1,720,989 research outputs found
Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging
Full text linkThe rise of the intelligent, local charging facilitation and environmentally friendly aspects of electric vehicles (EVs) has grabbed the attention of many end-users. However, there are still numerous challenges faced by researchers trying to put EVs into competition with internal combustion engine vehicles (ICEVs). The major challenge in EVs is quick recharging and the selection of an optimal charging station. In this paper, we present the most recent research on EV charging management systems and their role in smart cities. EV charging can be done either in parking mode or on-the-move mode. This review work is novel due to many factors, such as that it focuses on discussing centralized and distributed charging management techniques supported by a communication framework for the selection of an appropriate charging station (CS). Similarly, the selection of CS is evaluated on the basis of battery charging as well as battery swapping services. This review also covered plug-in charging technologies including residential, public and ultra-fast charging technologies and also discusses the major components and architecture of EVs involved in charging. In a comprehensive and detailed manner, the applications and challenges in different charging modes, CS selection, and future work have been discussed. This is the first attempt of its kind, we did not find a survey on the charging hierarchy of EVs, their architecture, or their applications in smart cities
Novel Approaches for State of Charge Modeling in Battery Management Systems
Full text linkOne of the key steps of any battery management system design is the representation of the open circuit voltage (OCV) as a function of the state of charge (SOC). The OCV-SOC relationship is very non-linear that is often represented using a polynomial that has log and inverse terms that are not defined around SOC equal to zero or one. The traditional response to this problem was only at the software level. In this thesis, I present a formal scaling approach to the OCV-SOC characterization in Li-ion batteries. I show that, through formal modeling and optimization, the traditional approach to OCV-SOC modeling can be significantly improved by selecting the proper value of . When the proposed technique is used a decrease in the maximum SOC error of 9\% is reported. The proposed approach is tested on data collected from multiple cells over various temperatures for OCV-SOC characterization and the results are presented. State-space model (SSM) and the Kalman filter have several applications in the emerging areas of automation and data science including in battery SOC estimation. In many such applications, the application of Kalman filtering requires model identification with the help of the observed data. I present the formulas with derivations for linear state-space model parameter estimation using the expectation maximization (EM) algorithm. Particularly, I derive the formulas for different special SSM cases of practical interest, such as the continuous white noise acceleration (CWNA) model. Through simulation, I show the benefits of these derivations for the special models in comparison with the generalized approach
NOVEL APPROACHES TO FAST OCV CHARACTERIZATION AND IMPROVED CAPACITY ESTIMATION IN LITHIUM ION BATTERIES
Full text linkThis thesis considers the problem of open circuit voltage (OCV) to state of charge (SOC) characterization in li-ion batteries for battery reuse applications. The traditional approach to OCV-SOC characterization is done by collecting voltage and current data through a slow discharge and charge process; this process usually takes about 60 hours. Such OCV-SOC characterization is performed on a few sample batteries because the OCV-SOC characterization is considered to be the same for new batteries coming out of the same manufacturing process. However, the characteristics of a battery may change as it is used for years in different environmental and usage conditions. Hence, they may need to be re-characterized before secondary use. Unlike primary characterization, the secondary characterization may have to be done faster in order to save time and cost. This thesis presents a faster approach for OCV-SOC characterization. The proposed approach in this thesis consists of constant-current profiles that halves in magnitude after a specified time. Such reductions allows us to fully deplete the battery; similarly, the battery is charged back with a reducing current profile in order to make sure the battery is fully charged. The resulting current profile reduces the total characterization time by 1/5. Secondly, we explore the idea of discharge and charge capacity of batteries. A traditional low-rate-OCV test consists of constant-current charging which results in a voltage drop based on the internal resistance and charging/discharging current. This thesis presents an approach to counteract this voltage drop, by appropriately over-charging and over-discharging the battery to obtain the most accurate representation of the capacity of the battery
Statistical Methods to Measure Reading Progression Using Eye-Gaze Fixation Points
Full text linkIn this thesis, we investigate methods to accurately track reading progression by analyzing eye-gaze fixation points, using commercially available eye tracking devices and without the imposition of unnatural movement constraints. In order to obtain the most accurate eye-gaze fixation point data possible, the current state of the art relies on expensive, cumbersome apparatuses. Eye-gaze tracking using less expensive hardware, and without constraints imposed on the individual whose gaze is being tracked, results in less reliable, noise-corrupt data which proves difficult to interpret. Extending the accessibility of accurate reading progression tracking beyond its current limits and enabling its feasibility in a real-world, constraint-free environment will enable a multitude of futuristic functionalities for educational, enterprise, and consumer technologies. We first discuss the ``Line Detection System'' (LDS), a Kalman filter and hidden Markov model based algorithm designed to infer from noisy data the line of text associated with each eye-gaze fixation point reported every few milliseconds during reading. This system is shown to yield an average line detection accuracy of 88.1\%. Next, we discuss a ``Horizontal Saccade Tracking System'' (HSTS) which aims to track horizontal progression within each line, using a least squares approach to filter out noise. Finally, we discuss a novel ``Slip-Kalman'' filter which is custom designed to track the progression of reading. This method improves upon the original LDS, performing at an average line detection accuracy of 97.8\%, and offers advanced capability in horizontal tracking compared to the HSTS. The performance of each method is demonstrated using 25 pages worth of data collected during readin
Estimation And Tracking Algorithm For Autonomous Vehicles And Humans
Full text linkAutonomous driving systems have experienced impressive growth in recent years. The present research community is working on several challenging aspects, such as, tracking, localization, path planning and control. In this thesis, first, we focus on tracking system and present a method to accurately track a moving vehicle. In the vehicle tracking, considering the proximity of surrounding vehicles, it is critical to detect their unusual maneuvers as quickly as possible, especially when autonomous vehicles operate among human-operated traffic. In this work, we present an approach to quickly detect lane-changing maneuvers of the nearby vehicles. The proposed algorithm is based on the optimal likelihood ratio test, known as Page test. Second, we consider another form of tracking: tracking the movements of humans in indoor settings. Indoor localization of staff and patients based on radio frequency identification (RFID) technology has promising potential application in the healthcare sector. The use of an active RFID in real-time indoor positioning system without any sacrifice of localization accuracy is intended to provide security, guidance and support service to patients. In this paper maximum likelihood estimation along with its Cramer-Rao lower bound of the locations of active RFID tags are presented by exploring the received signal strength indicator which is collected at the readers. The performance of real-time localization system is implemented by using an extended Kalman filter (EKF)
Performance Analysis of Coulomb Counting Approach for State of Charge Estimation in Li-Ion Batteries
Full text linkAccurate state of charge (SOC) estimation in rechargeable batteries is always a challenge since many parameters can affect the SOC of the battery. Amongst all the developed methods for SOC estimation, Coulomb counting has been one of the most common and traditional methods. Nevertheless, the accuracy of this method is debatable. It was assumed that Coulomb counting can accurately estimate SOC by assuming the battery capacity and initial SOC. In this thesis, we analyze the Coulomb counting method thoroughly and we showed that this method is susceptible uncertainties. The sources of uncertainties that affect Coulomb counting accuracy are: (i) current measurement error; (ii) current integration approximation error; (iii) battery capacity uncertainty; and the (iv) timing oscillator error/drift. The SOC error due to all these uncertainties can be categorized into two forms; time-cumulative and SOC-proportional. The time-cumulative error increases over time and can invalidate SOC estimation by Coulomb counting. The SOC-proportional error increase with the accumulated SOC and it can affect SOC accuracy within one cycle of charge/discharge. A simulation analysis is presented to demonstrate and verify the effect of these uncertainties under several realistic scenarios. We also have discussed the approaches to reduce these uncertainties
A Comparison of Battery Equivalent Circuit Model Parameter Extraction Approaches Based on Electrochemical Impedance Spectroscopy
Full text linkThis thesis compares three methods for estimating battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS). These methods are referred to as least squares (LS), exhaustive search (ES), and nonlinear least squares (NLS). The ES approach utilizes the LS method to roughly determine the lower and upper bounds of the ECM parameters, while the NLS approach incorporates a Monte Carlo run, allowing for different initial guesses to enhance the accuracy of EIS fitting. The proposed approaches are validated using both simulated and real-world EIS data. When the signal-to-noise ratio (SNR) is high, both the ES and NLS approaches exhibit better fitting accuracy compared to the LS approach. Furthermore, in the validation using simulated EIS data as well as actual EIS data obtained from LG 18650 and Molicel 21700 batteries, the NLS approach consistently outperforms the LS and ES approaches in terms of fitting accuracy. Additionally, the computational time required for the NLS approach is significantly shorter than that of the ES approach, and the NLS approach demonstrates only a minimal difference in computational time compared to the LS approach while providing significantly better fitting performance
Numerical Thermal Performance Analysis of a Phase Change Material-Air-Liquid Heat Exchanger Using Latent Heat Thermal Energy Storage
Full text linkDue to the mismatches in energy supply and demand in thermal systems, employing latent heat thermal energy storage using phase change materials (PCMs) is a reliable and effective solution. Compact heat exchangers are an essential component of thermal management systems in several industries, such as the HVAC industry, automotive, and many others. In this regard, this paper introduces a novel PCM-air-liquid heat exchanger to increase thermal system performance by providing a hybrid heat source to the airside. A novel numerical work based on multiphysics coupling of heat transfer and double fluid flow and phase change within a complex geometry is represented. Numerical heat transfer analysis is performed on the model based on three-dimensional computational fluid dynamics (CFD) simulation. In order to make a thorough thermal performance assessment, the dynamic behavior of the system is investigated for the heat exchanger in two studies of air heating and cooling mode and conducted for the PCM charging and discharging processes. Furthermore, the effect of airside flow variation on the thermal response of the system is studied, and the results are discussed based on the fluids temperature, heat transfer rate, and the PCM phase transition procedure. It is demonstrated from the results attained that the PCM can store excess thermal energy from the working fluid during the charging process and releases it to the airside during the discharging process. It is observed that the heating load of 323 kJ for the air-heating study and cooling load of 188 kJ for the air-cooling study is stored during the PCM charging process. This thermal energy provides up to 6 and 9 minutes of extra airside heating and cooling time during the PCM discharging process for the studies, respectively. The share of PCM latent component of the airside heat transfer is determined to be at an average of 51% between all simulation cases. It is also observed that by increasing the airflow rate, the discharged heating/cooling load is decreased slightly, and the heating/cooling time is reduced notably. The presented thermal energy storage system offers a unique solution for the start-stop function implemented in many hybrid and electric vehicles. During short periods of engine shutdown, the system could provide passenger thermal comfort and enable effective energy savings
Novel Approaches to Cognitive Load Estimation in Automated Driving Systems
No full textAutomation has become indispensable in all walks of everyday life. In driving environments, Automated Driving Systems (ADS) aid the driver by reducing the required workload and by improving road safety. However, the present-day ADS requires the human driver to remain vigilant at all times and be ready to take over whenever the driving task requires. Thus, continuous monitoring of the drivers is important for adopting ADS. Such monitoring can be done in ADS by measuring the cognitive load experienced by the drivers. Studies show various methods to estimate cognitive load, however, the state of the art in cognitive load estimation, particularly, the non-invasive ones suitable for ADS, still suffer from significant deficiencies. Thus, more research to improve the accuracy of cognitive load estimators is crucial for allowing the safe adoption of ADS. This thesis contains the analysis of non-invasive metrics that can be used as reliable indicators of cognitive load. Eye-tracking measures such as pupil size, eye-gaze, and eye-blinks from low-cost eye-trackers are analyzed. In addition to eye-tracking data, heart rate is also studied as an estimator of cognitive load. Furthermore, this thesis introduces a novel model-based approach to filter noisy physiological measurements for the real-time monitoring of cognitive load. The proposed measures will be beneficial to the development of more accurate metrics for cognitive load estimation, thereby contributing to the advancement of ADS. The thesis also contains a detailed description of two datasets collected at the HSLab.These datasets will be helpful to researchers interested in employing machine learning algorithms to develop predictive models of humans for applications in human-machine automation
Performance Analysis and Improvement of Electrochemical Impedance Spectroscopy for Online Estimation of Battery Parameters
Full text linkBattery internal impedance measurement is of great importance for a battery management system, which is tasked with ensuring the safety, efficiency, and reliability of a battery pack. Electrochemical impedance spectroscopy (EIS) is an active method of estimating battery impedance parameters, where an excitation signal spanning a wide frequency spectrum is applied to the battery to measure its response. Even though the EIS approach is accurate and reliable, it is limited to laboratory experiments due to its dependence on high-precision measurement systems. In order to benefit from the EIS approach in real-time applications, e.g., the battery management system of an electric vehicle, the impedance parameter estimation approach needs to be robust enough to perform with low-cost (and hence low-precision) sensors that are prone to measurement uncertainties. This thesis presents an approach to estimate the impedance parameters of a Li-Ion battery pack in the presence of high levels of noise. The proposed algorithm consists of fast Fourier transform, feature extraction, curve fitting, and least-squares estimation. The proposed approach is demonstrated using a swept sine wave that had frequency in the range of 0.01 Hz to 10 kHz as excitation signals. The results of the proposed parameter estimation algorithm are compared to that of recent work for objective performance comparison. Results show that the proposed algorithm significantly outperforms the previous method under high measurement noise scenarios without requiring any significant increase in computational resources
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