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Summary & Details

Design of Power Management Strategy Using Artificial Neural Networks for Mild Hybrid Electric Vehicles,"Presenter"

Delivery: Available on this site

Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20181829

Paper/Info type: AVEC

No.ThE1-6

Pages: 1-5(Total 5 p)

Date of publication: Jul 2018

Publisher: Others, Unknown

Language: English

Event: AVEC '18

Detailed Information

Category(E)	Hybrid EV Control I
Author(E)	1) Bo-Chiuan Chen
Abstract(E)	An adaptive power management strategy (APMS) is developed for the mild hybrid electric vehicle with a belt-driven starter generator (BSG). According to the state of charge (SOC) of the battery, a self-organizing fuzzy controller is used to adaptively adjust the equivalence factor which is used to convert the electric power usage to the equivalent fuel consumption. Equivalent fuel consumption minimization (ECMS) is used to obtain the optimal power split ratio between the engine and the BSG. Due to the high computation load of ECMS, an artificial neural network (ANN) is designed to replace the ECMS for the real-time implementation. Simulation results show that the proposed APMS with ANN can achieve the fuel economy close to that of the APMS with ECMS while reducing the computation load significantly.

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