Vehicle Energy Consumption Research Articles

A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies.

Energy-Efficient Lane Change Trajectory Planning for Highway Traffic Scenarios Considering Different Driving Needs

This paper proposes an energy-saving lane-changing trajectory model for intelligent electric vehicles at high speeds that considers different driving needs under multiple constraints to address the issues of simple lane-changing considerations and poor safety. An economic index is added to construct a multi-objective optimization function based on the comfort, safety, and efficiency of lane changing. The particle swarm optimization algorithm is used to solve the optimal lane-changing time. The weight of the index is obtained by analyzing different driving needs using the Analytic Hierarchy Process in different scenarios. The multi-objective function is adjusted to plan the optimal lane-changing trajectory that meets driving needs. The simulation shows that the proposed model can generate smooth and feasible lane-changing trajectories that meet different driving needs. The energy consumption analysis results indicate that the construction of economic indicators can effectively reduce the energy consumption of vehicles driving on highways. The tracking analysis results indicate that the target vehicle can smoothly and safely change lanes on the planned trajectory, verifying the effectiveness and rationality of the planned trajectory.

Thorough Analysis of the Reliability of Measurements on Chassis Roller Dynamometer and Accurate Energy Consumption Estimation for Electric Vehicles

The energy consumption of the electric test vehicle on the roller dynamometer test bench is thoroughly investigated in this work. Initially, we extensively examined the relevant testing approaches on chassis dynamometer test benches. Then, we presented the main issues and limitations of this type of testing facility. After that, an experimental analysis was performed by recording the NEDC and the WLTP-class 2 standard driving cycle test data. It was found that the power consumption associated with specific test maneuvers could be accurately simulated via the roller dynamometer system. However, the experimental test results showed some disparities compared to the expected results from the simulation models. These findings supported identifying the dynamic behavior resulting from the tire and roller contact. The LuGre friction model provided the solution, incorporating friction-related physical models such as stick-slip and the Stribeck effect. In addition, adjustments were made to the LuGre model to consider the dynamic influences of the roller on the tire. Finally, we created a systematic estimation model for energy consumption by integrating the boundary conditions of the test bench, a generic model for energy estimation of electric vehicles, and the proposed tire–roller contact model. The overall model requires only the measured traction speed of the test vehicle and the auxiliary energy losses as physical inputs. The results proved that the proposed model enabled the estimation of the total power consumption with high accuracy.

Low energy consumption traction control for centralized and distributed dual-mode coupling drive electric vehicle on split ramps

Distributed drive electric vehicle has good pass ability on split ramps, but it has high power demand for driving motors, resulting in low driving efficiency and limited economy. To address the aforementioned deficiencies, a centralized and distributed dual-mode coupling drive system (DMC) capable of drive force transfer between coaxial wheels is designed to avoid single-side wheel high-intensity driving and reduce vehicle energy consumption. To guarantee the longitudinal dynamics performance, a traction control based on online sliding mode extremum search (SES) was proposed, and the traction control based on the ρ_SES algorithm as a means to suppress the controller damage caused by the steady-state oscillation has also been adopted. The simulation and experiment results show that the dual-mode coupling drive electric vehicle significantly reduces the energy consumption and reduces the power demands of the drive motor than those of the distributed drive electric vehicle. Apparently, the ρ_SES algorithm based traction control suppresses the steady-state oscillations of the SES, which can protect the motor controller. This study lays the theoretical foundation for the use of DMC and achieving superior traction control for electric vehicles.

A three-heat source segmented heating control strategy based on waste heat recovery technology for electric vehicles

The control strategy of thermal management systems is crucial for electric vehicles to ensure thermal comfort of the cabin and thermal safety of the battery and motor. However, this will also impact the energy consumption of the vehicle. In this study, a three-heat source segmented heating control strategy was proposed to reduce the heating energy consumption of electric vehicles, with the three sources denoted motor waste heat, air, and positive temperature coefficient (PTC) heaters. Specifically, a simulation model of an integrated thermal management system for heat pump air conditioning, motor thermal management, and battery thermal management systems was first established and validated using published experimental results. Subsequently, the performance of three cabin heating modes was investigated based on the standard driving cycle. The optimal opening and closing strategies for each mode were discussed to ensure the temperature requirements of the cabin, and the optimal method for using motor waste heat and the PTC heater to heat the battery at low temperatures was explored. The segmented heating control strategy was developed by determining the heating priority for the battery and cabin, as well as the heating capacity of heating modes using air source. Compared to the traditional heating strategy of the cabin and battery, the proposed control strategy could reduce energy consumption by 18.49 % while running at an ambient temperature of − 10 °C for 2.5 h.

Multi-Agent Reinforcement Learning Based UAV Swarm Communications Against Jamming

The swarm relay and power allocation policy determines the bit error rate and the energy consumption of unmanned aerial vehicles (UAVs) and can be optimized based on the network and jamming model, which is rarely known by UAVs. In this paper, we propose a multi-agent reinforcement learning (RL)-based UAV swarm communication scheme to optimize the relay selection and power allocation against jamming. Based on the network topology, channel states, previous performance and observations shared by the neighboring UAVs, this scheme formulates the policy distribution to improve the policy exploration and applies a policy learning mechanism to stabilize the learning process. Based on transfer learning, the shared swarm experiences are exploited to accelerate the initial learning and improve policy optimization. A deep RL-based scheme is proposed to mitigate the state quantization error for the rapidly changing channel states under high swarm moving speed and thus further improve the anti-jamming performance. This scheme designs a policy network with four fully connected layers to approximate the policy distribution and uses another two neural networks to estimate the average policy distribution and the expected long-term utility, respectively, to update the policy network for stabilized deep learning. We investigate the computational complexity and derive the performance bound regarding the bit error rate, the energy consumption and the utility. Simulation and experimental results verify the performance gain of our proposed schemes over related works.

Target speed computation through predictive cruise control for vehicles energy consumption reduction

Target speed computation through predictive cruise control for vehicles energy consumption reduction

Electric vehicle energy consumption modelling and analysis: a Malaysia case study

Previously, the Electric vehicle (EV) range anxiety greatly hindered EV adoption. The fear of being stranded due to running out of battery becomes the main concern among EV drivers. This critical issue can be solved by deploying a bigger battery in EVs. However, deploying bigger size battery contributes to the higher price of the EV itself. Hence, the optimal design of the EV powertrain is a critical factor in ensuring EV meets the users’ needs and, simultaneously, can be owned by the potential owners at an affordable price. Accurate assessment of EV energy consumption is crucial in designing an optimal EV powertrain, particularly for developing energy-efficient driving techniques in both manual and autonomous driving. One method is to perform a real-life experiment to determine the EV energy consumption, which is expensive and time-consuming. An alternative to real-world testing is creating an EV energy consumption model with the existing driving cycles. This paper studies the parameters contributing to EV energy consumption based on EV driving cycles available in MATLAB Simulink. The findings of this study will provide insight into the factors that influence EV energy consumption and serve as a guideline in EV powertrain design.

Simulating the alteration in energy consumption at a zebra crossing considering different traffic rates of electric and rule-following autonomous vehicles

The progressive integration of autonomous vehicle (AV) technology holds the potential to reshape the prevailing traffic landscape. AVs have different driving characteristics than human-driven vehicles, which manifests itself in the strict adherence to speed limit, in giving priority to pedestrians, and in the pre-set headways they can keep. A traffic simulation environment was built around an unsignalized pedestrian crossing to measure the energy consumption of vehicles in the presence of AVs. The simulation environment was modified to adhere pedestrian-accepted gaps between vehicles in case of crossing. Considered vehicle types are yielding or not yielding human-driven, and AVs. Scenarios were built to model the AV traffic share, the different headways kept by AVs, and the various traffic volumes in each direction. The different driving behaviour and traffic share of AVs led to energy consumption changes, which were modelled through scenario analysis. The maximum energy consumption reduction of human-driven vehicles was 10.67% for yielding vehicles and 12.41% for non-yielding vehicles compared to the 0% AV traffic rate. Although, in case of AVs, the energy consumption increased in all scenarios compared to the basic version with only human-driven vehicles. In higher traffic scenarios, where only AVs were on the road, there was a substantial 35,92-96.55% increase in energy consumption, compared to the 0% AV ratio case. Thereby speed of vehicles, following distance and the number of stops affected the overall system efficiency. The results of this study can contribute to the understanding the impact of AVs which can support their introduction.

Energetic performance and life cycle carbon emission of CO2 automotive air conditioning system with an evaporative gas cooler and a dual-evaporator configuration in China

Enhanced actions on climate change have led CO2 air conditioner to take center stage in automotive field. However, its inefficient performance in hot climates limits its further application. A dual-evaporator (D-E) configuration, an evaporative gas cooler (EGC), and their combination (D-E + EGC) were first integrated with the system and experimentally studied in this paper in an attempt to alleviate this problem. Annual energy consumption and life cycle climate performance (LCCP) under four modes (baseline, D-E, EGC, and D-E + EGC) in China were estimated. Results show D-E increases evaporation pressure and EGC is able to decrease discharge pressure. D-E + EGC combines the advantages of both. Thus, improvement percentage in coefficient of performance (COP) brought by D-E + EGC is the highest, followed by those of EGC and D-E. This percentage is more significant at higher ambient temperatures, and even reaches 67.4 % at 45°C outdoor condition. In any mode, annual energy consumption and LCCP in southeast China are double or triple of those in other regions, which shows this system is of high energy consumption and high emission in southeast China. Compared with baseline mode, annual energy consumption and LCCP can be reduced by 3 ∼ 7 % for D-E, 14 ∼ 16 % for EGC, and 18 ∼ 22 % for D-E + EGC. Specifically, D-E + EGC could save up to 890 kWh of electricity and reduce carbon emission by up to 440kgCO2 per vehicle, indicating this technique is a promising way to reduce energy consumption and carbon emission for vehicles equipped with CO2 air conditioners.

Effect of Wind on Electric Vehicle Energy Consumption: Sensitivity Analyses and Implications for Range Estimation and Optimal Routing

The energy consumption of electric vehicles (EVs) depends on multiple factors. As it affects vehicle range, energy consumption must be accurately predicted. After a summary of the relevant literature, this paper focuses on two sensitivity studies: one on the impact of wind on energy consumption, and the other on the identifiability of wind in the absence of vehicles’ speed and acceleration profiles. The studies show that wind has a significant impact on the energy consumption for a trip, and without high-resolution knowledge of the acceleration and instantaneous velocity, minor variations in the wind condition do not drastically alter the energy consumption distribution. After that, data sources for the information on the wind velocity and direction are discussed. A data-driven approach based on fuzzy set theory is proposed to incorporate wind into the energy prediction; the best model from this approach shows a notable improvement (3.62%) over the currently implemented production-level predictive model for energy consumption on a data set of 35,139 real-world trips; the improvement is even more pronounced (∼ 7%) for trips with more substantial headwind or tailwind level. Recognizing the interplay between range prediction and route selection, we consider a Markov Decision Process (MDP) framework for battery-charge- and travel-time-aware optimal route planning that accounts for the impact of the wind and includes stops at the charging stations. Finally, we propose a framework that includes wind in the operation of EVs, which consists of learning the impact of wind, incorporating wind forecasting into range and energy prediction, and using that prediction to perform optimal routing.

Multi-Mode Switching Control Strategy for IWM-EV Active Energy-Regenerative Suspension Based on Pavement Level Recognition

Aiming at the problems of poor overall vibration reduction and high energy consumption of in-wheel motor-driven electric vehicle (IWM-EV) active suspension on mixed pavement, a multi-mode switching control strategy based on pavement identification and particle swarm optimization is proposed. First, the whole vehicle dynamic model containing active energy-regenerative suspension and the reference model was established, and the sliding mode controller and PID controller designed, respectively, to suppress the vertical vibration of the vehicle and the in-wheel motor. Second, a road grade recognition model based on the dynamic travel signal of the suspension and the road grade coefficient was established to identify the road grade, and then the dynamic performance and energy-feedback characteristics of suspension were optimized by particle swarm optimization. According to the results of pavement identification, the optimal solution of the suspension controller parameters under each working mode was divided and selected to realize the switch of the suspension working mode. The simulation results show that the control strategy can accurately identify the grade of road surface under the condition of mixed road surface, and the ride index of the optimized active energy-regenerative suspension is obviously improved, while some energy is recovered.

Potential and flexibility analysis of electric taxi fleets V2G system based on trajectory data and agent-based modeling

The utilization of Vehicle-to-Grid (V2G) technology, which facilitates the integration of electric vehicles (EVs) into the power grid, is promising for energy management systems. Among them, electric taxi (ET) is an important part of EVs. The relatively stable and predictable operating patterns of the ET fleets could have a scale effect, effectively managing load demand and supply with V2G technology. This paper investigates the potential and flexibility of V2G technology for urban power system based on real-world ET trajectory data. To evaluate the flexibility of V2G more precisely, a general framework is proposed to infer vehicle charging, energy consumption, and reconstruction of the State of Charge (SoC) for ETs. The V2G potential estimation model is established for each ET, followed by a bottom-up agent-based model to simulate flexibility changes in the system during various V2G events. The proposed methodology is tested on 19,900 ETs in Shenzhen over a month to explore the potential and flexibility of V2G technology. The results show that the V2G system could supply at least 50 MW for 1 h, 30 MW for 2 h, and 20 MW for 3 h during peak periods, while simultaneously recovering within 2–3 h, without disrupting the regular operations of ETs. The great potential of ETs to supply additional energy during peak periods and recover energy during valley periods is highlighted, which could lead to a more efficient and sustainable management of energy resources.

Calculation of Carbon Emissions and Study of the Emission Reduction Path of Conventional Public Transportation in Harbin City

As the northernmost megacity in China, the long winters, large population size, and unsustainable transport structure in Harbin determine that the city will produce relatively large carbon emissions. The transportation industry is one of the three greenhouse gas emission sources; therefore, the development of low-carbon transportation is imperative. This work compares commonly used carbon emission measurement methods and chooses a mileage method to classify the carbon emissions of conventional buses of different energy types used in Harbin in 2020. A multi-factor grey prediction model was constructed to predict the population size of Harbin and the number of conventional buses. After that, a scenario analysis method was used to analyze the fuel structure of buses in Harbin from three perspectives: a pessimistic scenario, a baseline scenario, and an optimistic scenario. The carbon emissions of conventional buses were calculated for Harbin from 2023 to 2030. Finally, by combining the prediction results and factors influencing carbon emission, a regular bus path to minimize carbon emissions is proposed. The outcome of this study shows that the carbon emission environment in Harbin will be improved by reducing vehicle energy consumption, optimizing energy structure, standardizing driving behavior, building intelligent transportation, giving priority to public transportation, and improving the road network structure.

Dynamic and real-time continuous look-ahead distance for autonomous vehicles: an explicit formulation

The advent of autonomous vehicles has brought about significant advancements in transportation technology, promising safer and more efficient means of travel. However, their full integration into society depends on the accuracy of path-tracking realised by a lateral controller. Lateral control is achieved by regulating the steering angle to minimise the lateral error between the vehicle and a target point at a look-ahead distance on the reference path. This paper investigates the look-ahead distance as it is considered a key parameter that impacts vehicle performance, stability, and energy consumption. A qualitative analysis is performed to deduct a set of rules to adapt the look-ahead distance to three parameters: vehicle velocity, road curvature, and road adherence. Then, an original explicit mathematical formulation is developed for the look-ahead distance as a function of the considered parameters. A fuzzy logic decision for the look-ahead distance is further established and compared with the formulated one. Both approaches are implemented on a look-ahead distance-based lateral controller based on the super-twisting sliding mode control. Simulation results carried out in a joint simulation between Simulink/MatLab and SCANeR TM Studio vehicle dynamics simulator demonstrate the effectiveness of the developed model on vehicle performance, stability, computational efficiency, and energy consumption.

Simulation model for rendering and analyzing the prediction of electric vehicle energy consumption in Matlab/Simulink

Abstract: This work presents the development of a simulation model for predicting the electricity consumption of electric vehicles (EV), with a focus on creating a model that enables precise monitoring of the electric vehicle battery's state. A simple method is provided for predicting the state of charge (SoC) of electric vehicle batteries without computational complexity and other processes. Relevance of the study is clearly demonstrated through the ease of adaptability to all types of electric vehicles, using reliable methodologies for predicting the energy consumption of electric vehicle. This approach aims to contribute to the advancement of the technological infrastructure of electric vehicles, their chargers, and routing application software.

Privacy-Aware Multiagent Deep Reinforcement Learning for Task Offloading in VANET

Offloading task to roadside units (RSUs) provides a promising solution for enhancing the real-time data processing capacity and reducing energy consumption of vehicles in the vehicular ad-hoc network (VANET). Recently, multi-agent deep reinforcement learning (MADRL)-based offloading approaches have been widely used for task offloading in VANET. However, existing MADRL-based approaches suffer from offloading preference inference (OPI) attack, which utilizes the vulnerability in the policy learning process of MADRL to mislead vehicles to offload tasks to malicious RSUs. In this paper, we first formulate a joint optimization of offloading action and transmitting power with the objective of minimizing the system cost, including local and edge costs, under the privacy requirement of protecting offloading preference during offloading policy learning process in VANET. Despite the non-convexity and centralized of this joint optimization problem, we propose a privacy-aware MADRL (PA-MADRL) approach to solve it, which can allow the offload decision of each vehicle to reach the Nash Equilibrium (NE) without leaking offloading preference. The key to resisting the OPI attack is to protect the offloading preference by 1)elaborately constructing the noise based on ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta,\Phi$</tex-math> </inline-formula> )-differential privacy mechanism and 2) adding it to the action selection and policy updating process of vanilla MADRL. We conduct a detailed theoretical analysis of the convergence and privacy guarantee of the proposed PA-MADRL, and extensive simulations are conducted to demonstrate the effectiveness, privacy-protecting capacity, and cost-efficiency of PA-MADRL approach.

Prediction of Electric Vehicle Energy Consumption in an Intelligent and Connected Environment

Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm.

Analysing the Energy Consumption of Urban Electric Vehicles for Real Driving Scenarios

Driving efficiently with an electric vehicle is a key requirement to reduce energy consumption. This article aims to develop a unique two-layer driving pattern recognition approach for the classification of roadway type along with traffic situation for a particular route. The main idea is to use this information to accurately predict the driving pattern and hence analyse the energy consumption of an electric vehicle driving along a specific route under real driving scenarios. The main novelty of the contribution to develop a classification technique based on representative drive cycles, which characterise a roadway type and traffic condition on the basis of certain features. The simulation results indicate a classification accuracy of 85.2–97.7% in driving pattern recognition resulting in prediction of energy consumption under real traffic and road conditions.

Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

Regenerative braking technology is a viable solution for mitigating the energy consumption of electric vehicles. Constructing a distribution strategy for regenerative braking force will directly affect the energy saving efficiency of electric vehicles, which is a technical bottleneck of battery-powered electric vehicles. The distribution strategy of the front- and rear-axle braking forces of electric vehicles that possess integrated front-wheel-drive arrangements is established based on the Economic Commission of Europe (ECE) regulations, which enables the clarification of the total braking force of the front axle. The regenerative braking torque model of the motor is adjusted to optimize the ratio of motor braking force to the whole front-axle braking force. The regenerative braking process of electric vehicles is influenced by many factors, such as driving speed and braking intensity, so regenerative braking presents characteristics of nonlinearity, time variability, delay, and incomplete models. By considering the impact of fuzzy controllers having better robustness, adaptability, and fault tolerance, a fuzzy control strategy is employed in this paper to accomplish the regenerative braking force distribution on the front axle. A regenerative braking model is created on the Simulink platform using the braking force distribution indicated above, and experiments are run under six specific operating conditions: New European Driving Cycle (NEDC), World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), China Light-Duty Vehicle Test Cycle-Passenger (CLTC-P), and New York City Cycle (NYCC). The findings demonstrate that in six typical cycling road conditions, the energy saving efficiency of electric vehicles has greatly increased, reaching over 15%. The energy saving efficiency during the WLTC driving condition reaches 25%, and it rises to 30% under the FTP-72, FTP-75, and CLTC-P driving conditions. Furthermore, under the NYCC road conditions, the energy saving efficiency exceeded 40%. Therefore, our results verify the effectiveness of the regenerative braking control strategy proposed in this paper.