# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
###### Abstract  
Battery management systems (BMSs) play a vital role in understanding battery performance under extreme conditions such as high C-rate testing, where rapid charge or discharge is applied to batteries. This study presents a novel BMS tailored for continuous monitoring, transmission, and storage of essential parameters such as voltage, current, and temperature in an NCA 18650 4S lithium-ion battery (LIB) pack during high C-rate testing. By incorporating deep learning, our BMS monitors external battery parameters and predicts LIB's health in terms of discharge capacity. Two experiments were conducted: a static experiment to validate the functionality of BMS, and an in operando experiment on an electrically propelled vehicle to assess real-world performance under high C-rate abuse testing with vibration. It was found that the external surface temperatures peaked at 55 \({}^{\circ}\)C during in operando flight, which was higher than that during static testing. During testing, the deep learning capacity estimation algorithm detected a mean capacity deviation of 0.04 Ah, showing an accurate state of health (SOH) by predicting the capacity of the battery. Our BMS demonstrated effective data collection and predictive capabilities, mirroring real-world conditions during abuse testing. lithium-ion batteries; deep learning; state of health; in operando monitoring; C-rate; electric propulsion; battery management system +
Footnote †: journal: _ batteries: _ batteries: _ batteries:  
indicators are derived from proprietary data acquisition (DAQ) setups employed for data collection, which can restrict flexibility and introduce logistical complexities to connectivity. DAQ configurations for LIBs can provide essential data such as voltage, current, and temperature for deep learning (DL) applications, but often suffer from being bulky, expensive, and tailored to specific research batteries or battery packs. Frequently, there is an insufficient description of the costs involved in data collection. However, DAQ collects the data required to calculate the battery performance parameters. DAQ also provides flexibility in collecting a wider range of data for various applications. Integrating DL techniques with advanced BMS presents a promising approach for the early prediction of battery performance, overcoming the challenges associated with data collection methods, and offering scalability for diverse applications. To explore the use of DL in the early validation prediction of battery performance, [PERSON] et al. [9] utilized a dataset gathered from a Maccor series 4000 tester, an expensive and highly accurate battery test system capable of driving voltages and currents, as well as accurately reporting values within \(\pm\)0.02% and \(\pm\)0.05% of the full-scale range. The temperature data were gathered, but no specific details were given on the type of DAQ used or the data rate. [PERSON] et al. [10] describe a novel extended Kalman filter (EKF) intent on improving SOC or SOH estimation performance using a Keithley 2461 source meter unit to measure cell voltage and currents. A thermocouple data logger was used for temperature data collection, with no specifics given about sensors except a mention of K-type thermocouples. Continuing the recent trend of data logging setups for DL data, [PERSON] et al. [11] developed a machine learning (ML) method using massive amounts of data for battery health prediction with electrochemical impedance spectroscopy (EIS). The dataset utilized by [PERSON] et al. [11] was obtained from another study by [PERSON] et al. [12], which focused on a reduced-order LIB life model. This dataset, used for both modern reduced-order and ML models, was captured with a Fujitsu TNW 5 V, 360 A battery cycler. While such sophisticated battery testing systems provide highly accurate data for DL and ML applications, they pose a significant challenge for operand studies owing to their size, cost, and complexity.