Twaice

Battery analytics software based on digital twins to optimize li-ion batteries

01.

Identification

Twaice supports companies across industries with predictive battery analytics software.

Twaice solves many problem such as degradation and safety issues of li-ion batteries by offering an advanced predictive analytics software. Twaice creates models of the batteries (once per battery type) and constantly feed these models with data from the battery management system (BMS) during actual operation of the battery. This means that there is one specific model per battery in the field, a concept that is commonly referred to as digital twin. This hybrid approach of empirical-analytical models & AI driven data analytics not only provides the current condition of the battery at all times but also allows unique aging and lifetime predictions. Putting this back into the context of the major challenges with regard to li-ion batteries: a) The technology supports OEMs [Original Equipment Manufacturers] of battery products by deploying the solution during the pre-series test and validation phase where the technology enables a more realistic (compared to lab tests with accelerated aging) picture of battery performance in actual operation. b) Battery systems usually need to be disassembled in order to analyze the current condition. With Twaice's algorithms with detect situations that impact the battery and update the model accordingly without any test required. c) The core of Twaice is the determination of the current state of health (capacity & impedance) and the prediction of these parameters. This enables optimal operation and topics such as predictive maintenance. More, the algorithm detect safety-relevant effects such as lithium plating early on. It reduce the CO2 impact of li-ion batteries by optimizing the complete lifecycle with improved development processes, a longer lifetime with optimal battery use and a second life for energy storages that are not suitable for the mobility application anymore.