Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model
Journal article, 2018

Efficient battery condition monitoring is of particular importance in large-scale, high-performance, and safety-critical mechatronic systems, e.g., electrified vehicles and smart grid. This paper pursues a detailed assessment of optimization-driven moving horizon estimation (MHE) framework by means of a reduced electrochemical model. For state-of-charge estimation, the standard MHE and two variants in the framework are examined by a comprehensive consideration of accuracy, computational intensity, effect of horizon size, and fault tolerance. A comparison with common extended Kalman filtering and unscented Kalman filtering is also carried out. Then, the feasibility and performance are demonstrated for accessing internal battery states unavailable in equivalent circuit models, such as solid-phase surface concentration and electrolyte concentration. Ultimately, a multiscale MHE-type scheme is created for State-of-Health estimation. This study is the first known systematic investigation of MHE-type estimators applied to battery management.

Battery management

moving horizon estimation (MHE)

state-of-charge (SOC) estimation

electrochemistry

Li-ion battery

state-of-health (SOH) estimation

Author

Xiaosong Hu

Chongqing University

University of California

D. P. Cao

Cranfield University

Bo Egardt

Chalmers, Electrical Engineering, Systems and control, Automatic Control

IEEE/ASME Transactions on Mechatronics

1083-4435 (ISSN)

Vol. 23 1 167-178

Subject Categories

Computational Mathematics

Probability Theory and Statistics

Control Engineering

DOI

10.1109/TMECH.2017.2675920

More information

Latest update

4/20/2018