Optimal driving during electric vehicle acceleration using evolutionary algorithms
IR@CMERI: CSIR- Central Mechanical Engineering Research Institute (CMERI), Durgapur
View Archive Info| Field | Value | |
| Title |
Optimal driving during electric vehicle acceleration using evolutionary algorithms
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| Creator |
Chakraborty, Debasri
Vaz, Warren Nandi, Arup Kr. |
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| Subject |
Electric vehicles
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| Description |
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.
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| Publisher |
Elsevier
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| Date |
2015
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| Type |
Article
PeerReviewed |
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| Identifier |
Chakraborty, Debasri and Vaz, Warren and Nandi, Arup Kr. (2015) Optimal driving during electric vehicle acceleration using evolutionary algorithms. Applied Soft Computing, 34. pp. 217-235.
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| Relation |
http://cmeri.csircentral.net/256/
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