Development of a Power Management System for Electric Vehicles with Multiple Power Sources

Author(s):  
Chi-Sheng Tsai ◽  
Ching-Hua Ting
2019 ◽  
Author(s):  
Dr Chris Watts ◽  
Dr Truong Quang Dinh ◽  
Dr Truong Minh Ngoc Bui ◽  
Dr James Marco

With fuel consumption of marine vessels accounting for an increasing portion of global fuel usage, improving the energy efficiency of vessels is essential for saving fuel and reducing greenhouse gas emissions. Hybrid-electric propulsion technologies offer a solution by interfacing multiple power sources, including batteries, with sophisticated energy management systems. An Agile Power Management System for marine vessels is presented by a Babcock led consortium with the University of Warwick (WMG) and Potenza Technology Ltd. The aim of this Innovate-UK funded project is to take advances in automotive energy management techniques and develop a modular marine power management system, addressing the latest guidance and legislation for marine applications. The system employs novel power management algorithms developed using Hardware-in-the-Loop (HIL) modelling techniques. Capable of interfacing energy storage with multiple power sources and loads, the algorithms seek to maximise overall efficiency by improving prime-mover operational envelopes, hence reducing emissions and fuel consumption.


2012 ◽  
Vol 518 ◽  
pp. 137-153 ◽  
Author(s):  
M. Arnold ◽  
C.A. Featherston ◽  
Matthew R. Pearson ◽  
J. Lees ◽  
Aleksander Kural

Autonomous structural health monitoring systems with independent power sources and wireless sensor nodes are increasingly seen as the best solution for monitoring a diverse range of machines and structures including pumps, bridges and aircraft. Powering these systems using harvested energy from ambient sources provides an attractive alternative to the use of batteries which may be either inaccessible for routine maintenance or unsuitable (for example in aerospace applications). A number of techniques are currently being considered including harvesting energy from vibration and thermal gradients. Harvesting energy can however lead to a highly variable power supply in opposition to the requirements of a wireless sensor node which requires continuous standby power with an additional capacity for power peaks during transmission of data. A power management system with embedded energy storage is therefore necessary in order to match supply and demand. Due to the low levels of power harvested in a number of applications, an important factor in the design of such a system is its efficiency to ensure sufficient power reaches the sensor node. Based on the requirements for a simple power management system for thermoelectric power harvesting consisting of a rectifier, a DC/DC convertors and a battery, this paper first examines the possibilities in terms of basic components with a number of commercially available units tested and characterised. Potential designs for a management system incorporating these components are then discussed and a blueprint for an optimal system is suggested.


2021 ◽  
Vol 11 (21) ◽  
pp. 9820
Author(s):  
Ahmed Hadi Ali AL-Jumaili ◽  
Yousif I. Al Mashhadany ◽  
Rossilawati Sulaiman ◽  
Zaid Abdi Alkareem Alyasseri

This review describes a cloud-based intelligent power management system that uses analytics as a control signal and processes balance achievement pointer, and describes operator acknowledgments that must be shared quickly, accurately, and safely. The current study aims to introduce a conceptual and systematic structure with three main components: demand power (direct current (DC)-device), power mix between renewable energy (RE) and other power sources, and a cloud-based power optimization intelligent system. These methods and techniques monitor demand power (DC-device), load, and power mix between RE and other power sources. Cloud-based power optimization intelligent systems lead to an optimal power distribution solution that reduces power consumption or costs. Data has been collected from reliable sources such as Science Direct, IEEE Xplore, Scopus, Web of Science, Google Scholar, and PubMed. The overall findings of these studies are visually explained in the proposed conceptual framework through the literature that are considered to be cloud computing based on storing and running the intelligent systems of power management and mixing.


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