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).
Pozzi, A., Ciaramella, G., Gopalakrishnan, K., Volkwein, S., Raimondo, D., Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries, Contributed paper, in 2018 IEEE Conference on Decision and Control (CDC), (United States, 17-19 December 2018), IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA 2018:<<PROCEEDINGS OF THE IEEE CONFERENCE ON DECISION & CONTROL>>, 6482-6487. 10.1109/CDC.2018.8619340 [http://hdl.handle.net/10807/193664]
Optimal design of experiment for parameter estimation of a Single Particle Model for lithium-ion batteries
Pozzi, Andrea
Primo
;
2018
Abstract
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).
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