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A Novel Back Propagation Neural Network Based on the Harris Hawks Optimization Algorithm for the Remaining Useful Life Prediction of Lithium-Ion Batteries

Author

Listed:
  • Yuyang Zhou

    (School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Zijian Shao

    (School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Huanhuan Li

    (Automotive Engineering Research Institute, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Jing Chen

    (Intelligent Manufacturing Institute, Taizhou Polytechnic College, 8 Tianxing Road, Gaoxin District, Taizhou 225300, China)

  • Haohan Sun

    (Automotive Engineering Research Institute, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Yaping Wang

    (School of Material Science & Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Nan Wang

    (Automotive Engineering Research Institute, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Lei Pei

    (Automotive Engineering Research Institute, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Zhen Wang

    (Automotive Engineering Research Institute, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Houzhong Zhang

    (Automotive Engineering Research Institute, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

  • Chaochun Yuan

    (Automotive Engineering Research Institute, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, China)

Abstract

Remaining useful life (RUL) serves as a pivotal metric for quantifying lithium-ion batteries’ state of health (SOH) in electric vehicles and plays a crucial role in ensuring their safety and reliability. In order to achieve accurate and reliable RUL prediction, a novel RUL prediction method which employs a back propagation (BP) neural network based on the Harris Hawks optimization (HHO) algorithm is proposed. This method optimizes the BP parameters using the improved HHO algorithm. At first, the circle chaotic mapping method is utilized to solve the problem of the initial value. Considering the problem of local convergence, Gaussian mutation is introduced to improve the search ability of the algorithm. Subsequently, two key health factors are selected as input features for the model, including the constant-current charging isovoltage rise time and constant-current discharging isovoltage drop time. The model is validated using aging data from commercial lithium iron phosphate (LiFePO 4 ) batteries. Finally, the model is thoroughly verified under an aging test. Experimental validation using training sets comprising 50%, 60%, and 70% of the cycle data demonstrates superior predictive performance, with mean absolute error (MAE) values below 0.012, root mean square error (RMSE) values below 0.017 and mean absolute percentage error (MAPE) within 0.95%. The results indicate that the model significantly improves prediction accuracy, robustness and searchability.

Suggested Citation

  • Yuyang Zhou & Zijian Shao & Huanhuan Li & Jing Chen & Haohan Sun & Yaping Wang & Nan Wang & Lei Pei & Zhen Wang & Houzhong Zhang & Chaochun Yuan, 2025. "A Novel Back Propagation Neural Network Based on the Harris Hawks Optimization Algorithm for the Remaining Useful Life Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 18(14), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3842-:d:1705215
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    References listed on IDEAS

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