TY - GEN
T1 - Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries
AU - Pozzi, Andrea
AU - Ciaramella, Gabriele
AU - Gopalakrishnan, Krishnakumar
AU - Volkwein, Stefan
AU - Raimondo, Davide M.
PY - 2018
Y1 - 2018
N2 - Advanced battery management systems rely on dynamical models in order to provide safe and profitable battery operations. Such models need to be suitable for control and estimation purposes while, at the same time, as accurate as possible. This feature can be satisfied only if model parameters are accurately estimated. In this work we investigate the design of optimal experiments in order to minimize the uncertainty of the parameters of the Single Particle Model, in the context of Lithium-ion battery. Simulation results show the effectiveness of the proposed methodology when compared with standard current profiles (e.g. constant current).
AB - Advanced battery management systems rely on dynamical models in order to provide safe and profitable battery operations. Such models need to be suitable for control and estimation purposes while, at the same time, as accurate as possible. This feature can be satisfied only if model parameters are accurately estimated. In this work we investigate the design of optimal experiments in order to minimize the uncertainty of the parameters of the Single Particle Model, in the context of Lithium-ion battery. Simulation results show the effectiveness of the proposed methodology when compared with standard current profiles (e.g. constant current).
KW - Optimal Experimental Design
KW - Optimal Experimental Design
UR - http://hdl.handle.net/10807/193664
U2 - 10.1109/CDC.2018.8619340
DO - 10.1109/CDC.2018.8619340
M3 - Conference contribution
T3 - PROCEEDINGS OF THE IEEE CONFERENCE ON DECISION & CONTROL
SP - 6482
EP - 6487
BT - 2018 IEEE Conference on Decision and Control (CDC)
T2 - 57th IEEE Conference on Decision and Control
Y2 - 17 December 2018 through 19 December 2018
ER -
