Data analysis and visualization of electric vehicle engineering

We have visualized data using Excel to understand the relationship between battery chemistries and capacity. Examined Python code for tuning a Proportional – Integral – Derivative (PID) controller to understand its role in improving control accuracy. The challenges and benefits of PID tuning are discussed, highlighting the importance of precise tuning for optimal performance, efficiency, and safety. Analysing the impact of various battery form factors, including cylindrical, pouch, and prismatic options, on system cost, safety, and durability. Furthermore, the paper explores design trade-offs in electric vehicle development, emphasizing the need for balancing competing parameters such as performance, durability, and cost-effectiveness. The characteristics of various battery form factors (cylindrical, pouch, and prismatic) and Light Detection and Ranging (LiDAR) sensors (mechanical, solid-state, and hybrid) are compared, providing insights into their suitability for different applications. Overall, this article offers a comprehensive overview of the intricate relationships between battery chemistries, PID controllers, and design trade-offs in the development of efficient and sustainable electric vehicles. Conducted a comprehensive cost-benefit analysis for different LiDAR sensor models, comparing mechanical scanning, solid-state, and hybrid LiDAR sensors, with a focus on durability, scanning speed, and cost implications. Beside the aforementioned, we have also presented technical findings in a concise format using tables, aiding the engineering division in making informed decisions for various projects.