Utah State University

Ford Global Technolgoies, LLC

Massachusetts Institute of Technology

University of Colorado

Alliance for Sustainable Energy, LLC

Michael Evzelman

Regan Andrew Zane

Dragan Maksimovic

Richard Dyche Anderson

Gregory L Plett

Daniel Jes Costinett

Kandler Alan Smith

Michael Scott Trimboli

Karl D Iagnemma

Anette E Hosoi

Youzhi Liang

Matthew F Demers

Jose R Alvarado

The systems and methods described herein are directed towards a solid state pumping system that utilizes an electric field applied across a channel formed within the solid state pump to move electro-rheological (ER) fluid from an inlet fluidly coupled to a first end of the channel to an outlet fluidly coupled to a second end of the channel. The solid state pumping system may include first, second and third plate with the second plate disposed between the first and third plate. The second plate may include a channel having first and second circuits coupled to opposing sides of the channel. In an embodiment, in response to a voltage applied thereto, the first and second circuits can provide an electric field voltage across the channel such that in response to the electric field voltage the ER fluid moves from the first end to the second end of the channel.

Solid state pump using electro-rheological fluid

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.

Model predictive control and optimization for battery charging and discharging

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.

Autonomous battery control and optimization

For battery control, an apparatus includes a shared bus and a plurality of isolated direct current (DC) to DC bypass converters. Each bypass converter is associated with one battery unit. Inputs of each bypass converter are in parallel electrical communication with the associated battery unit. Outputs of each bypass converter are in parallel electrical communication with the shared bus. Each bypass converter estimates a battery state for each battery unit and controls the battery state to a reference state.

Battery control

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Logan, UT, United States of America

Michael Evzelman