Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges …
P Dong, J Zhao, X Liu, J Wu, X Xu, Y Liu… - … and Sustainable Energy …, 2022 - Elsevier
The rapid development of intelligent and connected technologies is conducive to the
efficient energy utilization of hybrid electric vehicles (HEVs). However, most energy …
efficient energy utilization of hybrid electric vehicles (HEVs). However, most energy …
Hybrid electric vehicles: A review of energy management strategies based on model predictive control
X Lü, S Li, XH He, C Xie, S He, Y Xu, J Fang… - Journal of Energy …, 2022 - Elsevier
At present, hybrid electric vehicles are regarded as an effective way to solve global
environmental pollution and energy shortage. Energy management strategy is the core …
environmental pollution and energy shortage. Energy management strategy is the core …
Risk assessment and mitigation in local path planning for autonomous vehicles with LSTM based predictive model
Accurate trajectory prediction of surrounding vehicles enables lower risk path planning in
advance for autonomous vehicles, thus promising the safety of automated driving. A low-risk …
advance for autonomous vehicles, thus promising the safety of automated driving. A low-risk …
EVDHM-ARIMA-based time series forecasting model and its application for COVID-19 cases
The time-series forecasting makes a substantial contribution in timely decision-making. In
this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) …
this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) …
Hierarchical predictive control for electric vehicles with hybrid energy storage system under vehicle-following scenarios
For electric vehicles with hybrid energy storage system, driving economy depends not only
on novel energy management strategies but also on load power demand. In order to …
on novel energy management strategies but also on load power demand. In order to …
Cross-type transfer for deep reinforcement learning based hybrid electric vehicle energy management
Developing energy management strategies (EMSs) for different types of hybrid electric
vehicles (HEVs) is a time-consuming and laborious task for automotive engineers …
vehicles (HEVs) is a time-consuming and laborious task for automotive engineers …
Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses
The Plug-in hybrid vehicle (PHEV) has been progressively penetrated in the urban public
transport system and seen a foreseeable fast growth in the future. Within this horizon, energy …
transport system and seen a foreseeable fast growth in the future. Within this horizon, energy …
Machine learning aided air traffic flow analysis based on aviation big data
Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air
traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft …
traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft …
A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle
Energy management strategy (EMS) has a huge impact on the energy efficiency of hybrid
electric vehicles (HEVs). Recently, fast-growing number of studies have applied different …
electric vehicles (HEVs). Recently, fast-growing number of studies have applied different …
A comprehensive study of speed prediction in transportation system: From vehicle to traffic
In the intelligent transportation system (ITS), speed prediction plays a significant role in
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …
supporting vehicle routing and traffic guidance. Recently, a considerable amount of research …