Strategies for maximal energy recovery in a battery manufacturing process
|Coordinator||Mälardalens högskola - Akademin för hållbar samhälls- och teknikutveckling, Västerås|
|Funding from Vinnova||SEK 499 999|
|Project duration||January 2018 - June 2018|
|Venture||Strategic innovation programme for process industrial IT and automation – PiiA|
Purpose and goal
The main objective of the project was to contribute to increasing knowledge levels in the field of efficient, large-scale battery manufacturing, as well as creating an overall model for the recovery and reuse of residual heat from different process stages in a battery factory. The work was divided into four different subtasks that were intended to be carried out within the project. The focus being on creating a simplified model of the battery manufacturing process, an optimal design for heat recovery, and a conceptual solution for datadriven decision support.
Expected results and effects
The project has resulted in a better knowledge of the battery manufacturing processes, from the preparation of materials to the production and formation of the battery cells. Furthermore, the project has identified a number of possible process steps where heat recovery should be profitable, with a relatively short payback period. The project propose how the process could be monitored and decision support provided. Short-tem, the industrial partners have a better understanding of how novel solutions could be developed. Long-term, the knowledge about future R&D needs has improved.
Planned approach and implementation
The project has mapped, described and created a simplified model of important process steps. Based on the available information, strategies were developed to investigate the potential for internal heat recovery. The theoretically best heat recovery potential was calculated, and a more "practical" approach used, where considering solutions that were considered to be most profitable. In parallel, a conceptual model of how different process steps can be visualized and monitored, while deviations and microtrends of different parameters could be calculated and analysed.