| 引用本文: | 陈路明,廖自力,魏曙光,等.电传动装甲车辆负载功率预测实时能量管理策略.[J].国防科技大学学报,2022,44(6):173-183.[点击复制] |
| CHEN Luming,LIAO Zili,WEI Shuguang,et al.Real-time energy management strategy for electric drive armored vehicles with load power prediction[J].Journal of National University of Defense Technology,2022,44(6):173-183[点击复制] |
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| 电传动装甲车辆负载功率预测实时能量管理策略 |
| 陈路明,廖自力,魏曙光,张征 |
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(陆军装甲兵学院 兵器与控制系, 北京 100072)
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| 摘要: |
| 针对电传动装甲车辆负载功率预测功能缺失导致控制作用滞后的问题,提出一种具有较高负载功率预测精度的实时能量管理策略。在分析整车结构的基础上,采用理论分析和数据拟合方法,建立各动力源数学模型。将差分自回归移动平均模型和自适应马尔可夫链两种预测方法相结合,设计非平稳趋势性负载功率组合预测方法。在非线性模型预测控制框架下,构建多目标优化函数,采用序列二次规划法在有限时域内实时求解最优控制指令,优化多动力源协调控制过程。依托硬件在环仿真平台进行多路面行驶实验,对比有无功率预测的能量管理控制效果。结果表明,改进的实时能量管理策略对未来负载功率具有较好的预见性,能够显著优化多动力源协调控制过程,提升车辆燃油经济性,稳定母线电压和电池荷电状态,对传统模型预测控制下的工程应用场景具有一定借鉴意义。 |
| 关键词: 电传动 功率预测 模型预测控制 能量管理策略 |
| DOI：10.11887/j.cn.202206021 |
| 投稿日期：2020-11-10 |
| 基金项目:国家自然科学基金资助项目(51507190)；国家部委基金资助项目(301051102) |
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| Real-time energy management strategy for electric drive armored vehicles with load power prediction |
| CHEN Luming, LIAO Zili, WEI Shuguang, ZHANG Zheng |
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(Weapons and Control Department, Army Academy of Armored Forces, Beijing 100072, China)
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| Abstract: |
| Aiming at the lack of load power prediction function in electric drive armored vehicles leading to the lag of control action, a real-time energy management strategy with higher load power prediction accuracy was proposed. Based on analyzing the whole vehicle′s structure, each power source′s mathematical model was established using theoretical analysis and data fitting methods. Combining the two forecasting methods of auto regressive integrated moving average model and adaptive Markov chain, a combination forecasting method of non-stationary trend load power was designed. Under the framework of nonlinear model predictive control, a multi-objective optimization function was constructed, and the sequential quadratic programming method was utilized to solve the optimal control command in real-time in the finite time domain. The multi-power source was optimized and coordinated. Relying on the hardware-in-the-loop simulation platform, multi-road driving experiments were carried out, and energy management control effects with or without power prediction method were compared. The results show that the improved real-time energy management strategy has good predictability for future load power. It can significantly optimize the coordinated control process of multiple power sources, improve vehicle fuel economy, stabilize bus voltage and battery state of charge. Moreover, it has specific reference significance in engineering application scenarios under traditional model predictive control. |
| Keywords: electric drive power prediction model predictive control energy management strategy |
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