AAC 2022 Paper Abstract

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Paper TuAT3.2

Wu, Hai (General Motors), Wang, Yue-Yun (General Motors R&D and Strategic Planning), Rober, Kevin B. (General Motors)

Battery Power Prediction for Protecting Droop Cells from Over-Discharging

Scheduled for presentation during the Regular Session "Energy Storage and Charging Systems" (TuAT3), Tuesday, August 30, 2022, 10:20−10:40, Pfahl Hall 140

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on March 29, 2024

Keywords Battery management systems

Abstract

The estimation of battery parameters and states at both battery pack and cell levels are studied using the extended Kalman filter (EKF). The estimation results are evaluated with Chevy BOLT electric vehicle data. The cell level estimation when applying EKF is more challenging and an appropriate scaling of cell parameters is required, due to the fact that the current and voltage values of a cell are quite different in magnitude. This estimation study also shows that the cell-level parameter estimation can provide important health information to a battery management system (BMS) for diagnostics and prognostics. The cell-level estimates are then used to predict the voltage limited battery pack power available to a vehicle when a weak cell or droop cell occurs. This power limit can avoid over-discharging a droop cell and protect it from further damage.

 

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