Details

Autor(en) / Beteiligte

• Naha, Arunava
• Han, Seongho
• Agarwal, Samarth
• Guha, Arijit
• Khandelwal, Ashish
• Tagade, Piyush
• Hariharan, Krishnan S.
• Kolake, Subramanya Mayya
• Yoon, Jongmoon
• Oh, Bookeun

Titel

An Incremental Voltage Difference Based Technique for Online State of Health Estimation of Li-ion Batteries

Ist Teil von

• Scientific reports, 2020-06, Vol.10 (1), p.9526-9526, Article 9526

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2020

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.