Sensitivity Analysis of Reinforcement Learning-Based Hybrid Electric Vehicle Powertrain Control

2021 ◽  
Vol 14 (3) ◽  
Author(s):  
Zhengyu Yao ◽  
Jordan Olson ◽  
Hwan-Sik Yoon
Keyword(s):  
Sensitivity Analysis ◽  
Reinforcement Learning ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Powertrain Control ◽  
Hybrid Electric ◽  
Vehicle Powertrain

Optimal Dual-Mode Hybrid Electric Vehicle Powertrain Architecture Design for a Variety of Loading Scenarios

2014 ◽  
Author(s):  
Alparslan Emrah Bayrak ◽  
Yi Ren ◽  
Panos Y. Papalambros
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Fuel Efficiency ◽  
Architecture Design ◽  
Optimization Strategy ◽  
Passenger Cars ◽  
Hybrid Electric ◽  
Distribution Of Power ◽  
Architecture Development ◽  
Vehicle Powertrain

A hybrid-electric vehicle powertrain architecture consists of single or multiple driving modes, i.e., connection arrangements among engine, motors and vehicle output shaft that determine distribution of power. While most architecture development work to date has focused primarily on passenger cars, interest has been growing in exploring architectures for special-purpose vehicles such as vans or trucks for civilian and military applications, whose weights or payloads can vary significantly during operations. Previous findings show that the optimal architecture can be sensitive to vehicle weight. In this paper we investigate architecture design under a distribution of vehicle weights, using a simulation-based design optimization strategy with nested supervisory optimal control and accounting for powertrain complexity. Results show that an architecture under a single load has significant differences and lower fuel efficiency than an architecture designed to work under a variety of loading scenarios.

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