Driving-Style-Based Codesign Optimization of an Automated Electric Vehicle: A Cyber-Physical System Approach

TLDR

The study proposes a cyber‑physical system framework to co‑design plant and controller parameters of an automated electric vehicle so that its dynamic performance, drivability, and energy use adapt optimally to different driving styles. The framework integrates unsupervised machine‑learning driving‑style recognition, adaptive control algorithms for aggressive, moderate, and conservative styles, and objective‑based parameter optimization to tune plant and controller settings. Experiments demonstrate that the optimized vehicle performs well across all three driving styles, confirming the feasibility and effectiveness of the CPS‑based co‑design approach.

Abstract

This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.