Vehicle Energy Consumption Research Articles

How to Incorporate Autonomous Vehicles into the Carbon Neutrality Framework of China: Legal and Policy Perspectives

To achieve the announced 2060 carbon neutrality goal, the government of China has recently established its “1 + N” legal and policy framework. However, the emerging autonomous vehicle technology, which is developing rapidly, is not included. Considering the significant potential of autonomous vehicle technology in reducing carbon emissions from the transportation sector, whether and how to integrate it into the current framework can be crucial for China. The existing literature typically does not regard the emission reduction impacts of autonomous vehicles as a major issue, and there are still few studies available aiming to ensure enhancement of emission reduction induced by autonomous vehicle technology from a legal and policy perspective. This article attempts to fill the above research gaps with a three-step research scheme. Specifically, this work begins with a comprehensive and detailed analysis of the possible wide range of factors affecting energy consumption and emissions of autonomous vehicles. Next, an in-depth discussion of the feasibility of incorporating autonomous vehicle technology into the current “1 + N” legal and policy framework is provided. Finally, several recommendations corresponding to these factors are proposed from legal and policy perspectives: (i) adoption of industrial incentives to mitigate research and development costs and risks at the production level and to expand market demand at the consumption level; (ii) priority promotion of the commercialization of autonomous vehicles in the taxi market by filling legal gaps and applying regulatory measures; (iii) development of efficient transportation policies and land use policies. This article provides a systematic and practical scheme for inclusion of autonomous vehicles in China’s legal and policy framework to realize the carbon neutrality goal. The proposed recommendations can be referenced for other governments that may face similar challenges and future development of possible uniform rules at the global level.

Estimating energy consumption and charging duration of electric vehicle in multigraph

The two main features of electric vehicles are their ability to recuperate energy and their spent time in charging stations, including the waiting time in the queue and the battery charge duration. In this paper, we focus on estimating energy consumption and charging duration in an Electric Vehicles Routing Problem on a multigraph that incorporates a queuing model at charging stations. We develop a mathematical model and propose an Adaptive heuristic to solve the problem. The heuristic approach is compared with an exact solution on small-size instances and a Tabu Search algorithm. Computational results are also provided in larger and more realistic instances. Furthermore, we find that increasing the waiting time in the queue may lead to higher costs whereas the existence of alternative paths may reduce expenses. Finally, we conduct a sensitivity analysis to illustrate the impression of the model parameters on the solution.

Real-time self-adaptive Q-learning controller for energy management of conventional autonomous vehicles

Reducing emissions and energy consumption of autonomous vehicles is critical in the modern era. This paper presents an intelligent energy management system based on Reinforcement Learning (RL) for conventional autonomous vehicles. Furthermore, in order to improve the efficiency, a new exploration strategy is proposed to replace the traditional decayed ε-greedy strategy in the Q-learning algorithm associated with RL. Unlike traditional Q-learning algorithms, the proposed self-adaptive Q-learning (SAQ-learning) can be applied in real-time. The learning capability of the controllers can help the vehicle deal with unknown situations in real-time. Numerical simulations show that compared to other controllers, Q-learning and SAQ-learning controllers can generate the desired engine torque based on the vehicle road power demand and control the air/fuel ratio by changing the throttle angle efficiently in real-time. Also, the proposed real-time SAQ-learning is shown to improve the operational time by 23% compared to standard Q-learning. Our simulations reveal the effectiveness of the proposed control system compared to other methods, namely dynamic programming and fuzzy logic methods.

Multimodal Transportation Route Optimization of Cold Chain Container in Time-Varying Network Considering Carbon Emissions

To solve the multimodal transport route optimization problem considering carbon emission, the vehicle speed has time-varying characteristics, and the customer has a time window limit. The carbon emission of multimodal transport system is affected by the energy consumption of transport vehicles in the time-varying network. The time-varying network is uncertain, and carbon emissions may continue to rise after a gradual decline. Based on this, this study established the sum of the carbon emission cost, transportation cost, penalty cost for exceeding the time window, and the damage cost of the cold chain cargo as the objective function. A route optimization model of cold chain container multimodal transportation was established. Static and dynamic optimization scenarios were designed and a hummingbird evolutionary genetic algorithm was used to solve the model. The effectiveness of the model and the practical value of the study are verified by the empirical analysis of the multimodal transport network of the Yangtze River Delta economic group. Results show that the dynamic model of the time-varying network can more truly reflect the transportation of the multimodal transport network and meet the efficiency requirements for the cold chain container multimodal transport. This study aims to solve the time-varying network under cold chain route optimization of container intermodal transportation, provides new insights for related businesses and a theoretical basis for reasonable multimodal transport route decisions.

Road-Section-Based Analysis of Vehicle Emissions and Energy Consumption

To monitor air pollution on roads in urban areas, it is necessary to accurately estimate emissions from vehicles. For this purpose, vehicle emission estimation models have been developed. Vehicle emission estimation models are categorized into macroscopic models and microscopic models. While the calculation is simple, macroscopic models utilize the average speed of vehicles without accounting for the acceleration and deceleration of individual vehicles. Therefore, limitations exist in estimating accurate emissions when there are frequent changes in driving behavior. Microscopic emission estimation models overcome these limitations by utilizing the trajectory data of each vehicle. In this method, the total emissions in a road segment are calculated by adding together the emissions from individual vehicles. However, most research studies consider the total vehicle emissions in a road section without considering the difference in vehicle emissions at different locations of a selected road section. In this study, a road segment between two intersections was divided into sub-sections, and energy consumption and emission generation were analyzed. Since there are unique driving behaviors depending on the section of the road segment, energy consumption and emission generation patterns were identified. The findings of this study are expected to provide more detailed and quantitative data for better modeling of energy consumption and emissions in urban areas.

Speed Trajectory Optimization Method for Electric Vehicles Based on Driving Style

In real driving conditions, speed trajectory optimization is an important part of the energy-saving driving assistance system of smart cars, and it can contribute to increase the cruising range. However, the existing speed trajectory optimization research for vehicles mainly focuses on energy consumption and does not consider whether the optimized trajectory can meet the driver’s driving style requirements. In response to this problem, this article establishes an electric vehicle energy consumption model and discusses the difference between the trajectory reflecting driving style (TRDS) and the optimal trajectory of energy consumption (OTOEC). Based on the driving speed trajectory and acceleration trajectory of the driver, this article obtains trajectory clusters reflecting the driving style of the driver by trajectory data mining and establishes the driving style characteristic space (DSCS) based on these clusters and proposes a speed trajectory optimization method based on driving style finally. Compared with the TRDS, the energy consumption of the trajectory obtained by this method can reduce significantly. The trajectory obtained by the method can reflect the driving style of the driver to a large extent compared with the OTOEC. The simulation results verify the effectiveness of the method.

Event-Driven Energy-Efficient Driving Control in Urban Traffic for Connected Electric Vehicles

The traffic light in urban areas dominates the traffic flow, resulting in variation of energy consumption of the vehicles involved. To mitigate the impact of traffic bias on the energy efficiency of electric vehicles (EVs), this article proposes an event-driven energy-efficient driving control (EEDC) strategy based on a receding horizon two-stage control framework, which harnesses the Internet of Vehicles to incorporate the traffic light and preceding vehicle for adaption of different driving scenarios. At the core of the first stage are the vehicle driving event classification rules, which classified the urban traffic scenarios into four events. This article contributes to empirical solutions on the design of the traffic scenario classifier considering conflict goals, including driving efficiency and safety. In the second stage, the speed trajectory in each driving event is optimized using Pontryagin’s minimum principle to reduce vehicle energy consumption. A real-time solution for the energy-efficient driving problem is derived with the consideration of vehicle dynamics, control input, and speed limit constraints. Finally, extensive simulations and road tests are conducted to evaluate the effectiveness of the EEDC. The results show that the EEDC is excellent in energy efficiency improvement over two benchmark strategies in different traffic scenarios while satisfying the constraints in inter-vehicle driving safety and travel time. Moreover, the road tests demonstrate that the EEDC is capable of energy saving in real-world driving.

Energy and environmental impacts of shared autonomous vehicles under different pricing strategies

The introduction of vehicle automation, shared mobility, and vehicle electrification will bring about changes in urban transportation, land use, energy, and the environment. The accurate estimation of these effects is therefore essential for sustainable urban development. However, existing research on estimating the energy and environmental effects of shared autonomous electric vehicles generally ignores the interaction between land-use and transportation systems. This study, therefore, analyzes the long-term effects of shared autonomous vehicles (SAVs) from the perspective of land use and transportation integration. Different SAV pricing scenarios are also developed to explore the optimal pricing strategy for low carbon–oriented SAVs. Moreover, the study has further assessed the effect of vehicle electrification on vehicle emissions and energy consumption. The results have shown a nonlinear relationship between SAV fares and their transportation, land-use, energy, and environmental effects. Under an appropriate pricing strategy, SAV deployment could reduce PM2.5 emission and energy consumption by 56–64% and 53–61%, respectively. With the further introduction of vehicle electrification, these can rise to 76% and 74%.

Prediction of Electric Vehicle Energy Consumption by Combining Real Vehicle Data and Machine Learning Methods

Prediction of Electric Vehicle Energy Consumption by Combining Real Vehicle Data and Machine Learning Methods

An Unmanned Helicopter Energy Consumption Analysis

The number of operations incorporating E-VTOL aircrafts is increasing each year, and the optimization of the energy consumption of such vehicles is a major problem. In this paper, a small-scale ARCHER helicopter’s energy consumption is analyzed, wherein different flight conditions, main rotor revolutions, and flight control system settings are considered. The helicopter dynamic model was developed in the FLIGHTLAB environment and was then validated based on flight test data. The model used for the calculation of energy consumption was developed using the electric and dynamic characteristics of the main rotor, electric motor, and transmission system. The main part of this work concerns the analysis of electric energy consumption during the vehicle’s flight via the use of an automatic flight control system (AFCS) that ensures repeatable flight conditions. The AFCS was designed such that it includes both path and attitude control to provide hover and cruise control modes. The helicopter’s energy consumption was analyzed during different phases of flight, when executing maneuvers, and using different main rotor angular velocities to perform - a given task. The results show that the level of energy consumption significantly depends on the helicopter’s main rotor revolutions, flight speed, and the maneuvers performed. The proposed methodology can be used in prospective energy-efficient mission planning and UAV helicopter design.

Influence of towing position on hydrodynamic characteristics of an Amphibious Tractor

Amphibious tractors are capable of towing loads over land and water, which will save time effectively and reduce the cost of transport between water and land. Due to their smaller displacement and more fragile stability, the hydrodynamic characteristics of amphibious tractors are quite different from those of tugboats. The towing position will significantly impact the attitude and resistance characteristics of the vehicle. In this paper, the towing position of a light amphibious tractor is thoroughly investigated, in order to improve hydrodynamic characteristics and reduce energy consumption of vehicles. First of all, the towing force effect is researched based on the force equilibrium model. Secondly, a numerical model is established referring to the Reynolds Averaged Navier-Stokes method, which is validated by towing experiments. Moreover, various towing position forms are evaluated to research the towing effects and working principles. Eventually, a coupled model of pitch and heave motion is established to investigate the dynamic response in the longitudinal plane. Results indicate that towing positions at the rear of the vehicle, combined with low height, are conducive to the hydrodynamic performance of amphibious tractors.

Research on the Intelligent Energy Governance of Parallel Hybrid Vehicle Based on Deep Learning

To realize the intelligent energy governance of hybrid vehicles, a Deep-Q-Network energy controller based on the construction of parallel hybrid vehicle model is proposed, which aiming at energy loss problem of parallel hybrid vehicles and combining deep learning with reinforcement learning, and it is simulated through the ADVISOR software platform and compared with the traditional fuzzy logic strategy. The experimental results indicate that the DQN-based control strategy proposed in this paper reduces both the energy consumption and exhaust emissions of parallel hybrid vehicles. Compared with the traditional fuzzy control strategy, fuel consumption is reduced by 0.43L while the fuel economy increases by 10.9%. and exhaust gas such as CO44, CO, NOxx the emission were reduced by 28.9%, 0.2%, and 7.4%, respectively. It shows the feasibility and effectiveness of the proposed methods.

Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption

Increasing the range of electric vehicles (EVs) is possible with the help of eco-driving techniques, which are algorithms that consider internal and external factors, like performance limits and environmental conditions, such as weather. However, these constraints must include critical variables in energy consumption, such as driver preferences and external vehicle conditions. In this article, a reasonable energy-efficient non-linear model predictive control (NMPC) is built for an electric two-wheeler vehicle, considering the Paris-Brussels route with different driving profiles and driver preferences. Here, NMPC is successfully implemented in a test bed, showing how to obtain the different parameters of the optimization problem and the estimation of the energy for the closed-loop system from a practical point of view. The efficiency of the brushless DC motor (BLCD) is also included for this test bed. In addition, this document shows that the proposal increases the chance of traveling the given route with a distance accuracy of approximately 1.5% while simultaneously boosting the vehicle autonomy by almost 20%. The practical result indicates that the strategy based on an NMPC algorithm can significantly boost the driver’s chance of completing the journey. If the vehicle energy is insufficient to succeed in the trip, the algorithm can guide the minimal State of Charge (SOC) required to complete the journey to reduce the driver energy-related uncertainty to a minimum.

An adaptive artificial bee colony algorithm enhanced by Deep Q-Learning for milk-run vehicle scheduling problem based on supply hub

To meet market challenges and reduce costs, supply hubs are broadly applied to achieve just-in-time production. In this paper, a milk-run vehicle scheduling problem considering the ratio of parts is proposed based on the supply hub mode. A mathematical model is therefore established, aiming to minimize the energy consumption of electric vehicles and the time penalty of early or late distribution simultaneously in the scheduling horizon. To solve the proposed problem which is inherently NP-hard, an adaptive artificial bee colony algorithm enhanced by deep Q-learning network (AABC-DQN) is constructed, where the deep Q-learning network is used to reasonably select the neighborhood search operators to systematically expand the search range. Experimental results have demonstrated the superiority of the proposed AABC-DQN, which outperforms the benchmark algorithms in both solution quality and convergence ability.

An Optimization Model for Structuring a Car-Sharing Fleet Considering Traffic Congestion Intensity

Ever-growing mobility and traffic congestion within urban areas make the need for a sustainable form of transport inevitable. Traffic congestion has a significant effect on the amount of energy consumption of a vehicle and, as a result, on its associated environmental impacts. Any decision-making regarding structuring a fleet without taking into account the traffic congestion level (TCL) will lead to a less sustainable fleet with higher environmental and economic costs. To address this issue, this study examines the effects of the traffic congestion intensity level on the fleet structure of an urban car-sharing company over a certain planning period. We present a new optimization framework for finding an optimal vehicle composition of the fleet of an urban car-sharing company considering the energy consumption of vehicles at different traffic congestion levels. The results show that electric vehicles (EVs) are more competitive than diesel vehicles (DVs) in high-peak traffic congestion from the outset of the planning period. In addition, we perform a sensitivity analysis to take into account the effects of specific uncertain parameters such as the energy and purchasing costs of EVs on the total cost of ownership. As expected, the purchasing price of EVs, energy prices of DVs, and increase in diesel prices have the highest impact on the total cost.

Computational Models for Forecasting Electric Vehicle Energy Demand

Electric vehicles (EV) are fast becoming an integral part of our evolving society. There is a growing movement in advanced countries to replace gas-driven vehicles with EVs towards cutting down pollution from emissions. When fully integrated into society, electric vehicles will share from energy available on the grid; therefore, it is important to understand consumption profiles for EVs. In this study, some computation models are developed from predicting day-ahead energy consumption for electric vehicles in the city of Barcelona. Five different machine learning algorithms namely support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANN), decision tree (DT), and ensemble learners were used to train the forecasting models. The hyperparameters for each of the ML algorithms were tuned by Bayesian optimization algorithm. In order to propose efficient features for modeling EV demand, two different model structures were investigated, named Type-I and Type-II model. In the instance of the Type-I model, seven regressors representing the consumption of the previous seven days were considered as input features. The Type-II models considered only the EV consumption on the previous day and on the same day in the previous week. Based on the results in this study, we find that the performance of the Type-II models was as good as the Type-I models across all the algorithms considered although less input features were considered. Overall, the all algorithms employed in this study gave about 75-80% model accuracy based on the R 2 performance criterion. The models formulated in this study may prove useful for planning and unit commitment functions in city energy management functions.

Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database

Electric vehicles in a short time will make up the majority of the fleet of vehicles used in general. This state of affairs will generate huge sets of data, which can be further investigated. The paper presents a methodology for the analysis of electric vehicle data, with particular emphasis on the energy consumption parameter. The prepared database contains data for 123 electric vehicles for analysis. Data analysis was carried out in a Python environment with the use of the dabl API library. Presentation of the results was made on the basis of data classification for continuous and categorical features vs. target parameters. Additionally, a heatmap Pearson correlation coefficient was performed to correlate the energy consumption parameter with the other parameters studied. Through the data classification for the studied dataset, it can be concluded that there is no correlation against energy consumption for the parameter charging speed; in contrast, for the parameters range and maximum velocity, a positive correlation can be observed. The negative correlation with the parameter energy consumption is for the parameter acceleration to 100 km/h. The methodology presented to assess data from electric vehicles can be scalable for another dataset to prepare data for creating machine learning models, for example.

Influence of the Reynolds Number on the Aerodynamic Performance of a Small Rotor

The use of small rotors has increased due their applications in drones and UAVs. In order to improve the global performance of these aerial vehicles, it is necessary to understand the aerodynamics of small rotors, since this is related to the global energy consumption of such vehicles. Most of the computational fluid dynamics (CFD) studies found in the literature that are related to the analysis of small rotors employ fully turbulent models, despite the low-to-moderate Reynolds numbers of these applications. This paper presents CFD simulations for a small rotor at hover at different Reynolds numbers using fully turbulent and transitional SST k−ω turbulence models. Numerical results show that thrust and torque are close to experimental measurements, showing differences of less than 5% for both fully turbulent and transitional models. However, significant differences were observed between the fully turbulent and the transitional models when studying the boundary-layer development and separation. As the Reynolds number was increased, it was observed that at the tip of the blade, these differences were reduced, but at mid-span, the differences were more obvious.

A meta-heuristic-based energy efficient route modeling for EVs integrating start/stop and recapturing energy effect

The research interest in electric vehicles (EVs) has recently sparked exponentially on account of curbing carbon emissions and paucity of fuel resources. No doubt, these vehicles are ecologically sustainable and move smoothly at a moderate speed that is economical on fuel, but their limited range due to the battery reserve demands an efficient use of energy resources. An immediate solution to overcome this problem lies in regulating the EV route, with optimum energy usage. Thus, this study presents a mathematical model that accounts for vehicle energy consumption with the vehicle’s start/stop energy expenditure. To achieve the objective of maximizing the energy-efficiency, it also introduces Amplified-ACO (A2CO), a routing algorithm based on Ant Colony Optimization (ACO) principles that make use of the probabilistic selection model (DS2 relies on Distance, Speed, and State-of-Charge). This study simulates the proposed model over the real-time map of Chandigarh (India) and provides a comparative study against some of the nature-inspired meta-heuristic approaches such as classical ACO and Particle Swarm Optimization (PSO). The proposed model justifies its significance under various parameters, viz. energy consumption, travel time, total travel distance, and remaining SoC.

Optimal Design of Energy Sources for a Photovoltaic/Fuel Cell Extended-Range Agricultural Mobile Robot

Powertrain electrification in the agricultural vehicles is still in the initial stages. This article analyzes the energy behavior of a Photovoltaic/Fuel Cell Agricultural Mobile Robot (PV/FCAMR) as the preliminary step before development. This concept incorporates three energy storage sources for the powertrain: a battery pack, a Fuel Cell (FC) system, and a Photovoltaic (PV) system. This paper proposes an approach based on the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms to determine the sizes of the FC and battery of an FCAMR. A differential drive mobile robot was used as a case study to extract the typical working cycles of farming applications. The FCAMR vehicle model was developed in MATLAB/Simulink to evaluate vehicle energy consumption and performance. For the energy analysis and evaluation, the FCAMR was tested based on two realistic working cycles comprising circular and rectangular moving patterns. The results showed that the proposed arrangement could extend the FCAMR autonomy by 350% as opposed to the pure electric system. This allows for at least 8 h of work with a tank filled with 150 g hydrogen and a PV system with a 0.5 m2 monocrystalline solar panel. The simulation results have demonstrated the relevance and robustness of this approach in relation to various working cycles. The cost comparison between the theoretical and optimization sizing methods showed at least an 8% decrease for the FCAMR. Furthermore, adding the PV system extended the vehicle’s range by up to 5%. This study provides an optimal solution for energy sources sizing of mobile robots as futuristic agricultural vehicles.