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A smart AIS based portable wireless electric charging vehicles

As the production of electric vehicle has scaled up in the recent years in order to meet the objective of lowering the carbon footprint and eco-friendly transportation. The raise in the production of electric vehicles in accordance with hike in the price of petroleum and diesel has shifted the huge market share of the automobile industry from ICE engines to the battery powered engines. The shift in turn pushes the demand for installation of charging stations for electric vehicles at most locations. But the installation of such EV charging base stations requires high capital and sophisticated spatial infrastructure in densely populated area. Therefore, the paper proposes the AIS based mobile wireless charging system for electric vehicles which is cost effective and reliable. The system is best suited for densely populated areas, parking arenas at theatres, malls, parks etc., The Wireless Sensor Network is implemented to effectuate adaptive intelligent system, therefore leading to better accuracy and modularity.


Introduction
In modern technological world, digitalization of resources is predominant in order to simulate or process the solution for the real-time problem without causing damages to the physical entities. Therefore, the need for procuring the physical parameters of tangible bodies and changes in the phases of the tangible environment has increased to effectuate spontaneous solutions for instantaneous problems. The need has fortified the development of transducers. But yet, for complex real-world problems, there was a necessity to integrate multiple system architectures as the limitations of hardware design hinder the arbitrary goal of an all-in-one system. For such complex problems, the network of transducers is an obligation to provide solutions with intelligent computation. Therefore, in electronics the wireless sensor network (WSN) plays the fundamental role of acquiring the complex physical parameters to process the required solution. Apart from this, as there's a growing demand in electric vehicle production, the demand extends to the charging base stations for the electric vehicles. But the budget required for installing the base station is too high for densely populated areas. It is to be noted that, if the base charging stations increase, the adoption of electric vehicles would also increase proportionally. As the capital requirement is high, there's a less count of base stations in the current scenario. Moreover, charging the electric vehicles require numerous physical parameters, for R e t r a c t e d which the WSN is adopted by expertise in charging the vehicle. But the WSN itself doesn't function with intelligence in the case of any changes sensed in the parameters. Therefore, an adaptive and intelligent system (AIS) is a need [1]. To be precise, to build a cost effective reliable electric vehicle charging system it requires a complex sensor networks and instead of charging through cables, wireless charging can be adopted. Mostly such a system is best suited for locations with high population density as well as the parking arenas of malls, theatres, hotels, parks etc., as the wireless charging is advantageous in such places. Thus, the paper proposes the AI-WSN system based mobile electric charging station, which finds its application in the densely populated areas and parking arenas [2]. The proposed system adopts magnetic resonance technology therefore sustaining the efficiency level and duration of charging as compared to the wired charging without compromising the abilities of wired charging [3]. Moreover, the system is cost effective and simple in design.

Research background
The recent proposal [4] "Novel wireless charging algorithms to charge mobile wireless sensor network by using reinforcement learning" proposes a machine learning model in which the wireless sensor network operates with high efficiency in acquiring the physical parameters. The extended WSN model is proposed for efficiency traction, network optimization and so on. The significance of MWSN is, it travels to the position of the sensor nodes and begins the charging of the vehicle. In this, the reinforcement learning (RL) model is implemented. Though the model offers lowcost implementation, the station itself is not mobile friendly and the model is complex in design aspects. The paper [5] "An improved algorithm for dispatching the minimum numberof electric charging vehicles for wireless sensor networks" focuses on scheduling the electric charging vehicles for the wireless sensor networks. The authors conclude that the power consumption of the wireless sensor network implemented by the proposed algorithm consumes lesser power than the existing algorithms. However, the paper fails to provide improvised solution for minimal wireless sensor networks and its learning model. In paper [6] "A Review of Wireless Charging Nodes in Wireless Sensor Networks" the author did a comparative study among the various wireless charging technologies to charge the wireless sensors [7]. In this, the WSN is divided into two clusters in which the either of the two clusters have respective charging vehicle. The trajectory of the charging vehicle is assessed based on the energy awareness concept and the initial point of the charging vehicles corresponds to the nodes energy. Nevertheless, the paper doesn't provide any proof of concept and the dynamic charging capability of life-time critical WSN. In paper [8] "A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning Technique", the author proposes a hybrid model known as dynamic wireless charging which is used to subtle the problems in network topological infrastructure [9]. The charge state estimator -CSE is implemented as unsupervised machine learning model which makes the system significantly operate well on the basis of latency and packet lost ratio [10].   figure 3 represents the charging unit section that consists of microcontroller, RF receiver, WSN and wireless power transfer transmitter circuitry. Once the electric vehicle approaches the parking slot, the infrared sensor of the parking slot section checks the position of the electric vehicle. If the vehicle is mispositioned, the display will command the driver to position the vehicle in the right manner. Once it is positioned properly, the wireless sensor network of charging unit requests for permitting the charging. If the WSN of electric vehicle unit allows, then the WPT transmitter section begins transmitting the power to the electric vehicle. The receiver WPT of vehicle unit captures the power transferred from the charging unit wirelessly. The magnetic resonance technology is used to wirelessly transfer the power as it doesn't compromise the fast-charging ability and efficiency of wired charging. The voltage sensor will be checking the battery level of the electric vehicle, once it is completely charged the request to stop the transmission of power is sent through RF transmitter of vehicle to the RF receiver section of charging unit. The charging unit is actually integrated with the mobile mechanical unit which can move from one place to another in the parking arena. The simple circuitry and compactness of the system makes the system cost effective, maintenance less and portable, moreover, as the magnetic resonance technology is used, the system is also efficient and supports quick charging.

Design and implementation
The proposed system comprises three units in which the parking slots circuitry, electric vehicle circuitry and charging unit circuitry combined imparts the innovation. Each of these units has the microcontroller to initialize and distribute the control signals. The WSN may possess many sensors include temperature, pressure, etc.

Microcontroller
The microcontroller used in the parking slot circuit, it is the low-cost mixed signal microcontroller of Texas instruments. The MSP430FR2000 is an embedded device which has 16-bit RISC architecture with the wide voltage range of 1.8 to 3.6V. It consists of 12 GPIO pins and 8 interrupt pins and finds it effective applications in battery pack appliances Figure 4. For the charging unit and electric vehicle circuitry a dedicated special purpose microcontroller is employed. The BQ500210 involves in intelligent power transfer between the base station and the vehicle. It has improved parasitic metal object detection (PMOD) which promotes safety during wireless power transfer. Moreover, the digital demodulation features significantly simplifies the solution and is immune to high temperatures Figure 5. The below is the block diagram of BQ500210, Figure 5. The above schematic is the block diagram of BQ500210 microcontroller.

WSN
The wireless sensor networks comprise of multiple sensors which is powered wirelessly. To collaborate with all the values produced by the sensors a WSDA -2000 is employed. WSDA is defined as wireless sensor data aggregator. The WSDA therefore enables high speed data aggregation and it is remote accessible. The reinforced learning model is adopted for AI-WSN.

Infrared Sensor
The infrared sensor is employed in motion sensing applications. In the parking slot it is used to emit the infrared rays onto the slot on which electric vehicle has to be positioned. If the infrared rays

LCD display
The liquid crystal display is employed to display the information that is outputted from the circuitry. The 16*2 LCD modules are used to display such information.

RF Node transmitter & Receiver
The 433 MHz radio frequency module is utilized to transfer the signals if the vehicle is mispositioned. The RF transmitter is placed in the electric vehicle section and the receiver is placed in the charging unit. Figure 6. Voltage sensor module that is compatible with the MSP microcontroller.

Voltage Sensor
The voltage sensor is used to measure the voltage level in the battery figure 6. The operational voltage range that the sensor can measure is from 0 to 35V. It consists of two pull up SMD resistors of range 30k and 7.5 K Ohms.

Wireless Power Transfer
The electromagnetic resonance charging is based on the principle of making a wireless power transfer between two coils, which are tuned to resonate at the single frequency. Based on the electromagnet resonant coupling, the current is injected in the primary coil which creates an oscillating magnetic field in its vicinity based on the size, the magnetic field in turn when resonated with the frequency in the second coil generates the electrical energy in the second coil. The magnetic resonance charging is the most effective way of wireless charging as it doesn't compromise with the abilities of wired charging. The transmitter section is installed in mobile charging unit in the proposed system, which minimizes the cost effectively Figure 7.

Future enhancement
In future, the WSN can be improvised by defining new learning and implementation algorithm. The mobile unit can be uniquely designed with remote control features. Therefore, if a vehicle's battery is low, the priority is raised for that vehicle, so that the mobile unit moves near to that vehicle first.

Conclusion
The magnetic resonance wireless power transfer is the most effective technology which is reliable for electric vehicle charging in the modern era. As the technology is most efficient and cost effective, it is preferred. Moreover, the employment of wireless sensor network increases the accuracy and optimizes the network stability. The innovative approach of charging circuit installed in the mobile unit is cheaper compared to installing a fixed base station. The system is also reliable and modular when compared to the existing fixed station.