Artificial intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell vehicles

M Fayyazi, P Sardar, SI Thomas, R Daghigh, A Jamali… - Sustainability, 2023 - mdpi.com
Environmental emissions, global warming, and energy-related concerns have accelerated
the advancements in conventional vehicles that primarily use internal combustion engines …

A systematic review of reinforcement learning application in building energy-related occupant behavior simulation

H Yu, VWY Tam, X Xu - Energy and Buildings, 2024 - Elsevier
The building and construction industry has consistently been a major contributor to energy
consumption and carbon emissions. With stochastic interactions between occupants and …

[HTML][HTML] A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization

Z Wang, L Huang, S Yang, D Li, D He… - Alexandria Engineering …, 2023 - Elsevier
There are many tricky optimization problems in real life, and metaheuristic algorithms are the
most effective way to solve optimization problems at a lower cost. The dung beetle …

Health-awareness energy management strategy for battery electric vehicles based on self-attention deep reinforcement learning

C Wu, J Peng, H He, J Ruan, J Chen, C Ma - Journal of Power Sources, 2024 - Elsevier
The economical and safe energy management strategy (EMS) is essential for a battery
electric vehicle (BEV). With an improved deep deterministic policy gradient (DDPG)-based …

Cyber-physical models for distributed CAV data intelligence in support of self-organized adaptive traffic signal coordination control

W Lin, H Wei - Expert Systems with Applications, 2023 - Elsevier
While more studies have been focused on adaptive traffic signal control (ATSC) algorithms
to learn the control policy from interactions with the traffic environment by using connected …

Model-Free Prescribed Performance Control of Adaptive Cruise Control Systems via Policy Learning

J Zhao, B Jia, Z Zhao - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
Model-free control does not require precise system dynamic information, but rather meets
performance requirements by directly designing a control law. This method is particularly …

Hierarchical energy management for extended-range electric vehicles considering range extender dynamic coordination

L Han, X Zhou, N Yang, H Liu, C Xiang - Journal of Power Sources, 2024 - Elsevier
Due to the dynamic characteristics of the range extender, the energy management
commands of the range-extender electric vehicle (EREV) cannot be tracked well, which …

A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva's public transportation

S Zakeri, D Konstantas, S Sorooshian… - Artificial Intelligence …, 2024 - Springer
This paper proposes a novel decision-support system (DSS) to assist decision-makers in the
ULTIMO project with integrating Autonomous Vehicles (AVs) in Geneva, Switzerland …

A deep reinforcement learning-based approach for autonomous lane-changing velocity control in mixed flow of vehicle group level

Z Wang, H Huang, J Tang, L Hu - Expert Systems with Applications, 2024 - Elsevier
As an important driving behavior, lane-changing has a great impact on the safety and
efficiency of traffic flow interacting with surrounding vehicles, especially in mixed traffic flows …

Energy management controllers: strategies, coordination, and applications

MS Bakare, A Abdulkarim, AN Shuaibu… - Energy …, 2024 - Springer
Energy management controllers (EMCs) are pivotal for optimizing energy consumption and
ensuring operational efficiency across diverse systems. This review paper delves into the …