Energy management control for supplying partner selection protocol in mobile peer-to-peer three-dimensional streaming

2011 ◽  
Vol 25 (5) ◽  
pp. 752-769 ◽  
Author(s):  
Haifa Raja Maamar ◽  
Graciela Roman Alonso ◽  
Azzedine Boukerche ◽  
Emil Petriu
Keyword(s):  
Energy Management ◽  
Three Dimensional ◽  
Partner Selection ◽  
Peer To Peer ◽  
Management Control ◽  
Selection Protocol

scholarly journals Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2338
Author(s):  
Sofia Agostinelli ◽  
Fabrizio Cumo ◽  
Giambattista Guidi ◽  
Claudio Tomazzoli
Keyword(s):  
Artificial Intelligence ◽  
Renewable Energy ◽  
Energy Management ◽  
Three Dimensional ◽  
Cost Effective ◽  
Automation System ◽  
Climate Conditions ◽  
Digital Twin ◽  
Building Energy Management ◽  
Computing Paradigm

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements.

Energy Management of a Grid-Intergrated Hybrid Peer-to-Peer Renewable Charging Station for Electric Vehicles

2018 ◽  
Author(s):  
Lindiwe Bokopane ◽  
Kanzumba Kusakana ◽  
Herman Jacobus Vermaak
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Peer To Peer ◽  
Charging Station

Analysis and Control of Torque Split in Hybrid Electric Vehicles by Incorporating Powertrain Dynamics

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Mehran Bidarvatan ◽  
Mahdi Shahbakhti
Keyword(s):  
Steady State ◽  
Energy Consumption ◽  
Energy Management ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Internal Combustion Engines ◽  
Dynamic Models ◽  
Management Control ◽  
Drive Cycle ◽  
Hybrid Electric

Hybrid electric vehicle (HEV) energy management strategies usually ignore the effects from dynamics of internal combustion engines (ICEs). They usually rely on steady-state maps to determine the required ICE torque and energy conversion efficiency. It is important to investigate how ignoring these dynamics influences energy consumption in HEVs. This shortcoming is addressed in this paper by studying effects of engine and clutch dynamics on a parallel HEV control strategy for torque split. To this end, a detailed HEV model including clutch and ICE dynamic models is utilized in this study. Transient and steady-state experiments are used to verify the fidelity of the dynamic ICE model. The HEV model is used as a testbed to implement the torque split control strategy. Based on the simulation results, the ICE and clutch dynamics in the HEV can degrade the control strategy performance during the vehicle transient periods of operation by around 8% in urban dynamometer driving schedule (UDDS) drive cycle. Conventional torque split control strategies in HEVs often overlook this fuel penalty. A new model predictive torque split control strategy is designed that incorporates effects of the studied powertrain dynamics. Results show that the new energy management control strategy can improve the HEV total energy consumption by more than 4% for UDDS drive cycle.

scholarly journals Energy Management Control of Fuel Cell Electric Bus Basing on Fuzzy Rule

2021 ◽  
Vol 2002 (1) ◽  
pp. 012073
Author(s):  
Renguang Wang ◽  
Dong Hao ◽  
Yanyi Zhang ◽  
Xiangfei Meng
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Fuzzy Rule ◽  
Management Control ◽  
Electric Bus

Intelligent energy management control of vehicle air conditioning via look-ahead system

2011 ◽  
Vol 31 (16) ◽  
pp. 3147-3160 ◽  
Author(s):  
Hamid Khayyam ◽  
Saeid Nahavandi ◽  
Eric Hu ◽  
Abbas Kouzani ◽  
Ashley Chonka ◽  
...  
Keyword(s):  
Energy Management ◽  
Air Conditioning ◽  
Management Control ◽  
Look Ahead

Modeling and energy management control design for a fuel cell hybrid passenger bus

2014 ◽  
Vol 246 ◽  
pp. 736-746 ◽  
Author(s):  
Kyle Simmons ◽  
Yann Guezennec ◽  
Simona Onori
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Cell Hybrid ◽  
Control Design ◽  
Management Control ◽  
Fuel Cell Hybrid

Optimal adaptation equivalent factor of energy management strategy for plug-in CVT HEV

2018 ◽  
Vol 233 (4) ◽  
pp. 877-889 ◽  
Author(s):  
Xinyou Lin ◽  
Qigao Feng ◽  
Liping Mo ◽  
Hailin Li
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Adaptation Strategy ◽  
Management Control ◽  
Energy Management Strategy ◽  
Driving Mode ◽  
Driving Cycles ◽  
Equivalent Factor

This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF.

Application of State Transition Energy Management Control Algorithm in Fuel Cell

2021 ◽  
Author(s):  
Zheng Pan ◽  
Qihong Xiao ◽  
Yangliang Chen
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
State Transition ◽  
Motor Vehicle ◽  
Transition Energy ◽  
Simulation Software ◽  
Management Control ◽  
Hydrogen Consumption ◽  
Management Algorithm ◽  
Battery Energy

Dynamic programming algorithms are widely used in motor vehicle fuel cells, and can help battery energy management control to perform error analysis. The paper designs the decision-making process of fuel cell charge and discharge management based on the state transition energy management algorithm, which is used to analyse the cumulative causes of errors and the corresponding results. The article uses simulation software to simulate the algorithm proposed in this paper, and finds that the algorithm is an energy management optimization decision, and the error of the hydrogen consumption obtained by the algorithm relative to the theoretical optimal hydrogen consumption is less than 0.25%.

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