Data-Driven Thermal Anomaly Detection in Large Battery Packs

The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage and temperature data from multiple Li-ion battery cells. Mean-based residuals are generated for cell groups and evaluated using Principal Component Analysis. The evaluated residuals are then thresholded using a cumulative sum control chart to detect anomalies. The mild external short circuits associated with cell balancing are detected in the voltage signals and necessitate voltage retraining after balancing. Temperature residuals prove to be critical, enabling anomaly detection of module balancing events within 14 min that are unobservable from the voltage residuals. Statistical testing of the proposed approach is performed on the experimental data from a battery electric locomotive injected with model-based anomalies. The proposed anomaly detection approach has a low false-positive rate and accurately detects and traces the synthetic voltage and temperature anomalies. The performance of the proposed approach compared with direct thresholding of mean-based residuals shows a 56% faster detection time, 42% fewer false negatives, and 60% fewer missed anomalies while maintaining a comparable false-positive rate

DETC2008-49886 PIEZOELECTRIC T-BEAM MICROACTUATORS

ABSTRACT This paper introduces a novel T-beam actuator fabricate

DETC2010-28370 DISPLACEMENT AND BLOCKING FORCE PERFORMANCE OF PIEZOELECTRIC T-BEAM ACTUATORS

ABSTRACT In this paper, we present the experimental validation of the detailed models developed for the flexural motion of piezoelectric T-beam actuators. With a T-shaped cross-section, and bottom and top flange and web electrodes, a cantilevered beam can bend in both in-plane and out-of-plane directions upon actuation. Analytical models predict the tip displacement and blocking force in both directions. Mechanical dicing and flange electrode deposition was used to fabricate six meso-scale T-beam prototypes. The T-beams were experimentally tested for in-plane and out-of-plane displacements, and out-of-plane blocking force. The analytical models closely predict the T-beam displacement and blocking force performance. A nondimensional analytical model predict that all T-beam designs for both in-plane and outof-plane actuation, regardless of scale, have nondimensional displacement and blocking force equal to nondimensional voltage. The results from experiments are favorably compared with this theoretical prediction

Protocol for a sequential, prospective meta-analysis to describe coronavirus disease 2019 (COVID-19) in the pregnancy and postpartum periods.

We urgently need answers to basic epidemiological questions regarding SARS-CoV-2 infection in pregnant and postpartum women and its effect on their newborns. While many national registries, health facilities, and research groups are collecting relevant data, we need a collaborative and methodologically rigorous approach to better combine these data and address knowledge gaps, especially those related to rare outcomes. We propose that using a sequential, prospective meta-analysis (PMA) is the best approach to generate data for policy- and practice-oriented guidelines. As the pandemic evolves, additional studies identified retrospectively by the steering committee or through living systematic reviews will be invited to participate in this PMA. Investigators can contribute to the PMA by either submitting individual patient data or running standardized code to generate aggregate data estimates. For the primary analysis, we will pool data using two-stage meta-analysis methods. The meta-analyses will be updated as additional data accrue in each contributing study and as additional studies meet study-specific time or data accrual thresholds for sharing. At the time of publication, investigators of 25 studies, including more than 76,000 pregnancies, in 41 countries had agreed to share data for this analysis. Among the included studies, 12 have a contemporaneous comparison group of pregnancies without COVID-19, and four studies include a comparison group of non-pregnant women of reproductive age with COVID-19. Protocols and updates will be maintained publicly. Results will be shared with key stakeholders, including the World Health Organization (WHO) Maternal, Newborn, Child, and Adolescent Health (MNCAH) Research Working Group. Data contributors will share results with local stakeholders. Scientific publications will be published in open-access journals on an ongoing basis

Flowing Electrolyte Metal Batteries

Flowing electrolyte metal batteries (FEMBs) are a new class of energy storage devices with metal anodes and flowing electrolytes. In this presentation, electrochemical models predict that flowing electrolyte can accelerate diffusion-limited transport, reducing impedance, increasing stability, and improving coulombic efficiency and cycle life. Electrolyte flow toward the metal electrode reduces and, for flow velocities above a critical speed, changes the sign of the electrolyte impedance, allowing faster charging without exceeding voltage limits. The critical flow rate is very slow (~μm/s) and directly proportional to the charging current density. Concentration of diffusion flux at dendrite tips is responsible for accelerated dendrite growth in metal batteries, so introduction of creeping electrolyte flow normal to electrodes levels ion concentration and eliminates dendrites above the critical speed. Creeping normal flow also significantly reduces solid electrolyte interphase (SEI) growth rate [1]. Creeping Poiseuille and Couette flows that are parallel to the electrodes have no impact on impedance or SEI layer growth, but do reduce dendrite growth, albeit much less effectively than normal flow [2]. Both creeping normal and parallel flows eliminate/reduce dendrite growth, so introducing electrolyte flow, in general, is a promising method to control dendrite growth. As dead Li formation is tied to dendrite growth, and as creeping normal flow eliminates dendrites and reduces SEI layer growth [3], creeping normal flows significantly improve coulombic efficiencies, and cycle life. The low flow rates indicate potential for practical applications. [1] Parekh, Mihir N., Christopher D. Rahn, and Lynden A. Archer. "Controlling dendrite growth in lithium metal batteries through forced advection." Journal of Power Sources 452 (2020): 227760. [2] Parekh, Mihir N., and Christopher D. Rahn. "Reducing Dendrite Growth in Lithium Metal Batteries by Creeping Poiseuille and Couette Flows." Journal of the Electrochemical Society (2020). [3] Parekh, Mihir N., and Christopher D. Rahn. “Solid electrolyte interphase growth on lithium metal electrodes with normal electrolyte flow.’’ Journal of the Electrochemical Society (Submitted). Figure 1 <jats:p /