Arima Masahito, Lin Lei, Fukui Masahiro
Research & Development Strategy Department, Daiwa Can Company, Sagamihara 252-5183, Japan.
Graduate School of Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan.
Sensors (Basel). 2022 Jul 9;22(14):5156. doi: 10.3390/s22145156.
The main analyzed aspect of lithium-ion battery (LIB) degradation so far has been capacity fading. On the other hand, interest in efficiency degradation has also increased in recent years. Battery aggregation, which is expected to absorb the surplus of variable renewable energies such as photovoltaic energy, is affected by efficiency degradation in terms of the decreases in the economic gain and renewable energy use. Reusable LIBs could be used as aggregation components in the future; naturally, the variety of charge-discharge efficiencies might be more complex. To improve the operation efficiency of aggregation, including that obtained using reusable LIBs, we propose the Kalman-filter-based quasi-unsupervised learning of the characteristic profiles of LIBs. This method shows good accuracy in the estimation of charge-discharge energy. It should be emphasized that there are no reports of charge-discharge energy estimation using the Kalman filter. In addition, this study shows that the incorrect open-circuit voltage function for the state of charge, which is assumed in the case of a reused battery, could be applied as the reference for the Kalman filter for LIB state estimation. In summary, it is expected that this diagnosis method could contribute to the economic and renewable energy usage improvement of battery aggregation.
到目前为止，锂离子电池（LIB）退化的主要分析方面一直是容量衰减。另一方面，近年来对效率退化的关注也有所增加。有望吸收光伏能源等可变可再生能源过剩电量的电池聚合，在经济收益和可再生能源利用减少方面会受到效率退化的影响。可重复使用的锂离子电池未来可作为聚合组件使用；自然而然地，充放电效率的多样性可能会更加复杂。为了提高聚合的运行效率，包括使用可重复使用的锂离子电池所获得的效率，我们提出了基于卡尔曼滤波器的锂离子电池特性曲线准无监督学习方法。该方法在充放电能量估计方面显示出良好的准确性。应当强调的是，目前尚无使用卡尔曼滤波器进行充放电能量估计的相关报道。此外，本研究表明，在电池重复使用情况下所假设的不正确的充电状态开路电压函数，可作为卡尔曼滤波器用于锂离子电池状态估计的参考。总之，预计这种诊断方法有助于提高电池聚合的经济性和可再生能源利用率。
