In:
Scientific Reports, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2020-06-12)
Abstract:
Accurate state of health (SOH) estimation of rechargeable batteries is important for the safe and reliable operation of electric vehicles (EVs), smart phones, and other battery operated systems. We propose a novel method for accurate SOH estimation which does not necessarily need full charging data. Using only partial charging data during normal usage, 10 derived voltage values ( $${v}_{sei}$$ v s e i ) are collected. The initial $${v}_{sei}$$ v s e i point is fixed and then for every 1.5% increase in the Coulomb counting, other points are selected. The difference between the $${v}_{sei}$$ v s e i values ( $$\Delta {v}_{sei}$$ Δ v s e i ) and the average temperature during the charging form the feature vector at different SOH levels. The training data set is prepared by extrapolating the charging voltage curves for the complete SOH range using initial 400 cycles of data. The trained artificial neural network (ANN) based on the feature vector and SOH values can be used in any battery management system (BMS) with a time complexity of only $$O({n}^{4})$$ O ( n 4 ) . Less than 1% mean absolute error (MAE) for the test cases has been achieved. The proposed method has a moderate training data requirement and does not need any knowledge of previous SOH, state of charge (SOC) vs. OCV relationship, and absolute SOC value.
Type of Medium:
Online Resource
ISSN:
2045-2322
DOI:
10.1038/s41598-020-66424-9
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2020
detail.hit.zdb_id:
2615211-3
Permalink