Implicit multilinear modeling

The paper introduces a subclass of nonlinear differential-algebraic models of interest for applications. By restricting the nonlinearities to multilinear polynomials, it is possible to use modern tensor methods. This opens the door to new approximation and complexity reduction methods for large scale systems with relevant nonlinear behavior. The modeling procedures including composition, decomposition, normalization, and multilinearization steps are shown by an example of a local energy system with a nonlinear electrolyzer, a linear buck converter and a PI controller with saturation.

Design, Control, Management, and Performance Analysis of PV-Battery Supercapacitor DC-System Using Buck Converter

The intermittent nature of photovoltaic energy necessitates the incorporation of storage devices to ensure the continuality of loads feeding. In addition, it is important to model, control, and verify the operating of the designed system before implementation. Furthermore, the integration of power electronic interfaces plays a significant role in protecting the system and benefiting from solar energy. To this end, a buck converter is chosen to charge the battery and supply the supercapacitor. The control strategy is based on the maximum power point tracking techniques when the management algorithm recommends MPPT function mode. Otherwise, a feedback constant voltage PI controller is designed. Indeed, perturb and observe and incremental conductance are implemented and compared to analyze the system efficiency within the management strategy to charge the battery, switch between the controllers, and feed a supercapacitor in case of full battery charge. The obtained results using MATLAB/SIMULINK platform confirm the behaviour of the proposed strategy.

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.