scholarly journals Data-Driven Reinforcement Learning–Based Real-Time Energy Management System for Plug-In Hybrid Electric Vehicles

2016 ◽  
Vol 2572 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Xuewei Qi ◽  
Guoyuan Wu ◽  
Kanok Boriboonsomsin ◽  
Matthew J. Barth ◽  
Jeffrey Gonder
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
Data Driven ◽  
Energy Management System ◽  
Hybrid Electric

scholarly journals Project and Development of a Reinforcement Learning Based Control Algorithm for Hybrid Electric Vehicles

2022 ◽  
Vol 12 (2) ◽  
pp. 812
Author(s):  
Claudio Maino ◽  
Antonio Mastropietro ◽  
Luca Sorrentino ◽  
Enrico Busto ◽  
Daniela Misul ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
Pollutant Emissions ◽  
Energy Management System ◽  
Modular Software ◽  
Accurate Design ◽  
Hybrid Electric

Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered.

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