Model-Based Lithium-Ion Battery Resistance Estimation From Electric Vehicle Operating Data
Journal article, 2018

State-of-health estimates of batteries are essential for onboard electric vehicles in order to provide safe, reliable, and cost-effective battery operation. This paper suggests a method to estimate the 10-s discharge resistance, which is an established battery figure of merit from laboratory testing, without performing the laboratory test. Instead, a state-of-health estimate of batteries is obtained using data directly from their operational use, e.g., onboard electric vehicles. It is shown that simple dynamical battery models, based on a current input and a voltage output, with model parameters dependent on temperature and state of charge, can be derived using AutoRegressive with eXogenous input models, whose order can be adjusted to describe the complex battery behavior. Then, the 10-s discharge resistance can be conveniently computed from the identified model parameters. Moreover, the uncertainty of the estimated resistance values is provided by the method. The suggested method is validated with usage data from emulated electric vehicle operation of an automotive lithium-ion battery cell. The resistance values are estimated accurately for a state-of-charge and temperature range spanning typical electric vehicle operating conditions. The identification of the model parameters and the resistance computation are very fast, rendering the method suitable for onboard application.

Author

Giuseppe Giordano

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Jonas Sjöberg

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Verena Klass

Royal Institute of Technology (KTH)

Mårten Behm

Royal Institute of Technology (KTH)

Göran Lindbergh

Royal Institute of Technology (KTH)

IEEE Transactions on Vehicular Technology

0018-9545 (ISSN)

Vol. 67 5 3720-3728

Subject Categories

Communication Systems

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/TVT.2018.2796723

More information

Latest update

5/14/2018