Vehicle Energy Consumption Research Articles

Comparisons of Driving Characteristics between Electric and Diesel-Powered Bus Operations along Identical Bus Routes

The energy consumption profiles of conventional fuelled and electric vehicles are different due to the fundamental differences in the driving characteristics of these vehicles, which have been actively researched elsewhere but mostly on the basis of uncommon geographical contexts. This study, therefore, collected driving data on electric and conventional diesel buses running along exactly the same set of bus routes in Hong Kong during normal daily revenue operations. This enabled a fair comparison of driving characteristics for both types of bus under identical real-life, on-road driving conditions, which highlighted the originality and contributions of this study. A three-step approach was adopted to carry out detailed driving pattern analyses, which included key driving parameters, speed–acceleration probability distributions (SAPDs), and vehicle-specific power (VSP) distributions. Results found that route-based comparisons did highlight important differences in driving patterns between electric and diesel buses that might have been smoothed out by analyses with mixed-route datasets. In particular, the spread, intensity, and directions of these differences were found to be exaggerated at the route-based level. The differences in driving patterns varied across different routes, which has significant implications on vehicle energy consumption. Government agencies and/or bus operators should make references to these results in formulating electric bus deployment plans.

Amphibious vehicle's resistance optimization through neural networks and genetic algorithms

Amphibious vehicles, as a new type of aquatic and terrestrial transport platform, are increasingly involved in the existing transportation system. Resistance is a key factor that affects the efficiency and energy consumption of vehicles in aquatic sailing. Resistance reduction optimization design is a focal point and challenge in the design process of amphibious vehicles. In this paper, a resistance performance optimization method has been proposed based on neural networks and genetic algorithms. First, key parameters for the shape design are extracted based on a thorough understanding of the vehicle's performance. These parameters are used to construct a parameterized design space. Second, a training set is obtained based on the Latin hypercube sampling method and numerical calculation methods, and a test set is randomly generated. To achieve better resistance prediction performance, a method based on the genetic algorithm-optimized backpropagation Neural Network is proposed. Next, the resistance performance of the two operating conditions is optimized through the non-dominated sorting genetic algorithm II, and optimized configuration parameters are obtained, which has a 22.71% energy-saving ratio at cruising speed. Finally, the optimized configuration is analyzed using numerical calculation methods to validate the resistance prediction and optimization methods.

Optimizing routing and scheduling of shared autonomous electric taxis considering capacity constrained parking facilities

This paper focuses on routing and scheduling of autonomous electric vehicles to provide reservation-based shared ride services, while a set of parking facilities with limited capacity are used for vehicle intermittent charging. A mixed-integer linear program model is formulated in the form of a vehicle routing problem with satellite facilities (VRPSF), subject to a series of additional time and capacity-related constraints. The objective of the model is to minimize the total operating costs of the system, including those related to vehicle miles traveled and the deployed vehicle fleet size. The number of vehicles inside each parking facility is tracked so as to ensure that the capacity is never exceeded throughout the service horizon. A customized solution method based on an adaptive large neighborhood search algorithm with an explicit treatment of parking facility choices is developed. A series of numerical experiments, consisting of both hypothetical examples and a real-world case study in Hangzhou, China, have been conducted to evaluate the effectiveness and applicability of the proposed model and algorithm. The results demonstrate that ride-sharing services and parking facilities have the potential to significantly reduce the total vehicle energy consumption and operating costs for a shared autonomous electric taxi (SAET) operator in practical scenarios.

Optimization of energy flow in thermal management of electric vehicles based on real vehicle testing and digital twin simulation

The thermal system is one of the most critical parts of electric vehicles since its performance directly affects the vehicle's energy consumption and emissions. The current digital twin technology in the auto industry lacks a thermodynamic model focusing on energy consumption, and the control strategy lacks an adversary model, which does not allow for an in-depth discussion of energy consumption without sufficient use of data. This paper aims to study the optimization of the thermal system coupled energy flow in electric vehicles with digital twin technologies. The vehicle thermal management digital twin technology discussed in this paper establishes the digital twin model of the vehicle thermodynamic system that focuses on energy consumption. Meanwhile, vehicle energy flow experiments are carried out to analyze the energy consumption of key components under typical high energy consumption conditions. Accordingly, this research optimizes the control logic to optimize energy consumption and enhance vehicle ranges without changing the hardware architecture. The results show that after the optimization of the control strategy, the electricity consumption under high-temperature, low-temperature, and dehumidification conditions is reduced by 890 W, 456 W, and 1594.9 W, respectively. The vehicle driving mileages are increased by 12.51 km, 6.16 km, and 22.42 km, respectively.

Application of Improved Ant Colony Algorithm in Optimizing the Charging Path of Electric Vehicles

In current traffic congestion scenarios, electric vehicles (EVs) have the problem of reduced battery life and continuous decline in endurance. Therefore, this study proposes an optimization method for electric vehicle charging scheduling based onthe ant colony optimization algorithm with adaptive dynamic search (ADS-ACO), and conducts experimental verification on it. The experiment revealed that in the four benchmark functions, the research algorithm has the fastest convergence speed and can achieve convergence in most of them. In the validation of effectiveness, the optimal solution for vehicle time consumption under the ADS-ACO algorithm in the output of the algorithm with a stationary period and a remaining battery energy of 15 kW·h was 2.146 h in the regular road network. In the initial results of 15 kW·h under changes in road conditions from peak to peak periods, the total energy consumption of vehicles under the research algorithm was 4.678 kW·h and 4.656 kW·h under regular and irregular road networks, respectively. The change results were 4.509 kW·h and 4.656 kW·h, respectively. The initial results of 10 kW·h were 4.755 kW·h and 4.873 kW·h, respectively. The change results were 4.461 kW·h and 4.656 kW·h, respectively, which are lower than the comparison algorithm. In stability verification, research algorithms can find the optimal path under any conditions. The algorithm proposed in the study has been demonstrated to be highly effective and stable in electric vehicle charging path planning. It represents a novel solution for electric vehicle charging management and is expected to significantly enhance the range of electric vehicles in practical applications.

Finding the optimal reliable energy consumption path for electric vehicles under rainfall conditions

ABSTRACT This paper presents a new path-finding problem to ensure reliable energy consumption for electric vehicles (EVs) under rainfall conditions. The objective function of the proposed model aims to find a reliable path that minimises energy consumption while ensuring a certain probability of completing the trip without exhausting a given battery energy budget. By considering the influence of adverse weather conditions at different periods, the existing model is expanded. To address the non-additivity and non-linearity characteristics of the optimisation model, an enhanced heuristic algorithm is proposed, incorporating inequality techniques, the K-shortest algorithm, and path-updating strategies. Lastly, the proposed algorithm is validated using Hong Kong’s grid-based road network as a case study, which demonstrates the correctness and effectiveness of the algorithm. The results indicate that by considering adverse weather conditions, the estimation of energy consumption can be significantly improved in terms of accuracy, achieving more efficient and reliable optimal path recommendations.

Research on Efficient Suspension Vibration Reduction Configuration for Effectively Reducing Energy Consumption

Reducing vehicle energy consumption is crucial for sustainable development, especially in the context of energy crises and environmental pollution. Energy regenerative suspension offers a promising solution, yet its practical implementation faces challenges like inertial mass issues, cost, and reliability concerns. This study introduces a novel suspension configuration, optimizing shock absorber technology with energy regenerative principles. The objective is to drastically cut energy consumption. Through a frequency domain analysis, this study identifies the root causes of increased energy consumption and worsened vibration in traditional suspensions. This study presents a comparative analysis of the frequency-domain characteristics between the novel suspension configuration and the traditional one. This study reveals that the new configuration exhibits a low-pass filtering effect on the shock absorber’s velocity, effectively minimizing vibrations in the low-frequency range, while mitigating their impact in the high-frequency range. This approach mitigates the trade-off between increased energy consumption and worsened vibration in the high-frequency range, making it a promising solution. Simulations show that this configuration significantly reduces acceleration by 7.04% and suspension power consumption by 10.47% at 60 km/h on the D-level road, while maintaining handling stability. This makes it a promising candidate for future energy-efficient suspension systems.

Comparison of Tire Rolling Resistance Measuring Methods for Different Surfaces

The rolling resistance of car tires is one of the most important parameters characterizing tires today. This resistance has a very significant contribution to the energy consumption of wheeled vehicles. The climate crisis has forced tire and car manufacturers to place great emphasis on the environmental impact of their products. Paradoxically, the development of electric vehicles has led to an even greater importance of rolling resistance, because in electric vehicles, a large part of the influence of grade resistance and inertial resistance has been eliminated due to re-generative braking, which resulted in rolling resistance and air resistance remain as the most important factors. What is more, electric and hybrid vehicles are usually heavier, so the rolling resistance is increased accordingly. To optimize tires for rolling resistance, representative test methods must exist. Unfortunately, the current standards for measuring rolling resistance assume that tests are carried out in conditions that are far from real road conditions. This article compares the results of rolling resistance tests conducted in road conditions with the results of laboratory tests conducted on roadwheel facilities. The overview of results shows that the results of tests conducted in accordance with ISO and SAE standards on steel drums are very poorly correlated with more objective results of road tests. Significant differences occur both in the Coefficients of Rolling Resistance (CRR) and in the tire ranking. Only covering the drums with replicas of road surfaces leads to a significant improvement in the results obtained. For investigations of rolling resistance in non-steady-state conditions, the flat track testing machine (TTF), equipped with asphalt cassettes, is shown to provide measurement data in agreement with the road test data.

Driving Profiles of Light Commercial Vehicles of Craftsmen and the Potential of Battery Electric Vehicles When Charging on Company Premises

This paper examines the extent to which it is possible to replace conventional light commercial vehicles in the heating, ventilation and air conditioning and plumbing trade with battery electric vehicles with an unchanged usage profile. GPS trackers are used to record the position data of 22 craft vehicles with combustion engines from eleven companies over the duration of one working week. Within this paper, various assumptions (battery capacity and average consumption) are made for battery electric vehicles and the charging power on the company premises. The potential of battery electric vehicles is evaluated based on the assumption that they are charged only on company premises. Using the collected data and the assumptions made, theoretical state of charge curves are calculated for the vehicles. The driving profiles of the individual vehicles differ greatly, and the suitability of battery electric vehicles should be considered individually. Battery capacity, vehicle energy consumption and charging power at the company have a substantial influence on the suitability of battery electric vehicles. Furthermore, there are differences between vehicles that can charge on the company premises at night and those that cannot or can only do so on some days.

Experimental platform for studying energy regeneration in electric vehicle powertrains

Investigation into the energy consumption in electric vehicles (EVs) plays a pivotal role in determining their autonomy and assessing the electric system performance across diverse operational scenarios. This study focuses on the concept of energy regeneration, encompassing the recovery and storage of kinetic mechanical energy during braking or descent in EVs. Employing control systems in power electronics becomes necessary to establish a seamless workflow across operational quadrants to ensure efficient energy regeneration in an electric machine functioning as both a motor and a generator. To seamlessly integrate new technologies into practical applications, it is essential to conduct thorough evaluations in laboratories prior to deployment. This paper introduces an experimental platform specifically designed to analyze energy consumption and storage in EVs by emulating their powertrains in a controlled laboratory environment. The platform comprises key components for emulating the powertrain of a single-motor electric vehicle with single-axle traction, including a power converter configured in two quadrants, an energy storage system, a primary rotating electric machine, and a mechanically coupled point load torque (another motor). This paper provides a detailed guide on implementing such a laboratory and for facilitating the testing of diverse motor technologies and controllers under varied operational conditions. This comprehensive approach allows for the assessment of electromechanical system efficiency, focusing on both energy recovery and comprehensive control of electric power converters. Validation tests conducted under urban conditions and on steep terrains demonstrate the effectiveness of the platform in analyzing the energy efficiency of both the induction machine and the power controller.

Impacts of On-board Energy Storage Devices on the Energy Consumption of Train Operations

Urban rail transit systems consume significant electrical power, with traction accounting for 50% of the total energy (from China Urban Rail Transit Association [1]). As operational mileage and vehicle counts rise, so does energy consumption, underscoring the importance of vehicular energy conservation. This study evaluates the impact of on-board energy storage devices on train energy efficiency. Using operational data from Changsha Metro Line 5 and incorporating literature reviews and simulations, the energy-saving potential was assessed. Integrating these storage devices with vehicle energy monitoring systems shows promising potential for energy conservation and operational efficiency. This research presents a viable strategy to reduce energy consumption in urban rail vehicles, enhancing system competitiveness.

Electric vehicle energy consumption taking into account the route topology

Purpose. Determining the impact of the route topology factor on the costs of mechanical work of an electric vehicle is the main task of this work. The impact is determined by calculating the costs of mechanical work during the movement of an electric vehicle, taking into account energy recovery. The task also includes assessment of the forces acting on an electric vehicle using the example of the 2014 Nissan Leaf AZEO. Methodology. The paper uses a mathematical model that estimates the amount of mechanical work required to overcome one of the chosen routes, taking into account energy recovery. Evaluation is performed using the most common standardized cycle WLTC class 3b. Findings. The result of the research is a developed mathematical model that will allow one to effectively estimate the amount of mechanical work to overcome the given route and the possible recovery energy. The proposed method makes it possible to determine the most economical route from the starting point to the destination, taking into account the cost of mechanical energy. Originality. A description of the main components affecting the consumption of electricity is given, taking into account the full picture of the forces acting on the electric vehicle during movement. Practical value. The obtained results are of practical importance for choosing the most optimal route of the electric vehicle, which contributes to the efficient use of energy. The proposed technique can be used in practice to plan routes from the point of view of maximum energy recovery.

Efficiency Analysis of Electric Vehicles with AMT and Dual-Motor Systems

With the rapid development of automobiles, energy shortages and environmental pollution have become a growing concern. In order to decrease the energy consumption of electric vehicles (EVs), this study aims to improve EV efficiency with AMT and dual-motor systems. Firstly, the paper establishes an Automated Manual Transmission (AMT) model for EVs, which is then simulated using MATLAB R2022a software. In order to eliminate the impact of gear ratio selection, the genetic algorithm is used to optimize the AMT gear ratios. Meanwhile, a dual-motor EV model is constructed, and three different torque distribution schemes are simulated and analyzed. The results indicate that due to the elongation of the energy transmission chain in AMT-equipped EVs, energy losses increase, leading to some improvement in optimized power consumption. However, these EVs remain inferior to those with only a single-stage main reducer. The study also found that the torque distribution based on optimal efficiency further improves results.

Transitioning to low GWP refrigerants in automobiles: A numerical simulation study on R1234yf/R152a’s heat transfer characteristics in porous microchannels

Automobile air conditioning systems significantly contribute to vehicle energy consumption. The transition to new, environmentally sustainable refrigerants in place of traditional ones is a critical trend shaping the future of this industry. The prevalent refrigerant R134a, used in automotive air conditioning, is environmentally detrimental, necessitating the development of low Global Warming Potential refrigerants as viable long-term alternatives. This study examines the flow heat transfer characteristics of the proposed refrigerant blend R1234yf/R152a in porous microchannel tubes via numerical simulation. It assesses the impact of heat flux, mass flow rate, saturation temperature, and wall superheat on the Jakob number, vaporization core density, and boiling heat transfer coefficient. Furthermore, it compares these parameters with those of R134a and R1234yf to evaluate the potential of R1234yf/R152a as a substitute. The findings indicate that the Jakob number differences among the three refrigerants are negligible. Notably, R1234yf/R152a consistently exhibits higher vaporization core density and the highest boiling heat transfer coefficient, coupled with the lowest pressure drop, thereby affirming its suitability as a heat transfer-efficient alternative. These insights could inform the enhancement and gradual transition from R134a in automotive air conditioning systems.

SMITS: Social and Mobility aware Intelligent Task Scheduling in Vehicular Fog Computing — A Federated DRL Approach

Vehicular fog computing (VFC) has become an enticing research hot spot to provide resources for the Internet of Vehicles (IoV) application requests. Moreover, with the massive expansion of services in vehicular fog networks such as augmented reality, 3-D gaming, and autonomous driving, the allocation of fog computing resources to IoV applications constraining the deadline and energy consumption of the vehicles is becoming challenging. In this paper, we investigate a task scheduling problem in vehicular fog networks to jointly optimize the success rate of vehicular tasks and energy consumption of vehicles by considering the moving paths of the vehicles and the social relationships among the vehicles. In this problem, we model the social relationship among the vehicles based on their communication patterns and social characteristics to improve the success rate of the tasks. We also incorporate the mobility of vehicles using the Markov renewal process (MRP) technique. Initially, we formulate a mixed integer nonlinear programming (MINLP) problem for the proposed problem and further prove it to be an NP-hard problem. To solve the proposed problem, we design a federated deep reinforcement learning (FDRL) mechanism by converting the problem into a Markov decision process (MDP) problem. We compare our proposed algorithm with existing scheduling approaches, and simulation results show that the proposed algorithm performs efficiently for task scheduling problems. The proposed algorithm achieved a substantial improvement of 12% in the maximization of the success rate and 36% in the minimization of the energy consumption compared to the existing algorithms.

Online Model Adaption for Energy Management in Fuel Cell Electric Vehicles (FCEVs)

The growing interest in low-impact mobility technologies has elevated the significance of fuel cell electric vehicles (FCEVs) in the automotive sector. Given the complexity of the resulting powertrain, the need for an effective energy management strategy (EMS) becomes essential to optimize efficiency and energy consumption in vehicles with diverse energy sources. Model-based control is the main approach to address the EMS in electrified vehicles. In particular, fuel cell power is commonly represented through a 1D look-up table using the current demand as input to simplify the implementation in a vehicle control unit. Uncertainties that may be implemented in maps due to simplifying hypotheses, dynamics, ageing, etc., can be propagated to powertrain control, motivating the adoption of adaptive look-up tables for FC modelling. In this study, an extended Kalman filter (EKF) is proposed to adapt the look-up table to actual FC behaviour by measuring its power and gradually correcting calibration errors, drift, and ageing. Subsequently, a standard equivalent consumption minimization strategy (ECMS) is employed to control the FCEV. The fuel cell model is calibrated with experimental data from an FCEV. The results demonstrate that the adaptive strategy outperforms the base calibration. Following an extensive simulation campaign, an improvement of 1.1% in fuel consumption was observed. Remarkably, after just one hour of operation, there was a notable 85% reduction in fuel cell power estimation error, even when the EMS was initially fed a biased look-up table.

Reducing energy consumption of vertical take-off and landing unmanned aerial vehicle using hybrid technical solutions

Reducing energy consumption of vertical take-off and landing unmanned aerial vehicle using hybrid technical solutions

Energy Consumption of Electric Vehicle with Roof-Mounted Solar Panel in Various Driving Situations

Energy Consumption of Electric Vehicle with Roof-Mounted Solar Panel in Various Driving Situations

Effective Modeling of CO2 Emissions for Light-Duty Vehicles: Linear and Non-Linear Models with Feature Selection

Predictive modeling is important for assessing and reducing energy consumption and CO2 emissions of light-duty vehicles (LDVs). However, LDV emission datasets have not been fully analyzed, and the rich features of the data pose challenges in prediction. This study aims to conduct a comprehensive analysis of the CO2 emission data for LDVs and investigate key prediction model characteristics for the data. Vehicle features in the data are analyzed for their correlations and impact on emissions and fuel consumption. Linear and non-linear models with feature selection are assessed for accuracy and consistency in prediction. The main behaviors of the predictive models are analyzed with respect to vehicle data. The results show that the linear models can achieve good prediction performance comparable to that of nonlinear models and provide superior interpretability and reliability. The non-linear generalized additive models exhibit enhanced accuracy but display varying performance with model and parameter choices. The results verify the strong impact of fuel consumption and powertrain attributes on emissions and their substantial influence on the prediction models. The paper uncovers crucial relationships between vehicle features and CO2 emissions from LDVs. These findings provide insights for model and parameter selections for effective and reliable prediction of vehicle emissions and fuel consumption.

Evaluation of Technological Configurations of Residential Energy Systems Considering Bidirectional Power Supply by Vehicles in Japan

To reduce CO2 emissions in the residential and transportation sectors, distributed energy technologies, such as photovoltaic power generation (PV), stationary storage batteries (SBs), battery electric vehicles (BEVs), and vehicle-to-home (V2H) systems, are expected to be introduced. The objective of this study was to analyze the impact of the installed capacity of PV and SB, the type of vehicle, and their combination on the economic and environmental performance of the total energy consumption of residences and vehicles. Thus, this study developed a model to optimize the technological configuration of residential energy systems, including various vehicle types and driving patterns. The simulation results showed that it is more economically and environmentally efficient to install a BEV and a V2H system in households with longer parking times at the residence and to install an SB in addition to these technologies in households with shorter parking times at the residence. Furthermore, comparing a gasoline vehicle and an SB, the most economical combination, with a BEV and a V2H system and with a BEV, a V2H system, and an SB, estimated the carbon tax rate necessary for cost equivalence. The result indicated that the carbon tax rate needs to be increased from its current level.