Model-based Knowledge Management in HV Battery Development 2024-01-2902

In the dynamic landscape of battery development, the quest for improved energy storage and efficiency has become paramount. The contemporary energy transition, coupled with growing demands for electric vehicles, renewable energy sources, and portable electronic devices, has underscored the critical role that batteries play in our modern world. To navigate this challenging terrain and harness the full potential of battery technology, a well-defined and comprehensive data strategy resp. knowledge management strategy are indispensable. Conversely, the imminent and rapid progression of artificial intelligence (AI) is poised to have a substantial impact on the forthcoming landscape of work and the methodologies organizations employ for the management of their knowledge management (KM) procedures. Conventional KM endeavors encompass a spectrum of activities such as the creation, transmission, retention, and evaluation of an enterprise’s knowledge over the entire knowledge lifecycle. However, these efforts frequently overlook the ongoing advancements within the domain of AI. Consequently, organizations grapple with the integration of AI into their operational milieu to harness enhanced efficiency in outcomes. This paper will draw upon the tenets of the already established KM strategies in AVL High Voltage Energy Systems Team and AI-centric paradigm tailored for the implementation of KMS within organizational frameworks. Our proposed approach serves to fortify the foundations of KM strategy by outlining the ways in which AI interfaces with existing operational procedures. This, in turn, enables a comprehensive comprehension of the potential roles AI could assume in the intricate interplay between knowledge workers and AI systems.