Drive cycle pattern is different for different countries which depends on their traffic density, road condition and driver discipline. Drive cycle influences HEV`s components design, sizing and their ratings. Standard drive cycle data doesn't reveal much information to determine efficient and economic design of HEV`s components. In this research paper measurement and analysis of real time Indian road drive cycles (IRDC) are carried out for urban roads, state highway, national highway and express Highway where vehicles have their most run. Real time drive cycle data will expose impact of driver’s skills, traffic, road conditions and short acceleration / deceleration period, which can be represented on drive cycle chart. Analysis of IRDC in terms of rate of acceleration and deceleration, top speed, average speed with road length and analysed mathematically to find energy and power required for acceleration, normal operation and energy harvested during deceleration. Based on information from IRDC HEV`s components initial size are estimated. Initial estimated size is optimized to make HEV`s components design more efficient and economic. Teaching and learning based optimization algorithm (TLBO) and Multi objective genetic algorithm (MOGA) are used to optimize HEV`s components. Constraint of optimization algorithm are like engine and motor rating should be selected such that it has effective top speed with enough acceleration capability and can run enough distance to reach destination according to Indian urban, state, national and express highway pattern where cities are very closed compared with other countries and its regeneration component design should able to harvest maximum deceleration energy. For economic operation of HEV’s, running cost in terms of Rs. / Km. should be minimum.
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