Battery capacity estimation plays an important role in the normal operation of electric vehicles. In this work, we presented a data-driven approach for capacity estimation of batteries based on voltage dependent health indicators. A difference-based model of discharge voltage and capacity was built. Next, two health indicators are constructed from partial voltage curves, and correlations between capacity and health indicators are investigated. Afterward, the capacity estimation approach based on Gaussian process regression model is expounded. To validate the accuracy of the proposed method, a case study is carried out. Results demonstrate that RMSE and RMSPE of capacity estimation are lower than 1% compared with actual capacity.
Machine condition monitoring (MCM) has become an important tool to avoid sudden machine breakdown and gaining more economic profits. Tasks including early fault detection and monotonic degradation assessment are important in MCM. For the incipient fault detection, statistics such as kurtosis, Gini index are widely utilized, but they cannot give an accurately incipient fault detection time, and many fluctuations may exhibit. For the monotonic degradation assessment, root-mean-square are commonly used, however, it is sensitive to energy, and cannot show distinct degradation tendency in an early fault state. Those drawbacks have limited the development of practical MCM algorithms. To address those issues, this paper proposed four parameterized statistics for simultaneously early fault detection and monotonic degradation assessment. The four parameterized statistics can be health indicators and simplify the MCM algorithms, which can be beneficial to the practical MCM applications.
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