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. The model can identify defective cells, cells with internal short circuits (ISCs), and cells with possible thermal runaway (TR) using a small portion of one-cycle data. Classifiers in the ML model demonstrate satisfactory performance and robustness.

Make use of data from Battery Electric Vehicles. Use pre-trained data-driven models to analyze and optimize battery usage. Validate and verify the analysis, and prediction results by comparing them to simulation results of physics-based models and to battery test results by advanced event-driven data analytics.

The novel BMS concept of Smart Battery for transportation and grid storage with improved safety lifetime is introduced. The key feature is the bypass device, capable of cell-level load management with minimized load impact leading to pulsed current operation which is known to slow down degradation processes. An advanced AI-based lifetime optimizer capable of online training is recognizing the early signs of stress and inserts optimized relaxation time.

In this work, we introduce a method for the synthetic generation of capacity fade curves based on limited battery test or operation data, aiming to augment the datasets used by data-driven models for degradation prediction. We validate our method by testing the performance of both a shallow machine learning and a deep learning model using different datasets incorporating realistic conditions such as cell-to-cell variations, measurement noise, varying charge-discharge conditions, and capacity recovery.

Advances in laboratory-based characterizationtechniques have yielded powerful insights into the structure-function relationship ofelectrodes, yet there is still far to go. Further improvements rely, in part, on gaining a deeperunderstanding of complex physical heterogeneities in the materials.

This presentation shows how to implement AI control in the lab and pilot plant, how we can measure materials acceleration, and ultimately demonstrate it for a couple of materials like electrodes, electrolytes, and some manufacturing processes. Our 10-30x acceleration is enabled by our unique platform for accelerated electrochemical energy storage research (PLACES/R) that covers the entire materials research value chain. Select discoveries will be shown on >100 cells.

Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved.

In this work, we introduce a method for the synthetic generation of capacity fade curves based on limited battery test or operation data, aiming to augment the datasets used by data-driven models for degradation prediction. We validate our method by testing the performance of both a shallow machine learning and a deep learning model using different datasets incorporating realistic conditions such as cell-to-cell variations, measurement noise, varying charge-discharge conditions, and capacity recovery.

This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV).

Optimization of lifetime performance, early identification of anomalies during production, and description of the health of the fleet are just some of the imperative insights that a battery analytics solution should provide. Enterprise Battery Intelligence (EBI) leverages best-in-class battery data analytics to provide Automotive OEMs with a digital thread across their battery lifecycle. In this talk, we will discuss why this digital thread is business-critical and will provide use cases from the automotive industry.

Estimating the state of health (SoH) of lithium-ion (Li-ion) batteries is a challenging task due to cross-coupling and dependency between aging mechanisms. An accurate estimation is particularly essential for second-life batteries to facilitate their successful implementation in the new application.

Transforming battery data into operational decisions requires not only developing accurate machine learning (ML) models but also understanding how these models make predictions in the context of expert knowledge. In this contribution, we present our recent work on explainable machine learning applied to battery research, with an outlook on how semantic technologies - e.g., ontologies - can help to ingest the deluge of inputs, outputs, models, and patterns into actionable battery intelligence.

Basic monitoring of the BMS is carried out by derived measurements during the validation of battery cells after development. The wide range of customer-specific and geographical usage profiles can only be tested or simulated in advance to a limited extent. For a comprehensive performance or health status, further metrics from the current cell state or effects from tolerable deviations from the target value during production and development are needed.

In this lecture, I will discuss a digital twin developed in the context of ARTISTIC project1 and which gives the promise to accelerate the optimization of the manufacturing process of LIBs.