Maximum battery runtime and low power dissipation are the key points for energy harvesting devices development. Therefore, an accurate battery model, describing the static and dynamic battery behaviour, plays an important role in estimating battery state over time and in a different operating conditions. This work proposes a dynamic hybrid model to approximate the battery State of Charge (SOC) and the discharge characteristic, using a swarm-intelligence optimization algorithm, the Continuous Flock of Starling Optimization (CFSO). Simulation and results are shown, highlighting the efficiency of the presented identification strategy.
A novel method for dynamic battery model identification based on CFSO / Lucaferri V.; Lozito G.M.; Fulginei F.R.; Salvini A.. - ELETTRONICO. - (2019), pp. 57-60. (Intervento presentato al convegno 15th Conference on Ph.D. Research in Microelectronics and Electronics, PRIME 2019 tenutosi a che nel 2019) [10.1109/PRIME.2019.8787760].
A novel method for dynamic battery model identification based on CFSO
Lozito G. M.;
2019
Abstract
Maximum battery runtime and low power dissipation are the key points for energy harvesting devices development. Therefore, an accurate battery model, describing the static and dynamic battery behaviour, plays an important role in estimating battery state over time and in a different operating conditions. This work proposes a dynamic hybrid model to approximate the battery State of Charge (SOC) and the discharge characteristic, using a swarm-intelligence optimization algorithm, the Continuous Flock of Starling Optimization (CFSO). Simulation and results are shown, highlighting the efficiency of the presented identification strategy.
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