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Overview

Battery-powered devices have become ubiquitous in the modern world, from tiny headsets to cameras, cell phones and laptops to hybrid and electric vehicles. Yet the battery is not a new invention. Battery artifacts date back to the early centuries A.D. (the Baghdad battery) and electric cars were favored over their gasoline counterparts in the late nineteenth century because of higher reliability. However, the uncertainty in determining battery life plagued electric vehicles then as it does now. Consequences of battery exhaustion may range from reduced performance to operational impairment and even to catastrophic failures. Most new battery research has focused on the development of new materials in the pursuit of higher energy and power density, longer cycle-life and shorter recharge times. However, successfully predicting and managing the life of batteries is in equal parts necessary for widespread adoption of these cutting edge technologies.

Battery Health Management

Project goals

  • Investigate prognostic algorithms
  • Provide framework for a variety of prognostic applications
  • Improve state of the art of battery health management
  • Demonstrate capability on hardware
  • Integrate prognostics with decisioning framework

Application: eUAV Battery Health Management

One of the most critical applications of this technology is in the field of electric vehicles. Electric UAVs (unmanned aerial vehicles) have become the new face of green aviation. They are being increasingly deployed in military, civilian and scientific missions all over the globe. However, like ground vehicles, battery powered electric UAVs suffer from uncertainties in estimating the remaining charge and hence most flight plans are highly conservative in nature. Usually combustion based powertrains run within narrow bands of RPMs with metered fuel delivery. This combined with a known volume fuel tank allows reasonably accurate predictions of remaining use time or travel distance. Batteries on the other hand, decrease in capacity with time and usage. Various factors like ambient storage temperatures and the SOC at which the battery was stored affects capacity fade. Additionally, the amount of usable charge of a battery for a given discharge profile is not only dependent on the starting SOC, but also other factors like battery health and the discharge or load profile imposed.

This problem is more pronounced in battery powered electric UAVs since different flight regimes like takeoff/landing and cruise have different power requirements and a dead stick condition (battery shut off in flight) can have catastrophic consequences. A reliable battery life prediction integrated with the decisioning process of the operator/pilot can prevent such mishaps while optimizing operational efficiency.

Questions to be answered

  • Can the current mission be completed?
    • Given the health of the battery, is there enough charge left for anticipated load profile (within allowable uncertainty bounds)
    • Dominant metrics: state of charge (SOC), state of health (SOH)
  • Can future missions be completed?
    • Given the health of the battery, at what point can typical future missions not be met?
    • Dominant metrics: end of life (EOL), state of health (SOH)
  • Mission definition:
    • Aeronautics: one or more defined flights with ancillary power demands
    • Space: Sequential unique or repeated tasks
  • Develop a model that makes a prediction of end-of-charge and end-of-life based on rapid state of health (SOH) assessment

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