A battery management system (BMS) plays a crucial role to ensure the safety, efficiency, and reliability of a rechargeable Li-ion battery pack. State of charge (SOC) estimation is an important operation within a BMS. Estimated SOC is required in several BMS operations, such as remaining power and mileage estimation, battery capacity estimation, charge termination, and cell balancing. The open-circuit voltage (OCV) look-up-based SOC estimation approach is widely used in battery management systems. For OCV lookup, the OCV–SOC characteristic is empirically measured and parameterized a priori. The literature shows numerous OCV–SOC models and approaches to characterize them and use them in SOC estimation. However, the selection of an OCV–SOC model must consider several factors: (i) Modeling errors due to approximations, age/temperature effects, and cell-to-cell variations; (ii) Likelihood and severity of errors when the OCV–SOC parameters are rounded; (iii) Computing system requirements to store and process OCV parameters; and (iv) The required computational complexity of real-time OCV lookup algorithms. This paper presents a review of existing OCV–SOC models and proposes a systematic approach to select a suitable OCV–SOC for implementation based on various constraints faced by a BMS designer in practical application.

Automated driving systems (ADSs) are becoming ubiquitous to reduce the workload of drivers and improve road safety. However, present-day ADS lacks accurate and effective driver monitoring systems. Driver monitoring systems use physiological measurements, such as pupil dilation, eye-gaze, and eye-blinks, in order to monitor the cognitive load experienced by the drivers. With advances in eye-tracking technology, pupil dilation is emerging as a reliable measure of cognitive load in ADS. However, pupil dilation as a measure of cognitive load suffers from many factors, such as confounding effects, noise, and personal attributes, to name a few. Hence, in order to improve cognitive load estimation in ADS, other noninvasive measures must be studied and incorporated. In this article, various eye-gaze metrics are studied and evaluated as a measure of cognitive load based on data collected from 16 drivers in a simulated driving scenario using a driving simulator.

This paper is the third part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling of lithium-ion batteries. The first part of the series proposed models to quantify various sources of uncertainties in the OCV models; the second part of the series presented systematic data collection approaches to compute the uncertainties in the OCV to state of charge (SOC) models. This paper uses data collected from 28 OCV characterization experiments, performed according to the data collection plan presented in the second part, to compute and analyze three OCV uncertainty metrics: cell-to-cell variations, C-Rate error, and curve fitting error. The computed metrics showed that a lower C-Rate resulted in smaller errors in the OCV-SOC model and vice versa. The results reported in this paper establish a relationship between the C-Rate and the uncertainty of the OCV-SOC model. Further, it was observed that the magnitude of cell-to-cell variations varied with the battery SOC and that it was not significantly affected by the C-Rate at which the experimental data was collected. The analysis in this paper also found that widely used polynomial modeling approaches for OCV-SOC curve modeling, incur significant errors. The approaches and results presented in this paper can be useful to battery researchers for quantifying the tradeoff between the time taken to complete the OCV characterization test and the corresponding uncertainty in the OCV-SOC modeling. Further, quantified uncertainty model parameters can accurately characterize the uncertainty in various BMS functionalities, such as SOC and state of health estimation. The insights presented in this paper can be useful to collect more accurate data for training machine learning models in SOC estimation.

Battery management systems (BMS) are important for ensuring the safety, efficiency and reliability of a battery pack. Estimating the internal equivalent circuit model (ECM) parameters of a battery, such as the internal open circuit voltage, battery resistance and relaxation parameters, is a crucial requirement in BMSs. Numerous approaches to estimating ECM parameters have been reported in the literature. However, existing approaches consider ECM identification as a joint estimation problem that estimates the state of charge together with the ECM parameters. In this paper, an approach is presented to decouple the problem into ECM identification alone. Using the proposed approach, the internal open circuit voltage and the ECM parameters can be estimated without requiring the knowledge of the state of charge of the battery. The proposed approach is applied to estimate the open circuit voltage and internal resistance of a battery.

Battery management systems (BMSs) rely on the prior characterization of the open circuit voltage (OCV) against the state of charge (SOC) for accurate SOC estimation in real-time. The OCV-SOC characterization is done offline in a laboratory setting using sample batteries. Various quantities defined for the OCV-SOC characterization process may determine the performance indicators of the battery, such as battery capacity, SOC and available power. Clearly defined OCV-SOC characterization will result in better performance and predictability of the BMS in electric vehicle applications. This paper is the second part of a series of papers about empirical approaches to open-circuit voltage (OCV) modeling and its performance comparison in lithium-ion batteries. The first part of the series introduced various sources of uncertainties in the OCV models and established a theoretical relationship between uncertainties and the performance of a battery management system. In this paper, clearly defined approaches for low-rate OCV data collection are described in detail. The data collection is designed with consideration to several parameters that affect the experimental time and the performance of the BMS. Based on the proposed approach, data is collected from 16 battery cells through 28 different experiments and analyzed. The results demonstrate the importance of a clearly defined data collection plan for the accuracy of SOC estimation in battery management systems.