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Performance & Efficiency Improvement of Electric Vehicle Power Train
Technical Paper
2019-28-2483
ISSN: 0148-7191, e-ISSN: 2688-3627
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Event:
NuGen Summit
Language:
English
Abstract
Introduction: The advent of electric mobility is changing the conventional mobility techniques and their application in automobiles across all segments. This development comes with challenges ranging across varied sub -systems in a vehicle including Power Train, HVAC, Accessories, etc. Objective: This paper would concentrate on the Power train related sub systems & improvement of the same both in terms of Efficiency & Performance. Methodology: The electric power train consists of three major sub parts: 1. Motor Unit 2. Controller with Power electronics 3. Battery Pack with BMS We would concentrate on improving the overall efficiency and performance of all these subsystems while they perform in vehicle environment and work in tandem by deploying following techniques: a. Improved Regenerative Braking for converting vehicles Kinetic energy into electrical energy using specific algorithms and control techniques b. Optimization of Design Specs and duty cycle based on real world driving cycles. c. Innovative Heat dissipation techniques to minimize energy loss to heat. d. Efficient Electrical to Chemical Energy conversion and vice versa through use of optimization techniques based on battery characterization Data. Results: Based on our Initial Proof of concept level experimentation we expect a improvement in efficiency of the tune 15-20 % through these optimization techniques. Innovation/New Idea: These techniques have not been adopted using a system level approach as per our knowledge. The innovation is in the integration of various techniques to create a better Power Train as a whole. Conclusion: These techniques when optimized and applied together can significantly improve the efficiency of electric vehicles and make them a more viable option.
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Citation
Pareek, D., "Performance & Efficiency Improvement of Electric Vehicle Power Train," SAE Technical Paper 2019-28-2483, 2019, https://doi.org/10.4271/2019-28-2483.Data Sets - Support Documents
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