AI/ML cycle life prediction is emerging as the broadly transformational capability; initial focus has been on urgent transportation needs. Argonne’s expanded scope includes diverse stationary markets, where useful life extends beyond 80% capacity and use-protocols are complex and changeable (e.g., stacking or role changes during asset life) and requires alternative research strategies. This talk compares results with various ML approaches and with the use of both experimental and synthetic data.

Over the last decade, pharmaceutical co-innovation partnerships have gotten new life-saving products to market faster. Given the importance decreasing time-to-market for battery innovations, we discuss how the playbook for accelerated drug discovery is being adapted to the battery industry. We compare and contrast these two industries from both business and technical perspectives, discuss how co-innovation partnerships are being successfully structured for battery R&D, and showcase several case studies from Aionics partners.

As the automotive industry enters a second phase of electrification with clearly defined leaders, OEMs are using Enterprise Battery IntelligenceTM (EBI) platforms to get products to market faster to create sustainable competitive advantage. With EBI, OEMs are delivering on their roadmaps by moving away from black box technologies, improving traditional performance indicators to lower consumer barriers to entry, and leveraging efficiencies across the supply chain to ensure best-in-class results.

GBatteries will present an innovative and compelling way to charge unaltered Li-ion batteries, powered by machine learning. One which reduces irreversible chemical reactions and allows for ultra-fast charging.

Pulse charging (PC) of Li-ion batteries is known to improve their lifetime. In this work, an experimental setup is developed to identify an optimal PC frequency for energy efficient operation for large (50Ah) Li-ion cells. The Smart Battery (SB) architecture is introduced and its use for the implementation of optimal PC is described. SB can generate current pulses of any frequency, so energy efficient PC is possible without external hardware.

This presentation will focus on developing novel diagnostic and state estimation techniques which uniquely take advantage of the temperature of the cell and not just voltage and current. The latest work uses knowledge of the entropic behavior of the individual electrodes to extract loss of lithium inventory (LLI), loss of active material (LAM), or stoichiometric drift, under realistic real-world operating conditions.

By collecting battery data from the field and building up the battery digital twin in the cloud, the degradation of batteries can be monitored online on electrode level and the information regarding the degradation modes can be extracted from the data. Here, we present a degradation diagnosis framework for lithium-ion batteries by integrating field data, impedance-based modeling, and artificial intelligence, revolutionizing the degradation identification with accurate and robust estimation of both capacity and power fade together with degradation mode analysis.

How to establish a fast and accurate model with physics implications still remains an unsolved headache for the community. Herein, we establish a battery safety risk classification modeling framework based on a machine learning algorithm that can accurately and rapidly classify the potential safety risk level.

ALGOLiON has developed quantitative algorithms in software solutions that extends the early warning period of thermal runaway from seconds to days. The software can be integrated as an embedded solution in the BMS, on cloud platforms for telematic data, and as programs for diagnostic instruments and OBD applications. The method works by monitoring battery current and voltage that are affected by changes in cell chemistry due to precursor defects prior to failure. The advance warning is sufficient for initiating appropriate preventive action to protect products and people.

This talk focuses on the BEEP battery analytics platform developed by the Energy & Materials Team at TRI. BEEP aims to accelerate battery development by making battery testing faster and less expensive. The platform automates battery cycling experiments and subsequent data analysis. To address bottlenecks in the battery development process, the system has the ability to make early outcome predictions in order to reduce testing time and conduct closed loop optimization to minimize the total number of experiments. More recently, research capacity of machine learning based state of health evaluation, prediction, and optimization are developed leveraging the BEEP platform, enabling rapid iterations of battery testings.

Successful commercial innovation in battery technology is uniquely difficult because of interrelated and coupled challenges stemming from atomic-scale all the way through GWh-level manufacturing. Cuberg, as part of Northvolt, has pioneered a vertically-integrated approach toward battery innovation that combines excellence in simulation, data science, high-throughput testing, cell design, process development, supply chain, and customer distribution to deliver the world's first lithium metal battery at commercial scale.

We demonstrated the use of the powerful machine learning tool to develop the “safety envelope” of lithium-ion batteries for electric vehicles that provides the range of mechanical loading conditions ensuring safe operation. The daunting challenge of obtaining a large databank of battery tests was overcome by utilizing a high-accuracy finite element model of a pouch cell to generate over 2500 numerical simulations.

The use of ALD to become an industry leader in materials optimization in the battery space is predicated on our ability to optimize coating solutions through an iterative process between coating the materials and electrochemical testing. In this work we have employed Voltaiq analysis software as a tool to efficiently explore ALD coatings as a means of stabilizing Ni-rich NMC surfaces, enabling increased capacity retention and high voltage utilization.