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Technical Paper

Integrated Engine States Estimation Using Extended Kalman Filter and Disturbance Observer

2019-10-22
2019-01-2603
Accurate estimation of engine state(s) is vital for engine control systems to achieve their designated objectives. The fusion of sensors can significantly improve the estimation results in terms of accuracy and precision. This paper investigates using an Extended Kalman Filter (EKF) to estimate engine state(s) for Spark Ignited (SI) engines with the external EGR system. The EKF combines air path sensors with cylinder pressure feedback through a control-oriented engine cycle domain model. The model integrates air path dynamics, torque generation, exhaust gas temperature, and residual gas mass. The EKF generates a cycle-based estimation of engine state(s) for model-based control algorithms, which is not the focus of this paper. The sensor and noise dynamics are analyzed and integrated into the EKF formulation. To account for ‘non-white’ disturbances including modeling errors and sensor/actuator offset, the EKF engine state(s) observer is augmented with disturbance state(s) estimation.
Technical Paper

Quantification of Linear Approximation Error for Model Predictive Control of Spark-Ignited Turbocharged Engines

2019-09-09
2019-24-0014
Modern turbocharged spark-ignition engines are being equipped with an increasing number of control actuators to meet fuel economy, emissions, and performance targets. The response time variations between engine control actuators tend to be significant during transients and necessitate highly complex actuator scheduling routines. Model Predictive Control (MPC) has the potential to significantly reduce control calibration effort as compared to the current methodologies that are based on decentralized feedback control strategies. MPC strategies simultaneously generate all actuator responses by using a combination of current engine conditions and optimization of a control-oriented plant model. To achieve real-time control, the engine model and optimization processes must be computationally efficient without sacrificing effectiveness. Most MPC systems intended for real-time control utilize a linearized model that can be quickly evaluated using a sub-optimal optimization methodology.
Technical Paper

A Voice and Pointing Gesture Interaction System for On-Route Update of Autonomous Vehicles’ Path

2019-04-02
2019-01-0679
This paper describes the development and simulation of a voice and pointing gesture interaction system for on-route update of autonomous vehicles’ path. The objective of this research is to provide users of autonomous vehicles a human vehicle interaction mode that enables them to make and communicate spontaneous decisions to the autonomous car, modifying its pre-defined autonomous route in real-time. For example, similar to giving directions to a taxi driver, a user will be able to tell the car «Stop there» or «Take that exit». In this way, the user control/spontaneity vs interaction flexibility dilemma that current autonomous vehicle concepts have, could be solved, potentially increasing the user acceptance of this technology. The system was designed following a level structured state machine approach. The simulations were developed using MATLAB and VREP, a robotics simulation platform, which has accurate vehicle and sensor models.
Technical Paper

Modeling and Learning of Object Placing Tasks from Human Demonstrations in Smart Manufacturing

2019-04-02
2019-01-0700
In this paper, we present a framework for the robot to learn how to place objects to a workpiece by learning from humans in smart manufacturing. In the proposed framework, the rational scene dictionary (RSD) corresponding to the keyframes of task (KFT) are used to identify the general object-action-location relationships. The Generalized Voronoi Diagrams (GVD) based contour is used to determine the relative position and orientation between the object and the corresponding workpiece at the final state. In the learning phase, we keep tracking the image segments in the human demonstration. For the moment when a spatial relation of some segments are changed in a discontinuous way, the state changes are recorded by the RSD. KFT is abstracted after traversing and searching in RSD, while the relative position and orientation of the object and the corresponding mount are presented by GVD-based contours for the keyframes.
Technical Paper

An Immersive Vehicle-in-the-Loop VR Platform for Evaluating Human-to-Autonomous Vehicle Interactions

2019-04-02
2019-01-0143
The deployment of autonomous vehicles in real-world scenarios requires thorough testing to ensure sufficient safety levels. Driving simulators have proven to be useful testbeds for assisted and autonomous driving functionalities but may fail to capture all the nuances of real-world conditions. In this paper, we present a snapshot of the design and evaluation using a Cooperative Adaptive Cruise Control application of virtual reality platform currently in development at our institution. The platform is designed so to: allow for incorporating live real-world driving data into the simulation, enabling Vehicle-in-the-Loop testing of autonomous driving behaviors and providing us with a useful mean to evaluate the human factor in the autonomous vehicle context.
Technical Paper

Real-Time Reinforcement Learning Optimized Energy Management for a 48V Mild Hybrid Electric Vehicle

2019-04-02
2019-01-1208
Energy management of hybrid vehicle has been a widely researched area. Strategies like dynamic programming (DP), equivalent consumption minimization strategy (ECMS), Pontryagin’s minimum principle (PMP) are well analyzed in literatures. However, the adaptive optimization work is still lacking, especially for reinforcement learning (RL). In this paper, Q-learning, as one of the model-free reinforcement learning method, is implemented in a mid-size 48V mild parallel hybrid electric vehicle (HEV) framework to optimize the fuel economy. Different from other RL work in HEV, this paper only considers vehicle speed and vehicle torque demand as the Q-learning states. SOC is not included for the reduction of state dimension. This paper focuses on showing that the EMS with non-SOC state vectors are capable of controlling the vehicle and outputting satisfactory results. Electric motor torque demand is chosen as action.
Technical Paper

Knock Thresholds and Stochastic Performance Predictions: An Experimental Validation Study

2019-04-02
2019-01-1168
Knock control systems are fundamentally stochastic, regulating some aspect of the distribution from which observed knock intensities are drawn. Typically a simple threshold is applied, and the controller regulates the resultant knock event rate. Recent work suggests that the choice of threshold can have a significant impact on closed loop performance, but to date such studies have been performed only in simulation. Rigorous assessment of closed loop performance is also a challenging topic in its own right because response trajectories depend on the random arrival of knock events. The results therefore vary from one experiment to the next, even under identical operating conditions. To address this issue, stochastic simulation methods have been developed which aim to predict the expected statistics of the closed loop response, but again these have not been validated experimentally.
Technical Paper

Use of Machine Learning for Real-Time Non-Linear Model Predictive Engine Control

2019-04-02
2019-01-1289
Non-linear model predictive engine control (nMPC) systems have the ability to reduce calibration effort while improving transient engine response. The main drawback of nMPC for engine control is the computational power required to realize real-time operation. Most of this computational power is spent linearizing the non-linear plant model at each time step. Additionally, the effectiveness of the nMPC system relies heavily on the accuracy of the model(s) used to predict the future system behavior, which can be difficult to model physically. This paper introduces a hybrid modeling approach for internal combustion engines that combines physics-based and machine learning techniques to generate accurate models that can be linearized with low computational power. This approach preserves the generalization and robustness of physics-based models, while maintaining high accuracy of data-driven models. Advantages of applying the proposed model with nMPC are discussed.
Technical Paper

A Look-Ahead Model Predictive Optimal Control Strategy of a Waste Heat Recovery-Organic Rankine Cycle for Automotive Application

2019-04-02
2019-01-1130
The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system is a challenge for real time application. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. This paper proposes a look-ahead control strategy to explore the potential of increased power recovery from a simulated WHR system. In the look-ahead control, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model.
Technical Paper

A Systems Approach in Developing an Ultralightweight Outside Mounted Rearview Mirror Using Discontinuous Fiber Reinforced Thermoplastics

2019-04-02
2019-01-1124
Fuel efficiency improvement in automobiles has been a topic of great interest over the past few years, especially with the introduction of the new CAFE 2025 standards. Although there are multiple ways of improving the fuel efficiency of an automobile, lightweighting is one of the most common approaches taken by many automotive manufacturers. Lightweighting is even more significant in electric vehicles as it directly affects the range of the vehicle. Amidst this context of lightweighting, the use of composite materials as alternatives to metals has been proven in the past to help achieve substantial weight reduction. The focus of using composites for weight reduction has however been typically limited to major structural components, such as BiW and closures, due to high material costs. Secondary structural components which contribute approximately 30% of the vehicle weight are usually neglected by these weight reduction studies.
Technical Paper

Modeling the Effect of Thermal Barrier Coatings on HCCI Engine Combustion Using CFD Simulations with Conjugate Heat Transfer

2019-04-02
2019-01-0956
Thermal barrier coatings with low conductivity and low heat capacity have been shown to improve the performance of homogeneous charge compression ignition (HCCI) engines. These coatings improve the combustion process by reducing heat transfer during the hot portion of the engine cycle without the penalty thicker coatings typically have on volumetric efficiency. Computational fluid dynamic simulations with conjugate heat transfer between the in-cylinder fluid and solid piston of a single cylinder HCCI engine with exhaust valve rebreathing are carried out to further understand the impacts of these coatings on the combustion process. For the HCCI engine studied with exhaust valve rebreathing, it is shown that simulations needed to be run for multiple engine cycles for the results to converge given how sensitive the rebreathing process is to the residual gas state.
Journal Article

Automotive Waste Heat Recovery after Engine Shutoff in Parking Lots

2019-04-02
2019-01-0157
1 The efficiency of internal combustion engines remains a research challenge given the mechanical friction and thermodynamic losses. Although incremental engine design changes continue to emerge, the harvesting of waste heat represents an immediate opportunity to address improved energy utilization. An external mobile thermal recovery system for gasoline and diesel engines is proposed for use in parking lots based on phase change material cartridges. Heat is extracted via a retrofitted conduction plate beneath the engine block after engine shutoff. An autonomous robot attaches the cartridge to the plate and transfers the heat from the block to the Phase Change Material (PCM) and returns later to retrieve the packet. These reusable cartridges are then driven to a Heat Extraction and Recycling Tower (HEART) facility where a heat exchanger harvests the thermal energy stored in the cartridges.
Technical Paper

A Heuristic Supervisory Controller for a 48V Hybrid Electric Vehicle Considering Fuel Economy and Battery Aging

2019-01-15
2019-01-0079
Most studies on supervisory controllers of hybrid electric vehicles consider only fuel economy in the objective function. Taking into consideration the importance of the energy storage system health and its impact on the vehicle’s functionality, cost, and warranty, recent studies have included battery degradation as the second objective function by proposing different energy management strategies and battery life estimation methods. In this paper, a rule-based supervisory controller is proposed that splits the torque demand based not only on fuel consumption, but also on the battery capacity fade using the concept of severity factor. For this aim, the severity factor is calculated at each time step of a driving cycle using a look-up table with three different inputs including c-rate, working temperature, and state of charge of the battery. The capacity loss of the battery is then calculated using a semi-empirical capacity fade model.
Technical Paper

The Ingress and Egress Strategies of Wheelchair Users Transferring Into and Out of Two Sedans

2018-04-03
2018-01-1321
The ability to independently transfer into and out of a vehicle is essential for many wheelchair users to achieve driving independence. The purpose of the current study is to build upon the previous exploratory study that investigated the transfer strategies of wheelchair users by observing YouTube videos. This observational study videotaped five wheelchair users transferring from their wheelchairs into two research vehicles, a small and mid-size sedan that were equipped with a 50mm grid. The goal of this study was to use these videos and vehicle grids to precisely identify ingress and egress motions as well as “touch points” in a controlled setting with a small sample of five male wheelchair users. Using the videos from multiple different camera perspectives, the participants’ ingress and egress transfers were coded, documenting the touch points and step-by-step action sequences.
Technical Paper

On Enhanced Fuzzy Sliding-Mode Controller and Its Chattering Suppression for Vehicle Semi-Active Suspension System

2018-04-03
2018-01-1403
This paper aims to present an enhanced fuzzy sliding-mode control scheme with variable rate reaching law for semi-active vehicle suspension systems, which can reduce chattering phenomena in high frequency compared with the sliding-mode controller with traditional exponent reaching law. First, an ideal-skyhook damping suspension system is taken as reference model; then the new control law is synthesized by employing the fuzzy logic control while considering the sliding-mode reaching segment characteristics, which can dynamically change the reaching rate to suppress chattering in closed-loop control systems; finally, simulation analysis is conducted under both random road and bump road surface, the results verified the effectiveness and feasibility of the proposed control scheme.
Technical Paper

Handling Deviation for Autonomous Vehicles after Learning from Small Dataset

2018-04-03
2018-01-1091
Learning only from a small set of examples remains a huge challenge in machine learning. Despite recent breakthroughs in the applications of neural networks, the applicability of these techniques has been limited by the requirement for large amounts of training data. What’s more, the standard supervised machine learning method does not provide a satisfactory solution for learning new concepts from little data. However, the ability to learn enough information from few samples has been demonstrated in humans. This suggests that humans may make use of prior knowledge of a previously learned model when learning new ones on a small amount of training examples. In the area of autonomous driving, the model learns to drive the vehicle with training data from humans, and most machine learning based control algorithms require training on very large datasets. Collecting and constructing training data set takes a huge amount of time and needs specific knowledge to gather relevant information.
Technical Paper

Pointing Gesture Based Point of Interest Identification in Vehicle Surroundings

2018-04-03
2018-01-1094
This article presents a pointing gesture-based point of interest computation method via pointing rays’ intersections for situated awareness interactions in vehicles. The proposed approach is compared with two alternative methods: (a) a point of interest identification method based on the intersection of the pointing ray with the point cloud (PoC) resulting from the vehicle sensors, and (b) the traditional ray-casting approach, where the point of interest is computed based on the first intersection of the pointing rays with locations stored in a 2D annotated map. Simulation results show that the presented method outperforms by 36.25% the traditional ray casting one. However, as it was expected, the sensor-based computation method is more accurate. The validation of our approach was conducted by experiments performed in a test track facility.
Technical Paper

Control Optimization of a Charge Sustaining Hybrid Powertrain for Motorsports

2018-04-03
2018-01-0416
The automotive industry is aggressively pursuing fuel efficiency improvements through hybridization of production vehicles, and there are an increasing number of racing series adopting similar architectures to maintain relevance with current passenger car trends. Hybrid powertrains offer both performance and fuel economy benefits in a motorsport setting, but they greatly increase control complexity and add additional degrees of freedom to the design optimization process. The increased complexity creates opportunity for performance gains, but simulation based tools are necessary since hybrid powertrain design and control strategies are closely coupled and their optimal interactions are not straightforward to predict. One optimization-related advantage that motorsports applications have over production vehicles is that the power demand of circuit racing has strong repeatability due to the nature of the track and the professional skill-level of the driver.
Technical Paper

An Advanced Automatic Transmission with Interlocking Dog Clutches: High-Fidelity Modeling, Simulation and Validation

2017-03-28
2017-01-1141
Fuel economy regulations have forced the automotive industry to implement transmissions with an increased number of gears and reduced parasitic losses. The objective of this research is to develop a high fidelity and a computationally efficient model of an automatic transmission, this model should be suitable for controller development purposes. The transmission under investigation features a combination of positive clutches (interlocking dog clutches) and conventional wet clutches. Simulation models for the torque converter, lock-up clutch, transmission gear train, interlocking dog clutches, wet clutches, hydraulic control valves and circuits were developed and integrated with a 1-D vehicle road load model. The integrated powertrain system model was calibrated using measurements from real-world driving conditions. Unknown model parameters, such as clutch pack clearances, compliances, hydraulic orifice diameters and clutch preloads were estimated and calibrated.
Technical Paper

Assessment of Model-Based Knock Prediction Methods for Spark-Ignition Engines

2017-03-28
2017-01-0791
Knock-limited engine operation is one of the most important constraints on fuel efficiency and performance that must be considered during the design, control algorithm development and calibration of spark-ignition engines. This research evaluates the accuracy of model-based knock prediction routines and their applicability for control-oriented applications over various engine operating conditions using commercial fuels. Two common methods of knock prediction, a generalized chemical kinetics model and an empirical induction-time correlation, are evaluated and compared against experimental data. The experimental investigation is conducted using a naturally aspirated 3.6L V6 engine, retrofitted with cooled Exhaust Gas Recirculation (EGR). Data are acquired from spark timing sweeps under knocking conditions at different engine speeds and loads in an engine dynamometer cell.
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