Generalization of Spectral Methods for High-Cycle Fatigue Analysis to Accommodate Non-Stationary Random Processes

Estimation of material fatigue life is an essential task in many engineering fields. When non-proportional loads are applied, the methodology to estimate fatigue life grows in complexity. Many methods have been proposed to solve this problem both in time and frequency domains. The former tends to give more accurate results, while the latter seems to be more computationally favorable. Until now, the focus of frequency-based methods has been limited to signals assumed to follow a stationary statistic process. This work proposes a generalization to the existing methods to accommodate non-stationary processes as well. A sensitivity analysis is conducted on the influence of the formulation’s hyper-parameters, followed by a numerical investigation on different signals and various materials to assert the robustness of the method.

Energy Optimal Routing of a Delivery Vehicle Fleet With Diverse Powertrains

In this paper, an algorithm framework is developed to find energy-optimal routes for a mixed fleet of delivery vehicles. The fleet could be composed of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and Internal Combustion Engine-powered conventional Vehicles (ICEVs) operating over the same service area from a common depot. Additionally, in the case of an HEV, the onboard energy management optimization determines the power split between the power sources on the vehicle based on the route information available. The framework presented in this paper takes into account information related to static conditions (such as topography, payload, and driving distance) and dynamic driving conditions (such as traffic incidents and traffic lights). The route optimization can then be done for various cost functions such as energy consumption, operating costs or maximizing goods throughput. The simulation results demonstrate elements of the route planning framework for benchmark grid problems and real-world road maps.

A Model Predictive Approach for the Coordination of Powertrain Control Systems

The design, calibration and integration of powertrain control algorithms has become significantly more complex in recent years, as the automotive industry faces increasing challenges in meeting consumer requirements and government regulations. Traditionally, the powertrain control engineering design process develops the engine and transmission controllers independently and then integrates them after an initial calibration. This process can lead to suboptimal performance and requires additional calibration and verification steps to improve the coordination of the various subsystems. This paper proposes a novel approach to achieve a systematic, high-level coordination, and optimization of the control strategy in an automotive powertrain system that will reduce overall calibration effort. Optimized set-points for engine and transmission controls are generated based on joint optimization of fuel consumption and drivability using Model Predictive Control to manage both continuous and discrete control variables. Simulation results confirm the control decisions made by the proposed coordinator match a well-calibrated production ECU with little tuning effort.

Adaptive Dynamics Learning for Small Fault Detection of Discrete-Time Nonlinear Uncertain Systems

This paper addresses the problem of small fault detection for discrete-time nonlinear uncertain systems. The problem is challenging due to (i) the considered system is subject to unstructured nonlinear uncertain dynamics; and (ii) the faults are considered to be “small” in the sense that system states and control inputs in faulty mode remain close to those in normal mode. To overcome these challenges, a novel adaptive dynamics learning based fault detection scheme is proposed. Specifically, an adaptive dynamics learning approach is first proposed to achieve the locally-accurate approximation of the system uncertain dynamics. Then, based on the learned knowledge, a novel residual system is designed by using the absolute measures of the change of the system dynamics resulting from the fault effect. An adaptive threshold is developed based on the residual system for real-time decision making, i.e., the fault is claimed to be detected when the associated residual signal becomes larger than the adaptive threshold. Rigorous analysis is performed to deduce the small fault detectability condition, which is shown to be significantly relaxed compared to those of existing fault detection methods. Extensive simulations have also been conducted to demonstrate the effectiveness and advantages of the proposed approach.

Sliding Mode Impedance and Stiffness Control of a Pneumatic Cylinder

Pneumatic double acting cylinders are able to provide inherent stiffness and force control for compliant motion control applications. Impedance control methods allow for a broad spectrum of mechanical properties of actuators to be achieved. The range of this spectrum can be increased by simultaneously controlling the actuator’s inherent stiffness and impedance, a concept explored in this paper. Presented here is a sliding mode impedance and stiffness controller for a servo-pneumatic double acting cylinder. Two proportional servo-valves are employed for simultaneous control of the virtual impedance and inherent stiffness of the pneumatic cylinder. Experimental results of tracking trajectories and contact are reported and discussed with respect to different approaches in the literature.

An Experimental Study of Longitudinal Tire Relaxation Constants for Vehicle Traction Dynamics Modeling

Existing literature on vehicle traction dynamics were reviewed for a variety of vehicle and tire dynamic models, some of which consider the pneumatic tires’ relaxation as a property of vehicle transient dynamics. In general, unlike the lateral relaxation counterpart, the longitudinal tire relaxation characteristics were mostly overlooked in tire transient dynamics modeling. As a continuation of the analytical study published in the 2018 DSCC Proceedings, the co-authors of this paper present an experimental study of the longitudinal tire relaxation characteristics of a Continental MPT 81 tire. Experimental results were obtained by conducting tests on an MTS Flat-Trac LTR tire testing machine. The experimental data is analyzed to investigate longitudinal tire relaxation characteristics as they relate to changes of tire conditions. The goal is to verify and refine the existing models suggested in the literature; as well as, discuss advantages and disadvantages of different test procedures and tire testing equipment. In particular, the paper investigates the longitudinal tire relaxation constant variation due to changes of wheel velocity, tire inflation pressure, and sine oscillations of tire slippage in the time and frequency domains. The paper concludes on the influence of the longitudinal tire relaxation constants on the tire/vehicle traction dynamics modeling.

Experimental Validations on Vision-Based Path Tracking With Preview Four Wheel Steering Control

In this paper, a vision-based path-tracking control strategy using four-wheel steering (4WS) is experimentally investigated via an automated ground vehicle (AGV). A low-cost monocular camera is used to continuously perceive the upcoming lane boundaries via capturing the preview road image frames. Based on the applied image processing algorithms, the vehicle lateral offset error with respect to the road center line and the heading angle error with respect to the road curvature are calculated in real time for the control purpose. The 4WS path-tracking controller is designed to minimize the two path-tracking errors of the AGV. The AGV with the 4WS system is utilized to perform the experimental tests on road to validate the path-tracking control design. For comparison, the road test is also conducted for the path-tracking control with only the front wheel steering. The experimental results show that the proposed 4WS is able to achieve better path-tracking performance.

Temperature Control to Reduce Capacity Mismatch in Parallel-Connected Lithium Ion Cells

The temperature and capacity of individual cells affect the current distribution in a battery pack. Non uniform current distribution among parallel-connected cells can lead to capacity imbalance and premature aging. This paper develops models that calculate the current in parallel-connected cells and predict their capacity fade. The model is validated experimentally for a nonuniform battery pack at different temperatures. The paper also proposes and validates the hypothesis that temperature control can reduce capacity mismatch in parallel-connected cells. Three Lithium Iron Phosphate cells, two cells at higher initial capacity than the third cell, are connected in parallel. The pack is cycled for 1500 Hybrid Electric Vehicles cycles with the higher capacity cells regulated at 40°C and the lower capacity cell at 20°C. As predicted by the model, the higher capacity and temperature cells age faster, reducing the capacity mismatch by 48% over the 1500 cycles. A case study shows that cooling of low capacity cells can reduce capacity mismatch and extend pack life.

Comparison of Estimation Techniques for the Crankshaft Dynamics of an Opposed Piston Engine

This study provides a comparison of a linear and unscented Kalman filter to estimate the dynamics of a two-stroke opposed piston engine. These engines maintain several advantages over conventional internal combustion engines including reduced heat transfer, higher power to weight ratio, and a larger expansion ratio. Additionally, a crank angle phasing is introduced between the two cranks to improve scavenging efficiency. However, the coupling of the two crankshafts through a large geartrain can cause high amounts of noise and vibration harshness (NVH) and mechanical efficiency losses. By removing the geartrain and controlling the decoupled crankshafts with motor-generators, the NVH can be minimized while maintaining the benefits of crank angle phasing. To control the input torque of the motors to the engine, accurate estimations of the crankshaft position and dynamics are necessary. While the unscented Kalman filter exhibits lower estimation error, the filter is sensitive to model uncertainty relating to cylinder pressure, demanding further investigation of the robustness of the nonlinear filter.

Optimal Mini Segway Control Using Non-Minimum Phase Sample and Hold Input

Our early work shows the reduction of feasible sampling period when sample and hold inputs (SHI) are used to convert a continuous-time non-minimum phase (NMP) system to a discrete-time minimum phase (MP) system, comparing to conventional zero-order hold. Consequently, high-gain discrete-time controllers can be designed and used to improve continuous-time NMP system performance since the resulting discrete-time system is MP. This paper demonstrates the performance improvements of a mini Segway robot through experiments utilizing a dual-loop control architecture. An inner-loop continuous-time controller stabilizes the mini Segway robot and the outer-loop discrete-time controller, designed based on the discrete-time MP system, is used to improve the overall system performance. Experimental results show that the mini Segway cart oscillation magnitudes are significantly reduced and its stability is also improved. This study also confirms the feasibility of implementing the SHI into a low cost microcontroller such as Arduino. That is, the additional computational load of SHIs is minimal.