Engineering Papers⌕ Search

Engineering topics

Evzelman, Michael

Publications and source records attributed to Evzelman, Michael.

4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

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↗
DOE OSTI2019

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.↗
DOE OSTI2017

Advanced Cell-Level Control for Extending Electric Vehicle Battery Pack Lifetime

A cell-level control approach for electric vehicle battery packs is presented that enhances traditional battery balancing goals to not only provide cell balancing but also achieve significant pack lifetime extension. These goals are achieved by applying a new life-prognostic based control algorithm that biases individual cells differently based on their state of charge, capacity and internal resistance. The proposed life control approach reduces growth in capacity mismatch typically seen in large battery packs over life while optimizing usable energy of the pack. The result is a longer lifetime of the overall pack and a more homogeneous distribution of cell capacities at the end of the first life for vehicle applications. Active cell balancing circuits and associated algorithms are used to accomplish the cell-level life extension objectives. This paper presents details of the cell-level control approach, selection and design of the active balancing system, and low-complexity state-of-charge, capacity, and series-resistance estimation algorithms. A laboratory prototype is used to demonstrate the proposed control approach. The prototype consists of twenty-one 25 Ah Panasonic lithium-Ion NMC battery cells from a commercial electric vehicle and an integrated BMS/DC-DC system that provides 750 W to the vehicle low voltage auxiliary loads.

25 ENERGY STORAGE↗