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

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 (R² = 0.962), adjusted R² = 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.

Keywords

Affiliated Institutions

• King Khalid University SA
• Karpagam Academy of Higher Education IN
• Government Medical College IN
• GLA University IN
• Koneru Lakshmaiah Education Foundation IN
• University of Nizwa OM
• Jimma University ET

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