Uncertainty optimization of pure electric vehicle interior tire/road noise comfort based on data-driven

2022 ◽  
Vol 165 ◽  
pp. 108300
Author(s):  
Haibo Huang ◽  
Xiaorong Huang ◽  
Weiping Ding ◽  
Mingliang Yang ◽  
Dali Fan ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Data Driven ◽  
Road Noise ◽  
Vehicle Interior ◽  
Uncertainty Optimization

Structurized sound design process of electric vehicle interior sound

2011 ◽  
Vol 130 (4) ◽  
pp. 2546-2546
Author(s):  
Thomas Kueppers ◽  
Jan-Welm Biermann ◽  
Jochen Steffens
Keyword(s):  
Electric Vehicle ◽  
Design Process ◽  
Sound Design ◽  
Vehicle Interior

A Deep Reinforcement Learning Based Car Following Model for Electric Vehicle

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Yuankai Wu ◽  
Huachun Tan ◽  
Jiankun Peng ◽  
Bin Ran
Keyword(s):  
Reinforcement Learning ◽  
Electric Vehicle ◽  
Electricity Consumption ◽  
Trust Region ◽  
Research Area ◽  
Data Driven ◽  
Car Following ◽  
Car Following Model ◽  
On The Road ◽  
Policy Optimization

Car following (CF) models are an appealing research area because they fundamentally describe longitudinal interactions of vehicles on the road, and contribute significantly to an understanding of traffic flow. There is an emerging trend to use data-driven method to build CF models. One challenge to the data-driven CF models is their capability to achieve optimal longitudinal driven behavior because a lot of bad driving behaviors will be learnt from human drivers by the supervised learning manner. In this study, by utilizing the deep reinforcement learning (DRL) techniques trust region policy optimization (TRPO), a DRL based CF model for electric vehicle (EV) is built. The proposed CF model can learn optimal driving behavior by itself in simulation. The experiments on following standard driving cycle show that the DRL model outperforms the traditional CF model in terms of electricity consumption.

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