As a result, as multidisciplinary research highlights in the fields of electrochemistry, materials science and intelligent algorithms, researching on the state of health estimation of lithium-ion batteries in energy storage power stations has attracted the attention of experts and scholars from various fields [ 6, 7, 8 ].
Among several battery technologies, lithium-ion batteries (LIBs) exhibit high energy efficiency, long cycle life, and relatively high energy density. In this perspective, the properties of LIBs, including their operation mechanism, battery design and construction, and advantages and disadvantages, have been analyzed in detail.
Among a great number of attribute data, the discharge quantity q of the cluster and the sharp voltage drop amplitude Δ uohm of the cluster and cells in it are extracted, and the orderliness of these characteristic data is analyzed by the information entropy to realize the effective estimation of the health state of the energy storage power station;
In the context of energy management and distribution, the rechargeable lithium-ion battery has increased the flexibility of power grid systems, because of their ability to provide optimal use of stable operation of intermittent renewable energy sources such as solar and wind energy .
Changes of the average value of the characteristic data for the energy storage power station in several days From Fig. 14, it can be seen that the average value of discharged quantity and the average value of sharp voltage drop have little change, which can simply reflect the aging degree of battery clusters in the energy storage power station.
For the energy storage power station in Hunan Province sampled in the paper, the entropy value Hq of discharged quantity is stable at 0.6931, and the entropy value HΔu of the sharp voltage drop amplitude is stable in the range of 1.2–1.4, consisting with ΔSOC statistical analysis of cells in the cluster;
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