Hybrid Optimal Theory and Predictive Control for Power Management in Hybrid Electric Vehicle

This paper presents a nonlinear-model based hybrid optimal control technique to compute a suboptimal power-split strategy for power/energy management in a parallel hybrid electric vehicle (PHEV). The power-split strategy is obtained as model predictive control solution to the power management control problem (PMCP) of the PHEV, i.e., to decide upon the power distribution among the internal combustion engine, an electric drive, and other subsystems. A hierarchical control structure of the hybrid vehicle, i.e., supervisory level and local or subsystem level is assumed in this study. The PMCP consists of a dynamical nonlinear model, and a performance index, both of which are formulated for power flows at the supervisory level. The model is described as a bi-modal switched system, consistent with the operating mode of the electric ED. The performance index prescribing the desired behavior penalizes vehicle tracking errors, fuel consumption, and frictional losses, as well as sustaining the battery state of charge (SOC). The power-split strategy is obtained by first creating the embedded optimal control problem (EOCP) from the original bi-modal switched system model with the performance index. Direct collocation is applied to transform the problem into a nonlinear programming problem. A nonlinear predictive control technique (NMPC) in conjunction with a sequential quadratic programming solver is used to compute suboptimal numerical solutions to the PMCP. Methods for approximating the numerical solution to the EOCP with trajectories of the original bi-modal PHEV are also presented in this paper. The usefulness of the approach is illustrated via simulation results on several case studies

Fault Detection in Surface PMSM with Applications to Heavy Hybrid Vehicles

This report explores detecting inter-turn short circuit (ITSC) faults in surface permanent magnet synchronous machines (SPMSM). ITSC faults are caused by electrical insulation failures in the stator windings and can lead to shorts to ground and even fires. This report proposes methods for detecting these faults using a moving horizon observer (MHO) to reduce the chance of electrical shocks and fires. Specifically, this report constructs a MHO for ITSC fault detection in SPMSM. ITSC fault tolerant control is investigated for a 2004 Toyota Prius hybrid vehicle having a traction SPMSM. Once the supervisory-level powertrain power flow control becomes aware of the presence of a fault and its degree from the MHO, the control (i) reduces the maximum possible vehicle speed to ensure SPMSM thermal constraints are not violated and (ii) switches to a traction motor input-output power efficiency appropriate for the degree of fault. These steps are taken during a fault rather than shutting down the traction motor to provide a “limp home” capability. The traction motor cannot simply be turned off because its rotation is not independent of drive wheel rotation. The control is demonstrated by simulating the Prius over a 40 s drive velocity profile with faults levels of 0.5%, 1%, 2%, and 5% detected at the midpoint of the profile. For comparison, the Prius is also simulated without a traction motor fault. Results show that the control reduced vehicle velocity upon detection of a fault to appropriate safe values. Further, the challenges of ITSC fault tolerant control for heavy hybrid vehicles are examined. This work is partially supported by the Department of Energy, Award No. DE-EE0005568. The authors would like to acknowledge the support of Greg Shaver and the Hoosier Heavy Hybrid Center of Excellence. S. Johnson, R. DeCarlo, and S. Pekarek are with the Department of Electrical and Computer Engineering at Purdue University, 610 Purdue Mall, West Lafayette, IN 47907 (email: [email protected], [email protected], [email protected]). R. Meyer is with the Department of Mechanical and Aerospace Engineering at Western Michigan University, 1903 West Michigan Avenue, Kalamazoo, MI 49008 (email: [email protected])

A Comparison of Permanent Magnet and Wound Rotor Synchronous Machines for Portable Power Generation

Permanent magnet and wound rotor synchronous machines (PMSMs and WRSMs) are often used in diesel engine-based portable power generation systems. In these applications, there is a growing desire to improve machine efficiency in order to reduce fossil fuel requirements. In addition, there is a desire to reduce mass to improve mobility. To attempt to address these competing performance objectives, a system analyst is confronted with numerous choices, including machine type (PM or WR), converter architecture (active/passive), and control. Herein, to assist the analyst, design tools capable of performing automated multi-objective optimization of PMSMs and WRSMs connected to both active and passive rectifiers are described. The tools are then used to derive tradeoffs between mass and efficiency for a 3 kW application

Gas Turbine Engine Behavioral Modeling

This paper develops and validates a power flow behavioral model of a gas tur- bine engine with a gas generator and free power turbine. “Simple” mathematical expressions to describe the engine’s power flow are derived from an understand- ing of basic thermodynamic and mechanical interactions taking place within the engine. The engine behavioral model presented is suitable for developing a supervisory level controller of an electrical power system that contains the en- gine connected to a generator and a large interconnection of many components, e.g., a naval ship power system powered by gas turbine engines. First principles engine models do not lend themselves to the preceding control development be- cause of their high granularity. The basis of the behavioral model development is the balance of energy flow across engine components; power flow is obtained by taking the time derivative of the energy flow. The behavioral model of a spe- cific engine utilizes constants and empirical fits of power conversion efficiencies obtained from data collected from a high-fidelity engine simulator. Behavioral models for a GE LM2500 and an engine similar to a GE T700 are constructed; the 2-norm normalized error between the simulator and behavioral model out- puts for both engines is 3.5% or less