Maximum Power Point Tracking Based on a New Hybrid Method for Automobile Exhaust Thermoelectric Generators

doi:10.3901/JME.2019.16.132

Abstract

Abstract: To improve the dynamic tracking speed and steady accuracy of the automotive exhaust thermoelectric generator DC/DC maximum power algorithm, a novel iterative tracking algorithm is proposed based on the combination of the improved incremental conductance method and adaptive variable step size BP artificial neural network. Besides, an interleaved buck converterwith voltage closed loop is used, then based on building its small signal model compensation design is given to ensure high efficiency and system stability. Comparative experiments under two different engine steady and the dynamic conditions are carried out with a new hybrid method (HM) proposed and the single improved incremental conductance (SⅡNC) method, single BP artificial neural network (SBP-ANN) algorithm. Simulation and experimental results verify the feasibility and availability of the proposed algorithm, comparing with the SⅡNC method and SBP-ANN algorithm respectively, and in the steady state, the HM is approximately equal to the actual value and the power losses are the least, thus it is suitable forautomotive exhaust thermoelectric generator scenarios. The maximum power tracking technology based on a new hybrid method proposed has a strong reference value for designing the stable and efficient automotive exhaust thermoelectric generation system.

Key words: automotive exhaust thermoelectric generator, hybrid method, interleaved Buck converter, maximum power tracking, power losses, voltage closed loop

CLC Number:

TM913

LI Xiaolong, XIE Changjun, HUANG Liang, QUAN Shuhai, FANG Wei. Maximum Power Point Tracking Based on a New Hybrid Method for Automobile Exhaust Thermoelectric Generators[J]. Journal of Mechanical Engineering, 2019, 55(16): 132-140.

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