Torque control strategy and optimization for fuel consumption and emission reduction in parallel hybrid electric vehicles

Abstract

To reduce fuel consumption and exhaust emissions in hybrid electric vehicles (HEVs), it is important to develop a well-organized energy management system (EMS). This paper proposes a torque control strategy coupled with optimization for a parallel HEV. A torque control strategy is developed first. In particular, a function to control the driving condition, called the internal combustion engine (ICE) torque control function, is introduced. This function controls the driving conditions (electric motor (EM) driving, ICE driving, and ICE driving assisted by EM) for reducing fuel consumption and exhaust emissions. This function depends on several design variables that should be optimized. Numerical simulation of HEV using Matlab/Simulink is so computationally intensive that a sequential approximate optimization (SAO) using a radial basis function network (RBF) is adopted to determine the optimal values of these design variables. As the result, the optimal ICE torque control function is determined with a small number of simulation runs. In this paper, CO 2 and NO x emissions are minimized simultaneously for reducing the fuel consumption and exhaust emission. Through numerical simulations using typical driving cycles, the trade-off between CO 2 and NO x emissions is clarified and the validity of the proposed torque control strategy coupled with the proposed optimization is examined.

Nonlinear prediction using radial basis function network incorporating coordinate transformation

“…Worldwide harmonized Light duty driving Test Cycle (WLTC) is the standard driving cycle for fuel consumption and emissions in the world (Kitayama et al, 2015). In this benchmark, the vehicle speed is predicted by using the torque and the engine speed.…”

Shape optimization of screwdriver handle for improving grasping comfort of precision and power grips

“…where α represents the weight coefficient, and p is the parameter that defines the Lp space. The p was set as 4 based on Kitayama and Natsume (2014) and Kitayama, Saikyo, Nishio, and Tsutsumi, (2015).…”

Kinematic and Dynamic Response of a Novel Engine Mechanism Design Driven by an Oscillation Arm

“…There are also other types of analyses developed in the FEA domain: static analysis, vibration analysis, fatigue analysis and CFD (Computed Fluid Dynamics) used for analysis of fluid flows prediction based on some mathematical and numerical methods [33][34][35]. Recently, these were extended to some novel challenging domains of interest which start to embed/develop numerical simulations accordingly: electromagnetic field or electrical field [36][37][38][39][40][41].…”