# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
###### Abstract
Measurement of the surface temperature of LIBs is crucial because of its direct correlation with their safety and performance [13]. Elevated temperatures can accelerate the degradation processes, increase the risk of thermal runaway, and compromise the overall stability of the battery system [13]. Monitoring and controlling the surface temperature is essential to prevent overheating and mitigate the potential hazards associated with LIBs [14]. With limitations imposed by computational power and storage, a compact design using a single integrated circuit is needed with the possible integration of cloud-based measurements [15]. A modern BMS is also a critical and underutilized repository of battery data that can be valuable for DL applications. In one study, [PERSON] et al. [16] developed a temperature-based BMS using linear regression to predict and prevent over-discharge in LIBs. Their setup utilized commercially available prismatic cells with lithium cobalt oxide (LCO) cathode, cycled using an eight-channel battery analyzer (BAn). The external temperatures were monitored using a resistance temperature detector (RTD) with a DAQ module. This setup successfully detected and prevented over-discharge within 0.1 V of the cutoff voltage. [PERSON] et al. [17] developed a method using Gaussian process regression (GPR) to predict the discharge capacity of LIBs after nail puncture, utilizing incremental capacity analysis data. Their approach achieved high accuracy in forecasting battery performance under damage conditions, with a mean coefficient of determination of 0.923. This study showcases how machine learning algorithms can be effectively applied to complex battery systems, particularly in scenarios involving abuse of the battery. In a separate study, [PERSON] et al. [18] examined ML-driven prognostication of SOC, SOH, and RUL by employing a GPR ML model. [PERSON] et al. [19] and other researchers elaborate on a spectrum of ML techniques for SOC estimation. [PERSON] et al. [20] presented an online RUL estimation technique that extracts pivotal data features, followed by algorithmic classification and prediction using a support vector machine-based ML model. [PERSON] al. [21] discuss a plethora of conventional methods for predicting RUL, encompassing both ML and non-ML paradigms. [PERSON] et al. [22] employed transfer and ensemble learning to predict capacity with lesser cycling data. [PERSON] et al. [23] utilized convolutional neural network short-term charging data to estimate maximum and remaining capacities. More information on the development of ML algorithms for battery life predictions can be found in our previous research [24]. However, there remains a critical gap in the development of practical, real-time BMS that can operate under high C-rate discharge conditions and integrate with advanced ML-based prediction models in real time. Along with the SOH, the SOC, RUL, and the C-rate are also important parameters for the performance of LIBs. C-rate is the ratio of battery current to the rated capacity. An increase in the C-rate of the battery causes a capacity fade or increase in battery degradation and a reduction in time to complete one full charge or discharge cycle [25]. Such increases in the C-rate are often required for applications like aviation [26], electric vehicle charging, etc. [27; 28]. [PERSON] et al. [29] performed testing on overcharge and over-discharge rates to investigate the fire and thermal characteristics of batteries. It was observed that the surface temperature of the battery increases with an increase in the C-rate. Previous work on high C-rates has also involved the development of accelerated degradation models of batteries [20]. In the DL domain, data holds paramount importance. However, current data collection methods often face challenges such as complex setups and sensor accuracy ambiguities.