Electric Vehicle Acceleration Research Articles

Electric vehicle power system in intelligent manufacturing based on soft computing optimization

Soft computing technology has attracted extensive attention in computer engineering and automatic control domains because it can deal with uncertainties, fuzziness, and complex practical problems. This study adopts a Genetic Algorithm (GA) in soft computing technology to realize the cooperative optimization of electric vehicle's dynamic and economic performance. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness, and complex practical problems, making GA an effective tool for solving complex optimization problems. Firstly, the electric vehicles' power system structure and energy management strategy are investigated and analyzed. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Thirdly, NSGA-II is used to construct the electric vehicles' power and energy configuration, with power and economic performance as the main optimization objectives. Meanwhile, a fuzzy logic controller is designed to adjust the parameters of GA online, so that the optimization process is closer to the actual operating conditions. Finally, the relevant variables are selected to achieve the optimization goal, the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the optimized electric vehicle's acceleration time is remarkably reduced, the dynamic performance is improved by more than 7 %, and the power loss is reduced by 5 %. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. This study provides a new idea and method for the performance optimization of electric vehicles. Moreover, it offers a valuable reference for the innovation and development of electric vehicle technology in the intelligent manufacturing field. This study indicates that electric vehicles could be more efficient, energy-saving, and environmentally friendly to serve people's travel needs in the future.

The Design Concept of Integrated Pedestrian Sidewalk with Solar Plants as a Form of Green Campus Development at the Bali Land Transportation Polytechnic

Abstract An important aspect of creating a viable campus environment is providing safe and comfortable walking paths. Proper walking paths give some advantages for students, including improved physical and mental health, transportation efficiency, and increased Student satisfaction and happiness. The pedestrian sidewalk area at Bali Land Transportation Polytechnic (Poltrada Bali) covers 3800 m2, experiencing high pedestrian activity but lacking protective roofing to enhance pedestrian comfort. Based on the pedestrian sidewalk area, a solar power plant as support for the green canopy construction combined with shading vines is possible. The generated electrical energy serves as the power source for the Public Electric Vehicle Charging Stations (PEVCS) in the campus parking lot as a support for the electric vehicle acceleration program in Bali province. In addition to PEVCS, energy extraction surplus is used as alternative energy for street lighting facilities in the campus area. In this study, the design concept of a shaded pedestrian sidewalk has been carried out, with multiple benefits as solar plants using Delphi as a variable identification and validation tool, and SketchUp software to visualize design shapes aesthetically. The results show that a pedestrian comfort level of 94% was obtained and the maximum power generation of 934,800 Wp, thus it was able to meet the 887,832 W campus operational power reserve needs, street and garden lights of 9,728 W, and the power requirements of two PEVCS units of 22,000 W with a surplus of 15,240 W electric power.

Research on the interaction between energy consumption and power battery life during electric vehicle acceleration

Most studies on the acceleration process of electric vehicle focus on reducing energy consumption, but do not consider the impact of the power battery discharge current and its change rate on the battery life. Therefore, this paper studied the interaction between electric vehicle energy consumption and power battery capacity attenuation during acceleration. First, a power battery life model for electric vehicle under driving conditions is established, and the percentage of battery capacity loss per kilometer is used to measure the capacity loss under different acceleration conditions. Then, the relationship between the percentage of battery capacity loss per kilometer and velocity and acceleration is explored, and the capacity attenuation mechanism of power battery under different acceleration processes is analyzed. Finally, the energy consumption and battery capacity attenuation is studied when the electric vehicle accelerated with multiple accelerations curves, and the interaction of the first acceleration and acceleration time on the electric vehicle energy consumption and the power battery capacity attenuation characteristics is discussed. The research results indicate that when the electric vehicle accelerates with different multiple accelerations curves, the change of energy consumption per kilometer and percentage of battery capacity loss per kilometer with acceleration and acceleration time is different, and the change of the two is basically opposite.

Selection of Charging Station Location based on Sustainability Perspective using AHP Method

The acceleration of electric vehicle (EV) development in Indonesia to support energy and environmental sustainability has taken over and attracted the attention of many parties. Various policies, programs, and research outputs have made the public aware of EVs. However, the availability of charging stations (CS) in the community and the optimal placement are matters of great concern as CS is an essential infrastructure that enhances EV development. Therefore, to support sustainable urban development, CS location selection must consider a sustainability perspective in decision-making. In this study, we employed Analytical Hierarchy Process (AHP) method to assess the best placement of CS in Surakarta based on a sustainability perspective. Ten sub-criteria were identified based on a literature review to establish a hierarchy structure of the problem. We distributed a questionnaire to five experts in different fields to assess the importance scale for each sub-criteria and five alternatives location. The priority value and rank of each sub-criteria and alternative were generated. We found that CS 1 obtains the highest-ranking of preferable sites. The level of water and vegetation damage, service capacity, and impact on society are the most critical parameters that must be considered carefully in choosing a CS location. This study supplements literature for location selection and the application of the AHP method.

Research on active sound control in EV in-vehicle based on engine order sound fitting

In order to upgrade the comfort and dynamic feeling of the sound in the cabin of electric vehicle acceleration and uniform driving, this paper conducted a study on active sound making control in EVs based on sound fitting. Sound fitting was analyzed by short-time Fourier technique (STFT), sound extraction and synthesis was tested by STFT and synthesis method was conducted by Kaiser window function. A study on the sound requirements of a Class A EV was completed, in which a sound design method integrated three objective dimensions of sound amplitude strengthening, energy distribution, and order composition in the vehicle speed range, so as to determine the sound design scheme of accelerated driving with both comfort and dynamic sense. By comparing the characteristics of two kinds of engine sound extraction, the correlation between dynamic sensation and engine sound amplitude was analyzed, and the active sound parameter selection was confirmed under two conditions of acceleration and uniform speed. Efforts were devoted to conduct the active sound system program workflow diagram and system hardware design and testing, and to complete the active sound generation system (ASGS) formation and accuracy testing of the electric vehicle based on the audio system speaker, and additionally the sound calibration and target verification of the ASGS under the static conditions of the real vehicle were carried out. The objective test shows that the ASGS reduces the sharpness under the two working conditions of acceleration and constant speed to 1.0 and 0.52 acum respectively, which effectively alleviates the uncomfortable feeling brought by the high-frequency order sound, and proves that the ASGS can enhance the comfort and dynamic quality characteristics of the electric vehicle interior sound.

Refrigerant flow distribution research for battery cooling coupled with cabin comfort based on dual-evaporator heat pump system for electric vehicle acceleration

Abstract The heat pump system employed with a dual evaporator for battery cooling coupled with cabin comfort is an innovative thermal management method. It can be inferred that the refrigerant thermal load distribution can trigger temperature fluctuations for thermal performance of both battery and cabin. To trade-off between the thermal management demands of battery and cabin, this study proposed a strategy to promote the decreasing of battery temperature and to ensure battery thermal uniformity with a higher priority. Hence a transient refrigerant flow rate distribution scheme with a minimum flow rate to satisfy battery thermal demands was designed. The results showed a significant reduction in the temperature fluctuations for cabin thermal comfort and BTM thermal controlling. It offers a satisfactory reference for refrigerant thermal load distribution strategy applied with the heat pump system connected to the battery and cabin by the dual evaporator.

Modeling of Working Machines Synergy in the Process of the Hybrid Electric Vehicle Acceleration

There are many different mathematical models that can be used to describe relations between energy machines in the power-split hybrid drive system. Usually, they are created based on simulations or measurements in bench (laboratory) conditions. In that sense, however, these are the idealized conditions. It is not known how the internal combustion engine and electrical machines work in real road conditions, especially during acceleration. This motivated the authors to set the goal of solving this research problem. The solution was to implement and develop the model predictive control (MPC) method for driving modes (electric, normal) of a hybrid electric vehicle equipped with a power-split drive system. According to the adopted mathematical model, after determining the type of model and its structure, the measurements were performed. There were carried out as road tests in two driving modes of the hybrid electric vehicle: electric and normal. The measurements focused on the internal combustion engine and electrical machines parameters (torque, rotational speed and power), state of charge of electrochemical accumulator system and equivalent fuel consumption (expressed as a cost function). The operating parameters of the internal combustion engine and electric machines during hybrid electric vehicle acceleration assume the maximum values in the entire range (corresponding to the set vehicle speeds). The process of the hybrid electric vehicle acceleration from 0 to 47 km/h in the electric mode lasted for 12 s and was transferred into the equivalent fuel consumption value of 5.03 g. The acceleration of the hybrid electric vehicle from 0 to 47 km/h in the normal mode lasted 4.5 s and was transferred to the value of 4.23 g. The hybrid electric vehicle acceleration from 0 to 90 km/h in the normal mode lasted 11 s and corresponded to the cost function value of 26.43 g. The presented results show how the fundamental importance of the hybrid electric vehicle acceleration process with a fully depressed gas pedal is (in these conditions the selected driving mode is a little importance).

COMPUTER SIMULATION OF ELECTRIC VEHICLE ACCELERATION PROCESSES WITH DIFFERENT POSITIONS OF THE MASS CENTER

Due to the electrification of modern vehicles the role of the electric drive is growing as the main mover. In conditions of increasing requirements for the safety controllability and energy efficiency of a vehicle on electric traction,it is actual to take into account the dynamic properties of a vehicle in various driving modes when developing an automatic control system. In the work it is investigated the influence of the mass center position on the redistribution of forces during acceleration on a straight-line section. Taking into consideration the position of the mass center in the control system allows redistributing the desired moment to the wheels with better adhesion to the surface, which increases the safety and controllability of the vehicle, as well as minimizes energy costs on wheels with the worst adhesion. The aim of the work is to investigate the influence of the mass center position on the dynamics of a vehicle with full, rear and front wheel drive using computer simulation. The mathematical description includes analytical expressions for the redistribution of the support reactions for each of the wheels, which makes it possible, on their basis, to carry out computer simulation of the electric vehicle acceleration on a straight-line section. For the indicated types of vehicle drives, a computer model has been developed that includes, in the automatic control system for torque redistribution, the coordinates of the mass center position, which are converted on the basis of analytical expressions into the physical parameters of the system.Computer simulation of acceleration from zero to one hundred km/h with full pressing of the accelerator pedal for nine different positions of the mass center and three types of drive was carried out. Data were obtained on the change in accelerations, support reactions and torque of wheels during acceleration at various mass center positions. Based on the results obtained, the most preferable coordinates of the mass center for each type of drive from the point of view of the acceleration dynamics on a straight section were determined. The developed computer model can be used to study the dynamics of an electric vehicle when cornering, as well as to study energy indicators in all dynamic driving modes.

Acceleration, Drive Cycle Efficiency, and Cost Tradeoffs for Scaled Electric Vehicle Drive System

This article investigates and quantifies, for varying drive system ratings (0.5–2.0 times the rating of a small and large reference system), the tradeoff relations between the electric vehicle acceleration performance and energy consumption during a wide range of drive cycles, using detailed load-dependent loss models. Additionally, the results are related to estimated drive system cost by transparently determined scalable electric motor and inverter cost models. When reducing the system rating to half, the cost is 83% of the small reference system and 76% of the large. The acceleration time (0–100 km/h) decreases nonlinearly with increasing system rating. Interestingly, the drive cycle energy consumption generally decreases with decreasing drive system rating, and most cycles show a minimum consumption with a downscaled drive system. For the small system, the strongest impact was noted for the HWFET cycle where the energy consumption is reduced 2% when downscaling the drive system by 0.5 relative to the reference system. For the large system, NYCC shows the largest reduction in energy consumption: 4% when scaled by 1.6 relative to the reference system.

Energy Optimization of Electric Vehicle's Acceleration Process Based on Reinforcement Learning

During a typical driving mission in the urban envi ronment, vehicle's dynamic performance is limited due to traffic congestion. When the vehicle could not accelerate with its maximum acceleration, the economic performance of acceleration process will have a certain room to improve. So in this paper, a pedal control strategy for electric vehicles' acceleration process is proposed, which could reduce energy consumption under congested traffic environment. Firstly, a reinforcement learning framework for vehicle's acceleration process is built, then the framework is constantly optimized through iterative computation. When the iteration is finished, the final state-action matrix will be used to express our control strategy. In order to ensure that the control strategy can fully balance vehicles' dynamic and economic performances, a regulation coefficient is defined and introduced into the calculation of rewards to determine whether the control strategy pays more attention to vehicle's economic performance or dynamic performance. The test results show that the energy consumption can be reduced by at least 5% within the vehicle's velocity being lower than 50km/h. When the velocity is lower than 20km/h, which is the average maximum velocity under traffic congestion, the energy consumption will reduce by more than 10% with the acceleration time only increasing by 2s, which suggests that our control strategy is suitable for use under traffic congestion and can effectively reduce energy consumption of an acceleration process.

Acceleration curve optimization for electric vehicle based on energy consumption and battery life

The existing research of electric vehicle acceleration curves optimization mainly focuses on minimum energy consumption, without considering the battery life. This paper focuses on solving a multi-objective optimization problem with two conflicting objectives: minimization of energy consumption per kilometer and minimization of percentage of battery capacity loss per kilometer during acceleration process. The influence of the number and the variation trend of accelerations on these two objectives are simultaneously considered, and the acceleration curves are optimized using the fast elitist non-dominated sorting genetic algorithm. The results obtained are selected by using the fuzzy theory. The results show that for the acceleration condition with zero initial velocity, the energy consumption per kilometer and the percentage of battery capacity loss per kilometer of multiple accelerations curves above the original acceleration curves all decreases. While for the high acceleration condition where initial velocity is not zero, the energy saving effect of the optimized multiple accelerations curves above the original condition is not obvious. Then, we analyze the reasons for energy consumption difference, and it is found that energy consumption per kilometer in overcoming accelerating resistance for optimization curves is much less than original condition for low velocity. It is also found that energy consumption per kilometer in accelerating resistance and aerodynamic resistance is large for high velocity, and there is little difference between optimized multiple accelerations curves and original condition.

The Sliding Mode Control about ASR of Vehicle with Four Independently Driven In-Wheel Motors Based on the Exponent Approach Law

Acceleration slip regulation control system is a new active safety technology. In this paper, through the research of the four-wheel independent drive electric vehicle, a sliding mode variable structure control algorithm based on exponent approach law is proposed, which is applied to the ASR system. This paper establishes a seven DOFs vehicle dynamics model, tests whether the ASR control strategy is efficient on the poor condition road. The simulation results show that the vehicle acceleration performance improvement rate increases by 43.5% and 58.5% with the control strategy. During the two simulation processes, the results indicate that the sliding mode variable structure control algorithm applied to ASR system has a good adaptation to good and slippery roads. The algorithm can greatly improve the four-wheel independent drive electric vehicle's acceleration performance.

Optimal driving during electric vehicle acceleration using evolutionary algorithms

Due to the limited amount of stored battery energy it is necessary to optimally accelerate electric vehicles (EVs), especially in urban driving cycles. Moreover, a quick speed change is also important to minimize the trip time. Conversely, for comfortable driving, the jerk experienced during speed changing must be minimum. This study focuses on finding a comfortable driving strategy for EVs during speed changes by solving a multi-objective optimization problem (MOOP) with various conflicting objectives. Variants of two different competing evolutionary algorithms (EAs), NSGA-II (a non-dominated sorting multi-objective genetic algorithm) and SPEA 2 (strength Pareto evolutionary algorithm), are adopted to solve the problem. The design parameters include the acceleration value(s) with the associated duration(s) and the controller gains. The Pareto-optimal front is obtained by solving the corresponding MOOP. Suitable multi-criterion decision-making techniques are employed to select a preferred solution for practical implementation. After an extensive analysis of EA performance and keeping online implementation in mind, it was observed that NSGA-II with the crowding distance approach was the most suitable. A recently proposed innovization procedure was used to reveal salient properties associated with the obtained trade-off solutions. These solutions were analyzed to study the effectiveness of various parameters influencing comfortable driving.