IMU-Based Virtual Road Profile Sensor for Vehicle Localization

A road profile can be a good reference feature for vehicle localization when a Global Positioning System signal is unavailable. However, cost effective and compact devices measuring road profiles are not available for production vehicles. This paper presents a longitudinal road profile estimation method as a virtual sensor for vehicle localization without using bulky and expensive sensor systems. An inertial measurement unit installed in the vehicle provides filtered signals of the vehicle’s responses to the longitudinal road profile. A disturbance observer was designed to extract the characteristic features of the road profile from the signals measured by the inertial measurement unit. Design synthesis based on a Kalman filter was used for the observer design. A nonlinear damper is explicitly considered to improve the estimation accuracy. Virtual measurement signals are introduced for observability. The suggested methodology estimates the road profile that is sufficiently accurate for localization. Based on the estimated longitudinal road profile, we generated spectrogram plots as the features for localization. The localization is realized by matching the spectrogram plot with pre-indexed plots. The localization using the estimated road profile shows a few meters accuracy, suggesting a possible road profile estimation method as an alternative sensor for vehicle localization

Decentralized and Real-Time Power Dispatch Control for an Islanded Microgrid Supported by Distributed Power Sources

Microgrids can deploy traditional and/or renewable power sources to support remote sites. Utilizing renewable power sources requires more complicated control strategies to achieve acceptable power quality and maintain grid stability. In this research, we assume that the grid stability problem is already solved. As a next step, we focus on how the power can be dispatched from multiple power sources for improved grid efficiency. Isolated microgrids frequently require reconfigurations because of the grid expansion or component failures. Therefore, the control strategies ideally should be implemented in a plug-and-play fashion. Moreover, these strategies ideally require no pre-knowledge of the grid structure, and as little communication with neighboring power sources as possible. The control objective is to minimize a cost function that can be adjusted to reflect the desire to minimize energy cost and/or losses. An algorithm is designed to satisfy a derived necessary condition of function optimality. Such conditions are obtained by formulating Lagrange functions. An equivalent grid model approximates the grid structure which was later confirmed to represent the grid behavior adequately. For decentralized operations, we execute the distributed control sequentially using a simple token communication protocol. The performance of the combined system identification-Lagrange function minimization algorithm is demonstrated through simulations

Post-Impact Stabilization during Lane Change Maneuver

This study addresses challenges in vehicle collisions, especially in non-front or non-rear impacts, causing rapid state changes and a loss of control. Electronic Stability Control (ESC) can stabilize a vehicle in minor impact cases, but it cannot effectively handle major collision cases. To overcome this, our research focuses on Post-Impact Stabilization Control (PISC). Existing PISC methods face issues like misidentifying collisions during cornering maneuvers due to assumptions of straight driving, rendering them ineffective for lane change accidents. Our study aims to design PISC specifically for cornering and lane change maneuvers, predicting collision forces solely from the ego vehicle’s data, ensuring improved collision stability control. We employ the unscented Kalman filter to estimate collision forces and develop a sliding mode controller with an optimal force allocation algorithm to counter the disturbances caused by collisions and stabilize the vehicle. Rigorous validation through simulations and tests with a driving simulator demonstrates the feasibility of our proposed methodology in effectively stabilizing vehicles during collision accidents, particularly in lane change situations

Energy Management Control Strategy for Saving Trip Costs of Fuel Cell/Battery Electric Vehicles

Fuel cell vehicles (FCVs) should control the energy management between two energy sources for fuel economy, using the stored energy in a battery or generation of energy through a fuel cell system. The fuel economy for an FCV includes trip costs for hydrogen consumption and the lifetime of two energy sources. This paper proposes an implementable energy management control strategy for an FCV to reduce trip costs. The concept of the proposed control strategy is first to analyze the allowable current of a fuel cell system from the optimal strategies for various initial battery state of charge (SOC) conditions using dynamic programming (DP), and second, to find a modulation ratio determining the current of a fuel cell system for driving a vehicle using the particle swarm optimization method. The control strategy presents the on/off moment of a fuel cell system and the proper modulation ratio of the turned-on fuel cell system with respect to the battery SOC and the power demand. The proposed strategy reduces trip costs in real-time, similar to the DP-based optimal strategy, and more than the simple energy control strategy of switching a fuel cell system on/off at the battery SOC boundary conditions even for long-term driving cycles

Position Estimator Design for a MEMS Top-Drive Electrostatic Rotary Actuator

The capacitance and rotor angle of a MEMS top-drive electrostatic rotary actuator do not have a linear relationship due to the non-ignorable fringe effect and low aspect ratio of the electrodes. Therefore, the position estimation is not as straightforward as that for a comb-drive linear actuator or a side-drive rotary actuator. The reason is that the capacitance is a nonlinear and periodic function of the rotor angle and is affected by the three-phase input voltages. Therefore, it cannot be approximated as a simple two-plate capacitor. Sensing the capacitance between a rotor and a stator is another challenge. The capacitance can be measured in the electrodes (stators), but the electrodes also have to perform actuation, so a method is needed to combine actuation and sensing. In this study, a nonlinear capacitance model was derived as a data-driven model that effectively represents the nonlinear capacitance with sufficient accuracy. To measure the capacitance accurately, the stator parts for actuation and those for sensing are separated. Using the nonlinear model and the capacitance measurement, an unscented Kalman filter was designed to mitigate the large estimation error due to the periodic nonlinearity. The proposed method shows stable and accurate estimation that cannot be achieved with a simple two-plate capacitor model. The proposed approach can be applied to a similar system with highly nonlinear capacitance

Development of Road Surface Detection Algorithm Using CycleGAN-Augmented Dataset

Road surface detection is important for safely driving autonomous vehicles. This is because the knowledge of road surface conditions, in particular, dry, wet, and snowy surfaces, should be considered for driving control of autonomous vehicles. With the rise of deep learning technology, road surface detection methods using deep neural networks (DNN) have been widely used for developing road surface detection algorithms. To apply DNN in road surface detection, the dataset should be large and well-balanced for accurate and robust performance. However, most of the images of road surfaces obtained through usual data collection processes are not well-balanced. Most of the collected surface images tend to be of dry surfaces because road surface conditions are highly correlated with weather conditions. This could be a challenge in developing road surface detection algorithms. This paper proposes a method to balance the imbalanced dataset using CycleGAN to improve the performance of a road surface detection algorithm. CycleGAN was used to artificially generate images of wet and snow-covered roads. The road surface detection algorithm trained using the CycleGAN-augmented dataset had a better IoU than the method using imbalanced basic datasets. This result shows that CycleGAN-generated images can be used as datasets for road surface detection to improve the performance of DNN, and this method can help make the data acquisition process easy

Degradation-Conscious Equivalent Consumption Minimization Strategy for a Fuel Cell Hybrid System

The design of an energy management strategy is critical to improving the fuel efficiency of a vehicle system with an alternative powertrain system, such as hybrid electric vehicles or fuel cell electric vehicles. In particular, in fuel cell electric vehicles, the energy management strategy should consider system degradation and fuel savings because the hardware cost of the fuel cell system is much higher than that of a conventional powertrain system. In this paper, an easily implantable near-optimal energy management controller is proposed. The proposed controller distributes power generation between the fuel cell and the battery to simultaneously minimize system degradation and fuel usage. The controller is designed to consider the degradation cost and fuel cost in the framework of the equivalent consumption minimization strategy concept. The proposed controller was validated with a fuel cell electric vehicle model in MATLAB/Simulink (MathWorks, Natick, MA, USA). The proposed control strategy showed significant overall cost reduction compared to a thermostat control strategy and a conventional Equivalent Consumption Minimization Strategy (ECMS) strategy

Design of a Human Evaluator Model for the Ride Comfort of Vehicle on a Speed Bump Using a Neural Artistic Style Extraction

The subjective evaluation of vehicle ride comfort is costly and time-consuming but is crucial for vehicle development. To reduce the cost and time, the objectification of subjective evaluation has been widely studied, and most of the approaches use a regression model between objective metrics and subjective ratings. However, the accuracy of these approaches is highly dependent on the selection of the objective metrics. In most of the methods, it is not clear that the selected metrics are sufficiently significant or whether all significant metrics are included in the selection. This paper presents a method to build a correlation model between measurements and subjective evaluations without using predefined features or objective metrics. A numerical representation of ride comfort was extracted from raw signals based on the idea of the artistic style transfer method. The correlation model was designed based on the extracted numerical representation and subjective ratings. The model has a much better accuracy than any other correlation models in the literature. This better accuracy is contributed to not only by using a neural network, but also by the extraction of the numerical representation of ride comfort using a pre-trained neural network

Recurrent Pulseless Ventricular Tachycardia Induced by Commotio Cordis Treated with Therapeutic Hypothermia

The survival rate of commotio cordis is low, and there is often associated neurological disability if return of spontaneous circulation (ROSC) can be achieved. We report a case of commotio cordis treated with therapeutic hypothermia (TH) that demonstrated a favorable outcome. A 16-year-old female was transferred to our emergency department (ED) for collapse after being struck in the chest with a dodgeball. She has no history of heart problems. She was brought to our ED with pulseless ventricular tachycardia (VT), and ROSC was achieved with defibrillation. She was comatose at our ED and was treated with TH at a target temperature of 33°C for 24 hours. After transfer to the intensive care unit, pulseless VT occurred, and defibrillation was performed twice. She recovered to baseline neurologic status with the exception of some memory difficulties