Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning

Chen, Zheng; Hu, Hengjie; Wu, Yitao; Zhang, Hongjie; Li, Guang; Liu, Yonggang

doi:10.1016/j.energy.2020.118931

Cited by 86 publications

(29 citation statements)

References 38 publications

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“…The panoramic analysis on electric powertrain behaviors and energy consumption in the real vehicle and the built model proves that the novel model validation method can prompt the accuracy of the control module in the constructed forward model, contributing to optimal design of vehicle control strategy. [33], parallel [34] and power-split [35] configurations. According to the investigation results, the developed novel method can comprehensively validate the forward model with different configurations coherently.…”

Section: B Model Validation In Simulationmentioning

confidence: 99%

Machine Learning-Based Vehicle Model Construction and Validation—Toward Optimal Control Strategy Development for Plug-In Hybrid Electric Vehicles

Zhang

Chen

et al. 2022

IEEE Trans. Transp. Electrific.

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Advances in machine learning inspire novel solutions for the validation of complex vehicle models, and spur an easy manner to promote energy management performance of complexly configured vehicles, such as plug-in hybrid electric vehicles (PHEVs). A constructed PHEV model, based on the four-wheel drive passenger vehicle configuration, is validated through an efficient virtual test controller (VTC) developed in this paper. The VTC is designed via a novel approach based on the least square support vector machine and random forest with the inner-interim data filtered by the ReliefF algorithm to validate the vehicle model as necessary. The paper discusses the process and highlights the accuracy improvements of the PHEV model that is achieved by implementing the VTC. The validity of the VTC is addressed by examining the PHEV model to mimic the characteristics of internal combustion engine, motor and generator behaviors observed through the benchmark test. Sufficient simulations and hardware-in-loop test are employed to demonstrate the capability of the novel VTC based model validation method in practical applications. The major novelty of this paper lies in the development of a VTC, by which the vehicle model can be efficiently developed, providing solid framework and enormous convenience for control strategy design.

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Section: B Model Validation In Simulationmentioning

confidence: 99%

Machine Learning-Based Vehicle Model Construction and Validation—Toward Optimal Control Strategy Development for Plug-In Hybrid Electric Vehicles

Zhang

Chen

et al. 2022

IEEE Trans. Transp. Electrific.

Self Cite

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“…In the energy sector, the combination of the MPC and DQN methods has been applied to respond effectively to disturbances in power generation of DERs [35][36][37] and uncertain behavior in DR programs [38,39]. Nevertheless, there is a scarcity of studies applying the DQN-based MPC approach to DCMs with integrated voltage-based DR and From Figure 2, the proposed control strategy is carried out in the following steps:…”

Section: Reinforcement Learning-based Model Predictive Controlmentioning

confidence: 99%

Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch

Thanh

Wang

2022

Energies

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Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries’ optimal dispatch operation for minimizing the entire DC microgrid’s operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries’ charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method.

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“…They used DQN to approximate the optimal actionvalue function. To contribute to the fuel efficiency of plug-in hybrid EVs, Chen et all [27] propose a stochastic model predictive control strategy for energy management, based on Reinforcement Learning. Furthermore, the authors employ the Q-learning algorithm to set a RL controller used in the optimization process.…”

Section: State Of the Artmentioning

confidence: 99%

Smart Scheduling of Electric Vehicles Based on Reinforcement Learning

Vizitiu¹,

Furtuna²,

Robu³

et al. 2022

Preprint

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As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of EVs at a larger scale. To this end, we propose a solution capable of addressing multiple EV charging scheduling issues, such as congestion management, scheduling a charging station in advance, and allowing EV drivers to plan optimized long trips using their EVs. The smart charging scheduling system we propose considers a variety of factors such as battery charge level, trip distance, nearby charging stations, other appointments, and average speed. Given the scarcity of data sets required to train the Reinforcement Learning algorithms, the novelty of the recommended solution lies in the scenario simulator, which generates the labelled datasets needed to train the algorithm. Based on the generated scenarios, we created and trained a neural network that uses a history of previous situations to identify the optimal charging station and time interval for recharging. The results are promising and for future work we are planning to train the DQN model using real-world data.

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Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning

Cited by 86 publications

References 38 publications

Machine Learning-Based Vehicle Model Construction and Validation—Toward Optimal Control Strategy Development for Plug-In Hybrid Electric Vehicles

Machine Learning-Based Vehicle Model Construction and Validation—Toward Optimal Control Strategy Development for Plug-In Hybrid Electric Vehicles

Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch

Smart Scheduling of Electric Vehicles Based on Reinforcement Learning

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