BATTERY

The increasing complexity of Electric Vehicle (EV) powertrains necessitates a robust, integrated, and flexible control strategy, centralized within the Electric Vehicle Control Unit (EVCU). This study shows the implementation of the Id = control strategy using the MATLAB/Simulink Motor Control Blockset under varying loading conditions and speed requirements. This study further goes on to show an implementation of a CC-CV charging controller for a Li-ion battery with multiple current control loops. The study is designed for compliance with the AUTOSAR (Automotive Open System Architecture) Classic Platform for compliance – ensuring modularity, portability and adherence to industry standards. The study results validate the performance of the Interior PMSM and the ability to generate C implementation and header files from the model-based engineering (MBE) design approach which can be further used for hardware-in-the-loop testing. This study concludes that the MBE and AUTOSAR approach produces a highly efficient framework for developing, validating and iterating on complex, multi-domain electric vehicle components.

This project focuses on developing an innovative web-based monitoring system tailored for deep cycle batteries. Serving as a repository of vital information, the system seamlessly amalgamates data from battery-connected sensors, securely storing it within a cloud-based database. Accessible via an intuitively designed web interface, users can effortlessly access essential battery insights, without the need for real-time updates. The system's ingenuity lies in its capacity to translate raw data into actionable insights. Extracted patterns and correlations inform the optimization of battery performance and the extension of its lifespan. The system's intelligence empowers informed decision-making, offering suggestions for adjustments to charging rates, discharge patterns, and operational strategies. These recommendations hold the potential to substantially enhance deep cycle battery longevity, mitigate maintenance costs, and elevate overall system efficiency. Furthermore, the system acts as a trusted guide in selecting deep cycle batteries tailored to specific needs. Conducting meticulous comparative analyses of battery performances and considering pivotal selection factors empowers users to make confident, well-informed decisions, even in the absence of visual aids Spanning applications across the renewable energy, marine, and automotive sectors, this allencompassing monitoring system revolutionizes deep cycle battery management. By prioritizing pertinent data and actionable insights over real-time updates, the system lays the groundwork for efficient, cost-effective, and well-informed battery systems, thus contributing to a sustainable energy landscape