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This paper studies the bi-directional power flow control between Plug-in Electric Vehicles (PEVs) and an electrical grid. A grid-tied charging system that enables both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) charging/discharging is modeled using SimPowerSystems in Matlab/Simulink environment. A bi-directional AC-DC converter and a bi-directional DC-DC buck-boost converter are integrated to charge and discharge PEV batteries. For AC-DC converter control, Predictive Current Control (PCC) strategy is employed to enable grid current to reach a reference current after one modulation period. In addition, Phase Lock Loop (PLL) and a band-stop filter are designed to lock the grid voltage phase and reduce harmonics. Bi-directional power flow is realized by controlling the mode of the DC-DC converter. Simulation tests are conducted to evaluate the performance of this bi-directional charging system.

The vehicle driving cycles affect the performance of a hybrid vehicle control strategy, as a result, the overall performance of the vehicle, such as fuel consumption and emission. By identifying the driving cycles of a vehicle, the control system is able to dynamically change the control strategy (or parameters) to the best one to adapt to the changes of vehicle driving patterns. This paper studies the supervised driving cycle recognition using pattern recognition approach. With pattern recognition method, a driving cycle is represented by feature vectors that are formed by a set of parameters to which the driving cycle is sensitive. The on-line driving pattern recognition is achieved by calculating the feature vectors and classifying these feature vectors to one of the driving patterns in the reference database. To establish reference driving cycle database, the representative feature vectors for four federal driving cycles are generated using feature extraction method.

This paper studies the hardware-in-the-loop (HiL) design of a power-split hybrid electric vehicle (HEV) for the research of HEV lithiumion battery aging. In this paper, an electrochemical model of a lithium-ion battery pack with the characteristics of battery aging is built and integrated into the vehicle model of Autonomie® software from Argonne National Laboratory. The vehicle model, together with the electrochemical battery model, is designed to run in a dSPACE real-time simulator while the powertrain power distribution is managed by a dSPACE MicroAutoBoxII hardware controller. The control interface is designed using dSPACE ControlDesk to monitor the real-time simulation results. The HiL simulation results with the performance of vehicle dynamics and the thermal aging of the battery are presented and analyzed.

Vehicle-to-Grid (V2G) service has a potential to improve the reliability and stability of the electrical grid due to the ability of providing bi-directional power flow from/to the grid. However, frequent charging/discharging may impact the battery lifetime. This paper presents the analysis of battery degradation in three scenarios. In the first scenario, different battery capacities are considered. In the second scenario, the battery degradation with various depth of discharge (DOD) are studied. In the third scenario, the capacity loss due to different charging regime are compared. The charging/discharging of plug-in electric vehicles (PEVs) are simulated in a single-phase microgrid system integrated with a photovoltaics (PV) farm, an energy storage system (ESS), and ten electric vehicle service equipment (EVSE). The battery degradation model is an energy throughput model, which is developed based on the Arrhenius equation and a power law relationship between time and capacity fading.

This paper studies the nonlinear model predictive control for a power-split Hybrid Electric Vehicle (HEV) power management system to improve the fuel economy. In this paper, a physics-based battery model is built and integrated with a base HEV model from Autonomie®, a powertrain and vehicle model architecture and development software from Argonne National Laboratory. The original equivalent circuit battery model from the software has been replaced by a single particle electrochemical lithium ion battery model. A predictive model that predicts the driver’s power request, the battery state of charge (SOC) and the engine fuel consumption is studied and used for the nonlinear model predictive controller (NMPC). A dedicated NMPC algorithm and its solver are developed and validated with the integrated HEV model. The performance of the NMPC algorithm is compared with that of a rule-based controller.

For spark-ignition gasoline engines operating under the wide speed and load conditions required for light duty vehicles, ignition quality limits the ability to minimize fuel consumption and NOx emissions via dilution under light and part load conditions. In addition, during transients including tip-outs, high levels of dilution can occur for multiple combustion events before either the external exhaust gas can be adjusted and cleared from the intake or cam phasing can be adjusted for correct internal dilution. Further improvement and a thorough understanding of the impact of the ignition system on combustion near the dilution limit will enable reduced fuel consumption and robust transient operation. To determine and isolate the effects of multiple parameters, a variable output ignition system (VOIS) was developed and tested on a 3.5L turbocharged V6 homogeneous charge direct-injection gasoline engine with two spark plug gaps and three ignition settings.

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.

This paper investigates the aging performance of the lithium ion cobalt oxide battery pack of a single shaft parallel hybrid electric vehicle (HEV) under different ambient temperatures. Varying ambient temperature of HEVs results in different battery temperature and then leads to different aging performance of the battery pack. Battery aging is reflected in the increasing of battery internal resistance and the decreasing of battery capacity. In this paper, a single shaft parallel hybrid electric vehicle model is built by integrating Automotive Simulation Model (ASM) from dSPACE and AutoLion-ST battery model from ECPower to realize the co-simulation of HEV powertrain in the common MATLAB/Simulink platform. The battery model is a physics-based and thermally-coupled battery (TCB) model, which enables the investigation of battery capacity degradation and aging. Standard driving cycle with differing ambient temperatures is tested using developed HEV model.

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.