Prediction Of The Remaining Useful Life Of Lithium-Ion Batteries Based On An Empirical Mode Approach With Artificial Neural Networks

Ozancan Bayrı; Sıtkı Akkaya

Makale detayları

Prediction Of The Remaining Useful Life Of Lithium-Ion Batteries Based On An Empirical Mode Approach With Artificial Neural Networks

Yazarlar:

Ozancan Bayrı | Sıtkı Akkaya

Yayıncı:

Manisa Celal Bayar University

Ana Başlıklar:

Elektrik Enerjisi Depolama

Dergi:

Celal Bayar Üniversitesi Fen Bilimleri Dergisi Vol.20 Issue 2, pp.1-13

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320857

pISSN:

1305-130X

Anahtar kelimeler:

Özet:

 Forecasting future capacities and estimating the remaining useful life, while incorporating uncertainty quantification, poses a crucial yet formidable challenge in the realm of battery health diagnosis and management. In this study, a data-driven model based on artificial neural networks ANN and signal decomposition techniques including Empirical Mode Decomposition EMD, Ensemble Empirical Mode Decomposition EEMD, and Empirical Wavelet Transform EWT is presented to predict the capacity value of lithium-ion batteries. Signal decomposition was performed using the discharge voltage values for four different batteries. A total of 22 features were obtained. The features of the signal decomposition methods were evaluated separately as well as hybrid approaches. Mean Squared Error MSE, Mean Absolute Percentage Error MAPE and Root Mean Square Error RMSE performance metrics are used in the proposed method and the values obtained are 3.6710-6, 0.001351 and 0.002311, respectively. According to the findings, the hybrid model proposed demonstrated positive results in terms of accuracy, adaptability, and robustness.

 Forecasting future capacities and estimating the remaining useful life, while incorporating uncertainty quantification, poses a crucial yet formidable challenge in the realm of battery health diagnosis and management. In this study, a data-driven model based on artificial neural networks ANN and signal decomposition techniques including Empirical Mode Decomposition EMD, Ensemble Empirical Mode Decomposition EEMD, and Empirical Wavelet Transform EWT is presented to predict the capacity value of lithium-ion batteries. Signal decomposition was performed using the discharge voltage values for four different batteries. A total of 22 features were obtained. The features of the signal decomposition methods were evaluated separately as well as hybrid approaches. Mean Squared Error MSE, Mean Absolute Percentage Error MAPE and Root Mean Square Error RMSE performance metrics are used in the proposed method and the values obtained are 3.6710-6, 0.001351 and 0.002311, respectively. According to the findings, the hybrid model proposed demonstrated positive results in terms of accuracy, adaptability, and robustness.

 Forecasting future capacities and estimating the remaining useful life, while incorporating uncertainty quantification, poses a crucial yet formidable challenge in the realm of battery health diagnosis and management. In this study, a data-driven model based on artificial neural networks ANN and signal decomposition techniques including Empirical Mode Decomposition EMD, Ensemble Empirical Mode Decomposition EEMD, and Empirical Wavelet Transform EWT is presented to predict the capacity value of lithium-ion batteries. Signal decomposition was performed using the discharge voltage values for four different batteries. A total of 22 features were obtained. The features of the signal decomposition methods were evaluated separately as well as hybrid approaches. Mean Squared Error MSE, Mean Absolute Percentage Error MAPE and Root Mean Square Error RMSE performance metrics are used in the proposed method and the values obtained are 3.6710-6, 0.001351 and 0.002311, respectively. According to the findings, the hybrid model proposed demonstrated positive results in terms of accuracy, adaptability, and robustness.

 Forecasting future capacities and estimating the remaining useful life, while incorporating uncertainty quantification, poses a crucial yet formidable challenge in the realm of battery health diagnosis and management. In this study, a data-driven model based on artificial neural networks ANN and signal decomposition techniques including Empirical Mode Decomposition EMD, Ensemble Empirical Mode Decomposition EEMD, and Empirical Wavelet Transform EWT is presented to predict the capacity value of lithium-ion batteries. Signal decomposition was performed using the discharge voltage values for four different batteries. A total of 22 features were obtained. The features of the signal decomposition methods were evaluated separately as well as hybrid approaches. Mean Squared Error MSE, Mean Absolute Percentage Error MAPE and Root Mean Square Error RMSE performance metrics are used in the proposed method and the values obtained are 3.6710-6, 0.001351 and 0.002311, respectively. According to the findings, the hybrid model proposed demonstrated positive results in terms of accuracy, adaptability, and robustness.

Makale detayları

Prediction Of The Remaining Useful Life Of Lithium-Ion Batteries Based On An Empirical Mode Approach With Artificial Neural Networks

Yazarlar:

Ozancan Bayrı | Sıtkı Akkaya

Yayıncı:

Manisa Celal Bayar University

Ana Başlıklar:

Elektrik Enerjisi Depolama

Dergi:

Celal Bayar Üniversitesi Fen Bilimleri Dergisi Vol.20 Issue 2, pp.1-13

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320857

pISSN:

1305-130X

Lithium-ion batteries

Signal decomposition

Artificial neural networks

Prediction

Sayı

Makale detayları

Prediction Of The Remaining Useful Life Of Lithium-Ion Batteries Based On An Empirical Mode Approach With Artificial Neural Networks

Yazarlar: Ozancan Bayrı | Sıtkı Akkaya

Yayıncı: Manisa Celal Bayar University

Ana Başlıklar: Elektrik Enerjisi Depolama

Dergi: Celal Bayar Üniversitesi Fen Bilimleri Dergisi Vol.20 Issue 2, pp.1-13

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001320857

pISSN: 1305-130X

Lithium-ion batteries

Signal decomposition

Artificial neural networks

Prediction

Sayı

Yazarlar:

Ozancan Bayrı | Sıtkı Akkaya

Yayıncı:

Manisa Celal Bayar University

Ana Başlıklar:

Elektrik Enerjisi Depolama

Dergi:

Celal Bayar Üniversitesi Fen Bilimleri Dergisi Vol.20 Issue 2, pp.1-13

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320857

pISSN:

1305-130X