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兵工学报 ›› 2021, Vol. 42 ›› Issue (10): 2130-2144.doi: 10.3969/j.issn.1000-1093.2021.10.009

• 论文 • 上一篇    下一篇

基于改进灰色马尔可夫链的电传动装甲车辆负载需求功率预测

刘春光1, 陈路明1, 张运银1, 张征1,2, 徐浩轩1   

  1. (1.陆军装甲兵学院 兵器与控制系, 北京 100072; 2.北京市遥感信息研究所, 北京 100192)
  • 发布日期:2021-11-03
  • 通讯作者: 陈路明(1991—),男,助理研究员,博士 E-mail:295170692@qq.com
  • 作者简介:刘春光(1981—),男,副教授,博士生导师。E-mail: ljzjb365@163.com
  • 基金资助:
    国家自然科学基金项目(51507190);武器装备预先研究项目(301051102)

Prediction of Demand Power of Electric Drive Armored Vehicle Based on Improved Grey Markov Chain

LIU Chunguang1, CHEN Luming1, ZHANG Yunyin1, ZHANG Zheng1,2, XU Haoxuan1   

  1. (1.Department of Weapons and Control, Army Academy of Armored Forces, Beijing 100072, China;2.Beijing Institute of Remote Sensing Information, Beijing 100192, China)
  • Online:2021-11-03

摘要: 为解决电传动装甲车辆负载需求功率预测精度偏低的问题,提出一种基于改进灰色马尔可夫链的组合功率预测方法。采用改进灰色预测方法和马尔可夫链预测方法对负载需求功率中的主体功率和残差功率进行预测,并在每个预测时刻将两种功率预测结果进行代数相加,建立改进灰色马尔可夫链组合预测方法。仿真结果表明:该负载需求功率组合预测方法相对于传统单一预测方法,能够较好地预测负载功率的强随机性变化特征,准确预测负载需求功率的变化趋势,预测精度最高提升了16.49%,可为后续能量管理策略提供有效参考信息。

关键词: 电传动装甲车辆, 负载需求, 灰色模型, 马尔可夫链, 功率预测

Abstract: A combined power prediction method based on improved grey Markov chain is proposed to solve the problem of low accuracy of demand power prediction for electric drive armored vehicles. The improved grey prediction method and Markov chain prediction method are used to predict the main power and residual power in the load demand power. The predicted results of demand and residual powers at each prediction moment are algebraically added to establish an improved grey Markov chain combination prediction method. The simulated results show that,compared with the traditional prediction method,the proposed demand power combination prediction method can better predict the strong randomness of load power and accurately predict the variation trend of demand power,and the prediction accuracy is increased by 16.49%,which can provide effective reference information for the energy management strategy.

Key words: electricdrivearmoredvehicle, loaddemand, greymodel, Markovchain, powerprediction

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