Power flow control-based regenerative braking energy utilization in ac electrified railways: Review and future trends
Regenerative braking energy (RBE) utilization plays a vital role in improving the energy
efficiency of electrified railways. To date, various power flow control-based solutions have …
efficiency of electrified railways. To date, various power flow control-based solutions have …
Reinforcement learning-based intelligent control strategies for optimal power management in advanced power distribution systems: A survey
Intelligent energy management in renewable-based power distribution applications, such as
microgrids, smart grids, smart buildings, and EV systems, is becoming increasingly important …
microgrids, smart grids, smart buildings, and EV systems, is becoming increasingly important …
Event-triggered fault-tolerant control for input-constrained nonlinear systems with mismatched disturbances via adaptive dynamic programming
In this paper, the issue of event-triggered optimal fault-tolerant control is investigated for
input-constrained nonlinear systems with mismatched disturbances. To eliminate the effect …
input-constrained nonlinear systems with mismatched disturbances. To eliminate the effect …
Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control
Advanced energy management strategy (EMS) can ensure healthy, stable, and efficient
operation of the on-board energy systems. Model Predictive Control (MPC) and Deep …
operation of the on-board energy systems. Model Predictive Control (MPC) and Deep …
Twin delayed deep deterministic policy gradient-based deep reinforcement learning for energy management of fuel cell vehicle integrating durability information of …
Deep reinforcement learning (DRL)-based energy management strategy (EMS) is attractive
for fuel cell vehicle (FCV). Nevertheless, the fuel economy and lifespan durability of proton …
for fuel cell vehicle (FCV). Nevertheless, the fuel economy and lifespan durability of proton …
Observer‐based optimal fault‐tolerant tracking control for input‐constrained interconnected nonlinear systems with mismatched disturbances
S Liu, N Xu, N Zhao, L Zhang - Optimal Control Applications …, 2024 - Wiley Online Library
This paper investigates an observer‐based optimal fault‐tolerant tracking control problem
for interconnected nonlinear systems with input constraints and mismatched disturbances …
for interconnected nonlinear systems with input constraints and mismatched disturbances …
Imitation reinforcement learning energy management for electric vehicles with hybrid energy storage system
Deep reinforcement learning has become a promising method for the energy management
of electric vehicles. However, deep reinforcement learning relies on a large amount of trial …
of electric vehicles. However, deep reinforcement learning relies on a large amount of trial …
Deep reinforcement learning-based energy management strategy for fuel cell buses integrating future road information and cabin comfort control
Conventional energy management strategy (EMS) for fuel cell vehicles (FCVs) aims to
optimize powertrain energy consumption while ignoring the air conditioning regulation …
optimize powertrain energy consumption while ignoring the air conditioning regulation …
[HTML][HTML] A review of the design process of energy management systems for dual-motor battery electric vehicles
Dual-motor battery electric vehicles (DM-BEVs) are a trending technology in the electric
vehicle market. They have the potential to achieve higher energy savings and dynamic …
vehicle market. They have the potential to achieve higher energy savings and dynamic …
Integrated thermal and energy management of connected hybrid electric vehicles using deep reinforcement learning
The climate-adaptive mymargin energy management system (EMS) holds promising
potential for harnessing the concealed energy-saving capabilities of connected plug-in …
potential for harnessing the concealed energy-saving capabilities of connected plug-in …