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Plett, Gregory L.

Publications and source records attributed to Plett, Gregory L..

GATE Center of Excellence in Innovative Drivetrains in Electric Automotive Technology Education (IDEATE) (Final Report)

The IDEATE project recognized that this is a critical point in history where advanced engineering solutions are required to propel the U.S. automotive industry irrevocably to the next level: the electrified drivetrain. The existing workforce is not sufficient to this task. It is imperative that automotive engineers with traditional backgrounds focused on internal-combustion and mechanical-drivetrain technologies be retrained with the most current advanced solutions in battery controls and vehicle power electronics. For the long-term success of the U.S. automakers, it is even more important that a future workforce be developed that has both broad and deep comprehension of the issues involved and the most advanced solutions known to these problems. Two campuses of the University of Colorado system joined forces to establish the GATE Center of Excellence in Innovative Drivetrains in Electric Automotive Technology Education (IDEATE). The University of Colorado Boulder (CU-Boulder) is widely regarded as having one of the top graduate programs in power electronics in the country; the University of Colorado Colorado Springs (UCCS) has unrivaled expertise in algorithms for automotive battery control. By collaborating, IDEATE built on our team’s proven strengths to develop innovative curricula and to initiate courses and programs that have provided (and continue to provide) students with a unique opportunity for holistic and specialty education in electric drivetrain technology (part-way through the project, we invited Utah State University to join the project, adding to our strength in power electronics for battery application).

42 ENGINEERING↗

Model predictive control and optimization for battery charging and discharging

An apparatus for model predictive control (“MPC”) is disclosed. A method and system also perform the functions of the apparatus. The apparatus includes a measurement module that receives battery status information from one or more sensors receiving information from a battery cell, and a Kalman filter module that uses a Kalman filter and the battery status information to provide a state estimate vector. The apparatus includes a battery model module that inputs the state estimate vector and battery status information into a battery model and calculates a battery model output, the battery model representing the battery cell, and an MPC optimization module that inputs one or more battery model outputs and an error signal in a model predictive control algorithm to calculate an optimal response. The optimal response includes a modification of the error signal.

Trimboli, Michael Scott↗

Autonomous battery control and optimization

An apparatus includes a battery state module that determines a battery state of each of a plurality of battery cells forming a battery unit. A battery state includes a health of the battery cell. A battery state of a battery cell differs from a battery state of other battery cells of the battery unit. Each battery cell is connected to a shared bus through a bypass converter that provides power from the battery cell to the shared bus. A charge/discharge modification module determines, based on battery state, an amount to vary a charging characteristic for each battery cell compared to a reference charging characteristic. Each charging characteristic varies as a function of a reference state. A charge/discharge module adjusts charging/discharging of a battery cell of the battery unit based on the charging characteristic of the battery cell.

Zane, Regan A.↗