Cyber-Physical System Based Optimization Framework for Intelligent Powertrain Control

Abstract

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle's dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented. Simulationbased parameter optimizations are carried out according to the objective functions. Simulation results show that an electric powertrain with intelligent controller can perform its tasks well under sport, eco, and normal driving modes. The vehicle further improves overall performance in vehicle dynamics, ride comfort, and energy efficiency. The results validate the feasibility and effectiveness of the proposed CPS-based optimization framework, and demonstrate its advantages over a baseline benchmark.

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

“…To further advance the existing CPS methods as well as their applications reported in [29][30][31], following contributions are made in this paper: 1) a CPS-based co-design optimization framework is proposed for an automated EV considering different driving styles; 2) a driving style recognition algorithm is developed using unsupervised learning method; 3) control algorithms are synthesized for typical driving styles with different protocol selections.…”

“…A driving event is a driving maneuver, such as acceleration, deceleration, turning, and lane change, which can be used to identify driving styles [28]. As mentioned above, this paper mainly focuses on longitudinal motion control, hence the adopted driving events are defined as [29]: 1) Event 1: 0-50km/h acceleration. In this event, the car is accelerated from 0 to 50 km/h.…”

“…The data can be overall classified into three groups according to the driver feedback as aggressive, conservative and moderate. These three driving styles are firstly defined as [29]- [34]: 1) Aggressive: Aggressive drivers exhibit frequent changes in throttle and brake pedal positions [32]. They drive with sharp and abrupt accelerations and decelerations, aiming at vehicle dynamic performance.…”

“…They drive with sharp and abrupt accelerations and decelerations, aiming at vehicle dynamic performance. This kind of behavior would result in higher fuel consumption and increased likelihood of accidents [29].…”

Characterization of Driver Neuromuscular Dynamics for Human–Automation Collaboration Design of Automated Vehicles

“…S one of the typical mechatronics applications in cyberphysical systems, automated vehicle has become an increasingly popular area of interest for both academia and industry [1][2][3][4][5][6]. Prior to transitioning to fully autonomous driving, highly automated driving will play an important role in the development of automated vehicle technologies [7][8][9]. We will see cars that are self-driving part of the time but must still be human-driven at other times.…”