Battery capacity and health prediction using GA-optimized neural networks

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Durmuş F., Karagöl S.

Neural Computing and Applications, cilt.38, sa.12, 2026 (Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 12
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s00521-026-12277-8
  • Dergi Adı: Neural Computing and Applications
  • Derginin Tarandığı İndeksler: Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, Index Islamicus, INSPEC, zbMATH, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
  • Anahtar Kelimeler: Battery capacity, Genetic algorithm, Neural networks, State of Health (SOH) Prediction
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

In this study, a method is proposed to predict the capacity of lithium-ion batteries with high accuracy. Three features, specifically the constant current mode charging time, the discharging time to reach cutoff voltage, and the average discharge voltage, were extracted from the voltage data recorded during the charge and discharge cycles. The Pearson correlation coefficients between these features and the battery capacities were analyzed, showing that the selected features are suitable for all batteries. The parameters of the Convolutional Neural Network (CNN), Back Propagation (BP), and Recurrent Neural Network (RNN) models were optimized using a Genetic Algorithm (GA) with 25%, 50%, and 75% of the training data. Instead of using standard or random values, the GA helped find the best parameter combinations in a shorter time by applying natural selection and genetic processes. The performance of the proposed method was evaluated using five metrics: Mean Square Error (MSE), Coefficient of Determination (R²), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). The results obtained from the Oxford experimental dataset were compared with other studies in the literature to verify the robustness and superior accuracy of the proposed method.