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Journal Article

Estimation on the Location of Peak Pressure at Quick Start of HEV Engine Employing Ion Sensing Technology

In this paper an estimation method on location of peak pressure (LPP) employing flame ionization measurement, with the spark plug as a sensor, was discussed to achieve combustion parameters estimation at quick start of HEV engines. Through the cycle-based ion signal analysis, the location of peak pressure can be extracted in individual cylinder for the optimization of engine quick start control of HEV engine. A series of quick start processes with different cranking speed and engine coolant temperature are tested for establishing the relationship between the ion signals and the combustion parameters. An Artificial Neural Network (ANN) algorithm is used in this study for estimating these two combustion parameters. The experiment results show that the location of peak pressure can be well established by this method.
Technical Paper

Lateral State Estimation for Lane Keeping Control of Electric Vehicles Considering Sensor Sampling Mismatch Issue

Vehicle lateral states such as lateral distance at a preview point and heading angle are indispensable for lane keeping control systems, and such states are normally estimated by fusing signals from an onboard vision system and inertial sensors. However, the sampling rates and measurement delays are different between the two kinds of sensing devices. Most of the conventional methods simply neglect measurement delay and reduce sampling rate of the estimator to adapt to the slow sensors/devices. However, the estimation accuracy is deteriorated, especially considering the delay of visual signals may not be constant. In case of electric vehicles, the actuators for steering and traction are motors that have high control frequency. Therefore, the frequency of vehicle state feedback may not match the control frequency if the estimator is infrequently updated. In this paper, a multi-rate estimation algorithm based on Kalman filter is proposed to provide lateral states with high frequency.
Technical Paper

An Experimental Study of the Effects of Coolant Temperature on Particle Emissions from a Dual Injection Gasoline Engine

Euro VI emission standards have set a very strict limitation on particulate matter emissions of Gasoline Direct Injection (GDI) engine. It is difficult for GDI engine to meet the Euro VI PN regulation (6×1011#/km) without a series of complicated after-treatment devices such as Gasoline Particulate Filter (GPF). Previous research shows that GDI vehicles under cold start condition account for more than 50% of both particle number and mass emissions during the entire NEDC driving cycle. Dual Injection Gasoline engine is based on the GDI engine by adding a set of port fuel injection system. The good mixing characteristics of the port fuel injection system can help to reduce the particulate matter emissions of the GDI engine during the cold start condition.
Technical Paper

Effects of Spark Timing with Other Engine Operating Parameters on the Particulate Emissions of a Dualinjection Gasoline Engine During Warm-up Conditions

Gasoline direct injection (GDI) has been a mainstream technology due to its higher thermal efficiency and better power output. However, with increasingly stringent emission regulations introduced (EURO VI PN limits: 6 x l011#/km), high particulate matter (PM) emission of GDI engine has been a serious problem that limits its further development. Previous studies have found that cold-start and warm-up operation conditions play the dominant role in engine-out particulate emissions. In this paper, emission characteristics during the cold-start were first studied by controlling the coolant temperature. A Cambustion DMS500 fast particle spectrometer was employed to analyze the PM emissions. In order to reduce the engine-out emissions of cold-start, a dual injection system which combines port-fuel-injection (PFI) and direct-injection (DI) was applied in a four-cylinder gasoline engine.
Technical Paper

Design and Analysis of a Novel Magnetorheological Fluid Dual Clutch for Electric Vehicle Transmission

A novel magnetorheological fluid dual clutch (MRFDC) for electric vehicle transmission is proposed in this article. The structure was based on the MR fluid clutch and traditional dual clutch equipped on internal combustion engine vehicle. Therefore the MRFDC combines the advantages of MR fluid clutch and dual clutch transmission (DCT) to achieve high control accuracy and fast response. The structure of MRFDC was designed by Unigraphics (UG) three-dimensional (3D) modeling software. Then, finite element analysis (FEA) for magnetic field was conducted by ANSYS under different applied currents from 0.1A to 1A with 0.1A space to obtain the relation between the applied current and magnetic field. In this article, Herschel-Bulkley model is used to predict the MR fluid behavior because of the high shear rate of MR fluid.
Technical Paper

Optimization-Based Control Strategy for Large Hybrid Electric Vehicles

Electric vehicles (EVs) have become a hot research topic due to the petroleum crisis and air pollution issues, and Hybrid EVs (HEVs) equipped with engines and motors are popular nowadays due to their advantage over Pure EVs. The energy distribution between the engine and the motor is the major task of the control strategy or energy management for HEVs. Rule-based and optimization-based approaches are developed in this area, but not much work has been done for large-size super-capacitor (SC) equipped HEVs, like Hybrid buses. In this paper, a new optimization-based control strategy for a hybrid bus equipped with SCs as the energy regeneration system is presented. Considering the driving patterns of a bus that is of frequent accelerations and decelerations, it is proposed to characterize each time instant by its speed and acceleration, and the energy distribution is optimized based on these two state variables.
Technical Paper

Energy Management Optimization for Plug-In Hybrid Electric Vehicles Based on Real-World Driving Data

Excellent energy consumption performance of a plug-in hybrid electric vehicle (PHEV) is usually attributed to its hybrid drive mode. However, the factors including vehicle performance, driver behavior and traffic status have been shown to cause unsatisfactory performance. This phenomenon leads to a necessity of study on energy consumption control strategies under real-world driving conditions. This paper proposes a new approach for energy management optimization of plug-in hybrid electric vehicles based on real-world driving data for two purposes. One is for improving the energy consumption of PHEV under real-world driving conditions and the other is for reducing the computational complexity of optimization methods in simulation models. In this process, the paper collected real-world driving record data from 180 drivers within 6 months. Then the principal component analysis (PCA) was employed to extract and define the hidden factors from the initial real-world driving data.
Technical Paper

Robust Speed Synchronization Control for an Integrated Motor-Transmission Powertrain System with Feedback Delay

Motor speed synchronization is important in gear shifting of emerging clutchless automated manual transmissions for battery electric vehicles (BEV) and other kinds of parallel shaft-based powertrains for hybrid electric vehicles (HEV). Difficulties of the problem mainly come from random delay induced by network communication and unknown load torques from air drag, oil drag, and friction torques, etc. To deal with these two factors, this paper proposes a robust speed synchronization controller based on act-and-wait control and disturbance observer. The former is a kind of periodical controller specially for regulating problems with feedback delay while the latter is a technique for active disturbance rejection. Firstly, the dynamic model of the motor shaft is formulated, and the system parameters are offline identified. The speed tracking problem is then transformed into a regulating one.
Technical Paper

System Characteristics of Direct and Secondary Loop Heat Pump for Electrical Vehicles

The electricity energy consumption for passenger cabin heating can drastically shorten the driving range for electric vehicles in cold climates. Mobile heat pump system is considered as an effective method to improve heating efficiency. This study investigates the system characteristics of mobile heat pump systems for electrical vehicle application. Based on KULI thermal management software, simulation models including HFC-R134a direct heat pump (DHP) and secondary loop heat pump (SLHP) were developed. The secondary loop employed in the SLHP includes a coolant pump, an indoor heater core and a plate heat exchanger, instead of an indoor condenser in the DHP. The use of a secondary loop has advantages to improve air outlet temperature uniformity. The simulation models were verified by measured data obtained from calorimeter experiments. By adopting simulation models, the effects of indoor and outdoor temperatures on system performance and cycle characteristics were discussed.
Technical Paper

Gearshift Control Based on Fuzzy Logic of a Novel Two-Speed Transmission for Electric Vehicles

Using highly efficient powertrain is one of the most important and effective approaches to increase the driving distance of electric vehicles (EVs). In this paper, a novel two-speed dual-clutch transmission (DCT) is proposed. The transmission is comprised of two traditional friction clutches and two-stage planetary gear sets. One clutch connects the input sun gear and the other connects the input carrier. The Simulink models including an electric motor and two-speed DCT are established. Gearshift schedule based on fuzzy logic which reflects the driver’s intensions is adopted to improve the dynamic and economic performance of the novel transmission. The simulation model is built using MATLAB/Simulink® to validate the effectiveness of the proposed gearshift schedule compared with the conventional two-parameter gearshift schedule. Simulation results show that both the dynamic and economic performance of the novel DCT for EVs are improved with the proposed fuzzy logic gearshift schedule.