Machine Learning and Deep Learning Approaches for Energy Management in Smart Grid 3.0 DOI
Amitkumar V. Jha, Bhargav Appasani, Deepak Kumar Gupta

et al.

Power systems, Journal Year: 2023, Volume and Issue: unknown, P. 121 - 151

Published: Jan. 1, 2023

Language: Английский

A physics-informed learning algorithm in dynamic speed prediction method for series hybrid electric powertrain DOI
Wei Liu, Chao Yang, Weida Wang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108643 - 108643

Published: May 23, 2024

Language: Английский

Citations

1
Optimal Rule-Interposing Reinforcement Learning-Based Energy Management of Series—Parallel-Connected Hybrid Electric Vehicles DOI Open Access
Lihong Dai, Peng Hu, Tianyou Wang

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(16), P. 6848 - 6848

Published: Aug. 9, 2024

P2–P3 series–parallel hybrid electric vehicles exhibit complex configurations with multiple power sources and operational modes, presenting a difficulty in developing efficient energy management strategies. This paper takes system-KunTye 2DHT system as the research object proposes deep reinforcement learning framework based on pre-optimized to improve consumption performance of vehicles. Firstly, control-oriented model is established its configuration characteristics. Then, optimal distribution motor under different operating modes pre-optimized, which aims reduce task’s dimensionality by equating two motors an equivalent motor. Subsequently, real-time traffic information connected conditions, utilized optimize between engine motors. Combining results, achieved. Finally, comparisons are made predictive control traditional Dynamic Programming Adaptive Equivalent Consumption Minimization Strategy, revealing proposed optimization algorithm’s promising potential reducing fuel consumption.

Language: Английский

Citations

1
Fault-tolerant control of underactuated unmanned surface vessels in presence of deception and injection attacks DOI
X. C. Meng, Guichen Zhang, Bing Han

et al.

Transactions of the Institute of Measurement and Control, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 31, 2024

This paper studies the trajectory tracking control problem of underactuated unmanned surface vessels (USVs) under influence internal and external uncertainties, actuator faults, injection deception attacks. In design, we separately designed virtual adaptive laws to compensate for time-varying gains suffered by kinematics channel combined event-triggered (ETC) mechanism establish an online approximator channels loss-of-effectiveness (LOE) bias faults system. Combining robust neural damping technology finite-time disturbance observer (FTDO), dynamic uncertainties disturbances are suppressed. Finally, a novel scheme is proposed. The achieves active compensation heterogeneous including attack signals. Lyapunov stability theory analysis shows that all signals system bounded. simulation results prove effectiveness this scheme.

Language: Английский

Citations

1
Type- and task-crossing energy management for fuel cell vehicles with longevity consideration: A heterogeneous deep transfer reinforcement learning framework DOI
Ruchen Huang,

Hongwen He,

Qicong Su

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124594 - 124594

Published: Oct. 8, 2024

Language: Английский

Citations

1
Machine Learning and Deep Learning Approaches for Energy Management in Smart Grid 3.0 DOI
Amitkumar V. Jha, Bhargav Appasani, Deepak Kumar Gupta

et al.

Power systems, Journal Year: 2023, Volume and Issue: unknown, P. 121 - 151

Published: Jan. 1, 2023

Language: Английский

Citations

2