Rapid optimal design of a multimode power split hybrid electric vehicle transmission. (February 2019)

• Record Type: Journal Article
• Authors: Anselma, Pier Giuseppe; Huo, Yi; Roeleveld, Joel; Emadi, Ali; Belingardi, Giovanni
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 3(2019:Mar.)
• Page Start: 740
• Page End: 762
• Publication Date: 2019-02
• Subjects: Acceleration evaluation -- energy management -- hybrid electric vehicle -- optimal control strategy -- optimal design -- powertrain modeling -- transmission design; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/0954407017750789
• Languages: English
• ISSNs: 0954-4070

Abstract

This work aims at presenting a design methodology capable of modeling, generating, and testing a large number of multimode power split hybrid electric vehicle transmission designs in a relatively short period of time. Design parameters include the planetary gear ratios, the final drive ratio, the configuration of hookups to link the hybrid powertrain components to the planetary gear sets and the locations of clutch connections between different nodes of the planetary gear sets. The system modeling approach is first presented, including formulations for each component (the vehicle and road load, the engine, the motor/generators and the battery). A rapid and automated modeling procedure is proposed for hybrid electric vehicle transmissions including multiple planetary gear sets and clutch connections. Two algorithms are subsequently presented that enable fast evaluation of fuel economy and acceleration performance of hybrid electric vehicle transmission designs, namely the enhanced Power-Weighted Efficiency Analysis for Rapid Sizing and the Rapid Efficiency-based Launching Performance Analysis algorithms. The developed design methodology is tested by first modeling and evaluating three hybrid electric vehicle designs from the state-of-art. Later, an investigation for optimal designs that can ameliorate the examined benchmarks is performed. Several millions of design options are rapidly generated and tested using the proposed procedure. The methodology is proved effective by quickly coming up with two sub-optimal designs. Fuel economy and acceleration performance are improved by 5.56% and 40.56%, respectively, compared to the corresponding best benchmarks.