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Parameter identification for lithium batteries: Model variable-coupling analysis and a novel cooperatively coevolving identification algorithm
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DOI：10.1016/j.energy.2022.125762.png)
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Abstract
En 中文
In the operational control of renewable energy system, the efficient parameter identification for lithium battery is of great importance. In this study, the parameter identification of lithium battery is modelled as a large-scale global optimization problem with thousands of dimensionalities. In addition, the developed identification model is proved to be a partial-separable problem by comprehensively analysing its variable-coupling re-lationships, and the detailed proof is also provided. In order to overcome the high-dimensional characteristic of the developed model, a novel algorithm namely incomplete multi-context cooperatively coevolving PSO (IMCCPSO) is developed, in which some efficient algorithmic mechanisms are proposed: On one hand, the non -separable variables are grouped together with each of the separable-variable components, and the context vectors for separable and non-separable variables are discriminatively reconstituted for balancing the local and global exploration; On the other hand, the coevolving efficiency index is proposed for selecting the group-size values and coevolving rules dynamically and adaptively. Experimental results show that the developed meth-odology can effectively identify the parameters of the evaluated lithium battery bank under typical load profiles, and the developed IMCCPSO also outperforms the compared state-of-the-art algorithms on identification accu-racy and robustness.
Keywords:
Parameter identification
Lithium battery
Large scale global optimization
Particle swarm optimization
Variable grouping