KEYWORDS: LabVIEW, Batteries, Design, Data modeling, Signal processing, Human-machine interfaces, Mathematical optimization, Data processing, Data acquisition, Computer simulations
With the rise in environmental awareness and the promotion of new energy vehicle policies, the electric vehicle (EV) market is rapidly expanding. Direct current (DC) charging stations, as a crucial infrastructure for electric vehicle charging, have made the design and optimization of their monitoring systems especially important. This study aims to design and implement an efficient and reliable DC charging station monitoring system for electric vehicles using the LabVIEW platform. The system primarily achieves real-time data collection, signal processing, State of Charge (SoC) estimation, and user interface optimization. Through Geographic Information Systems (GIS) and big data analysis, this study also assesses the spatial distribution and accessibility of charging stations to promote the rational layout of charging infrastructure. Experimental results show that the developed monitoring system can effectively improve charging efficiency, reduce system response time, and enhance user experience. Moreover, system performance is further improved through optimization using genetic algorithms and simulated annealing algorithms. The research results provide technical support and practical guidance for the development of electric vehicle charging infrastructure, playing a significant role in promoting the sustainable development of the electric vehicle industry.
With the popularity of online learning, it is becoming more and more important to evaluate and monitor the learning status of online learners. Understanding the learning status of learners helps educators provide personalized support and guidance to improve the effectiveness and quality of online learning. By analyzing the behavior logs of learners in the well-known open source E-learning system (MOODLE) and combining the learning theories of Gagne and Waldorf education, this study designed a model based on finite state automata to simulate the process of learning state transition of online learners. By describing the state set, input data, state transition function, initial state and termination state set in the designed model in detail, this study provides feasible ideas for E-learning platform to optimize teaching strategies and provide personalized learning support, so as to help learners achieve learning goals more effectively. The application of this model is expected to provide targeted guidance for online learning platforms and improve learners' learning experience and learning outcomes.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.