Minimizing residential distribution system operating costs through intelligently scheduled plug-in hybrid electric vehicle charging

Rising fuel prices and environmental concerns are threatening the stability of current electrical grid systems. These factors are pushing the automobile industry towards more effcient, hybrid vehicles. Current trends show petroleum is being edged out in favor of electricity as the main vehicular motive force. The proposed methods create an optimized charging control schedule for all participating Plug-in Hybrid Electric Vehicles in a distribution grid. The optimization will minimize daily operating costs, reduce system losses, and improve power quality. This requires participation from Vehicle-to-Grid capable vehicles, load forecasting, and Locational Marginal Pricing market predictions. Vehicles equipped with bidirectional chargers further improve the optimization results by lowering peak demand and improving power quality.

Development of Hardware-In-the-Loop Simulation System for Hybrid Electric Vehicle Study

The development of embedded control systems for a Hybrid Electric Vehicle (HEV) is a challenging task due to the multidisciplinary nature of HEV powertrain and its complex structures. Hardware-In-the-Loop (HIL) simulation provides an open and convenient environment for the modeling, prototyping, testing and analyzing HEV control systems. This thesis focuses on the development of such a HIL system for the hybrid electric vehicle study. The hardware architecture of the HIL system, including dSPACE eDrive HIL simulator, MicroAutoBox II and MotoTron Engine Control Module (ECM), is introduced. Software used in the system includes dSPACE Real-Time Interface (RTI) blockset, Automotive Simulation Models (ASM), Matlab/Simulink/Stateflow, Real-time Workshop, ControlDesk Next Generation, ModelDesk and MotoHawk/MotoTune. A case study of the development of control systems for a single shaft parallel hybrid electric vehicle is presented to summarize the functionality of this HIL system.

STUDY OF HYBRID ELECTRIC VEHICLE BATTERY MODELING AND CONTROL USING AUTONOMIE

This report presents the research results of battery modeling and control for hybrid electric vehicles (HEV). The simulation study is conducted using plug-and-play powertrain and vehicle development software, Autonomie. The base vehicle model used for testing the performance of battery model and battery control strategy is the Prius MY04, a power-split hybrid electric vehicle model in Autonomie. To evaluate the battery performance for HEV applications, the Prius MY04 model and its powertrain energy flow in various vehicle operating modes are analyzed. The power outputs of the major powertrain components under different driving cycles are discussed with a focus on battery performance. The simulation results show that the vehicle fuel economy calculated by the Autonomie Prius MY04 model does not match very well with the official data provided by the department of energy (DOE). It is also found that the original battery model does not consider the impact of environmental temperature on battery cell capacities. To improve battery model, this study includes battery current loss on coulomb coefficient and the impact of environmental temperature on battery cell capacity in the model. In addition, voltage losses on both double layer effect and diffusion effect are included in the new battery model. The simulation results with new battery model show the reduced fuel economy error to the DOE data comparing with the original Autonomie Prius MY04 model.

MODELING AND HARDWARE-IN-THE-LOOP SIMULATION OF POWER-SPLIT HYBRID ELECTRIC VEHICLES

Conventional vehicles are creating pollution problems, global warming and the extinction of high density fuels. To address these problems, automotive companies and universities are researching on hybrid electric vehicles where two different power devices are used to propel a vehicle. This research studies the development and testing of a dynamic model for Prius 2010 Hybrid Synergy Drive (HSD), a power-split device. The device was modeled and integrated with a hybrid vehicle model. To add an electric only mode for vehicle propulsion, the hybrid synergy drive was modified by adding a clutch to carrier 1. The performance of the integrated vehicle model was tested with UDDS drive cycle using rule-based control strategy. The dSPACE Hardware-In-the-Loop (HIL) simulator was used for HIL simulation test. The HIL simulation result shows that the integration of developed HSD dynamic model with a hybrid vehicle model was successful. The HSD model was able to split power and isolate engine speed from vehicle speed in hybrid mode.

Predictive control of hybrid vehicle powertrain for intelligent energy management

This thesis studies the minimization of the fuel consumption for a Hybrid Electric Vehicle (HEV) using Model Predictive Control (MPC). The presented MPC – based controller calculates an optimal sequence of control inputs to a hybrid vehicle using the measured plant outputs, the current dynamic states, a system model, system constraints, and an optimization cost function. The MPC controller is developed using Matlab MPC control toolbox. To evaluate the performance of the presented controller, a power-split hybrid vehicle, 2004 Toyota Prius, is selected. The vehicle uses a planetary gear set to combine three power components, an engine, a motor, and a generator, and transfer energy from these components to the vehicle wheels. The planetary gear model is developed based on the Willis’s formula. The dynamic models of the engine, the motor, and the generator, are derived based on their dynamics at the planetary gear. The MPC controller for HEV energy management is validated in the MATLAB/Simulink environment. Both the step response performance (a 0 – 60 mph step input) and the driving cycle tracking performance are evaluated. Two standard driving cycles, Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET), are used in the evaluation tests. For the UDDS and HWFET driving cycles, the simulation results, the fuel consumption and the battery state of charge, using the MPC controller are compared with the simulation results using the original vehicle model in Autonomie. The MPC approach shows the feasibility to improve vehicle performance and minimize fuel consumption.

DEVELOPMENT OF THE ECOCAR 3 PROPOSAL AND GUIDELINES FOR MODELING AND DESIGN IN YEAR ONE OF ECOCAR 3

This report summarizes the work done for the Vehicle Powertrain Modeling and Design Problem Proposal portion of the EcoCAR3 proposal as specified in the Request for Proposal from Argonne National Laboratory. The results of the modeling exercises presented in the proposal showed that: An average conventional vehicle powered by a combustion engine could not meet the energy consumption target when the engine was sized to meet the acceleration target, due the relatively low thermal efficiency of the spark ignition engine. A battery electric vehicle could not meet the required range target of 320 km while keeping the vehicle weight below the gross vehicle weight rating of 2000 kg. This was due to the low energy density of the batteries which necessitated a large, and heavy, battery pack to provide enough energy to meet the range target. A series hybrid electric vehicle has the potential to meet the acceleration and energy consumption parameters when the components are optimally sized. A parallel hybrid electric vehicle has less energy conversion losses than a series hybrid electric vehicle which results in greater overall efficiency, lower energy consumption, and less emissions. For EcoCAR3, Michigan Tech proposes to develop a plug-in parallel hybrid vehicle (PPHEV) powered by a small Diesel engine operating on B20 Bio-Diesel fuel. This architecture was chosen over other options due to its compact design, lower cost, and its ability to provide performance levels and energy efficiency that meet or exceed the design targets. While this powertrain configuration requires a more complex control system and strategy than others, the student engineering team at Michigan Tech has significant recent experience with this architecture and has confidence that it will perform well in the events planned for the EcoCAR3 competition.

SVPWM SWITCHING PATTERN FOR Z-SOURCE INVERTER, SIMULATION AND APPLICATION

The voltage source inverter (VSI) and current voltage source inverter (CSI) are widely used in industrial application. But the traditional VSIs and CSIs have one common problem: can’t boost or buck the voltage come from battery, which make them impossible to be used alone in Hybrid Electric Vehicle (HEV/EV) motor drive application, other issue is the traditional inverter need to add the dead-band time into the control sequence, but it will cause the output waveform distortion. This report presents an impedance source (Z-source network) topology to overcome these problems, it can use one stage instead of two stages (VSI or CSI + boost converter) to buck/boost the voltage come from battery in inverter system. Therefore, the Z-source topology hardware design can reduce switching element, entire system size and weight, minimize the system cost and increase the system efficiency. Also, a modified space vector pulse-width modulation (SVPWM) control method has been selected with the Z-source network together to achieve the best efficiency and lower total harmonic distortion (THD) at different modulation indexes. Finally, the Z-source inverter controlling will modulate under two control sequences: sinusoidal pulse width modulation (SPWM) and SVPWM, and their output voltage, ripple and THD will be compared.