Parameterisation of Electrical and Thermal Models at Module-Level for a Li-Ion Battery Digital Twin Simulation Platform

The monitoring and modelling of the Li-Ion Batteries behaviour is still a major technical challenge due to the non-linearities and coupled phenomena that determine their operation. Deployed Li-Ion Batteries (LIBs) are becoming increasingly large in scale and can consist of hundreds or thousands of individual cells. When these LIBs are in operation, there are some cell-to-cell unbalances concerning the temperature or State of Charge (SoC) of the cells composing every module. Those affect the performance of the LIBs and even their degradation. Currently, the module-level performance is evaluated based on single cell models, or averaged operating conditions, thus hindering the appropriate analysis of cell-to-cell variations. The pursuit of new, increasingly intelligent and heavier state estimation algorithms requires a significant amount of data and computational power, which can be challenging to deploy in current Battery Management System solutions. This work proposes module-level integrated electrical and thermal models based on equivalent circuits. Both have been developed and validated separately and then together on a Digital Twin Simulation Platform based on Cloud Computing. A methodology for parameterising these models is presented in this poster, and an experimental validation platform is presented.