Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250800063-11.doi: 10.11896/jsjkx.250800063

• Artificial Intelligence • Previous Articles     Next Articles

Multi-objective Charging Optimization Strategy Based on Improved Black-winged Kite Algorithm

ZHAO Xuejian1, ZHOU Jingjing1, DONG Wenkai2, TIAN Hao2, ZHANG Hongzhu2   

  1. 1 Key Lab of Broadband Wireless Communication and Sensor Network Technology,Ministry of Education,Nanjing University of Posts andTelecommunications,Nanjing 210003,China
    2 Jiangsu Province Engineering Technology Research Center of Optical Storage,Charging and Testing Integrated R&D and Application,Changzhou,Jiangsu 213000,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:ZHAO Xuejian,born in 1982,Ph.D,associate professor,is a member of CCF(No.88401M).His main research interests include data mining,artificial intelligence and smart grids.
  • Supported by:
    National Natural Science Foundation of China(62202223) and Changzhou International Cooperation Project (CZ20240007).

Abstract: Existing charging strategies for high-power charging technology suffer from limitations such as insufficient adaptability of static parameter settings,simplified models failing to adequately characterize multi-physics coupling effects,and traditional optimization algorithms prone to local optima.To address these issues,this paper proposes a multi-objective charging optimization strategy based on an improved black-winged kite algorithm(Improved BKA).Firstly,an electrochemical-thermal-aging multi-physics coupled model is established,encompassing electrical dynamics,thermal management,and aging mechanisms,to accurately characterize the complex physical behavior during high-power charging.A multi-objective optimization function is constructed with the goals of minimizing charging time,maximizing energy efficiency,and maximizing state-of-health(SOH) retention rate.Secondly,an efficient solution method for the objective function based on the Improved BKA is proposed.This method enhances the quality of initial solutions through a hybrid initialization strategy combining uniform and exponential distributions,dynamically balances global exploration and local exploitation capabilities via an adaptive step size mechanism grounded in Lyapunov stability theory,and efficiently handles safety constraints using convex projection-based constraint repair techniques.Finally,simulation experiments validate that compared to six representative algorithms including PSO and GWO,the Improved BKA strategy achieves at least a 17.0% improvement in charging speed,reaches 91.3% energy efficiency,controls the peak temperature below 44.2 ℃,and reduces the capacity fade rate by at least 21.0%.

Key words: Black-winged kite algorithm, Multi-objective charging, High-power charging, Multi-physics coupling model, Constraint handling

CLC Number: 

  • TP273
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