We are seeking a database diagnostic and analysis specialist to model battery measurement parameters. This would be in the area of diagnostics, prognostics, predictive technology methods to manage large groups of mission critical batteries and battery systems. The ideal candidate would be a student, or a recent graduate at the PhD candidate level, with training in engineering, statistics, mathematics, and information technology.

Battery, monitoring, and measurement techniques informal tutorials and training is available.

Task description follows:

Application of Prognostic Algorithms to large databases of monitored values in order to determine remaining useful life of stationary Lead Acid batteries and systems in Standby applications.

Typical battery aging factors are ohmic in nature (resistance, impedance, conductance, etc.) Other measurement parameters include Temperature, Voltage, Current, cycling data, ripple, etc. All measurement parameters are defined in IEEE Standards.

Algorithms for prediction could include, but are not limited to:

• autoregressive integrated moving average (ARIMA)
• extended Kalman filtering (EKF)
• Bayesian techniques
• relevance vector machine (RVM)
• particle filters
• Monte Carlo Simulations
• Neural techniques
• Etc.

The focus area is a database containing measurement data on over 1 million batteries with 500 million points of data that has been in continuous collection for over 15 years. Parameter collection and archival is continuing and is monitored by a patented web based collection and analysis system with human interaction. Applications include trend detection, alerting, fault detection, fault prediction, remote diagnosis, and remaining useful life estimation.

The ideal candidate must be a self-starter who can communicate with fellow workers at all skill levels, management, and customers. Communication skills include integration of prognostic data, algorithms into existing software analysis systems and modules.