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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.

Thanks to the actuation flexibility of their systems, electric vehicles with individual powertrains, including in-wheel and on-board motors, are a very popular research topic amongst various types of electrified powertrain architectures. The introduction of the individual electric powertrain provides great capacity for improvement of the vehicle’s energy efficiency and control performance. However, it also poses tremendous challenges concerning vehicle safety, due to the complex system dynamics and cooperation mechanisms between multiactuators. For an electric vehicle with independently controlled motors, because of design and manufacturing factors, the steady-state error of each motor output torque, and the flexibilities and nonlinear backlash of left and right drivetrains, can be different. This results in asymmetrical output characteristics of electric powertrain systems on the same axle.