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

Yuyang Zhou, Zijian Shao, Huanhuan Li (), Jing Chen, Haohan Sun (), Yaping Wang, Nan Wang, Lei Pei, Zhen Wang, Houzhong Zhang and Chaochun Yuan
Additional contact information
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

Energies, 2025, vol. 18, issue 14, 1-23

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.

Keywords: lithium-ion batteries; remaining useful life; back propagation neural network; Harris hawks optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/14/3842/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/14/3842/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:14:p:3842-:d:1705215

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().