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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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摘要
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.
Keyword:
Parameter identification
Lithium battery
Large scale global optimization
Particle swarm optimization
Variable grouping
期刊
IF:
9.4
论文数:
4.3W
被引数:
20.2W
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