Interactive test stand for electric vehicle components

The article discusses an interactive stand for testing the components of electric vehicles. They can also be used in the didactics of technical subjects such as vehicle construction and vehicle diagnostics with a focus on hybrid or full electric vehicles. The article discusses the individual components of the research stand and presents examples of their use in research and teaching. Particular attention was paid to the components responsible for energy storage on-board the vehicle and the charging process of the lithium-ion battery.

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Impact of the Air-Conditioning System on the Power Consumption of an Electric Vehicle Powered by Lithium-Ion Battery

Modelling and Simulation in Engineering ◽

10.1155/2013/935784 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 8

Author(s):

Brahim Mebarki ◽

Belkacem Draoui ◽

Boumediène Allaou ◽

Lakhdar Rahmani ◽

Elhadj Benachour

Keyword(s):

Lithium Ion Battery ◽

Electric Vehicle ◽

Electric Vehicles ◽

Air Conditioning ◽

Storage Capacity ◽

Lithium Ion ◽

Noise Pollution ◽

Air Conditioning System ◽

Energy Storage Capacity ◽

Basic Goal

The car occupies the daily universe of our society; however, noise pollution, global warming gas emissions, and increased fuel consumption are constantly increasing. The electric vehicle is one of the recommended solutions by the raison of its zero emission. Heating and air-conditioning (HVAC) system is a part of the power system of the vehicle when the purpose is to provide complete thermal comfort for its occupants, however it requires far more energy than any other car accessory. Electric vehicles have a low-energy storage capacity, and HVAC may consume a substantial amount of the total energy stored, considerably reducing the vehicle range, which is one of the most important parameters for EV acceptability. The basic goal of this paper is to simulate the air-conditioning system impact on the power energy source of an electric vehicle powered by a lithium-ion battery.

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Advanced Model of Hybrid Energy Storage System Integrating Lithium-ion Battery and Supercapacitor For Electric Vehicle Applications

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2020.2984426 ◽

2020 ◽

pp. 1-1

Author(s):

Tedjani Mesbahi ◽

Patrick Bartholomeus ◽

Nassim Rizoug ◽

Redha Sadoun ◽

Fouad Khenfri ◽

...

Keyword(s):

Energy Storage ◽

Lithium Ion Battery ◽

Electric Vehicle ◽

Storage System ◽

Lithium Ion ◽

Energy Storage System ◽

Hybrid Energy ◽

Hybrid Energy Storage ◽

Hybrid Energy Storage System ◽

Advanced Model

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Thermal and economic analysis of hybrid energy storage system based on lithium-ion battery and supercapacitor for electric vehicle application

Clean Technologies and Environmental Policy ◽

10.1007/s10098-020-01824-z ◽

2020 ◽

Author(s):

Vima Mali ◽

Brijesh Tripathi

Keyword(s):

Energy Storage ◽

Economic Analysis ◽

Lithium Ion Battery ◽

Electric Vehicle ◽

Storage System ◽

Lithium Ion ◽

Energy Storage System ◽

Hybrid Energy ◽

Hybrid Energy Storage ◽

Hybrid Energy Storage System

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An electric circuit model for a lithium-ion battery cell based on automotive drive cycles measurements

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i4.pp2798-2810 ◽

2021 ◽

Vol 11 (4) ◽

pp. 2798

Author(s):

Jaouad Khalfi ◽

Najib Boumaaz ◽

Abdallah Soulmani ◽

El Mehdi Laadissi

Keyword(s):

Lithium Ion Battery ◽

Electric Vehicle ◽

Electric Vehicles ◽

Measurement Data ◽

Lithium Ion ◽

Electrical Circuit ◽

Performance Criteria ◽

Battery Management System ◽

Battery Cell ◽

Driving Cycles

The on-board energy storage system plays a key role in electric vehicles since it directly affects their performance and autonomy. The lithium-ion battery offers satisfactory characteristics that make electric vehicles competitive with conventional ones. This article focuses on modeling and estimating the parameters of the lithium-ion battery cell when used in different electric vehicle drive cycles and styles. The model consists of an equivalent electrical circuit based on a second-order Thevenin model. To identify the parameters of the model, two algorithms were tested: Trust-Region-Reflective and Levenberg-Marquardt. To account for the dynamic behavior of the battery cell in an electric vehicle, this identification is based on measurement data that represents the actual use of the battery in different conditions and driving styles. Finally, the model is validated by comparing simulation results to measurements using the mean square error (MSE) as model performance criteria for the driving cycles (UDDS, LA-92, US06, neural network (NN), and HWFET). The results demonstrate interesting performance mostly for the driving cycles (UDDS and LA-92). This confirms that the model developed is the best solution to be integrated in a battery management system of an electric vehicle.

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