Applications include electric vehicles, medical devices, robotics, power systems, and underwater unmanned vehicles.
Battery health monitoring is an emerging technology field that seeks to predict the remaining useful life (RUL) of battery systems before they run out of charge. Such predictive measures require interpretation of large amounts of battery status data within a Bayesian prognostic framework.
A battery data acquisition and logging system was developed that processes and reports analog sensor data in real time for wireless transmittal. In combination with customized algorithms, the system is part of a novel battery health management structure for electric unmanned aerial vehicles (UAVs). Constructed with commercial off-the-shelf (COTS) parts, this low-cost and novel battery monitoring system (BMS) is adaptable to multiple types of battery chemistry, creating cross-platform capabilities for a wealth of sensing needs. In addition to use with electric UAVs, potential applications include electric vehicles (EVs), medical devices, instrumentation, and robotics.
The system uses an embedded processor board for digital signal acquisition and an embedded computer-on-module expansion board for recording and manipulating data. Custom-developed Ccode runs on both platforms and enables onboard data processing in addition to a binary data stream output via an RS-232 data link. Within the BMS structure, the system creates an ASCII log of connected sensors that transmits data to a laptop receiver. Measurements include battery voltage, temperature, and state of health for multiple Li-ion batteries simultaneously. Results are archived on a local memory card with flash memory capability. Wireless transmittal is accomplished with a serial output port attached to a wireless transmitter.
The system prevents occurrence of catastrophic battery failure by predicting the RUL of battery systems when used in combination with customized algorithms. It enables a critical, real-time monitoring capability that allows an immediate and controlled response to avoid battery failure. The system also provides a robust platform for multiple sensing needs, with the potential for miniaturization.
The system works in tandem with a NASA-developed algorithm to collect, interpret, and transmit critical battery health data in order to generate meaningful battery life information. The methodology does not simply provide time-to-failure estimates, but further generates a probability distribution over time that best encapsulates the uncertainties inherent in system models. Such information enables real-time monitoring capability beyond that which is currently available, particularly for applications where unanticipated battery performance may lead to catastrophic failures, such as aerospace and medical device systems. For EVs, the technology can help to mitigate the driving distance, battery life, and thermal uncertainties that plague high-cost EV batteries.
This work was done by Ed Koshimoto of Armstrong Flight Research Center. DRC-011-006