Experimental optimization of an IoT-driven load adaptive wireless charging system for electric vehicles using response surface methodology

利用响应面法对物联网驱动的电动汽车负载自适应无线充电系统进行实验优化

阅读:1

Abstract

The Internet of Things (IoT) based wireless electric vehicle (EV) charging system, which has the functionality described in this paper, was developed, deployed, and streamlined to address the limitations of the traditional plug-in and stationary wireless chargers. The system can resonant induce primary and secondary coils and also automatically adjusts to load variations via real-time measurements of temperature on the surface, current and voltage. The smart control of power transfer and heat management is possible by letting users easily change coil settings and provide real-time feedback on different loads and react to it with the use of NodeMCU microcontrollers and Message Queuing Telemetry Transport (MQTT)-based IoT communication. An in-depth design of experiment (DoE), which determined the best coil gap, input voltage and load level was done through Response Surface Methodology (RSM) Central Composite Design (CCD). Its resultant model has coefficient of determination (R(2) = 0.962), adjusted R(2) = 0.948 which is very predictive. The analysis conducted on Analysis of Variance (ANOVA) gave an F-value of 65.43 (p < 0.001) and it is evident that all the interaction terms are significant. The values of the predicted and measured efficiency were within the margin of 2.3% hence the results were very robust. The system was also effective to the tune of 93.5% and varied by 1.8% in the best of conditions on different loads. The IoT-adaptive configuration was 7.3% more efficient at transferring power, and 24.6% lower in coil temperature increase, charging time, and current stability and only 1.5 less efficient than traditional wired charging. An 116 ms feedback-actuation lag is used to guarantee safe coil surface temperatures of 52 °C. This proposed IoT-configurable wireless charger has been statistically tested, thermally compliant, and power-saving and incorporates real-time sensor wit and empirical optimization, scalable and smart grid-enabled EV charging infrastructure is possible.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。