Fast-charging of lithium-ion batteries is a critical requirement for wider adoption of electric vehicles. However, it is subject to several difficulties, such as inhomogeneous delithiation, local heating, and lithium plating. Various charging protocols have been developed to resolve these problems, most of which comprise the combinations of constant-current (CC) and constant-voltage (CV) charging steps. Although it is one of the two main components in conventional charging protocols, CV charging is usually applied in a restrictive manner only to supplement CC charging, and its potential implications as a fast-charging protocol have not yet been fully explored. Herein, we study the effects of a CV-only charging protocol on the fast-charging efficiency of high-rate LiNi_0.8Co_0.1Mn_0.1O₂ cathode particles prepared by ultrasonic spray pyrolysis. A 15 minute full-charging is achieved by a single CV charging step without a significant capacity loss in the early cycles. However, gradual degradation of specific capacity and charging efficiency was observed at later cycles. Based on electro- and physico-chemical analysis data, we propose a two-step CV charging protocol that can improve the cycle stability alleviating the damage to the cathode particles. By revisiting CV charging protocols, this work provides a better understanding of the CV charging process that can contribute to the design of new charging protocols with advanced fast chargeability.

IoT devices and sensor networks present new opportunities for measuring, monitoring, and guiding scientific experiments. Sensors, cameras, and instruments can be combined to provide previously unachievable insights into the state of ongoing experiments. However, IoT devices can vary greatly in the type, volume, and velocity of data they generate, making it challenging to fully realize this potential. Indeed, synergizing diverse IoT data streams in near-real time can require the use of machine learning (ML). In addition, new tools and technologies are required to facilitate the collection, aggregation, and manipulation of sensor data in order to simplify the application of ML models and in turn, fully realize the utility of IoT devices in laboratories. Here we will demonstrate how the use of the Argonne-developed Manufacturing Data and Machine Learning (MDML) platform can analyze and use IoT devices in a manufacturing experiment. MDML is designed to standardize the research and operational environment for advanced data analytics and AI-enabled automated process optimization by providing the infrastructure to integrate AI in cyber-physical systems for in situ analysis. We will show that MDML is capable of processing diverse IoT data streams, using multiple computing resources, and integrating ML models to guide an experiment.

The U.S. Department of Energy's Office of Scientific and Technical Information