Assessing the Performance of Model-Based Energy Saving Charging Strategies in Li-Ion Cells

Andrea Pozzi, Marcello Torchio, Davide M. Raimondo

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

Li-ion batteries are widely employed as sources of portable energy. Advanced Battery Management Systems (ABMSs) rely on models to optimize battery performance (e.g. time-charge, life-time). The aim of this work is to assess if an energy efficiency improvement with respect to standard charging protocols can be obtained from model-based optimization in the time or in the frequency domain. Time-domain optimization is first considered and it is shown that only negligible improvements can be obtained. Then, motivated by successful experiments (e.g. [1], [2]), frequency-domain optimization is also investigated. Within this context, we prove that linear models are inadequate for providing any improvement. Surprisingly, even the mostly used electrochemical model available in literature (P2D), fails to capture the energy dissipation reduction with sinusoidal input current. These results show that currently available models are not suitable for the design of model-based energy saving strategies.
Lingua originaleEnglish
Titolo della pubblicazione ospite2018 IEEE Conference on Control Technology and Applications, CCTA 2018
Pagine806-811
Numero di pagine6
DOI
Stato di pubblicazionePubblicato - 2018
Evento2nd IEEE Conference on Control Technology and Applications, CCTA 2018 - Danimarca
Durata: 21 ago 201824 ago 2018

Convegno

Convegno2nd IEEE Conference on Control Technology and Applications, CCTA 2018
CittàDanimarca
Periodo21/8/1824/8/18

Keywords

  • battery management systems
  • Model predictive control

Fingerprint

Entra nei temi di ricerca di 'Assessing the Performance of Model-Based Energy Saving Charging Strategies in Li-Ion Cells'. Insieme formano una fingerprint unica.

Cita questo