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In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current measurement error; (2) current integration approximation error; (3) battery capacity uncertainty; and (4) timing oscillator error/drift. It is demonstrated that the state-of-charge error produced can be either time-cumulative or state-of-charge-proportional. Time-cumulative errors accumulate over time and have the potential to render the state-of-charge estimation utterly invalid in the long term.The proportional errors of the state of charge rise with the accumulated state of charge and reach their worst value within one charge/discharge cycle. The study presents methods for reducing time-cumulative and state-of-charge-proportional mistakes through simulation analysis.
Kiarash Movassagh; Arif Raihan; Balakumar Balasingam; Krishna Pattipati. A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries. Energies 2021, 14, 4074 .
AMA StyleKiarash Movassagh, Arif Raihan, Balakumar Balasingam, Krishna Pattipati. A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries. Energies. 2021; 14 (14):4074.
Chicago/Turabian StyleKiarash Movassagh; Arif Raihan; Balakumar Balasingam; Krishna Pattipati. 2021. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries." Energies 14, no. 14: 4074.
Eye-tracking while reading is an emerging application where the goal is to track the progression of reading. The challenges for accurate tracking of the reading progression are due to the measurement noise of the eye-tracker and the rapid and uncertain movement of the eye gaze. Solutions to this problem developed in the recent past suffer from many limitations, such as the need to know the text context and the need to have a batch of one page of data for classification. In this paper, we relax these assumptions and develop a novel, real-time line classification approach. The proposed solution consists of an improved slip-Kalman smoother that is designed to detect new line returns and to reduce the variance in the eye-gaze measurements. After preprocessing of the data by the slip-Kalman smoother, a classification approach is employed to track the lines being read in real-time. Two such classifiers are demonstrated in this paper; one is based on Gaussian discriminants, and the other is based on support vector machines. The proposed approaches were tested using realistic eye-gaze data from seven participants. Analysis based on the collected data using the proposed algorithms shows significantly improved performance over existing methods.
Xiaohao Sun; Balakumar Balasingam. Reading Line Classification Using Eye-Trackers. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -10.
AMA StyleXiaohao Sun, Balakumar Balasingam. Reading Line Classification Using Eye-Trackers. IEEE Transactions on Instrumentation and Measurement. 2021; 70 (99):1-10.
Chicago/Turabian StyleXiaohao Sun; Balakumar Balasingam. 2021. "Reading Line Classification Using Eye-Trackers." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-10.
In this article, we present the datasets collected from nine different Li-ion batteries. These datasets contain voltage, current and time measurements during a full charge-discharge cycle of a battery at very low current (that is nearly at C/30 rate). Such low current rate data is suitable for open circuit voltage characterization. The collection of this data was done through the use of an Arbin battery cycler and a thermal chamber was used to control the test temperature. Data were collected over a wide range of temperatures from −25∘C to 50∘C.
Mostafa Shaban Ahmed; Balakumar Balasingam; K.R. Pattipati. Experimental data on open circuit voltage characterization for Li-ion batteries. Data in Brief 2021, 36, 107071 .
AMA StyleMostafa Shaban Ahmed, Balakumar Balasingam, K.R. Pattipati. Experimental data on open circuit voltage characterization for Li-ion batteries. Data in Brief. 2021; 36 ():107071.
Chicago/Turabian StyleMostafa Shaban Ahmed; Balakumar Balasingam; K.R. Pattipati. 2021. "Experimental data on open circuit voltage characterization for Li-ion batteries." Data in Brief 36, no. : 107071.
Real-time identification of electrical equivalent circuit models is a critical requirement in many practical systems, such as batteries and electric motors. Significant work has been done in the past developing different types of algorithms for system identification using reduced-order equivalent circuit models. However, little work was done in analyzing the theoretical performance bounds of these system identification approaches. Given that both voltage and current are measured with error, proper understanding of theoretical bounds will help in designing a system that is economical in cost and robust in performance. In this paper, we analyze the performance of a linear recursive least squares approach to equivalent circuit model identification and show that the least squares approach is both unbiased and efficient when the signal-to-noise ratio is high enough. However, we show that, when the signal-to-noise ratio is low – resembling the case in many practical applications – the least squares estimator becomes significantly biased. Consequently, we develop a parameter estimation approach based on total least squares method and show it to be asymptotically unbiased and efficient at practically low signal-to-noise ratio regions. Further, we develop a recursive implementation of the total least square algorithm and find it to be slow to converge; for this, we employ a Kalman filter to improve the convergence speed of the total least squares method. The resulting total Kalman filter is shown to be both unbiased and efficient in equivalent circuit model identification. The performance of this filter is analyzed using real-world current profiles under fluctuating signal-to-noise ratios. Finally, the applicability of the algorithms and analysis in this paper in identifying higher order electrical equivalent circuit models is explained.
Balakumar Balasingam; Krishna Pattipati. On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurements. IEEE Transactions on Instrumentation and Measurement 2021, PP, 1 -1.
AMA StyleBalakumar Balasingam, Krishna Pattipati. On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurements. IEEE Transactions on Instrumentation and Measurement. 2021; PP (99):1-1.
Chicago/Turabian StyleBalakumar Balasingam; Krishna Pattipati. 2021. "On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurements." IEEE Transactions on Instrumentation and Measurement PP, no. 99: 1-1.
The dataset contains the following three measures that are widely used to determine cognitive load in humans: Detection Response Task - response time, pupil diameter, and eye gaze. These measures were recorded from 28 participants while they underwent tasks that are designed to permeate three different cognitive difficulty levels. The dataset will be useful to those researchers who seek to employ low cost, non-invasive sensors to detect cognitive load in humans and to develop algorithms for human-system automation. One such application is found in Advanced Driver Assistance Systems where eye-trackers are employed to monitor the alertness of the drivers. The dataset would also be helpful to researchers who are interested in employing machine learning algorithms to develop predictive models of humans for applications in human-machine system automation. The data is collected by the authors at the Department of Electrical & Computer Engineering in collaboration with the Faculty of Human Kinetics at the University of Windsor under the guidance of their Research Ethics Board.
Prarthana Pillai; Prathamesh Ayare; Balakumar Balasingam; Kevin Milne; Francesco Biondi. Response time and eye tracking datasets for activities demanding varying cognitive load. Data in Brief 2020, 33, 106389 .
AMA StylePrarthana Pillai, Prathamesh Ayare, Balakumar Balasingam, Kevin Milne, Francesco Biondi. Response time and eye tracking datasets for activities demanding varying cognitive load. Data in Brief. 2020; 33 ():106389.
Chicago/Turabian StylePrarthana Pillai; Prathamesh Ayare; Balakumar Balasingam; Kevin Milne; Francesco Biondi. 2020. "Response time and eye tracking datasets for activities demanding varying cognitive load." Data in Brief 33, no. : 106389.
Internal resistance is one of the important parameters in the Li-Ion battery. This paper identifies it using two different methods: electrochemical impedance spectroscopy (EIS) and parameter estimation based on equivalent circuit model (ECM). Comparing internal resistance, the conventional parameter estimation method yields a different value than EIS. Therefore, a hysteresis-free parameter identification method based on ECM is proposed. The proposed technique separates hysteresis resistance from the effective resistance. It precisely estimated actual internal resistance, which matches the internal resistance obtained from EIS. In addition, state of charge, open circuit voltage, and different internal equivalent circuit components were identified. The least square method was used to identify the parameters based on ECM. A parameter extraction algorithm to interpret impedance spectrum obtained from the EIS. The algorithm is based on the properties of Nyquist plot, phasor algebra, and resonances. Experiments were conducted using a cellphone pouch battery and a cylindrical 18650 battery.
S M Rakiul Islam; Sung-Yeul Park; Balakumar Balasingam. Unification of Internal Resistance Estimation Methods for Li-Ion Batteries Using Hysteresis-Free Equivalent Circuit Models. Batteries 2020, 6, 32 .
AMA StyleS M Rakiul Islam, Sung-Yeul Park, Balakumar Balasingam. Unification of Internal Resistance Estimation Methods for Li-Ion Batteries Using Hysteresis-Free Equivalent Circuit Models. Batteries. 2020; 6 (2):32.
Chicago/Turabian StyleS M Rakiul Islam; Sung-Yeul Park; Balakumar Balasingam. 2020. "Unification of Internal Resistance Estimation Methods for Li-Ion Batteries Using Hysteresis-Free Equivalent Circuit Models." Batteries 6, no. 2: 32.
Electric vehicles are set to be the dominant form of transportation in the near future and Lithium-based rechargeable battery packs have been widely adopted in them. Battery packs need to be constantly monitored and managed in order to maintain the safety, efficiency and reliability of the overall electric vehicle system. A battery management system consists of a battery fuel gauge, optimal charging algorithm, and cell/thermal balancing circuitry. It uses three non-invasive measurements from the battery, voltage, current and temperature, in order to estimate crucial states and parameters of the battery system, such as battery impedance, battery capacity, state of charge, state of health, power fade, and remaining useful life. These estimates are important for the proper functioning of optimal charging algorithms, charge and thermal balancing strategies, and battery safety mechanisms. Approach to robust battery management consists of accurate characterization, robust estimation of battery states and parameters, and optimal battery control strategies. This paper describes some recent approaches developed by the authors towards developing a robust battery management system.
Balakumar Balasingam; Mostafa Ahmed; Krishna Pattipati. Battery Management Systems—Challenges and Some Solutions. Energies 2020, 13, 2825 .
AMA StyleBalakumar Balasingam, Mostafa Ahmed, Krishna Pattipati. Battery Management Systems—Challenges and Some Solutions. Energies. 2020; 13 (11):2825.
Chicago/Turabian StyleBalakumar Balasingam; Mostafa Ahmed; Krishna Pattipati. 2020. "Battery Management Systems—Challenges and Some Solutions." Energies 13, no. 11: 2825.
State of charge estimation is one of the key elements in battery management systems. Accurate estimation of state of charge in real time is crucial in many applications such as in electric vehicles and aerospace systems. As a result, state of charge modeling and real-time state of charge tracking remain active topics in the battery management systems research domain. One of the key steps in real-time state of charge estimation is the representation of the open circuit voltage as a parametrized function of the state of charge – these parameters will later be used in real-time state of charge estimation based on instantaneous voltage and current measurements. The accuracy of a real-time state of charge estimation scheme is built on the assumption that the open circuit voltage curve is error free. In this paper, we show an example where most of the traditional open circuit voltage characterization approaches would result in up to 10% worst-case state of charge error. Then we present a scaling approach that can reduce this worst-case modeling error to less than 1%. Later, we demonstrate how the proposed scaling approach can be incorporated in real-time state of charge estimation methods, such as the extended Kalman filter based ones. The proposed methods are demonstrated on data collected from nine different battery cells at 16 different temperatures ranging from -25°C to 50°C.
Mostafa Shaban Ahmed; Sheikh Arif Raihan; Balakumar Balasingam. A scaling approach for improved state of charge representation in rechargeable batteries. Applied Energy 2020, 267, 114880 .
AMA StyleMostafa Shaban Ahmed, Sheikh Arif Raihan, Balakumar Balasingam. A scaling approach for improved state of charge representation in rechargeable batteries. Applied Energy. 2020; 267 ():114880.
Chicago/Turabian StyleMostafa Shaban Ahmed; Sheikh Arif Raihan; Balakumar Balasingam. 2020. "A scaling approach for improved state of charge representation in rechargeable batteries." Applied Energy 267, no. : 114880.
In this paper, we consider the problem of tracking the eyegaze of individuals while they engage in reading. Particularly, we develop ways to accurately track the line being read by an individual using commercially available eye tracking devices. Such an approach will enable futuristic functionalities such as comprehension evaluation, interest level detection, and user-assisting applications like hands-free navigation and automatic scrolling. Further, the proposed approach will pave the way to develop technology which may generate valuable feedback to content makers, such as web designers, authors, educators and social media users. Existing commercial eye-trackers provide an estimated location of the eyegaze points every few milliseconds. However, these estimated gaze points are not sufficient to quantify reading progression – a specific eye-gaze activity. In this paper we propose algorithms to bridge the commercial gaze tracker outputs and informative eye-gaze patterns while reading. The proposed system consists of Kalman filters and hidden Markov models to parameterize these statistical models and to accurately detect the line being read. The proposed approach is shown to yield an improvement of 27.1% in line detection accuracy over line tracking using estimated eye-gaze points alone by the eye-tracker.
Stephen Bottos; Balakumar Balasingam. Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models. IEEE Transactions on Instrumentation and Measurement 2020, 69, 7857 -7868.
AMA StyleStephen Bottos, Balakumar Balasingam. Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models. IEEE Transactions on Instrumentation and Measurement. 2020; 69 (10):7857-7868.
Chicago/Turabian StyleStephen Bottos; Balakumar Balasingam. 2020. "Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models." IEEE Transactions on Instrumentation and Measurement 69, no. 10: 7857-7868.
A battery-management system (BMS) is essential for the safe, reliable, and efficient operation of a battery pack. The BMS uses three noninvasive measurements from the battery-voltage, current, and temperature-to gauge a battery's state of charge (SOC) and state of health (SOH). These estimates are used in BMS functions, such as the generation of optimal charging waveforms, cell balancing, and the activation of safety mechanisms.
Balakumar Balasingam; Krishna Pattipati. Elements of a Robust Battery-Management System: From Fast Characterization to Universality and More. IEEE Electrification Magazine 2018, 6, 34 -37.
AMA StyleBalakumar Balasingam, Krishna Pattipati. Elements of a Robust Battery-Management System: From Fast Characterization to Universality and More. IEEE Electrification Magazine. 2018; 6 (3):34-37.
Chicago/Turabian StyleBalakumar Balasingam; Krishna Pattipati. 2018. "Elements of a Robust Battery-Management System: From Fast Characterization to Universality and More." IEEE Electrification Magazine 6, no. 3: 34-37.
B. Balasingam; G.V. Avvari; Krishna R Pattipati; Yaakov Barshalom. Performance analysis results of a battery fuel gauge algorithm at multiple temperatures. Journal of Power Sources 2015, 273, 742 -753.
AMA StyleB. Balasingam, G.V. Avvari, Krishna R Pattipati, Yaakov Barshalom. Performance analysis results of a battery fuel gauge algorithm at multiple temperatures. Journal of Power Sources. 2015; 273 ():742-753.
Chicago/Turabian StyleB. Balasingam; G.V. Avvari; Krishna R Pattipati; Yaakov Barshalom. 2015. "Performance analysis results of a battery fuel gauge algorithm at multiple temperatures." Journal of Power Sources 273, no. : 742-753.
G.V. Avvari; B. Balasingam; Krishna R Pattipati; Yaakov Barshalom. A battery chemistry-adaptive fuel gauge using probabilistic data association. Journal of Power Sources 2015, 273, 185 -195.
AMA StyleG.V. Avvari, B. Balasingam, Krishna R Pattipati, Yaakov Barshalom. A battery chemistry-adaptive fuel gauge using probabilistic data association. Journal of Power Sources. 2015; 273 ():185-195.
Chicago/Turabian StyleG.V. Avvari; B. Balasingam; Krishna R Pattipati; Yaakov Barshalom. 2015. "A battery chemistry-adaptive fuel gauge using probabilistic data association." Journal of Power Sources 273, no. : 185-195.
B. Balasingam; G.V. Avvari; B. Pattipati; Krishna R Pattipati; Yaakov Barshalom. A robust approach to battery fuel gauging, part II: Real time capacity estimation. Journal of Power Sources 2014, 269, 949 -961.
AMA StyleB. Balasingam, G.V. Avvari, B. Pattipati, Krishna R Pattipati, Yaakov Barshalom. A robust approach to battery fuel gauging, part II: Real time capacity estimation. Journal of Power Sources. 2014; 269 ():949-961.
Chicago/Turabian StyleB. Balasingam; G.V. Avvari; B. Pattipati; Krishna R Pattipati; Yaakov Barshalom. 2014. "A robust approach to battery fuel gauging, part II: Real time capacity estimation." Journal of Power Sources 269, no. : 949-961.
B. Balasingam; G.V. Avvari; B. Pattipati; Krishna R Pattipati; Yaakov Barshalom. A robust approach to battery fuel gauging, part I: Real time model identification. Journal of Power Sources 2014, 272, 1142 -1153.
AMA StyleB. Balasingam, G.V. Avvari, B. Pattipati, Krishna R Pattipati, Yaakov Barshalom. A robust approach to battery fuel gauging, part I: Real time model identification. Journal of Power Sources. 2014; 272 ():1142-1153.
Chicago/Turabian StyleB. Balasingam; G.V. Avvari; B. Pattipati; Krishna R Pattipati; Yaakov Barshalom. 2014. "A robust approach to battery fuel gauging, part I: Real time model identification." Journal of Power Sources 272, no. : 1142-1153.
B. Pattipati; B. Balasingam; G.V. Avvari; K.R. Pattipati; Y. Bar-Shalom. Open circuit voltage characterization of lithium-ion batteries. Journal of Power Sources 2014, 269, 317 -333.
AMA StyleB. Pattipati, B. Balasingam, G.V. Avvari, K.R. Pattipati, Y. Bar-Shalom. Open circuit voltage characterization of lithium-ion batteries. Journal of Power Sources. 2014; 269 ():317-333.
Chicago/Turabian StyleB. Pattipati; B. Balasingam; G.V. Avvari; K.R. Pattipati; Y. Bar-Shalom. 2014. "Open circuit voltage characterization of lithium-ion batteries." Journal of Power Sources 269, no. : 317-333.