Optimal Control Strategy for Plug-in Electric Vehicles Based on Reinforcement Learning in Distribution Networks

2018 ◽  
Author(s):  
X. Z. Ye ◽  
T. Y. Ji ◽  
M. S. Li ◽  
Q. H. Wu
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Distribution Networks ◽  
Optimal Control Strategy

scholarly journals An optimal control strategy design for plug-in hybrid electric vehicles based on internet of vehicles

Energy ◽  
2021 ◽  
Vol 228 ◽  
pp. 120631
Author(s):  
Yuanjian Zhang ◽  
Yonggang Liu ◽  
Yanjun Huang ◽  
Zheng Chen ◽  
Guang Li ◽  
...  
Keyword(s):  
Optimal Control ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Optimal Control Strategy ◽  
Internet Of Vehicles ◽  
Hybrid Electric ◽  
Strategy Design

scholarly journals Optimal Control Strategy for Parallel Plug-In Hybrid Electric Vehicles Based on Dynamic Programming

2021 ◽  
Vol 12 (2) ◽  
pp. 85
Author(s):  
Ying Tian ◽  
Jiaqi Liu ◽  
Qiangqiang Yao ◽  
Kai Liu
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Optimal Control Strategy ◽  
Parallel Hybrid ◽  
Hybrid Electric

In this paper, the dynamic programming algorithm is applied to the control strategy design of parallel hybrid electric vehicles. Based on MATLAB/Simulink software, the key component model and controller model of the parallel hybrid system are established, and an offline simulation platform is built. Based on the platform, the global optimal control strategy based on the dynamic programming algorithm is studied. The torque distribution rules and shifting rules are analyzed, and the optimal control strategy is adopted to design the control strategy, which effectively improves the fuel economy of plug-in hybrid electric vehicles. The fuel consumption rate of this parallel hybrid electric vehicle is based on china city bus cycle (CCBC) condition.

scholarly journals Model-Free Reinforcement Learning for Branching Markov Decision Processes

2021 ◽  
pp. 651-673
Author(s):  
Ernst Moritz Hahn ◽  
Mateo Perez ◽  
Sven Schewe ◽  
Fabio Somenzi ◽  
Ashutosh Trivedi ◽  
...  
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Markov Decision Processes ◽  
Control Strategy ◽  
Natural Extension ◽  
Decision Processes ◽  
Optimal Control Strategy ◽  
Model Free ◽  
Learning Techniques ◽  
Markov Decision

AbstractWe study reinforcement learning for the optimal control of Branching Markov Decision Processes (BMDPs), a natural extension of (multitype) Branching Markov Chains (BMCs). The state of a (discrete-time) BMCs is a collection of entities of various types that, while spawning other entities, generate a payoff. In comparison with BMCs, where the evolution of a each entity of the same type follows the same probabilistic pattern, BMDPs allow an external controller to pick from a range of options. This permits us to study the best/worst behaviour of the system. We generalise model-free reinforcement learning techniques to compute an optimal control strategy of an unknown BMDP in the limit. We present results of an implementation that demonstrate the practicality of the approach.

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