Energy economy optimization and comprehensive performance improvement for PEMFC/LIB hybrid system based on hierarchical optimization

Lü, Xueqin; Meng, Ruidong; Deng, Ruiyu; Long, Liyuan; Wu, Yinbo

doi:10.1016/j.renene.2022.05.007

Cited by 18 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[13]. The working temperature of core components such as automotive motors and power batteries is directly related to the safety, efficiency, and energy consumption of the entire vehicle [14]. Therefore, the status and importance of thermal management technology in the research and development of new energy vehicle products are more prominent [15].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Control and Optimization Mechanism of Thermal Management of New Energy Vehicles

Huang

2022

Advances in Multimedia

View full text Add to dashboard Cite

Global issues such as global warming, rising sea level, melting Arctic and Antarctic glaciers, deterioration of the natural environment, and depletion of fossil energy have attracted increasing attention. As the concept of environmental protection has become increasingly popular, the concept of new energy automobiles has been proposed to keep the heat up. Especially in recent years, with the advantages of energy conservation, environmental protection, and low noise, new energy automobiles have changed from concept to industry and have been generally accepted and recognized by consumers in the consumer market. The increasingly stringent energy crisis and emission regulations have put forward more stringent requirements for modern automobiles. The thermal management of the new generation of intelligent automobiles is not only limited to simply solving the problem of engine heat dissipation but also involves reliability, power, economy, emissions, and comfort. It is an important vehicle development technology with many performances such as performance. The integrated thermal management of new energy vehicles includes engine cooling, oil cooling, air conditioning refrigeration, HVAC heating, supercharged intercooling, low cycle thermal fatigue, and thermal damage. For new energy vehicles such as hybrid and pure electric vehicles, it also includes motor cooling, motor controller cooling, and power battery temperature control. For the purpose of vehicle thermal management optimization design, this paper innovatively proposes IVTM technical solutions, relying on multidimensional numerical calculation coupling and multiobjective collaborative optimization control to integrate system design, scheme evaluation, performance analysis, dynamic control, and collaborative optimization. Through the integrated thermal management collaborative control strategy based on the full operating conditions of the new energy vehicle, the comprehensive improvement of multiple evaluation indicators such as system design performance, thermal management, and control performance and economy is achieved.

show abstract

Section: Introductionmentioning

confidence: 99%

Collaborative Control and Optimization Mechanism of Thermal Management of New Energy Vehicles

Huang

2022

Advances in Multimedia

View full text Add to dashboard Cite

show abstract

“…The use of swarm intelligence in control systems offers a decentralized method for addressing problems, allowing a collective of independent entities to dynamically and effectively interact. [11][12][13][14][15] Utilizations of Swarm Intelligence in Electric Vehicles Researchers have recently investigated the amalgamation of swarm intelligence with electric vehicle (EV) control systems, acknowledging the potential for heightened flexibility and optimization. The ever-changing nature of traffic circumstances and the fluctuating supply of energy in electric cars demands the implementation of clever control systems that are capable of making real-time changes.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Control of Electric Vehicle Drives using Swarm Robotics

Usanova,

Kumar,

Ikram

et al. 2024

E3S Web Conf.

View full text Add to dashboard Cite

This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.

show abstract

“…As a result, during a certain loading condition, the FC and battery seem to be the only ones to contribute the full load energy. MPPT and ECMS algorithms are hybridized for energy management to improve the motion range of mobile welding robots [40].…”

Section: Equivalent Consumption Minimisation Strategy (Ecms)mentioning

confidence: 99%

Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems

Venkatasatish

Dhanamjayulu

2023

Sustainability

View full text Add to dashboard Cite

This research proposes an improved energy management strategy (EMS) for a fuel cell hybrid power system for an electric aircraft based on a recently developed coyote optimization algorithm (COA). The suggested hybrid system consists of fuel cells and an energy storage system (ESS) to supply the required load in stable conditions. The distribution and performance of the hybrid electrical power system are determined by various energy sources. Consequently, having the best energy management system is essential for completing this work. The suggested EMS’s main objectives are to reduce hydrogen energy utilization and increase power source longevity. The proposed coyote optimization algorithm with external energy maximization strategy (COA-EEMS) and coyote optimization algorithm with equivalent consumption minimisation strategy (COA-ECMS) are tested with the help of the Opal-RT 5700 real-time HIL simulator and MATLAB/Simulink. The proposed algorithms confirm their robustness and higher efficiency by minimizing hydrogen fuel consumption compared to existing algorithms. The merits of the proposed algorithms are presented in detailed and compared with existing algorithms.

show abstract

Energy economy optimization and comprehensive performance improvement for PEMFC/LIB hybrid system based on hierarchical optimization

Cited by 18 publications

References 31 publications

Collaborative Control and Optimization Mechanism of Thermal Management of New Energy Vehicles

Collaborative Control and Optimization Mechanism of Thermal Management of New Energy Vehicles

Intelligent Control of Electric Vehicle Drives using Swarm Robotics

Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems

Contact Info

Product

Resources

About