# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 5 Conclusions  
In operando data collection from an electrically propelled vehicle needs a portable BMS capable of operating under abusive conditions, such as high C-rate discharge in LIBs. In this study, a novel BMS architecture was proposed to record, transmit, and receive data using edge and cloud frameworks that can handle high C-rate discharges. This BMS architecture builds upon traditional systems by replacing the conventional approach that relies on a CAN bus and an edge computer [54].  
Two types of tests have been performed: on-ground tests and in-air or in operando tests. The highest surface temperature, both on-ground and in-air, was observed at the end of the constant current phase of discharge. In-air tests exhibited an increased surface temperature of the battery, including a differential temperature between each cell in the battery pack, with the maximum surface temperature reaching close to 55 \({}^{\circ}\)C. This increase may be attributed to factors such as aging, SOC changes, and many more. However, such a significant increase in surface temperature can eventually lead to the failure of that cell or even the entire battery pack. Throughout the 42 cycles performed on-ground and the 20 cycles conducted in-air, the surface temperature increased over the cycles.  
Using the collected data, the CD-Net was deployed in operando to predict the SOH of the battery. The model's capacity prediction for the upcoming cycle was consistent during on-ground testing, with a mean deviation of \(-0.026\) Ah. However, during in-air collection, the predictions exhibited a mean difference of 0.046 Ah, despite fluctuations in capacity measured by the coulomb counting method. Within the threshold range of the current-voltage sensor (\(\pm 32\) A), the BMS was able to perform data collection effectively.  
Future works include increasing the volume of data collected to observe the temperature differential and make changes to the battery pack. To make electric propulsion more sustainable, batteries need to be monitored using the latest DL models integrated into BMSs with edge and cloud support.  
J.V.R.V.: Conceptualization, Methodology, Investigation, Data Curation, Visualization, Validation, Formal Analysis, Writing--Original Draft, Writing--Review and Editing; A.S.: Conceptualization, Methodology, Investigation, Visualization, Writing--Original Draft; M.S. ([PERSON]): Methodology, Investigation, Software, Data Curation, Writing--Review and Editing; M.S. ([PERSON]): Methodology, Visualization, Validation, Formal Analysis, Writing--Original Draft, Writing--Review and Editing; V.T.: Conceptualization, Investigation, Visualization, Formal Analysis, Writing--Review and Editing, Resources, Supervision, Project Administration, Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Office of Naval Research for the financial support grant N00014-22-1-2079 (program manager [PERSON]), the School of Aeronautics and Astronautics.  
Data will be made available on request.  
The authors declare no conflicts of interest.