# Evaluation of electric vehicle performance using driving cycle clustering based on motor-inverter losses and efficiency

## TL;DR

This paper presents a method to evaluate electric vehicle performance by analyzing motor and inverter efficiency and losses across different driving cycles.

## Contribution

The novelty lies in integrating motor and inverter modeling with clustering to assess performance while reducing computational complexity.

## Key findings

- Clustering reduces computational time without losing essential performance characteristics.
- IGBT and MOSFET inverters show significant differences in efficiency and power losses.
- The method enables accurate evaluation of EV performance across diverse driving cycles.

## Abstract

This paper introduces the methodology for evaluating electric vehicle (EV) performance by integrating motor and inverter efficiency analysis with total losses assessment across representative driving cycle simulations. The proposed methodology integrates finite element analysis (FEA) of the Interior Permanent Magnet Synchronous Motor (IPMSM) with comprehensive modeling of inverter power electronics to accurately assess power electro-mechanical and conversion losses under realistic operating conditions. By considering the joint influence of motor behavior and inverter dynamics, the methodology enables a more accurate evaluation of EV performance across diverse driving cycles, offering a deeper understanding of efficiency variations and loss distribution throughout real-world operation. Additionally, the methodology is designed to minimize computational complexity while maintaining high accuracy for performance evaluations. Three standard driving cycles the Federal Test Procedure (FTP), the Worldwide Harmonised Light Vehicle Test Procedure (WLTP), and the New European Driving Cycle (NEDC) were selected to capture diverse driving scenarios. Initially, motor performance was simulated across all operating points within these cycles. Subsequently, a clustering technique was applied to condense the dataset without compromising accuracy, enabling a direct comparison between full cycle and clustered simulations. The study also evaluates the influence of two inverter types, insulated-gate bipolar transistor (IGBT) and metal-oxide-semiconductor field-effect transistor (MOSFET), on motor efficiency and losses. A case study on a 48-slot IPMSM integrated into an EV system illustrates the approach. Results show that clustering significantly reduces computational time while preserving essential performance characteristics. Moreover, inverter modeling reveals notable differences in efficiency and power losses between IGBT and MOSFET-based systems. These findings highlight the necessity of integrating motor and power electronics modeling for comprehensive EV design.