Pierluigi Pisu

Role of Terrain Preview in Energy Management of Hybrid Electric Vehicles

Energy-management strategy plays a critical role in high fuel economy that modern hybrid electric vehicles can achieve, yet a lack of information about future driving conditions is one of the limitations of fulfilling the maximum fuel economy potential of hybrid vehicles. Today, with wider deployment of vehicle telematic technologies, prediction of future driving conditions, e.g., road grade, is becoming more realistic. This paper evaluates the potential gain in fuel economy if road grade information is integrated into the energy management of hybrid vehicles. Real-world road geometry information is utilized in power-management decisions by using both dynamic programming (DP) and a standard equivalent consumption minimization strategy (ECMS). At the same time, two baseline control strategies with no future information are developed and validated for comparison purposes. Simulation results show that road terrain preview enables fuel savings. The level of improvement depends on the cruising speed, control strategy, road profile, and the size of the battery.

Fault Detection for Robot Manipulators via Second-Order Sliding Modes

This paper presents a model-based fault detection (FD) and isolation scheme for rigid manipulators. A single fault acting on a specific actuator or on a specific sensor of the manipulator is detected (and, if possible, the exact location of the fault), and an estimation of the fault signal is performed. Input-signal estimator and output observers are considered in order to make the FD procedure possible. By using the suboptimal second-order sliding-mode (SOSM) algorithm to design the input laws of the observers, satisfactory stability properties of the observation error are established. The proposed algorithm is verified in simulation and experimentally on a COMAU SMART3-S2 robot manipulator.

A supervisory control strategy for series hybrid electric vehicles with two energy storage systems

Regardless of the topology of the hybrid electric vehicle, the essence of the HEV control problem is the instantaneous management of the power flows from the various energy storage devices to achieve the overall control objectives. In this paper we consider the case of energy management for a series hybrid powertrain configuration with two energy storage systems, i.e. batteries and ultracapacitors. The proposed power split algorithm is based on a modified instantaneous equivalent consumption minimization strategy (ECMS). The methodology could easily be applied to any kind of powertrain subject to the charge-sustaining constraint, and can readily be adapted to the specific characteristics of the components used in the powertrain.

Energy Management and Drivability Control Problems for Hybrid Electric Vehicles

Control strategies for hybrid-electric vehicles generally target several simultaneous objectives. The primary one is the minimization of the vehicle fuel consumption, while also attempting to minimize emissions and to maintain or enhance drivability. Hybrid electric vehicle improvements in fuel economy and emissions strongly depend on the energy management strategy and vehicle powertrain configuration. This paper presents an overview of state-of-the-art energy management approaches for the control of different hybrid electric powertrain configurations.

Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model

Advanced battery management systems rely on accurate cell- or module-level state-of-charge (SOC) information for effective control, monitoring, and diagnostics. Electrochemical models provide arguably the most accurate and detailed information about the SOC of lithium-ion cells. In this brief, two nonlinear observer designs are presented based on a reduced order electrochemical model. Both observers consist of a Luenberger term acting on nominal errors and a variable structure term for handling model uncertainty. Using Lyapunovs direct method, the design of the Luenberger term in each observer is formulated as a linear matrix inequality problem, whereas the variable structure term is designed assuming uncertainty bounds. Simulation and experimental studies are included to demonstrate the performance of the proposed observers.

Adaptive Threshold Based Diagnostics for Steer-By-Wire Systems

In the process of implementing modular fault diagnostics for X-by-Wire systems (Pisu et al., 2000), it was discovered that distinguishing between the model uncertainties and occurrence of faults is a real challenge. It is imperative to solve the problems that exist in using fixed observer and threshold, including the observer model mismatch, generated threshold misfit, diagnostic parameter uncertainties, and incorrect diagnostic output during high stress but normal vehicle operation. Compared to the fixed observer and threshold strategy, the improved adaptive threshold-based diagnostics described in this report are more robust and applicable because of the addition of adaptation into the diagnostic observer and threshold generator. The report also describes an approach to fault detection and isolation in the presence of model and parameter uncertainties. This approach has been successfully implemented in the NAVDyn (Non-Linear Analysis of Vehicle Dynamics) simulation model using Matlab/Simulink, and simulation results are provided to verify that the strategy and implementation are viable.

Robust Fault Diagnosis for a Horizontal Axis Wind Turbine

Abstract This paper presents an H∞ optimization-based approach for the detection and isolation of faults in a horizontal axis wind turbine. The primary residuals are generated from separate parity equations for each of the blade pitch and drivetrain subsystems. Then, a robust secondary residual filtering scheme is developed to remove undesirable cross coupling in fault-residual pairings while suppressing the effects of the strong nonlinearity of the aerodynamic rotor torque-speed relationships, the effects of unmodelled dynamics and noise. Solutions are obtained using H∞ and μ-synthesis tools. Sensor, actuator and system/parameter faults are diagnosed using specified information about the nature of the faults as well as sensor redundancy. Results are included demonstrating the diagnosis of individual faults specified with the benchmark model for wind turbine fault detection and isolation.

Attitude Tracking With Adaptive Rejection of Rate Gyro Disturbances

The classical attitude control problem for a rigid body is revisited under the assumption that measurements of the angular rates obtained by means of rate gyros are corrupted by harmonic disturbances, a setup of importance in several aerospace applications. This note extends previous methods developed to compensate bias in angular rate measurements by accounting for a more general class of disturbances, and by allowing uncertainty in the inertial parameters. By resorting to adaptive observers designed on the basis of the internal model principle, it is shown how converging estimates of the angular velocity can be used effectively in a passivity-based controller yielding global convergence within the chosen parametrization of the group of rotations. Since a persistence of excitation condition is not required for the convergence of the state estimates, only an upper bound on the number of distinct harmonic components of the disturbance is needed for the applicability of the method.

Control Strategies for Parallel Hybrid Electric Vehicles

Abstract This paper presents the development and the implementation of energy management algorithms for the control of a parallel hybrid-electric sport-utility vehicle. Two strategies based on two different control techniques have been prototyped, simulated and analyzed. In order to compare their performance in terms of fuel consumption improvement and emissions control, the algorithms are evaluated using a simulation model of the hybrid powertrain in the Matlab/Simulink environment. The algorithms have also been implemented in the ASCET -SD environment on-board a prototype SUV based on the Ford Explorer platform. This paper summarizes the evaluation phase of the program, and describes the energy management algorithms and their effectiveness in the simulation environment.

Vehicle chassis monitoring system

Abstract Fault detection and isolation is becoming one of the most important issues in vehicle control system design. The development of automotive systems shows an increasing integration of electronic sensors, microprocessors and actuators for improving the efficiency, reliability and performance of components as the engine, the suspension and the brakes, thus increasing the need for fault diagnosis. This paper presents the design of a new model-based fault diagnosis method for monitoring the vehicle chassis performance. A brake-by-wire system with electronic pedal and electrical brakes is considered in more detail showing the fault diagnosis methodology.