A Miniature Millimeter-Wave Radar Based Contactless Lithium Polymer Battery Capacity Sensing with Edge Artificial Intelligence

Abstract

It is widely known that the remaining capacity of any lithium polymer (Li-Po) rechargeable battery is hard to know precisely in real time. Battery management systems (BMS) are used to precisely monitor battery health including state of charge (SOC) and the remaining capacity. But, BMS is usually limited by its size, power consumption, and compatibility, which could potentially have a negative impact on the battery powered mission such as long distance drone flights. Therefore, in this study, we present a new approach for (Li-Po) battery capacity sensing using a miniature millimeter Wave radar array in real-time. We assessed our contactless battery capacity sensing method with a classifier algorithm using labeled data collected from real battery discharging load circuit experiments. According to the results, our technique achieved 98.8% classification accuracy across eight different battery capacity levels. The machine learning algorithm is computationally light and easily implementable on edge computing platforms such as the Raspberry Pi. This work confirms that it is feasible to sense the real-time remaining capacity of Li-Po batteries that can lead to a capacity-aware cognitive battery management system.