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

Autor: Subramanya Mayya Kolake, Ashish Khandelwal, Han Seongho, Piyush Tagade, Krishnan S. Hariharan, Arunava Naha, Jongmoon Yoon, Samarth Agarwal, Arijit Guha, Bookeun Oh
Rok vydání: 2020
Předmět:
Zdroj: Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
ISSN: 2045-2322
Popis: 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 ($${v}_{sei}$$vsei) are collected. The initial $${v}_{sei}$$vsei point is fixed and then for every 1.5% increase in the Coulomb counting, other points are selected. The difference between the $${v}_{sei}$$vsei values ($$\Delta {v}_{sei}$$Δvsei) 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(n4). 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.
Databáze: OpenAIRE
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