计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (6A): 250800063-11.doi: 10.11896/jsjkx.250800063

• 人工智能 • 上一篇    下一篇

基于改进黑翅鸢算法的多目标充电优化策略

赵学健1, 周晶晶1, 董文楷2, 田浩2, 张红烛2   

  1. 1 南京邮电大学宽带无线通信与传感网技术教育部重点实验室 南京 210003
    2 江苏省光储充检一体化研发应用工程技术研究中心 江苏 常州 213000
  • 出版日期:2026-06-16 发布日期:2026-06-12
  • 通讯作者: 赵学健(zhaoxj@njupt.edu.cn)
  • 基金资助:
    国家自然科学基金(62303239);常州市国际合作项目(CZ20240007)

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
  • Published:2026-06-16 Online: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).

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摘要： 针对大功率充电技术现有充电策略中静态参数设定适应性不足,简化模型难以充分表征多物理场耦合效应,以及传统优化算法易陷入局部最优等问题,提出一种基于改进黑翅鸢算法的多目标充电优化策略——Improved BKA(Improved Black-winged Kite Algorithm)。首先,建立了涵盖电气动态、热管理和老化机理的电化学-热-老化多物理场耦合模型,精确表征大功率充电过程的复杂物理特性,构建了以充电时间最小化、能量效率最大化和健康状态保持率最大化为目标的多目标优化函数。其次,提出一种改进黑翅鸢算法的目标函数高效求解方法,通过融合均匀分布与指数分布的混合初始化策略提升初始解的质量,通过基于Lyapunov稳定性理论的自适应步长机制动态平衡全局探索与局部开发能力,通过凸投影约束修复技术高效处理安全约束。仿真结果表明,相比PSO和GWO等6种算法,Improved BKA算法使充电速度提高17.0%以上,能量效率达91.3%,峰值温度可控制在44.2 ℃以内,容量衰减率降低21.0%以上。

关键词: 黑翅鸢算法, 多目标充电, 大功率充电, 多物理场耦合模型, 约束处理

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

中图分类号: 

  • TP273
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