Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 21
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

This paper proves that the trend of development of modern transport in the world is to maximize the level of providing the personal use of electric vehicles. This mechanism would also partially solve the environmental problems of mankind. To implement this idea, some global automakers have announced the decision of the full transition of production to electric vehicles. At the same time, for effective functioning of the electric-vehicle market, adequate infrastructure needs to be created. There is a positive trend in the annual growth of the charging-station network in developed countries, that characterizes the charging-station market as dynamic and promising, but mostly chaotic and imbalanced at the regional level.
The main hypothesis of the research is about the independence between the level of electric-vehicle market development and networks of charging stations. The object of the study is the Washington (USA) electric-vehicle market, as it is the market segment with the highest development characteristics.
To test the hypothesis, the authors provided a multifactor analysis of the local electric-vehicle market and the existing charging infrastructure. A comprehensive analysis of the electric-vehicle market and the charging-station network in Washington (USA) was performed, and the market characteristics were defined accordingly: the degree of electric-vehicle spread in the regional localities; the level of charging-station-network coverage and concentration; the ratio of electric vehicles to charging stations.
Authors identified the tendency of the state location to innovations connected with electric vehicles. Clusterization and recommendations according to the level of development of the electric-vehicle market aimed to balance and grow the total electric-vehicle market and connected infrastructure.
Go to article

Authors and Affiliations

Oleksandr Yakushev
1
ORCID: ORCID
Daniil Hulak
2
ORCID: ORCID
Oksana Zakharova
2
ORCID: ORCID
Yuliia Kovalenko
3
ORCID: ORCID
Oksana Yakusheva
2
ORCID: ORCID
Olesandr Chernyshov
4
ORCID: ORCID

  1. Social Security Department, Cherkasy State Technological University, Ukraine
  2. Department of Economics and Management, Cherkasy State Technological University, Ukraine
  3. Management and Financial & Economic Security Department, Donetsk National Technical University, Ukraine
  4. Department of Management of Non-Productive Sphere, Donetsk State University of Management, Ukraine
Download PDF Download RIS Download Bibtex

Abstract

The paper presents the characteristics of the attitude that students have towards electric cars and the significance of distinguished attitude elements in creating interest in the purchase of such vehicles. Electric cars are the new type of vehicles that have an electric motor and use the electricity stored in batteries. They are introduced to the market, but for various reasons the volume of sales is not high. So far, it is not sufficiently known how electric vehicles are assessed by Poles. The presented research is an attempt to know what the attitude towards this type of vehicle. The attitude model tested in this research includes three areas: knowledge about them, emotions that they evoke and potential behaviors. The participants were students of Rzeszów University of Technology – a group of young people who are potential consumers of new technologies. The obtained results indicate that electric cars are rather unknown. At the same time, they arouse great interest and their image is very positive. The attitude characteristics towards this type of vehicle is supplemented by perceived limitations: too high of a purchase price, lack of sufficient information about them and unsatisfactory technical parameters, mainly the long time needed to recharge the battery and the insufficiently long distance with one recharge. The interest in the purchase is dependent on positive emotions, and the lack of sufficient information is an obstacle in thinking about buying such a vehicle. Understanding the attitudes of Polish students towards electric cars can be helpful in adapting information about such cars to potential customers, which in turn may affect the level of interest and sales volume.

Go to article

Authors and Affiliations

Ryszard Klamut
Download PDF Download RIS Download Bibtex

Abstract

In the recent times, lot of research work carried out in the field of fuel cells explicitly divulges that it has the potential to be an ultimate power source in upcoming years. The fuel cell has more storing capacity, which enables to use in heavy power applications. In these applications, power conditioning is more vital to regulate the output voltage. Hence, we need a dc-dc converter to provide a constant regulated output voltage for such high-power system. Currently, many new converters were designed and implemented as per the requirement. This paper has made comparative study on several topologies of the quadratic high gain dc-dc converter and the applications where these topologies can be used when the fuel cell is given as a source. Also, we have compared various parameters of all the converters considered and generated the results with steady-state and dynamic study. In this article, we briefed the types of analysis carried on the dc-dc converter to study its performance. Moreover, various application of fuel cell is presented and discussed. This paper will be a handbook to the researchers who start to work on high gain dc-dc converter topologies with quadratic boost converter as a base. This article will also guide the engineers to concentrate on the fuel cell components where it needs to be explored for optimizing its operation.
Go to article

Authors and Affiliations

Divya Navamani Jayachandran
1
Jagabar Sathik
2
Tanmay Padhi
1
Aditi Kumari
1

  1. Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, Chennai, India
  2. Renewable Energy Lab, Prince Sultan University,11586, Riyadh, Saudi Arabia
Download PDF Download RIS Download Bibtex

Abstract

Electromobility and electric cars are the words that began to gain significance in the social discourse in Poland especially intensively since 2017. Then, along with the announcement of the „Plan for the Development of the Electromobility Market in Poland”, government declarations appeared regarding one million electric cars that are to be used on Polish roads by 2025. It is already known today that such a result in Poland is impossible to achieve in the assumed time. According to the report of the Polish Alternative Fuels Association-PSPA (Polish EV Outlook 2020), in the event of introducing subsidies for the purchase of cars or subsidies, such as the possibility of 100% VAT deduction by buyers of such vehicles, the number of electric cars in Poland in 2025 could be over 280 thousand pcs. Without such government support, the Polish electric car park will be twice smaller. High prices of electric cars are one of the key barriers limiting Poles in making decisions related to the purchase of a vehicle. The aim of this article is to analyse the current state of the social environment in relation to the topic of ecological, electric cars. To what extent is it beneficial for the potential car owner to change from a traditional (petrol or diesel) car to an electric car due to purely financial benefits and other aspects? The article consists of an overview – presenting aspects related to the socio-economic benefits of buying an electric car. It also contains specific calculations regarding the profitability of using such a car in Polish conditions.
Go to article

Authors and Affiliations

Krystian Majchrzak
1
Piotr Olczak
2
ORCID: ORCID
Dominika Matuszewska
3
ORCID: ORCID
Magdalena Wdowin
2
ORCID: ORCID

  1. Foundation Instaway Institute, Warszawa, Poland
  2. Mineral and Energy Economy Research Institute of the Polish Academy of Sciences, Kraków, Poland
  3. AGH University of Science and Technology, Kraków, Poland
Download PDF Download RIS Download Bibtex

Abstract

The placement of the battery box can have a massive impact on the aerodynamics of an electric vehicle. Although favourable from the viewpoint of vehicle dynamics, an underbody battery box may impair the vehicle aerodynamics. This study aims to quantify the effect of an underbody battery box on the drag force acting on an electric vehicle. Four different variants of the vehicle (original variant, lifted suspension, lifted suspension with an underbody battery box) are investigated by means of computational fluid dynamics. The underbody battery box was found to induce flow separation, resulting in a massive increase in drag force. As a solution, a battery box fairing was designed and tested. The fairing significantly reduced the increase in drag. The results of this study could contribute to the design of more stable and aerodynamically efficient electric vehicles.
Go to article

Bibliography

[1] Where the Energy Goes: Electric Cars. US DOE, US EPA. https://www.fueleconomy.gov/feg/atv-ev.shtml (accessed 20 March 2021).
[2] Simmonds N., Pitman J., Tsoutsanis P., Jenkins K., Gaylard A., Jansen W.: Complete body aerodynamic study of three vehicles. SAE Tech. Pap. (2017), 2017-01-1529.
[3] Ahmed S.R. Ramm G., Faltin G.: Some salient features of the time-averaged ground vehicle wake. SAE Transactions 93(1984), 2, 840222–840402, 473–503.
[4] Buchheim R., Deutenbach K.-R., Lückoff H.-J.: Necessity and premises for reducing the aerodynamic drag of future passenger cars. SAE Transactions 90(1981), 1, 810010–810234, 758–771.
[5] Cooper K.R., Bertenyi T., Dutil G. Syms, J. Sovran G.: The aerodynamic performance of automotive underbody diffusers. SAE Tech. Pap. (1998), 980030, 150–179.
[6] Potthoff J.: The aerodynamic layout of UNICAR research vehicle. In: Proc. Int. Symp. on Vehicle Aerodynamics, Wolfsburg, 1982.
[7] Katz J.: Race Car Aerodynamics: Designing for Speed. Bentley, 1995.
[8] Hucho W.: Aerodynamics of Road Vehicles. From Fluid Mechanics to Vehicle Engineering. Butterworth-Heinemann, 1987.
[9] Katz J.: Automotive Aerodynamics. Wiley, 2016. [10] Shinde, Gopal, Aniruddha Joshi, Kishor Nikam.: Numerical investigations of the drivAer car model using opensource CFD solver OpenFOAM. Tata Consult. Serv., Pune, 2013.
[11] DrivAer Model. https://www.mw.tum.de/en/aer/research-groups/automotive/drivaer/ (accessed 15 Apr. 2021).
[12] Jakirlic S., Kutej L., Hanssmann D., Basara B., Tropea C.: Eddy-resolving simulations of the notchback ‘DrivAer’ model: Influence of underbody geometry and wheels rotation on aerodynamic behaviour. SAE Tech. Pap. (2016), 2016-01-1602.
[13] abois M., Lakehal D.: Very-large eddy simulation (V-LES) of the flow across a tube bundle. Nucl. Eng. Des. 241(2011), 6, 2075–2085.
[14] Heft A.: Aerodynamic investigation of the cooling requirements of electric vehicles. PhD thesis, Technische Universität München, Munich 2014.
[15] Heft A.I., Indinger T. Adams N.A.: Introduction of a new realistic generic car model for aerodynamic investigations. SAE Tech. Pap. (2012), 2012-01-0168.
[16] Janssen L.J., Hucho W.H.: The effect of various parameters on the aerodynamic drag of passenger cars. In: Advances in Road Vehicle Aerodynamics (H.S. Stevens, Ed.), 1973. 223-254.
[17] Wright P.G.: The influence of aerodynamics on the design of Formula One racing cars. Int. J. Vehicle Des. 3(1982), 4, 383–397.
[18] Eagle Two. http://lodzsolarteam.p.lodz.pl/index.php/eagle-two/ (accessed 3 May 2021).
[19] Lanfrit M.: Best Practice Guidelines for Handling Automotive External Aerodynamics with Fluent. Fluent Deutschland, Darmstadt 2005.
[20] Ansys Fluent Mosaic – new mesh generation technology incorporating hexahedral and polyhedral elements. Symkom, Łódz 2019. https://symkom.pl/ansys-fluent-mosaic/ (accessed 16 March 2021).
[21] Ansys: Ansys Fluent User’s Guide. 2013.
[22] Schlichting H.: Boundary-Layer Theory. McGraw Hill, 1979.
[23] Miao L., Mack S., Indinger T.: Experimental and numerical investigation of automotive aerodynamics using DrivAer model. In: Proc. ASME 2015 Int. Design Engineering Technical Conferences and Computers and Information in Engineering Conf., Boston, Aug. 2–5, 2015. V003T01A039. ASME.
[24] Heft A.I., Indinger T., Adams N.A.: Experimental and numerical investigation of the DrivAer model. In: Proc. Fluids Engineering Division Summer Meeting, Rio Grande, July 8–12, 2012, FEDSM2012-72272, 41–51. ASME.
[25] Heft A.I., Indinger T., Adams N.: Investigation of unsteady flow structures in the wake of a realistic generic car model. In: Proc. 29th AIAA Applied Aerodynamics Conf., June 2011, 3669.
[26] Ashton N., West A., Lardeau S., Revell A.: Assessment of RANS and DES methods for realistic automotive models. Comput. Fluids 128(2016), 1–15.
[27] Guilmineau E., Deng G., Leroyer A., Queutey P., Wackers J., Visonneau M. (2016, June): Assessment of RANS and DES methods for the Ahmed body. In: Proc. ECCOMAS Cong. 2016 VII Eur. Cong. on Computational Methods in Applied Sciences and Engineering (M. Papadrakakis, V. Papadopoulos, G. Stefanou, V. Plevris, Eds.), Crete Island, 5-10 June 2016.
[28] Menter F.R.: Zonal two equation k − ! turbulence models for aerodynamic flows. In: Proc. 23rd Fluid Dynamics, Plasmadynamics, and Lasers Conf., Orlando, 6–9 July 1993, AIAA-93-2906.
[29] Ansys Inc.: Ansys Fluent 12.0 Theory Guide, 2009.
[30] Menter F.R.: Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J 32(1994), 8, 1598–1605.
[31] Sobczak K.: Numerical investigations of an influence of the aspect ratio on the Savonius rotor performance. J. Phys. Conf. Ser. 1101(2018), 1, 012034.
[32] Huang P.G., Bardina J., Coakley T.: Turbulence modeling validation, testing, and development. NASA Tech. Memorand. (1997), 110446, 147.
[33] Pawłucki M., Krys M.: CFD for Engineers. Helion, Gliwice 2020.
Go to article

Authors and Affiliations

Jakub Bobrowski
1
Krzysztof Sobczak
1

  1. Institute of Turbomachinery, Lodz University of Technology, 217/221 Wolczanska, 93-005 Łódz Poland
Download PDF Download RIS Download Bibtex

Abstract

Cells of a prototype powered wheelchair can be designed in various connections to provide different supply voltages which has impact on the efficiency of other wheelchair drive elements. The impact of cell configuration and resulting battery voltage on overall efficiency of power elements have been studied to determine the optimal configuration and voltage of the pack. A brief description of a battery energy storage system was given, and main requirements and variables were introduced to reveal the flexibility of the battery design. The efficiency versus supply voltage plots of a drive converter and battery charger were presented and discussed to find the optimal battery voltage. The motor design was analyzed from the fill factor perspective. The calculated efficiency parameters of all drive power elements were used to discuss and select an optimal battery cell configuration.

Go to article

Authors and Affiliations

Kristaps Vitols
Andrejs Podgornovs
Download PDF Download RIS Download Bibtex

Abstract

To reduce the influence of the disorderly charging of electric vehicles (EVs) on the grid load, the EV charging load and charging mode are studied in this paper. First, the distribution of EV charging capacity and state of charge (SOC) feature quantity are analyzed, and their probability density function is solved. It is verified that both EV charging capacity and SOC obey the skew-normal distribution. Second, considering the space-time distribution characteristics of the EV charging load, a method for charging load prediction based on a wavelet neural network is proposed, and compared with the traditional BP neural network, the prediction results show that the error of the wavelet neural network is smaller, and the effectiveness of the wavelet neural network prediction is verified. The optimization objective function with the lowest user costs is established, and the constraint conditions are determined, so the orderly charging behavior is simulated by the Monte Carlo method. Finally, the influence of charging mode optimization on power grid operation is analyzed, and the result shows that the effectiveness of the charging optimization model is verified.
Go to article

Bibliography

[1] Zang Haixiang, Fu Yuting, Chen Ming, Shen Haiping, Miao Liheng, Zhang Side, Wei Zhinong, Sun Guoqiang, Dynamic planning of EV charging stations based on improved adaptive genetic algorithm, Electric Power Automation Equipment, vol. 40, no. 01, pp. 163–170 (2020).
[2] YI T., Zhang C., Lin T. et al., Research on the spatial-temporal distribution of electric vehicle charging load demand, A case study in China, Journal of Cleaner Production, vol. 242, (2020), DOI: 10.1016/j.jclepro.2019.118457.
[3] Xiao Hao, Pei Wei, Kong Li, Multi-Objective Optimization Scheduling Method for Active Distribution Network with Large Scale Electric Vehicles, Transactions of China Electrotechnical Society, vol. 32, no. S2, pp. 179–189 (2017).
[4] Chen Z., Zhang Z., Zhao J. et al., An analysis of the charging characteristics of electric vehicles based on measured data and its application, IEEE Access, pp. 24475–24487 (2018).
[5] Hu Z., Zhank K., Zhank H., Pricing mechanisms design for guiding electric vehicle charging to fill load valley, Applied Energy, vol. 178, pp. 155–163 (2016).
[6] Xiong Junjie, Liu Tao, He Hao, Huang Yangqi, Zhang Weizhe, Research on electric vehicle charging strategy based on particle swarm optimization, Jiangxi Electric Power, vol. 42, no. 08, pp. 15–20 (2018).
[7] Chen Zhong, Liu Yi, Zhou Tao, Xing Qiang, Du Puliang, Optimal time-of-use charging pricing strategy of EVs considering mobile characteristics, Electric Power Automation Equipment, vol. 40, no. 04, pp. 96–102 (2020).
[8] Li Shichun,Wang Yang, Zhong Hao, Shu Zhengyu, Charge and discharge strategy of the combination optimization of electric private car, taxi group with aim at strengthening peak regulation, Renewable Energy Resources, vol. 38, no. 06, pp. 824–830 (2020).
[9] Zhang Z, Donk K., Pang X., Research on the EV charging load estimation and mode optimization methods, Archives of Electrical Engineering, vol. 68, no. 04, pp. 831–842 (2019).
[10] Hu Dequan, Guo Chunlin, Yu Qinbo, Yang Xiaoyan, Bi-Level Optimization Strategy of Electric Vehicle Charging Based on Electricity Price Guide, Electric Power Construction, vol. 39, no. 01, pp. 48–53 (2018).
[11] Hadian E., Akbari H., Farzinfar M., Saeed S., Optimal Allocation of Electric Vehicle Charging Stations with Adopted Smart Charging/Discharging Schedule, IEEE Access (2020).
[12] Mao T., Lau W., Shum C. et al., A regulation policy of EV discharging price for demand scheduling, IEEE Transactions on Power Systems, vol. 33, no. 02, pp. 1275–1288 (2017).
[13] Cao Y., Tang S., Li C. et al., An optimized EV charging model considering TOU price and SOC curve, IEEE Transactions on Smart Grid, vol. 3, no. 01, pp. 388–393 (2011).
[14] Zhang Y., You P., Cai L., Optimal charging scheduling by pricing for EV charging station with dual charging modes, IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 09, pp. 3386–3396 (2018).
[15] Cui Jindong, Luo Wenda, Zhou Niancheng, Research on Pricing Model and Strategy of Electric Vehicle Charging and Discharging Based on Multi View, Proceedings of the CSEE, vol. 38, no. 15, pp. 4438–4450+4644 (2018).
[16] Faddel S., Elsayed A.T., Mohammed O.A., Bilayer Multi-Objective Optimal Allocation and Sizing of Electric Vehicle Parking Garage, IEEE Transactions on Industry Applications, vol. 54, no. 3, pp. 1992–2001 (2018).
[17] Moghaddam Z., Ahmad I., Habibi D., Phung Q.V., Smart Charging Strategy for Electric Vehicle Charging Stations, IEEE Transactions on Transportation Electrification, vol. 4, no. 1, pp. 76–88 (2018).
[18] Han Gangtuan, Cao Yantao, Construction of planning system for electric vehicle charging infrastructure, Urban and Rural Development, vol. 45, no. 9, pp. 3945–3948 (2016).
[19] Xia Yunyun, Wen Shangsheng, Fang Fang, Reliability Assessment of LED Based on Kolmogorov- Smirnov Check, Acta Photonica Sinica, vol. 45, no. 09, pp. 26–31 (2016).
[20] Zhang Yi, Lu Fenghu, The Approximate Empirical Bayesian Estimation of Kurtosis and Skewness Coefficient, Journal of Jiangxi Normal University (Natural Science), vol. 40, no. 04, pp. 358–362 (2016).
[21] Tao He, Liu Wei, Fu Jingyuan, Lognormal distribution reliability model and its application, Statistics and Decision, vol. 35, no. 03, pp. 89–92 (2019).
[22] Zhao Daoli, Gu Weihao, Feng Yaping, Short time traffic flow prediction based on wavelet neural network, Microcomputer and Its Applications, vol. 36, no. 23, pp. 80–83 (2017).
[23] Yao Ronghuan, Data center KPI prediction based on wavelet neural network, Application of Electronic Technique, vol. 45, no. 06, pp. 46–49+53 (2019).
[24] Li Guoqing, Liu Zhao, Jin Guobin, Quan Ran, Ultra Short-term Power Load Forecasting Based on Randomly Distributive Embedded Framework and BP Neural Network, Power System Technology, vol. 44, no. 2, pp. 437–445 (2020).
[25] Tang Zhenhao, Zhao Gengnan, Cao Shengxian, Zhao Bo, Very Short-term Wind Direction Prediction Via Self-tuning Wavelet Long-short Term Memory Neural Network, Proceedings of the CSEE, vol. 39, no.15, pp. 4459–4468 (2019).
[26] Zhu Lulu, The Monte Carlo method and application, MFA Thesis, Faculty of Mathematics and Statistics, Central China Normal University, Wuhan (2014).
[27] Chen Rongjun, He Yongxiu, Chen Fenkai, Dong Mingyu, Li Dezhi, Guangfengtao, Long-term Daily Load Forecast of Electric Vehicle Based on System Dynamics and Monte Carlo Simulation, Electric Power, vol. 51, no. 09, pp. 126–134 (2018).
Go to article

Authors and Affiliations

Zhiyan Zhang
1
Hang Shi
1
Ruihong Zhu
1
Hongfei Zhao
2
Yingjie Zhu
3

  1. College of Electrical Information Engineering, Zhengzhou University of Light Industry, China
  2. State Grid Jiangsu Electric Power Co., Ltd. Maintenance Branch Company, China
  3. Nanjing Electric Power Design Institute Co., Ltd. China
Download PDF Download RIS Download Bibtex

Abstract

In this fast-changing environmental condition, the effect of fossil fuel in vehicle is a significant concern. Many sustainable sources are being studied to replace the exhausting fossil fuel in most of the countries. This paper surveys the types of electric vehicle’s energy sources and current scenario of the onroad electric vehicle and its technical challenges. It summarizes the number of state-of-the-art research progresses in bidirectional dcdc converters and its control strategies reported in last two decades. The performance of the various topologies of bidirectional dc-dc converters is also tabulated along with their references. Hence, this work will present a clear view on the development of state-of-the-art topologies in bidirectional dc-dc converters. This review paper will be a guide for the researchers for selecting suitable bidirectional traction dc-dc converters for electric vehicle and it gives the clear picture of this research field.

Go to article

Authors and Affiliations

Lavanya Anbazhagan
Jegatheesan Ramiah
Vijayakumar Krishnaswamy
Divya Navamani Jayachandra
Download PDF Download RIS Download Bibtex

Abstract

Numerous European countries experience a steady increase in the share of electric (EV) and hybrid electric (HEV) vehicles in the traffic stream. These vehicles, often referred to as low- or zero-emission vehicles, significantly reduce air pollution in the road environment. They also have a positive effect on noise levels in city centers and in the surroundings of low-speed roads. Nevertheless, issues related to modeling noise from electric and hybrid vehicles in the outdoor environment are still not fully explored, especially in the rural road settings. The article attempts to assess the degree of noise reduction around these roads based on different percentages of EVs in the traffic stream. Input data for noise modeling was obtained from 133 sections of homogeneous rural roads in Poland. Based on their analysis, it was first determined on how many of these road sections electric-vehicle-induced noise reduction would be possible, taking into account the traffic speeds occurring on them. Next, a computational algorithm that can be used to calculate noise reduction in the CNOSSOS-EU model is presented, and noise modeling is performed based on it for different percentages of electric vehicles in the traffic stream.
Go to article

Authors and Affiliations

Maciej Hałucha
1
ORCID: ORCID
Janusz Bohatkiewicz
2
ORCID: ORCID
Piotr Mioduszewski
3
ORCID: ORCID

  1. EKKOM Sp. z o.o., ul. dr Józefa Babinskiego 71B, 30-394 Cracow, Poland
  2. Tadeusz Kosciuszko Cracow University of Technology, Faculty of Civil Engineering, ul. Warszawska 24, 31-155 Cracow, Poland
  3. Gdansk University of Technology, Faculty of Mechanical Engineering and Ship Technology, ul. Gabriela Narutowicza 11/12, 80-233 Gdansk, Poland
Download PDF Download RIS Download Bibtex

Abstract

Electric cars (SE) are currently considered to be one of the best ways to reduce CO2 and other air emissions in the transport sector as well as noise in cities. They can reduce the dependency of road transport on imported oil in a visible way. Nevertheless, the demand for electricity for a large amount of SE in road transport is not insignificant and has an impact on the power system. The article analyzes the potential impact of SE on the demand, supply, structure and costs of electricity generation as well as emissions as a result of introducing 1 million SEs by 2025 on Polish roads, and tripling this number by 2035. The competitive electricity market model ORCED was used for the calculations. The results of the analysis indicate that regardless of the charging strategy, the demand for SEs causes a slight increase in the overall electricity demand in Poland and consequently also a slight increase in power generating costs. Even a large increase in SEs in road transport will result in a rather moderate demand for additional generation capacity, assuming that power companies will have some control over the mode of charging cars. The introduction of SEs will not reduce CO2 emissions compared to conventional cars in 2025, on the contrary will increase them regardless of the loading strategy. In 2035 however, the result depends on the charging scenario and both the increase or decrease of emissions is possible. Electric vehicles will increase SO2 net emissions, but they will contribute to a decrease in the net emissions of particulates and NOx.

Go to article

Authors and Affiliations

Uroš Radović
Download PDF Download RIS Download Bibtex

Abstract

Electric vehicles are predicted to blossom in Egypt in future years as an emerging technology in both the transportation and power sectors, contributing significantly to the decrease of fossil-fuel usage and CO2 emissions. As a result, to mitigate overloads of the vehicle energy demand on the nation’s electric grid, a solar PV system can be used to provide the electricity needs of an EV charging station. This objective of this paper is to present the design, simulation and economic analysis of a grid-connected solar-power system for an electric-charging station at a workplace in 6th October city, Egypt using PVSOL simulation tool to supply energy to the charging station and office-building appliances. The ideal orientation of the PV panels for maximum energy was determined using data from the photovoltaic geographical information system and predicted load- -profile patterns. The amount of electricity generated the efficiency of the PV power system, financial analysis in terms of investment costs and the return on assets, and the ability to reduce CO2 emissions are all estimated in this study. This system also evaluates annual energy predictions and is used for electric-vehicle charging, grid feeding, and appliance consumption. Due to the relatively high solar insolation in Egypt; PV production energy was 10,463 kWh per year and the annual yield is 1,786.69 kWh/kWp. Of the power from PV generation, 66% is utilized for charging the electric vehicle and 34% for electrical appliances. After applying the financial analysis for 20 years; the electricity production cost is 0.0032 $/kWh and the payback period for this proposed system is about five years. The annual energy costs after the installation of PV systems proposed system created a financial saving of 21%. The performance ratio of this system inverter is 84% and the monthly average of the electric vehicle SOC over a year doesn’t decrease out of 27% plus 5 tons of CO2 emissions per year were avoided. This research can be used as a recommendation for stakeholders who want to use this energy source for vehicle charging.

Go to article

Authors and Affiliations

Marwa M. Ibrahim
1
ORCID: ORCID

  1. Mechanical Engineering Department, National Research Centre (NRC), Dokki, Cairo, Egypt
Download PDF Download RIS Download Bibtex

Abstract

Energy management plays a crucial role in cabin comfort as well as enormously affects the driving range. In this paper energy balances contemplating the implementation of a heat pump and an expansion device in battery electric vehicles are elaborated, by comparing the performances of refrigerants R1234yf and R744, from –20°C to 20°C. This work calculates the coefficient of performance, energy requirements for ventilation (from 1 to 5 people in the cabin) and energy required with the implementation of a heat pump, with the employment of a code in Python with the aid of Cool- Prop library. The work ratio is also estimated if the work recovery device recuperates the work during the expansion. Comments on the feasibility of the implementation are as well explicated. The results of the analysis show that the implementation of an expansion device in an heat pump may cover the energy requirement of the compressor from 27% to more than 35% at 20°C in cycles operating with R744, and from 15% to more than 20% with refrigerant R1234yf, considering different compressor efficiencies. At –20°C, it would be possible to recuperate between around 30 and 24%. However, the risk of suction when operating with R1234yf at ambient temperatures below –10°C shows that the heat pump can only operate with R744. Thus, it is the only refrigerant that achieves the reduction of energy consumption at these temperatures.
Go to article

Bibliography

  1. Global electric car sales by key markets, 2010-2020 – Charts – Data & Statistics IEA, https://www.iea.org/data-and-statistics/charts/global-electric-car-sales-by-key- markets-2015-2020 (accessed 17 March 2021).
  2. Rietmann N., Hügler B., Lieven T.: Forecasting the trajectory  of  electric  vehicle sales and the consequences for worldwide CO2 emissions. J. Clean. Prod. 261(2020), 121038. https://doi.org/10.1016/j.jclepro.2020.121038.
  3. Greaves , Backman H., Ellison A.B.: An empirical assessment of the feasibility of battery electric vehicles for day-to-day driving. Transport. Res. A-Pol. 66(2014), 226–237. https://doi.org/10.1016/j.tra.2014.05.011.
  4. Kempton W.: Electric vehicles: Driving range. Energ. 1 (2016), 1–2. https:// doi.org/ 10.1038/nenergy.2016.131.
  5. Klamut : Attitude towards electric vehicles. Research  on the students of a tech- nical university. Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi PAN 107(2018), 105–118 (in Polish). https://doi.org/10.24425/123719.
  6. Varga O., Sagoian A., Mariasiu F.: Prediction of electric vehicle range: A comprehensive review of current issues and challenges. Energies 12(2019), 946. https://doi.org/10.3390/en12050946.
  7. Lajunen , Suomela   J.:   Evaluation   of   energy   storage   system   requirements for hybrid mining loaders. IEEE T. Veh. Technol. 61(2012), 3387–3393. https:// doi.org/10.1109/TVT.2012.2208485.
  8. Garg ,  Chen  F.,  Zhang  J.: State-of-the-art of designs studies for batteries packs  of electric vehicles. In: Proc. IET Int. Conf. on Intelligent and Connected Vehicles (ICV 2016). https://doi.org/10.1049/cp.2016.1181.
  9. Hannan M.A., Hoque M.M., Hussain A., Yusof Y., Ker P.J.: State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applica- tions: Issues and recommendations. IEEE Access 6(2018), 19362–19378. https://org/10.1109/ACCESS.2018.2817655.
  10. Petitjean C., Guyonvarch G., Benyahia M., Beauvis R.: TEWI analysis for different automotive air conditioning systems. In: Proc. The Future Car Congress 2000, 2000-01–1561. https://doi.org/10.4271/2000-01-1561.
  11. Guyonvarch G., Aloup C., Petitjean C., De  Monts  De  Savasse :  42  V  electric air conditioning systems (E-A/CS) for  low  emissions,  architecture,  comfort and safety of next generation vehicles. In: Proc. The Future Transportation Tech- nology Conf. & Expo. 2001, 2001-01–2500. https://doi.org/10.4271/2001-01-2500.
  12. Bashirpour-Bonab H.: Thermal behavior of lithium batteries used in electric  ve- hicles using phase change materials. Int. J. Energ. Res. 44(2020), 12583–12591. https://doi.org/10.1002/er.5425.
  13. Karimi G., Li X.: Thermal management of lithium-ion batteries for electric vehicles. Int. J. Energ. Res. 37(2013), 13–24. https://doi.org/10.1002/er.1956.
  14. Kizilel R., Lateef A., Sabbah R., Farid M., Selman J.R., Al-Hallaj S.:  Passive control of temperature excursion and uniformity in high-energy Li-ion bat- tery packs at high current and ambient temperature. J. Power Sources 183(2008), 1, 370–375. https://doi.org/10.1016/j.jpowsour.2008.04.050.
  15. Agarwal ,  Sarviya  R.M.:  Characterization  of  Commercial  Grade  Paraffin  wax as Latent Heat Storage material for Solar dryers. Materials Today 4(2017), 779–789, Proc. 5th Int. Conf. on Materials Processing and Characterization (ICMPC 2016). https://doi.org/10.1016/j.matpr.2017.01.086.
  16. Ettouney H., Alatiqi , Al-Sahali M., Al-Hajirie K.: Heat transfer enhance- ment in energy storage in spherical capsules filled with paraffin wax and metal beads. Energ. Convers. Manage. 47(2006), 211–228. https://doi.org/10.1016/j.enconman. 2005.04.003.
  17. Heath A.: Amendment to the Montreal protocol on substances that  deplete  the ozone layer (Kigali amendment). Int. Legal Mater. 56(2017), 193–205. https:// doi.org/10.1017/ilm.2016.2.
  18. Lee Y., Jung D.: A brief performance comparison  of  R1234yf  and  R134a  in  a bench tester for automobile applications. Appl. Therm. Eng. 35(2012), 240–242. https://doi.org/10.1016/j.applthermaleng.2011.09.004.
  19. Ozgur A.E., Kabul A., Kizilkan : Exergy  analysis  of  refrigeration  systems using an alternative refrigerant (hfo-1234yf) to R-134a. Int. J. Low-Carb. Technol. 9(2014), 56–62. https://doi.org/10.1093/ijlct/cts054.
  20. Vaghela K.: Comparative evaluation of an automobile air – conditioning  system using R134a and its alternative refrigerants. Energy Proced. 109(2017), 153–160, Int. Conf. on Recent Advancement in Air Conditioning and Refrigeration, RAAR 2016, 10-12 November 2016, Bhubaneswar. https://doi.org/ 10.1016/j.egypro. 2017. 03.083.
  21. Reasor P., Aute V., Radermacher R.: Refrigerant R1234yf performance com- parison investigation. Refrigeration and Air Conditioning Conference 8, 2010.
  22. Cho H., Lee H., Park : Performance characteristics of an automobile air condi- tioning system with internal heat exchanger using refrigerant R1234yf. Appl. Therm. Eng. 61(2013), 563–569. https://doi.org/10.1016/j.applthermaleng.2013.08.030.
  23. Direk M., Kelesoglu A., Akin A.: Drop-in  performance  analysis  and  effect  of IHX for an automotive air conditioning system with R1234yf as a replacement of R134a. SV-JME 63(2017), 314–319. https://doi.org/10.5545/sv-jme.2016.4247.
  24. Feng L., Hrnjak P.: Experimental Study of an Air Conditioning-Heat Pump Sys- tem for Electric Vehicles. In: Proc: SAE 2016 World Exhibit., 2016-01–0257. https://doi.org/10.4271/2016-01-0257.
  25. Wu , Zhou G., Wang M.: A comprehensive assessment of refrigerants for cabin heating and cooling on electric vehicles. Appl. Therm. Eng. 174(2020), 115258. https://doi.org/10.1016/j.applthermaleng.2020.115258.
  26. Maina P., Huan Z.: A review of carbon dioxide as a refrigerant in refrigeration technology. Afr. J. Sci. 111(2015). https://doi.org/10.17159/sajs.2015/20140258.
  27. Song X., Lu D., Lei Q., Cai Y., Wang , Shi J., Chen J.: Experimental study   on heating performance of a CO2 heat pump system for an electric bus. Appl. Therm. Eng. 190(2021), 116789. https://doi.org/10.1016/j.applthermaleng.2021.116789.
  28. Wu D., Hu B., Wang Z.: Vapor compression heat pumps with pure low-GWP refrigerants. Renew. Sust. Energ. Rev. 138(2021), 110571. https://doi.org/10.1016/ j.rser.2020.110571.
  29. Lorentzen G.: Revival of carbon dioxide as a refrigerant. International Journal of Refrigeration 17(1994), 292–301. https://doi.org/10.1016/0140-7007(94)90059-0.
  30. Großmann H.: Comparing the refrigerant R1234yf and CO2. ATZ Worldw 118(2016), 70. https://doi.org/10.1007/s38311-016-0119-0.
  31. Ma Y., Liu Z., Tian H.: A review of transcritical carbon dioxide heat pump and refrigeration cycles. Energy 55(2013), 156–172. https://doi.org/10.1016/j.energy. 03.030.
  32. Li W., Liu Y., Liu R., Wang , Shi J., Yu Z., Cheng L., Chen J.L.: Perfor- mance evaluation of secondary loop low-temperature heat pump system for frost pre- vention in electric vehicles. Appl. Therm. Eng. 182(2021), 115615. https://doi.org/ 10.1016/j.applthermaleng.2020.115615.
  33. Menken J.C., Ricke M., Weustenfeld  A.,  Koehler  J.:  Simulative  analysis of secondary loop automotive refrigeration systems operated with an HFC and carbon dioxide. SAE Int. J. Passeng. Cars-Mech. Syst. 9(2016), 434–440. https://doi.org/ 10.4271/2016-01-9107.
  34. Wang , Yu B., Hu J., Chen L., Shi J., Chen J.: Heating performance char- acteristics of CO2 heat pump system for electrical vehicle in a cold climate. Int. J.Refrig. 85(2018), 27–41. https://doi.org/10.1016/j.ijrefrig.2017.09.009.
  35. Wang , Wang D., Yu,B., Shi J., Chen J.: Experimental and numerical in- vestigation of a CO2 heat pump system for electrical vehicle with series gas coolerconfiguration. Int. J. Refrig. 100(2019), 156–166. https://doi.org/10.1016/j.ijrefrig. 2018.11.001.
  36. Bruno F., Belusko M., Halawa : CO2 refrigeration and heat pump systems – A comprehensive review. Energies 12(2019), 15, 2959. https://doi.org/10.3390/ en12152959.
  37. Baek J.S., Groll E.A., Lawless B.: Piston-cylinder work producing expansion device in a transcritical carbon dioxide cycle. Part I: experimental investigation. Int. J. Refrig. 28(2005), 141–151. https://doi.org/10.1016/j.ijrefrig.2004.08.006.
  38. Ferrara G., Ferrari L., Fiaschi , Galoppi  G.,  Karellas  S.,  Secchi  R.,  Tempesti D.: A small power recovery expander for heat pump COP improvement. Energ. Proced. 81(2015), 1151–1159, 69th Conf. Ital. Therm. Eng. Assoc., ATI 2014. https://doi.org/10.1016/j.egypro.2015.12.140.
  39. Kohsokabe H., Funakoshi S., Tojo K., Nakayama , Kohno K., Kurashige  K.: Basic operating characteristics of CO2 refrigeration cycles with expander- compressor unit 10 (2006). 
  40. Specific Heat Capacities of Air – (Updated 7/26/08). https://www.ohio.edu/mecha nical/thermo/property_tables/air/air_Cp_Cv.html (accessed 6 March 2021).
  41. Abas N., Kalair A.R., Khan  ,  Haider  A.,  Saleem  Z.,  Saleem  M.S.:  Natu-  ral and synthetic refrigerants, global warming: A review. Renew. Sust. Energ. Rev. 90(2018), 557–569. https://doi.org/10.1016/j.rser.2018.03.099.
  42. Bell H., Wronski J., Quoilin S., Lemort V.: Pure and pseudo-pure fluid thermophysical property evaluation and the open-source thermophysical property li- brary CoolProp. Ind. Eng. Chem. Res. 53(2014), 6, 2498–2508. https://doi.org/ 10.1021/ie4033999.
  43. Richter M., McLinden M.O., Lemmon E.W.: Thermodynamic Properties of 2,3,3,3-Tetrafluoroprop-1-ene (R1234yf): Vapor Pressure and p–ρ–T Measurements and an Equation of State. ACS Publications (2011). https://doi.org/10.1021/ je200369m.
  44. Span , Wagner W.: A new equation  of  state  for  carbon  dioxide  covering  the fluid region from  the triple-point temperature  to 1100 K at pressures  up to 800 MPa.  J. Phys. Chem. Ref. Data 25(1996), 1509–1596. https://doi.org/10.1063/1.555991.
  45. Fukuda ,  Kojima  H.,  Kondou  C.,  Takata  N.,  Koyama S.:  Experimen-   tal assessment on performance of a heat pump cycle using R32/R1234yf and R744/R32/R1234yf. In; Proc. Int. Refrigeration and Air Conditioning Conf. 2016.
  46. Shin Y., Cho H.: Performance comparison of a truck refrigeration system  with R404A, R134a, R1234yf, and R744 refrigerants under frosting conditions. Int. J. Air-Cond. Ref. 24(2016), 1650005.https://doi.org/10.1142/S201013251650005X.
Go to article

Authors and Affiliations

Maria Laura Canteros
1
Jiri Polansky
2

  1. Czech Technical University in Prague, Jugoslávských partyzánu 1580/3, 160 00 Prague 6 – Dejvice, Czech Republic
  2. ESI Group, Brojova 16, 326 00 Plzen, Czech Republic
Download PDF Download RIS Download Bibtex

Abstract

Transformer efficiency and regulation, are to be maintained at maximum and minimum respectively by optimal loading, control, and compensation. Charging of electric vehicles at random charging stations will result in uncertain loading on the distribution transformer. The efficiency reduces and regulation increases as a consequence of this loading. In this work, a novel optimization strategy is proposed to map electric vehicles to a charging station, that is optimal with respect to the physical distance, traveling time, charging cost, the effect on transformer efficiency and regulation. Consumer and utility factors are considered for mapping electric vehicles to charging stations. An Internet of Things platform is used to fetch the dynamic location of electric vehicles. The dynamic locations are fed to a binary optimization problem to find an optimal routing table that maps electric vehicles to a charging station. A comparative study is carried out, with and without optimization, to validate the proposed methodology.
Go to article

Authors and Affiliations

R. Venkataswamy
1
ORCID: ORCID
K. Uma Rao
2
ORCID: ORCID
P. Meena
3
ORCID: ORCID

  1. CHRIST (deemed to be university)
  2. RV College of Engineering©
  3. BMS College of Engineering, India
Download PDF Download RIS Download Bibtex

Abstract

The loss of power and voltage can affect distribution networks that have a significant number of distributed power resources and electric vehicles. The present study focuses on a hybrid method to model multi-objective coordination optimisation problems for dis- tributed power generation and charging and discharging of electric vehicles in a distribution system. An improved simulated annealing based particle swarm optimisation (SAPSO) algorithm is employed to solve the proposed multi-objective optimisation problem with two objective functions including the minimal power loss index and minimal voltage deviation index. The proposed method is simulated on IEEE 33-node distribution systems and IEEE-118 nodes large scale distribution systems to demonstrate the performance and effectiveness of the technique. The simulation results indicate that the power loss and node voltage deviation are significantly reduced via the coordination optimisation of the power of distributed generations and charging and discharging power of electric vehicles.With the methodology supposed in this paper, thousands of EVs can be accessed to the distribution network in a slow charging mode.

Go to article

Authors and Affiliations

Huiling Tang
Jiekang Wu
Download PDF Download RIS Download Bibtex

Abstract

This paper presents an analysis of electric vehicle charging station operation based on a dual active bridge topology. Two cases are considered: one with the use of a medium frequency planar transformer, the other with a conventional Litz winding transformer. An analysiswas performed using both solutions in order to compare the performance characteristics of the system for both cases and to present the differences between each transformer solution. The analysis was based on tests carried out on the full-scale model of an electric vehicle charging station, which is the result of the project "Electric vehicle charging system integrated with lighting infrastructure" realized by the Department of Drives and Electrical Machines, Lublin University of Technology. The results presented in the paper show that the conventional transformer used in the research achieved better results than the planar transformer. Based on the results obtained, the validity of using both solutions in electric vehicle charging stations was considered.
Go to article

Authors and Affiliations

Maciej Rudawski
1
ORCID: ORCID
Karol Fatyga
1
ORCID: ORCID
Łukasz Kwaśny
1

  1. Lublin University of Technology, ul. Nadbystrzycka 38d, 20-618 Lublin, Poland
Download PDF Download RIS Download Bibtex

Abstract

This paper discusses three variants of how e-mobility development will affect the Polish Power System. Multivariate forecasts of annual new registrations of electric vehicles for up to seven years are developed. The forecasts use the direct trend extrapolation methods, methods based on the deterministic chaos theory, multiple regression models, and the Grey model. The number of electric vehicles in use was determined for 2019‒2025 based on the forecast new registrations. The forecasts were conducted in three variants for the annual electric energy demand in 2019‒2025, using the forecast number of electric vehicles and the forecast annual demand for electric energy excluding e-mobility. Forecasts were conducted in three variants for the daily load profile of power system for winter and summer seasons in the Polish Power system in 2019‒2025 based on three variants of the forecast number of electric vehicles and forecast relative daily load profiles.

Go to article

Authors and Affiliations

P. Piotrowski
D. Baczyński
S. Robak
M. Kopyt
M. Piekarz
M. Polewaczyk
Download PDF Download RIS Download Bibtex

Abstract

In recent years there has been an increasing demand for electric vehicles due to their attractive features including low pollution and increase in efficiency. Electric vehicles use electric motors as primary motion elements and permanent magnet machines found a proven record of use in electric vehicles. Permanent magnet synchronous motor (PMSM) as electric propulsion in electric vehicles supersedes the performance compared to other motor types. However, in order to eliminate the cumbersome mechanical sensors used for feedback, sensorless control of motors has been proposed. This paper proposes the design of sliding mode observer (SMO) based on Lyapunov stability for sensorless control of PMSM. The designed observer is modeled with a simulated PMSM model to evaluate the tracking efficiency of the observer. Further, the SMO is coded using MATLAB/Xilinx block models to investigate the performance at real-time.
Go to article

Authors and Affiliations

Soundirarajan Navaneethan
1
Srinivasan Kanthalakshmi
2
S. Aandrew Baggio

  1. Department of Instrumentation and Control Systems Engineering, PSG College of Technology, Coimbatore, 641004, Tamilnadu, India
  2. Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, 641004, Tamilnadu, India
Download PDF Download RIS Download Bibtex

Abstract

From the perspective of a virtual power plant (VPP) with electric vehicles (EVs), a self-scheduling strategy considering the response time margin (RTM) and state of charge margin (SOCM) is proposed. Firstly, considering the response state of the state of charge (SOC) and charge-discharge state of EVs, a VPP based response capacity determination model of EVs is established. Then, RTM and SOCM indexes are introduced on the basis of the power system scheduling target and the EV users’ traveling demands. The RTM and SOCM indices are calculated and then are used to generate a priority sequence of responsive EVs for the VPP. In the process of the scheduling period and rolling iteration, the scheduling schemes of the EVs in the VPP for multiple time periods are determined. Finally, the VPP self-scheduling strategy is validated by taking an VPP containing three kinds of EV users as an example. Simulation results show that with the proposed strategy, the VPP is able to respond to the scheduling power from the power system, while ensuring the traveling demands of the EV users at the same time.

Go to article

Authors and Affiliations

Fengshun Jiao
Yongsheng Deng
Duo Li
Bo Wei
Chengyan Yue
Meng Cheng
Yapeng Zhang
Jiarui Zhang
Download PDF Download RIS Download Bibtex

Abstract

This paper presents a review of the electromagnetic field and a performance analysis of a radial flux interior permanent magnet (IPM) machine designed to achieve 80 kW and 125 Nmfor an electric and hybrid traction vehicle. The motor consists of a 12-slot stator with a three-phase concentrated winding as well as an 8-pole rotor with V-shaped magnets. Selected motor parameters obtained from an IPM prototype were compared with the design requirements. Based on the electromagnetic field analysis, the authors have indicated the parts of the motor that should be redesigned, including the structure of the rotor core, aimed at enhancing the motor’s performance and adjusting segmentation for magnet eddy current loss reduction. In addition, iron and PM eddy current losses were investigated. Moreover, transient analysis of current peak value showed that the current may increase significantly compared to steady-state values.Amap of transient peak current load vs. torque load plotted against rotor speed was provided. Based on the numeric and analytical results of physical machine parameters, the authors indicate that collapse load during the motor’s operation may significantly increase the risk of permanent magnet (PM) demagnetization. It was also found that collapse load increases the transient torque, which may reduce the lifetime of windings.

Go to article

Authors and Affiliations

Adrian Młot
ORCID: ORCID
Marcin Kowol
Janusz Kołodziej
Andrzej Lechowicz
Piotr Skrobotowicz
Download PDF Download RIS Download Bibtex

Abstract

The development of electric vehicles (EV) necessitates the search for new solutions for configuring powertrain systems to increase reliability and efficiency. The modularity of power supplies, converters, and electrical machines is one such solution. Among modular electric machines, dual three-phase (DTP) motors are the most common in high-power drives. To simplify low and medium power drives for EVs based on DTP PM motor, it is proposed to use a BLDC drive and machine of the simplest design – with concentrated windings and surface mounted PMs on the rotor. To study and create such drives, an improved mathematical model of DTP PM machine was developed in this work. It is based on the results of 2D FEM modeling of the magnetic field. According to the developed method, the dependences of the self and mutual inductances between all phase windings from the angle of rotor position and loads of different motor modulus were determined. Based on these inductances, the circuit computer model of DTP PM machine was created in the Matlab/Simulink. It has a high simulation speed and a high level of adequacy, which is confirmed by experimental studies with a mock-up sample of the electric drive system.
Go to article

Authors and Affiliations

Ihor Shchur
1
Damian Mazur
2
ORCID: ORCID
Olekcandr Makarchuk
1 3
Ihor Bilyakovskyy
1
Valentyn Turkovskyi
1
Bogdan Kwiatkowski
4
ORCID: ORCID
Dawid Kalandyk
5

  1. Department of Electric Mechatronics and Computer-Controlled Electromechanical Systems, Lviv Polytechnic National University, Lviv 79013, Ukraine
  2. Department of Electrical Engineering and Fundamentals of Computer Science, Rzeszow University of Technology, Rzeszow 35-959, Poland
  3. Faculty of Electrical Engineering, Czestochowa University of Technology, Czestochowa 42-200, Poland
  4. Department of Electrical Engineering and Fundamentals of ComputerScience, Rzeszow University of Technology, Rzeszow 35-959, Poland
  5. Doctoral School of Engineering and Technical Sciences at the Rzeszow University of Technology, Rzeszów 35-959, Poland
Download PDF Download RIS Download Bibtex

Abstract

To improve the curve driving stability and safety under critical maneuvers for four-wheel-independent drive autonomous electric vehicles, a three-stage direct yaw moment control (DYC) strategy design procedure is proposed in this work. The first stage conducts the modeling of the tire nonlinear mechanical properties, i.e. the coupling relationship between the tire longitudinal force and the tire lateral force, which is crucial for the DYC strategy design, in the STI (Systems Technologies Inc.) form based on experimental data. On this basis, a 7-DOF vehicle dynamics model is established and the direct yaw moment calculation problem of the four-wheel-independent drive autonomous electric vehicle is solved through the nonsingular fast terminal sliding mode (NFTSM) control method, thus the optimal direct yaw moment can be obtained. To achieve this direct yaw moment, an optimal allocation problem of the tire forces is further solved by using the trust-region interior-point method, which can effectively guarantee the solving efficiency of complex optimization problem like the tire driving and braking forces allocation of four wheels in this work. Finally, the effectiveness of the DYC strategy proposed for the autonomous electric vehicles is verified through the CarSim-Simulink co-simulation results.
Go to article

Bibliography

  1.  H. Wang, K. Xu, Y. Cai, and L. Chen, “Trajectory planning for lane changing of intelligent vehicles under multiple operating conditions”, J. Jiangsu Univ. Nat. Sci. 40(3), 255‒260 (2019).
  2.  Y. Li, B. Zhang, and X. Xu, “Robust control for permanent magnet in-wheel motor in electric vehicles using adaptive fuzzy neural network with inverse system decoupling”, Trans. Can. Soc. Mech. Eng. 42(3), 286‒297 (2018).
  3.  Y. Li, H. Deng, X. Xu, and W. Wang, “Modelling and testing of in-wheel motor drive intelligent electric vehicles based on co-simulation with Carsim/Simulink”, IET. Intell. Transp. Syst. 13(1), 115‒123 (2019).
  4.  D. Zhang, G. Liu, H. Zhou, and W. Zhao, “Adaptive sliding mode fault-tolerant coordination control for four-wheel independently driven electric vehicles”, IEEE. Trans. Ind. Electron. 65(11), 9090‒9100 (2018).
  5.  T. Chen, X. Xu, L. Chen, H. Jiang, Y. Cai, and Y. Li, “Estimation of longitudinal force, lateral vehicle speed and yaw rate for four-wheel independent driven electric vehicles”, Mech. Syst. Signal. Process. 101, 377‒388 (2018).
  6.  T. Chen, X. Xu, Y. Cai, H. Jiang, and X. Sun, “Reliable sideslip angle estimation of four-wheel independent drive electric vehicle by information iteration and fusion”, Math. Probl. Eng. 2018, 9075372 (2018).
  7.  H. Zhang, J. Liang, H. Jiang, Y. Cai, and X. Xu, “Stability research of distributed drive electric vehicle by adaptive direct yaw moment control”, IEEE Access. 7, 106225‒106237 (2019).
  8.  L.D. Novellis, A. Sorniotti, P. Gruber, J. Orus, J.R. Fortun, J. Theunissen and J. D. Smet, “Direct yaw moment control actuated through electric drivetrains and friction brakes: Theoretical design and experimental assessment”, Mechatronics. 26, 1‒15 (2015).
  9.  Y. Chen, J. Hedrick, and K. Guo, “A novel direct yaw moment controller for in-wheel motor electric vehicles”, Veh. Syst. Dyn. 51(6), 925‒942 (2013).
  10.  A. Goodarzi, F. Diba, and E. Esmailzadeh, “Innovative active vehicle safety using integrated stabilizer pendulum and direct yaw moment control”, J. Dyn. Syst-Trans. ASME. 136(5), DS-12-1335 (2014).
  11.  S. Ding and J. Sun, “Direct yaw-moment control for 4WID electric vehicle via finite-time control technique”, Nonlinear Dyn. 88(1), 239‒254 (2017).
  12.  S. Ding, L. Liu, and W. Zheng, “Sliding mode direct yaw-moment control design for in-wheel electric vehicles”, IEEE. Trans. Ind. Electron. 64(8), 6752‒6762 (2017).
  13.  W. Huang, P. Wong, K. Wong, C. Vong, and J. Zhao, “Adaptive neural control of vehicle yaw stability with active front steering using an improved random projection neural network”, Veh. Syst. Dyn. 59(3), 396‒414 (2021), doi: 10.1080/00423114.2019.1690152.
  14.  J. Wagner and J. Keane, “A strategy to verify chassis controller software-dynamics, hardware, and automation”, IEEE Trans. Syst. Man Cybern. Part A-Syst. Hum. 27(4), 480‒493 (1997).
  15.  M. Reiter and J. Wagner, “Automated automotive tire inflation system–effect of tire pressure on vehicle handling”, IFAC Proceedings 47(3), 638‒643 (2010).
  16.  Y. Shi, Q. Liu, and F. Yu, “Design of an adaptive FO-PID controller for an in-wheel-motor driven electric vehicle”, SAE Int. J. Commer. Veh. 10, 265‒274 (2017).
  17.  H. Guo, F. Liu, F. Xu, H. Chen, D. Cao, and Y. Ji, “Nonlinear model predictive lateral stability control of active chassis for intelligent vehicles and its FPGA implementation”, IEEE Trans. Syst. Man Cybern. Part A-Syst. Hum. 49(1), 2‒13 (2017).
  18.  Q. Meng, T. Zhao, C. Qian, Z. Sun, and P. Ge, “Integrated stability control of AFS and DYC for electric vehicle based on non-smooth control”, Int. J. Syst. Sci. 49(7), 1518‒1528 (2018).
  19.  J. Song, “Development and comparison of integrated dynamics control systems with fuzzy logic control and sliding mode control”, J. Mech. Sci. Technol. 27(6), 1853‒1861 (2013).
  20.  J. Wang and R. He, “Hydraulic anti-lock braking control strategy of a vehicle based on a modified optimal sliding mode control method”, Proc. Inst. Mech. Eng. Part D-J. Aut. 233(12), 3185‒3198 (2019).
  21.  X. Sun, Y. Cai, C. Yuan, S. Wang, and L. Chen, “Fuzzy sliding mode control for the vehicle height and leveling adjustment system of an electronic air suspension”, Chin. J. Mech. Eng. 31(25), (2018), doi. 10.1186/s10033-018-0223-8.
  22.  S. Chen, J. Wang, M. Yao, and Y. Kim, “Improved optimal sliding mode control for a non-linear vehicle active suspension system”, J. Sound. Vib. 395, 1‒25 (2017).
  23.  Z. Yang, D. Zhang, X. Sun, W. Sun, and L. Chen, “Nonsingular Fast Terminal Sliding Mode Control for a Bearingless Induction Motor”, IEEE Access. 5, 16656‒16664 (2017).
  24.  E. Mousavinejad, Q. Han, F. Yang, Y. Zhu, and L. Vlacic, “Integrated control of ground vehicles dynamics via advanced terminal sliding mode control”, Veh. Syst. Dyn. 55(2), 268‒294 (2017).
  25.  A. Asiabar and R. Kazemi, “A direct yaw moment controller for a four in-wheel motor drive electric vehicle using adaptive sliding mode control”, Proc. Inst. Mech. Eng. Part K-J. Multi-Body Dyn. 233(3), 549‒567 (2019).
  26.  J. Zhang and J. Li, “Integrated vehicle chassis control for active front steering and direct yaw moment control based on hierarchical structure”, Trans. Inst. Meas. Control. 41(9), 2428‒2440 (2019).
  27.  S. Yue and Y. Fan, “Hierarchical direct yaw-moment control system design for in-wheel motor driven electric vehicle”, Int. J. Automot. Technol. 19(4), 695‒703 (2018).
  28.  X. Chen, J. Yang, D. Zhang, and J. Liang, “Complete large margin linear discriminant analysis using mathematical programming approach”, Pattern Recogn. 46(6), 1579‒1594 (2013).
  29.  R.H. Byrd, M.E. Hribar, and J. Nocedal, “An interior point algorithm for large-scale nonlinear programming”, SIAM J. Optim. 9(4), 877‒900 (1999).
  30.  R.H. Byrd, J.C. Gilbert, and J. Nocedal, “A trust region method based on interior point techniques for nonlinear programming”, Math. Progr. 89(1), 149‒185 (2000).
  31.  K. Pan and Y. Lu, “Analysis on vehicle dynamic simulating sti tire model used in driving simulator”, Auto Eng. 2, 28‒30 (2009).
  32.  Q. Xia, L. Chen, X. Xu, Y. Cai, H. Jiang, T. Chen, and G. Pan, “Running states estimation of autonomous four-wheel independent drive electric vehicle by virtual longitudinal force sensors”, Math. Probl. Eng. 2019, 8302943 (2019), doi: 10.1155/2019/8302943.
  33.  J. Tian, J. Tong, and S. Luo, “Differential steering control of four-wheel independent-drive electric vehicles”, Energies 11(11), 2892 (2018).
  34.  T. Chen, X. Xu, Y. Li, W. Wang, and L. Chen, “Speed-dependent coordinated control of differential and assisted steering for in-wheel motor driven electric vehicles”, Proc. Inst. Mech. Eng. Part D-J. Automob. Eng. 232(9), 1206‒1220 (2018).
  35.  L. Chen, T. Chen, X. Xu, Y. Cai, H. Jiang, and X. Sun, “Multi-objective coordination control strategy of distributed drive electric vehicle by orientated tire force distribution method”, IEEE Access. 6, 69559‒69574 (2018).
  36.  P. Herman and W.Adamski, “Non-adaptive velocity tracking controller for a class of vehicles”, Bull. Pol. Acad. Sci. Tech. Sci. 65(4) 459‒468 (2017).
  37.  Y. Li, H. Wu, X. Xu, Y. Cai, and X. Sun, “Analysis on electromechanical coupling vibration characteristics of in-wheel motor in electric vehicles considering air gap eccentricity”, Bull. Pol. Acad. Sci. Tech. Sci. 67(5), 851‒862 (2019).
  38.  X. Zhang, H. He, J. Nie, and L. Chen, “Performance analysis of semi-active suspension with skyhook-inertance control”, J. Jiangsu Univ. Nat. Sci. 39(5), 497‒502 (2018).
  39.  Y.Li, B.Zhang, and X.Xu, “Decoupling control for permanent magnet in-wheel motor using internal model control based on back- propagation neural network inverse system”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 961‒972 (2018).
  40.  S. Jiang, P. Wong, R. Guan, Y. Liang, and J. Li, “An efficient fault diagnostic method for three-phase induction motors based on incremental broad learning and non-negative matrix factorization”, IEEE Access 9, 17780‒17790 (2019).
  41.  H. Ye, G. Li, S. Ding, and H. Jiang, “Direct yaw moment control of electric vehicle based on non-smooth control technique”, J. Jiangsu Univ. Nat. Sci. 39(6), 640‒646 (2018).
  42.  H. Qiu, Z. Dong, and Z. Lei, “Simulation and experiment of integration control of ARS and DYC for electrical vehicle with four wheel independent drive”, J. Jiangsu Univ. Nat. Sci. 37(3), 268‒276 (2016).
  43.  S. Ding, L. Liu, and J. H. Park, “A novel adaptive nonsingular terminal sliding mode controller design and its application to active front steering system”, Int. J. Robust Nonlinear Control 29(12), 4250‒4269 (2019).
  44.  S. Ding and W. Zheng, “Nonsingular terminal sliding mode control of nonlinear second-order systems with input saturation”, Int. J. Robust Nonlinear Control 26(9) 1857‒1872 (2016).
  45.  H. Jiang, F. Cao, and W. Zhu, “Control method of intelligent vehicles cluster motion based on SMC”, J. Jiangsu Univ. Nat. Sci. 39(4), 385‒39 (2018).
  46.  B. Xu, G. Shi, W. Ji, F. Liu, and S. Ding, H. Zhu, “Design of an adaptive nonsingular terminal sliding model control method for a bearingless permanent magnet synchronous motor”, Trans. Inst. Meas. Control 39(12), 1821‒1828 (2017).
  47.  X. Yu, J. Yin, and S. Khoo, “Generalized Lyapunov criteria on finite-time stability of stochastic nonlinear systems”, Automatica 107,183‒189 (2019).
  48.  Y. Ma, Z. Zhang, Z. Niu, and N. Ding, “Design and verification of integrated control strategy for tractor-semitrailer AFS/DYC”, J. Jiangsu Univ. Nat. Sci. 39(5), 530‒536 (2018).
  49.  J. Wang, X. Yu, Z. Hui, and X. Hu, “Influence of running speed and lateral distance on vehicle transient aerodynamic characteristics during curve crossing”, J. Jiangsu Univ. Nat. Sci. 38(3), 249‒253 (2017).
  50.  C. Huang, L. Chen, C. Yun, H. Jiang, and Y. Chen, “Integrated Control of Lateral and Vertical Vehicle Dynamics Based on Multi-agent System”, Chin. J. Mech. Eng. 27(2), 304‒318 (2014).
  51.  W. Liu, R. Wang, C. Xie, and Q. Ye, “Investigation on adaptive preview distance path tracking control with directional error compensation”, Proc. Inst. Mech. Eng. Part I-J. Syst. Control Eng. 234(4), 484‒500 (2019), doi: 10.1177/0959651819865789.
  52.  T. Coleman and Y. Li, “A trust region and affine scaling interior point method for nonconvex minimization with linear inequality constraints”, Math. Progr. 88(1), 1‒31 (2000).
  53.  F. Leibfritz and E. Mostafa, “An interior point constrained trust region method for a special class of nonlinear semidefinite programming problems”, SIAM J. Optim. 12(4), 1048‒1074 (2002).
  54.  M. Rojas and T. Steihaug, “An interior-point trust-region-based method for large-scale non-negative regularization”, Inverse Probl. 18(5), 1291‒1307 (2002).
  55.  J. Bonnans and C. Pola, “A trust region interior point algorithm for linearly constrained optimization”, SIAM J. Optim. 7(3), 717‒731 (1997).
  56.  J. Erway and P. Gill, “A subspace minimization method for the trust-region step”, SIAM J. Optim. 20(3), 1439‒1461 (2010).
Go to article

Authors and Affiliations

Xiaoqiang Sun
1 2
Yujun Wang
1
Yingfeng Cai
1
Pak Kin Wong
3
Long Chen
2
ORCID: ORCID

  1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang Jiangsu, China
  2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China
  3. Department of Electromechanical Engineering, University of Macau, Taipa, Macau

This page uses 'cookies'. Learn more