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Energy management is one of the key challenges for the development of Hybrid Electric Vehicle (HEV) due to its complex powertrain structure. Hardware-In-the-Loop (HIL) simulation provides an open software architecture which enables rapid prototyping HEV energy management system. This paper presents the investigation of the energy management system for a single shaft parallel hybrid electric vehicle using dSPACE eDrive HIL system. The parallel hybrid electric vehicle, energy management system, and low-level Electronic Control Unit (ECU) were modeled using dSPACE Automotive Simulation Models and dSPACE blocksets. Vehicle energy management is achieved by a vehicle-level controller called hybrid ECU, which controls vehicle operation mode and torque distribution among Internal Combustion Engine (ICE) and electric motor. The individual powertrain components such as ICE, electric motor, and transmission are controlled by low-level ECUs.

This paper presents the development of a Stochastic Knock Detection (SKD) method for combustion knock detection in a spark-ignition engine using a model based design approach. The SKD set consists of a Knock Signal Simulator (KSS) as the plant model for the engine and a Knock Detection Module (KDM). The KSS as the plant model for the engine generates cycle-to-cycle accelerometer knock intensities following a stochastic approach with intensities that are generated using a Monte Carlo method from a lognormal distribution whose parameters have been predetermined from engine tests and dependent upon spark-timing, engine speed and load. The lognormal distribution has been shown to be a good approximation to the distribution of measured knock intensities over a range of engine conditions and spark-timings for multiple engines in previous studies.

This paper studies model predictive control algorithm for Hybrid Electric Vehicle (HEV) energy management to improve HEV fuel economy. In this paper, Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of power-split HEV. A dedicated model predictive control method is developed to predict vehicle speed, battery state of charge (SOC), and engine fuel consumption. The power output from the engine, motor, and the mechanical brake will be adjusted to match driver's power request at the end of the prediction window while minimizing fuel consumption. The controller model is built on Matlab® MPC toolbox® and the simulations are based on MY04 Prius vehicle model using Autonomie®, a powertrain and fuel economy analysis software, developed by Argonne National Laboratory. The study compares the performance of MPC and conventional rule-base control methods.

The ability to operate a spark-ignition (SI) engine near the knock limit provides a net reduction of engine fuel consumption. This work presents a real-time knock control system based on stochastic knock detection (SKD) algorithm. The real-time stochastic knock control (SKC) system is developed in MATLAB Simulink, and the SKC software is integrated with the production engine control strategy through ATI's No-Hooks. The SKC system collects the stochastic knock information and estimates the knock level based on the distribution of knock intensities fitting to a log-normal (LN) distribution. A desired knock level reference table is created under various engine speeds and loads, which allows the SKC to adapt to changing engine operating conditions. In SKC system, knock factor (KF) is an indicator of the knock intensity level. The KF is estimated by a weighted discrete FIR filter in real-time.