Modeling and Integrated Optimization of Power Split and Exhaust Thermal Management on Diesel Hybrid Electric Vehicles

Abstract

To simultaneously achieve high fuel efficiency and low emissions in a diesel hybrid electric vehicle (DHEV), it is necessary to optimize not only power split but also exhaust thermal management for emission aftertreatment systems. However, how to coordinate the power split and the exhaust thermal management to balance fuel economy improvement and emissions reduction remains a formidable challenge. In this paper, a hierarchical model predictive control (MPC) framework is proposed to coordinate the power split and the exhaust thermal management. The method consists of two parts: a fuel and thermal optimized controller (FTOC) combining the rule-based and the optimization-based methods for power split simultaneously considering fuel consumption and exhaust temperature, and a fuel post-injection thermal controller (FPTC) for exhaust thermal management with a separate fuel injection system added to the exhaust pipe. Additionally, preview information about the road grade is introduced to improve the power split by a fuel and thermal on slope forecast optimized controller (FTSFOC). Simulation results show that the hierarchical method (FTOC + FPTC) can reach the optimal exhaust temperature nearly 40 s earlier, and its total fuel consumption is also reduced by 8.9%, as compared to the sequential method under a world light test cycle (WLTC) driving cycle. Moreover, the total fuel consumption of the FTSFOC is reduced by 5.2%, as compared to the fuel and thermal on sensor-information optimized controller (FTSOC) working with real-time road grade information.

Calculation of Intake Oxygen Concentration through Intake CO2 Measurement and Evaluation of Its Effect on Nitrogen Oxide Prediction Accuracy in a Heavy-Duty Diesel Engine

“…This interest can be confirmed by several studies that have recently been reported in the literature. Some examples are provided in [1][2][3][4][5][6], in which the authors show the advantages of model-based control for several applications, including vehicle speed management, hybrid powertrain energy management and engine management. In [1], the authors proposed a dynamic programming-based optimal speed-planning algorithm for heavy-duty vehicles based on V2X (vehicle-to-everything) communication and look-ahead function.…”

“…In [2], the authors proposed a predictive driver-coaching system for fuel-economy driving in hybrid electric trucks based on upcoming static-map and dynamic traffic data. In [3], the authors described a hierarchical-model predictive-control framework that can be used to coordinate the power split and the thermal management of the exhaust in diesel hybrid electric vehicles, with the aim of reducing fuel consumption and optimizing the exhaust temperature. In [4], the authors applied a model-based technique to identify the optimal combustion parameters for an 8.42 L diesel engine by exploiting artificial neural networks and polynomial functions.…”