnG) strategy tailored for plug-in hybrid electric vehicle (PHEV) configurations. The I-PnG strategy is aimed at optimizing economic cruising in high-speed scenarios; however, it demonstrates applicability across diverse conditions without deterioration in performance. I-PnG is a data-driven autonomous driving solution developed through a deep Q network (DQN), featuring two distinct modes: ECO and SPORT, the former prioritizes energy economy, while the latter emphasizes dynamic performance enhancement. The results indicate that our proposed strategy significantly optimizes energy management compared to the baseline methods. Specifically, during variable-speed cruising, I-PnG ECO achieves a maximum energy efficiency improvement of 16.13 % and a cost reduction of 7.74 %, while I-PnG SPORT exhibits a maximum energy efficiency improvement of 13.37 % and a cost reduction of 3.92 %. Under the Highway Fuel Economy Test (HWFET), I-PnG ECO and I-PnG SPORT further improve energy efficiency by up to 22.94 % and 19.04 %, respectively, with corresponding cost savings of 18.47 % and 13.35 %.

Achieving deep transport energy demand reductions in the United Kingdom

The transport sector is a crucial yet challenging area to decarbonize, given its heavy reliance on fossil fuel usage, carbon-intensive infrastructure and car-centric lifestyles. It remains the largest contributor to local air pollution in cities yet has the potential to improve people's physical and mental health. This research investigated the potential contribution of transport energy demand reduction to climate change mitigation and improving public health. Using a comprehensive bottom-up modelling framework, the Transport Energy and Air pollution Model (TEAM), this study provides an integrated assessment of the impacts of deep mobility-related energy demand reductions, including lifecycle carbon emissions, local air pollution and health impacts. Using a sociotechnical scenario approach and the UK as a case study, this research reveals that energy demand reductions of up to 61 % by 2050 compared to baseline levels are achievable and can enhance citizens' quality of life. Business as usual approaches which rely on a technical transition miss the legislated carbon budgets and result in higher energy demand in 2050. More comprehensive scenarios deliver a reduction of up to 72 % in total lifecycle carbon emissions by 2050, with approximately half of the reduction achieved through mode shifting and avoiding travel, while the other half comes from vehicle energy efficiency, electrification, and downsizing of the vehicle fleets. The research shows that it can lead to significant co-benefits such as improved local air pollution and public health. The feasibility and practicality of policy measures and integrated strategies identified for achieving deep transport-energy demand reductions are discussed.

Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily

Developments in artificial intelligence techniques allow for an improvement in sustainable mobility strategies with particular reference to energy consumption estimates of electric vehicles (EVs). This research proposes a vehicle energy model developed on the basis of deep neural network (DNN) technology. This study also explores the potential application of the model developed for the movement data of new vehicles in the province of Enna, Sicily, Italy, which are characterized by numerous attractors and the increasing number of hybrid and electric cars circulating. The energy model for electric vehicles shows high accuracy and versatility, requiring vehicle velocity and acceleration as input data to predict energy consumption. This research article also provides recommendations for the energy modeling of electric vehicles and outlines additional steps for model development. The implemented methodological approach and its results can be used by transport decision-makers to plan new transport policies in Italian cities aimed at optimizing vehicle charging infrastructure. They can also help vehicle users accurately estimate energy consumption, generate maps, and identify locations with the highest energy consumption.

Numerical modeling of heat transfer mechanism in remote sensing bulb of thermal expansion valves

Thermal management of automotive systems has garnered a lot of focus in recent times to improve the energy efficiency of vehicles and address the challenge of global warming. In combustible fuel vehicles, the heat pump compressor is driven by the engine power which leads to changes in operating condition of the cycle while the vehicle is in motion. In electric vehicles, a combined thermal management strategy handles both cabin and battery pack cooling/heating depending on the ambient conditions and power requirements by adjusting refrigerant flow rates and flow directions with sophisticated valves. Irrespective of the vehicle type, effective variable refrigerant flow control is prerequisite to ensure optimum thermal comfort with minimum energy. Thermal expansion valves (TXV) are the most widely used method for regulating mass flow rate of refrigerant during the expansion step of the vapor compression cycle based on the principle of trying to maintain a fixed degree of superheat at the evaporator outlet. Conventional models of the thermal expansion valve ignore the time lag between fluctuation of temperature at the evaporator outlet and the corresponding change in pressure of the TXV remote sensing bulb, treating the valve as an instantaneous element during transient operation. This paper presents a novel method for determining the temperature profile and time constant of the remote sensing bulb response using numerical methods, in an attempt to capture the dynamics of thermal expansion valve in active heat pump cycle. Finite difference method was used to solve for the conduction heat transfer between the bulb and the heat exchanger outlet tube in perfect thermal contact applying the necessary boundary conditions. This improved modeling approach will be helpful in better prediction of the dynamic behavior of thermal expansion valves and how it influences the evaporative heat exchanger performance during transient operations to determine control algorithm and strategies.

A study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation

This paper is a study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation. It aims to develop a prediction model for the energy efficiency of electric vehicles and compare it with the results of chassis dynamometer tests. Driving tests were conducted on a chassis dynamometer, which included various test cycles of the 5-cycle test. Using MATLAB Simulink, a vehicle dynamics analysis model for electric vehicles was constructed, and analytical research was conducted focusing on light-duty electric vehicles. By comparing and analyzing the experimental data obtained from the chassis dynamometer with the data calculated through simulation, it is possible to verify the accuracy of the electric vehicle energy efficiency prediction model. And the reliability of the simulation model was secured by comparing the experimental data obtained through chassis dynamometer test with the simulation results of the developed electric vehicle energy efficiency prediction model. There is good agreement between the experimental and simulation results. In most areas, the error rate remains below 3 %, indicating that the simulation model closely follows the experimental results from the chassis dynamometer. Based on these results, it is expected that the developed simulation model can be utilized for measuring the energy efficiency of electric vehicles.

Hierarchical predictive optimal control for range extension of EV with ANN based torque control for IPMSM drives

This study presents a novel methodology to enhance the energy efficiency of Electric Vehicles (EVs) while maintaining dynamic stability and driving comfort, even on uneven roads, using onboard vehicle measurements. The approach involves a hierarchical control scheme that integrates a model predictive controller with a torque vectoring algorithm to optimize torque demand and accurately anticipate road-specific torque requirements. The proposed control is designed and implemented on an EV with Interior Permanent Magnet Synchronous Motors (IPMSM) on four wheel drives. The optimal torque control is realised through an Artificial Neural Network (ANN) - based motor torque control scheme. The design is validated through tests in real-world driving scenarios. In comparison with conventional methods, the proposed method shows a 37% increase in energy efficiency across different test conditions, thereby resulting in an increase in EV driving range. These advancements are realised without substantial modifications to the EV’s drivetrain, representing a significant step forward in sustainable and efficient electric mobility.

Aerodynamic Analysis of Si Jayaraya: A CFD Approach to Energy-Efficient Vehicle Design

Aerodynamic Analysis of Si Jayaraya: A CFD Approach to Energy-Efficient Vehicle Design

Increasing the energy efficiency of an electric vehicle powered by hydrogen fuel cells

The article considers fuel cell electric vehicle energy efficiency improvement by means of traction battery remanent state of charge modern control algorithms application which based on fuzzy logic. Fuel cell vehicle traction system used as experimental setup for the study includes brushless dc motor, lithium-ion batteries pack and electronic differential approach implementation. The results of simulation modelling in MATLAB-Simulink software comparing conventional PID-controller based and fuzzy logic based controller for vehicle speed control systems are presented. Fuzzy logic controller provides an electric vehicle dynamic behavior tuning for different driving modes automatically transferring the control system to economy mode when it possible. However, the control system is able to provide fast dynamic behavior when it is required. The proposed control algorithm is tested by means of developed experimental setup. Experimental results validate conducted simulation modelling results and the proposed control algorithm. Application of the proposed fuzzy logic control algorithm to control the electric vehicle dynamic behavior allows energy efficiency improvement and driving range increase.

The Impact of Variable Ambient Temperatures on the Energy Efficiency and Performance of Electric Vehicles during Waste Collection

The market for electric cars (EVs) is growing quickly, which has led to a diversity of models and significant technological advancements, particularly in the areas of energy management, charging, range, and batteries. A thorough analysis of the scientific literature was conducted to determine the operational and technical parameters of EVs’ performance and energy efficiency, as well as the factors that influence them. This article addresses the knowledge gap on the analysis of ambient temperature-related parameters’ effects on electric garbage trucks operating in particular urban traffic conditions for selective waste collection. To optimize vehicle routes, a computational model based on the Vehicle Routing Problem was used, including the Ant Colony Optimization algorithm, considering not only the load capacity of garbage trucks but also their driving range, depending on the ambient temperature. The results show that the median value of collected bulky waste for electric waste collection vans, depending on the ambient temperature, per route is 7.1 kg/km and 220 kg/h. At a temperature of −10 °C, the number of points served by EVs is 40–64% of the number of points served by conventional vehicles. Waste collection using EVs can be carried out over short distances of up to 150 km, which constitutes 95% of the optimized routes in the analyzed case study. The research contributed to the optimal and energy-efficient use of EVs in variable temperature conditions.

Potential Power Output from Vehicle Suspension Energy Harvesting Given Bumpy and Random-Surfaced Roads

The energy efficiency of vehicles is a crucial challenge relating to sustainable energy preservation and regeneration methods. Regenerative breaking has proven feasible, and there is interest in whether harvesting energy from a vehicle’s suspension is similarly feasible. We here provide methods for estimating the amount of power that can be regenerated from the suspension for given vehicle and road parameters. We show that a reasonable road model is a generalised Gaussian process known as AR(1). Using this model, we can derive the key equation used in the ISO 8608 standard for measuring road roughness, such that the AR(1) parameters can be related to the measured road roughness data. We find that the road roughness coefficient of ISO 8608 and the diffusion coefficient of the AR(1) road are equal up to a factor. We provide an analytical expression for the maximum amount of power that can be generated for given road and car parameters, derived via Fourier analysis. We further model harvesting from large bumps using Simulink. These results help to estimate the potential power output given the measured road data.

An Examination of Wireless Charging Technologies for Electric Vehicles Powered by New Energy

In recent years, the increasing prevalence of new energy electric vehicles in the automotive industry has led to a yearly growth in the number of electric vehicles. This trend highlights the increasing importance of wireless charging technology. This paper centers on the advancements in wireless charging technology for new energy-efficient vehicles. It assesses the current state of the technology, both domestically and internationally, and delves extensively into the operational principles of the pivotal wireless charging technology. The findings indicate that wireless charging, as a novel charging approach for electric vehicles, possesses considerable potential and warrants additional research and implementation. The objective of this paper is to provide a significant point of reference for the advancement of wireless charging technology.

A novel prediction model of total drag coefficient of an MPV vehicle based on LSA optimization

Abstract Environmental degradation and energy scarcity are caused by the ongoing rise in the number of automobiles. The most efficient way to lower air resistance while driving is to boost vehicle energy efficiency and reduce energy consumption. In this case, a specific MPV model serves as the study subject in this work, and we use CFD simulation to estimate the drag coefficient along with the selection of important factors. Furthermore, to increase prediction accuracy, a lightning search algorithm (LSA)-based BP-neural-network prediction model is created and developed. The results show that the average relative error reduction of 2.83% in the drag resistance model optimized by the lightning search method represents a significant improvement in accuracy. This phenomenon can be explained by the LSA’s ability to resolve the BP neural network’s initial weight and threshold uncertainty as well as the issue of local minima being quickly reached. The high prediction accuracy drag prediction model, obtained herein, is useful for creating and producing automobile shapes with low drag coefficients.

SimulatorBridger: System for Monitoring Energy Efficiency of Electric Vehicles in Real-World Traffic Simulations

SimulatorBridger: System for Monitoring Energy Efficiency of Electric Vehicles in Real-World Traffic Simulations

Convex Approach to Real-Time Multiphase Trajectory Optimization for Urban Air Mobility

Electric vertical takeoff and landing technology has emerged as a promising solution to alleviate ground transportation congestion. However, the limited capacity of onboard batteries enforces hard time constraints for vehicle operations in a complex urban environment. Therefore, a constrained, real-time trajectory optimization method is necessary to enable safe and energy-efficient vehicle flight. Unfortunately, most existing trajectory optimization methods suffer from low computational efficiency, unpredictable convergence processes, and strong dependence on a good initial trajectory. In this paper, we employ a successive convex programming approach to solve the multiphase minimum-energy-cost electric vertical takeoff and landing vehicle trajectory optimization problem for urban air mobility applications. The main contribution is the development and validation of two novel convexification methods that find approximate optimal solutions to the multiphase nonlinear trajectory optimization problem in real time by solving a sequence of convex subproblems. Specifically, the first method transforms the original nonlinear problem into a sequence of second-order cone programming problems through a convenient change of variables and lossless convexification, while the second approach achieves a similar goal without the need for control convexification. The resulting convex subproblems can be solved reliably in real time by advanced interior point methods. The proposed methods are demonstrated through numerical simulations of two different urban air mobility scenarios and compared with the solution obtained from GPOPS-II, a state-of-the-art general-purpose optimal control solver. Our proposed sequential convex programming methods can obtain near-optimal solutions with faster computational speed than GPOPS-II.

ECONOMIC MECHANISMS FOR THE DEVELOPMENT OF URBAN TRANSPORT AND LOGISTICS SYSTEMS

The article proposes the definition and substantiation of economic mechanisms for the development of urban transport and logistics systems that are appropriate in Ukraine, acceptable and implemented for use within "smart" cities on the basis of sustainability, reliability and safety, based on certain signs and principles of understanding sustainable transport and logistics systems that should be built on: understanding the use of means of transport with a low level of impact on the environment, include both the industrial component of the system and public and private traffic, transport-oriented development, ecological means of transport, car sharing, as well as construction and/ or the protection of urban transport and logistics systems that are energy efficient, take into account measures to save space and promote a healthy lifestyle. The proposed approaches provide for obtaining an economic effect by creating a greater added value from each unit of used resource compared to traditional methods and linear business models. Such economic incentives should contribute to the market demand for energy-efficient vehicles in two ways: - through the gravity effect, by increasing the demand for such environmentally attractive vehicles from consumers, users and the population of the urban agglomeration; - due to the "push-out" effect, which increases the attractiveness of new vehicle developments for manufacturers. These methods can be most effective when combined. In the first case, when the demand for vehicles that will meet the established criteria of ecological efficiency increases, in the second case, in the case of creating a critical market mass with sufficient leverage to justify specific developments and/or equipment on the part of manufacturers, where the development and implementation of environmentally friendly and of energy-efficient vehicles is considered as one of the tools for reducing the impact of transport and logistics systems on the environment of "smart" cities that strive to increase innovation and obtain competitive advantages.

Modelling the factors influencing carbon efficiency of consumer choice to promote energy-efficient vehicles

This study investigates the main factors influencing carbon emissions from Four Wheel Passenger Vehicles (FWPVs) in Sri Lanka, an emerging economy where the transport sector contributes 12 % of total emissions. Pioneering a new methodology, we integrate System Dynamics (SD) modeling and decomposition analysis to comprehensively map the factors affecting fuel efficiency for the first time in Sri Lanka. Employing the additive Logarithmic Mean Divisia Index (LMDI I), we analyze a comprehensive time series dataset covering all petrol, diesel, and electric-powered FWPVs registered between 2015 and 2019. Our study delves into significant drivers of CO2 emissions, including population dynamics, vehicle ownership trends, annual travel distances, fuel types, engine capacities, and technological advancements. Additionally, we quantify the impact of fiscal policies on FWPV emissions, revealing a surge in emissions in 2018 and 2019 driven by consumer preferences for high-capacity vehicles. Our findings offer critical insights for crafting effective policies to promote energy-efficient vehicles and mitigate carbon emissions in the transport sector, emphasizing the importance of targeted fiscal measures. By imposing taxes on high-capacity vehicles and incentivizing the adoption of low-emission options through tax variations and renewable energy incentives, policymakers can influence consumer behavior and drive significant emission reductions. Our study underscores the need for ongoing monitoring and evaluation of fiscal policies to refine existing measures and accelerate the transition towards a sustainable, low-carbon transportation sector in Sri Lanka.

Power management for fuel-cell electric vehicle using Hybrid SHO-CSGNN approach

The increasing connectivity of vehicles through intelligent transportation systems presents a significant research challenge: how to simultaneously optimize power management operations and vehicle dynamics to enhance energy efficiency. This paper proposes a novel approach to address this challenge by integrating the Sea-Horse Optimization Algorithm (SHO) and Contrastive Self-Supervised Graph Neural Network (CSGNN), referred to as the SHO-CSGNN technique. The primary objectives of this approach are to reduce computation time and improve energy efficiency in electric vehicles (EVs) powered by fuel cells. The SHO is employed to optimize the power consumption of the EV by dynamically adjusting power management operations based on real-time data and vehicle dynamics. On the other hand, the CSGNN is utilized to predict the vehicle's range performance by analyzing various factors such as road conditions, traffic patterns, and driving behavior. By combining these two approaches, the SHO-CSGNN technique can effectively optimize power management operations while considering the dynamic nature of the driving environment. The proposed technique is implemented and evaluated on the MATLAB platform, where it is compared with existing methods, including the Salp Swarm Algorithm (SSA), Firefly Algorithm (FFA), and Genetic Algorithm (GA). Experimental results demonstrate that the SHO-CSGNN technique outperforms all current methods, achieving a computation time reduction of 3.3 seconds compared to existing techniques. This study contributes to the advancement of power management strategies for EVs, highlighting the effectiveness of the SHO-CSGNN technique in enhancing energy efficiency and computational efficiency in real-world applications. The findings of this research have implications for the development of more efficient and sustainable transportation systems, paving the way for the widespread adoption of electric vehicles.

Improving the Energy Efficiency of Vehicles by Ensuring the Optimal Value of Excess Pressure in the Cabin Depending on the Travel Speed

Improving the Energy Efficiency of Vehicles by Ensuring the Optimal Value of Excess Pressure in the Cabin Depending on the Travel Speed

Enhancing energy recovery in automotive suspension systems by utilizing time-delay

This study introduces time-delay active control technology to with the aim of enhancing the system's energy harvesting potential and ride smoothness. The time-delay active suspension represents a nonlinear, multivariable system, and stability analysis in this context is intricate. The mainly challenge lies in effectively leveraging time delay to augment the system's energy collection potential while harmonizing the vehicle's ride comfort and energy efficiency. Addressing this issue, our study proposes enhancing the suspension's energy recovery capability through time-delay control. Initially, the impact of time-delay control parameters on energy collection ability under various operational conditions was analyzed, establishing that appropriate time-delay parameters can enhance energy harvesting capacity. Subsequently, to balance the relationship between vehicle comfort and energy efficiency, the research introduces a method for constructing an optimized objective function based on linear equivalent excitation, thereby quantifying the relationship between system time-domain vibrational response, time-delay control parameters, and external excitations. This approach enables the comprehensive optimization of the suspension system's comfort, safety, and energy efficiency. The control system's stability was analyzed using cluster treatment of characteristic roots method. Finally, simulations and experiments were conducted to evaluate the effectiveness of time-delay active across different scenarios, confirming the efficacy of the proposed control methodology.