New Orleans, Louisiana
June 26, 2016
June 26, 2016
August 28, 2016
Energy Conversion and Conservation
This paper describes how a team of five students, all but one of them undergraduates, successfully developed a State of Charge Indicator (SOCE) for the Li/CFx battery. The pedagogical methods that led to a successful outcome were originally proposed in an ASEE conference paper by the supervising professor nearly a decade ago.
Improving the accuracy of a SOCI for the Li/CFx battery is difficult due to the flat discharge profile of the battery and its non-linear response to ambient temperature. To account for these effects, an Artificial Neural Network (ANN) was designed to run on the MSP430 microcontroller. The ANN was developed and trained with data acquired from a mathematical model and laboratory testing of a Li/CFx cell. The ANN uses voltage, current, and ambient temperature for its inputs, and outputs the State of Charge (SOC) of the cell in the form of a five LED display. For military use, difficult constraints on temperature, power consumption, cost, and size were imposed.
Understanding the battery is the first task. Students performed discharge curves on a number of Li/CFx cells. From these tests, the flat discharge profile challenge became evident. A charge counter algorithm was selected, modified for temperature, rate of discharge, amount of discharge, power consumption, and battery history. Finding a simple charge counter in the technical literature, a circuit which nearly fit under a dime, a first version SOCI prototype was developed which successfully monitored voltage, current, and ambient temperature. Subsequent prototypes improved on accuracy, power consumption, and cost.
A team of five students, all but one of them undergraduates, worked on this project and learned from it for over 30 months. Assessment of their learning will follow up on the methods proposed by one of the authors nearly a decade ago. Assessment also includes quality of positions found by the students: at SpaceX, Agilent, Orbital Technologies, to an engineering faculty position, and to a large electrical equipment manufacturer. The success led to a reputation for quality work that secured follow-on Defense projects as will be described in the paper.
Hess, H. L., & William, E. J. (2016, June), Student Project to Develop a Neural Network-based State of Charge Indicator for Primary Batteries Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.25923
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