一种基于蚁群算法的串联电池组路径规划策略

doi:10.3969/j.issn.1674-1951.2021.08.004

华电技术 ›› 2021, Vol. 43 ›› Issue (8): 27-32.doi: 10.3969/j.issn.1674-1951.2021.08.004

一种基于蚁群算法的串联电池组路径规划策略

陈洋(), 程乐峰(), 邹涛^*()

广州大学机械与电气工程学院,广州 510006

收稿日期:2021-04-27 修回日期:2021-07-28 出版日期:2021-08-25 发布日期:2021-08-24
通讯作者: 邹涛
作者简介:陈洋（1988—）,女,黑龙江肇东人,讲师,博士,从事电池均衡管理、电能存储与变换等方面的研究（E-mail: zdchenyang@163.com）。
程乐峰（1990—）,男,湖北黄冈人,副教授,博士,从事配电自动化、演化博弈论和复杂网络建模等方面的研究（E-mail: chenglefeng@gzhu.edu.cn）。
基金资助:
广东省自然科学基金项目(2020A1515011247);广东省教育厅“创新强校工程”项目(自科类)(2020KQNCX054)

A path planning strategy based on ant colony algorithm for series-connected battery packs

CHEN Yang(), CHENG Lefeng(), ZOU Tao^*()

Department of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Received:2021-04-27 Revised:2021-07-28 Online:2021-08-25 Published:2021-08-24
Contact: ZOU Tao

摘要/Abstract

摘要：

碳达峰、碳中和背景下,电动汽车、电力储能的规模日益扩大,对锂离子电池高效、快速均衡的需求也在不断增长,锂离子电池的均衡电路设计,尤其是路径规划显得尤为重要,为此提出了一种基于蚁群算法的串联电池组路径规划策略。首先采用图模型表示不同电池单元之间的均衡路径;然后建立最佳均衡效率和速度模型,并采用实用的启发式群体智能算法,即蚁群算法求解这2个模型。以13节串联电池均衡系统为例,验证了所提出路径规划策略的有效性。

关键词: 碳中和, 电力储能, 电动汽车, 锂离子电池, 蚁群算法, 图模型, 路径规划, 串联电池组, 双层均衡结构

Abstract:

To pursue carbon neutrality and carbon peaking, the scale of electric vehicles and electric energy storage is expanding. With the ever-growing demand for efficient and fast equalization of Li-ion batteries, the design, especially the path planning, of equalization circuits for Li-ion batteries becomes crucial. An ant colony algorithm-based path planning strategy for series-connected battery packs is proposed. First, a graph model is used to represent the equalization paths between different battery units. Then, the optimal equalization efficiency and speed models are established,and are solved by an ant colony algorithm,a practical heuristic swarm intelligence algorithm. Finally, taking an equalization system with 13 series-connected batteries as an example,the effectiveness of the proposed path planning strategy is verified.

Key words: carbon neutrality, electricity storage, EV, Li-ion battery, ant colony algorithm, graph model, path planning, series-connected battery packs, two-layer balancing structure

中图分类号:

TP391.9:TM911

陈洋, 程乐峰, 邹涛. 一种基于蚁群算法的串联电池组路径规划策略[J]. 华电技术, 2021, 43(8): 27-32.

CHEN Yang, CHENG Lefeng, ZOU Tao. A path planning strategy based on ant colony algorithm for series-connected battery packs[J]. Huadian Technology, 2021, 43(8): 27-32.

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参考文献 13

[1]	李勇琦, 雷旗开, 王浩, 等. 基于BP神经网络的梯次利用电池健康状态诊断[J]. 华电技术, 2021, 43(7):42-46.
	LI Yongqi, LEI Qikai, WANG Hao, et al. State of health estimation for echelon-used batteries based on BP neural network[J]. Huadian Technology, 2021, 43(7):42-46.
[2]	KIM H S, PARK K B, PARK S H, et al. A new two-switch flyback battery equalizer with low voltage stress on the switches [C]// 2009 IEEE Energy Conversion Congress and Exposition. IEEE, 2009:511-516.
[3]	DU W, HUANG X, YANG S, et al. A novel equalization method with defective-battery-replacing for series-connected lithium battery strings [C]// Energy Conversion Congress and Exposition, ECCE 2009. IEEE, 2009, 58:1806-1811.
[4]	MOODY M. A high reliability battery management system[J]. Journal of Power Sources, 1986, 18(2/3):223-231. doi: 10.1016/0378-7753(86)80078-7
[5]	彭维珂, 聂椿明, 陈衡, 等. 基于智能算法的空冷火电机组负荷预测研究[J]. 华电技术, 2021, 43(3):57-64.
	PENG Weike, NIE Chunming, CHEN Heng, et al. Study on load forecasting for air cooling thermal power units based on intelligent algorithm[J]. Huadian Technology, 2021, 43(3):57-64.
[6]	DAOWD M, OMAR N, BOSSCHE P, et al. Passive and active battery balancing comparison based on MATLAB simulation [C]// 2011 Vehicle Power & Propulsion Conference. IEEE, 2011:1-7.
[7]	ZHANG W, GONG X, HAN G, et al. An improved ant colony algorithm for path planning in one scenic area with many spots[J]. IEEE Access, 2017, 5:13260-13269. doi: 10.1109/ACCESS.2017.2723892
[8]	SHANG Y, BING X, YANG J, et al. A delta-structured switched-capacitor equalizer for series-connected battery strings [C]// 2017 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2017:4493-4496.
[9]	章文浦, 王强钢. 基于遗传算法的分布式多能互补能源系统优化配置[J]. 华电技术, 2021, 43(1):52-58.
	ZHANG Wenpu, WANG Qianggang. Optimized allocation of multi-energy complementary distributed energy system based on genetic algorithm[J]. Huadian Technology, 2021, 43(1):52-58.
[10]	WANG X, CHOI T M, LIU H, et al. A novel hybrid ant colony optimization algorithm for emergency transportation problems during post-disaster scenarios[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 48(4):545-556. doi: 10.1109/TSMC.2016.2606440
[11]	ZHOU Y, ZHANG J, WANG Y. Performance analysis of the (1+1) evolutionary algorithm for the multiprocessor scheduling problem[J]. Algorithmica, 2015, 73(1):21-41. doi: 10.1007/s00453-014-9898-0
[12]	CALLE J, RIVERO J, CUADRA D, et al. Extending ACO for fast path search in huge graphs and social networks[J]. Expert Systems with Applications, 2017, 86:292-306. doi: 10.1016/j.eswa.2017.05.066
[13]	CHEN Y, LIU X, FATHY H K, et al. A graph-theoretic framework for analyzing the speeds and efficiencies of battery pack equalization circuits[J]. International Journal of Electrical Power & Energy Systems, 2018, 98:85-99. doi: 10.1016/j.ijepes.2017.11.039

均衡器组件	图论元素
储能电容器、电感等	转换节点
电池单体	目标节点
能量传递路径	有向边

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一种基于蚁群算法的串联电池组路径规划策略

A path planning strategy based on ant colony algorithm for series-connected battery packs

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