Urban vehicle design system

This project presents a comprehensive preliminary design tool for the sizing of key subsystems in an electric vehicle, with a specific focus on urban driving applications. The tool integrates a genetic algorithm (GA) to optimize critical design parameters, including battery capacity (kWh), wheel diameter (inches) and a structural coefficient representing chassis material utilization. The objective of the GA is to minimize the simulated energy consumption (expressed in kWh) while ensuring that the overall vehicle mass (the sum of the subsystem masses, auxiliary systems, and a fixed extra mass) remains within a tight tolerance of the target mass. The tool employs detailed subsystem models to estimate both energy consumption and mass distribution. The battery sizing model calculates the required energy capacity based on an effective state-of-charge window and a reference consumption value, converting this capacity into an estimated battery mass through a cell-based model. The motor sizing function derives the necessary power from dynamic force calculations—accounting for aerodynamic drag, rolling resistance, grade resistance, and acceleration forces—and estimates motor mass using an empirical power-to-mass ratio. In parallel, the tyre sizing module not only computes the physical dimensions and mass of the wheels but also models additional energy losses due to tire deformation, which depend on the deviation of the wheel diameter from a reference value. The chassis is sized through a volume-based approach, comparing external and internal volumes to derive a structural mass based on material density and a configurable structural coefficient. Furthermore, the tool includes an auxiliary mass model that estimates the weight of additional components, such as cooling systems and power electronics (e.g., the inverter), as functions of the motor power and battery capacity. The overall objective function aggregates the individual subsystem masses and compares the total to the predefined target mass; any deviation beyond the acceptable tolerance results in a substantial penalty being applied to the fitness score. The GA iteratively explores the solution space, seeking the optimal set of parameters that minimizes energy consumption while strictly adhering to the mass constraints. Final outputs include the optimal design parameters for the battery, motor, wheels, and chassis, the corresponding simulated energy consumption, the total vehicle mass, and an estimated maximum range calculated from the effective battery capacity and baseline consumption. This integrated tool provides a rapid, preliminary evaluation of design trade-offs, supporting the early-stage development of urban electric vehicles by highlighting the interplay between subsystem sizing, energy efficiency, and overall vehicle weight.