• RAFATH UNNISA
• Smt.E.KALPANA

The state-of-health (SOH) estimation of lithium-ion batteries (LIBs) is of great importance to the safety of systems. In this, a novel ensemble learning method is proposed to accurately estimate the SOH of LIBs. The feature is defined as voltage, current, current capacity, and discharge capacity are extracted as the key health indicator for the LIBs. The Pearson correlation analysis is performed to select four optimal indicators that are used as inputs of the prediction model. A random learning algorithm named extreme learning machine (ELM) is applied to extract the mapping knowledge relationship between the health indicators and the SOH due to its fast-learning speed and efficient tuning mechanism. Moreover, an ensemble learning structure is proposed to reduce the prediction error of the ELM models. The accuracy and reliability of the estimation results are then markedly improved by creating a trustworthy decision-making rule to assess the veracity of the output of each individual ELM model and exclude unreliable outputs. The testing results on public data sets show that the proposed method can accurately estimate the SOH in 1 ms and is robust to the operating temperature. The lower root-mean-square error (RMSE) is as low as 0.78%. The proposed method does not require any additional hardware or downtime of the system, which makes the method suitable for online practical applications.

ensemble extreme learning machine (ELM), lithium-ion battery (LIB), state of health (SOH) root-mean-square error(RMSE).

"Analysis the state of health of lithium-ion batteries using ensemble extreme learning method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.h70-h76, May-2023, Available :http://www.jetir.org/papers/JETIR2305712.pdf

"Analysis the state of health of lithium-ion batteries using ensemble extreme learning method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pph70-h76, May-2023, Available at : http://www.jetir.org/papers/JETIR2305712.pdf