• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2017, Vol. 53 ›› Issue (16): 44-51.doi: 10.3901/JME.2017.16.044

• 特邀专栏:汽车先进动力系统的设计、优化与控制(下) • 上一篇    下一篇

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基于凸优化的车载复合电源参数匹配

宋传学, 周放, 肖峰, 常成, 邵玉龙   

  1. 吉林大学汽车仿真与控制国家重点实验室 长春 130022
  • 收稿日期:2016-08-25 修回日期:2016-12-29 发布日期:2017-08-20
  • 通讯作者: 周放(通信作者),男,1988年出生,博士研究生。主要研究方向为车载复合电源技术。E-mail:zhoufang13@mails.jlu.edu.cn E-mail:zhoufang13@mails.jlu.edu.cn
  • 作者简介:宋传学,男,1959年出生,博士,教授,博士研究生导师。主要研究方向为汽车系统动力学,汽车振动噪声分析与控制。E-mail:songchx@126.com
  • 基金资助:
    吉林省重点科技攻关资助项目(20150204017GX)

Parameter Matching of On-board Hybrid Energy Storage System Based on Convex Optimization Method

SONG Chuanxue, ZHOU Fang, XIAO Feng, CHANG Cheng, SHAO Yulong   

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022
  • Received:2016-08-25 Revised:2016-12-29 Published:2017-08-20

摘要: 提出一种电动汽车复合电源参数快速匹配方法,对复合电源参数和能量管理策略同时进行优化。为避免动态规划或者进化算法在求解此类优化问题时出现的计算负担大或结果次优的问题,应用凸优化原理对优化问题进行求解。对复合电源模型进行线性近似,并引入新变量对优化问题中的目标函数和约束进行转换,将优化问题转化为凸优化问题,最后应用Matlab/cvx工具箱进行求解。应用动态规划求解此优化问题并对比优化结果:凸优化和动态规划结果相差在4%以内,凸优化保证了全局最优;凸优化耗时不超过100 s,明显优于动态规划,有良好的工程应用潜力。凸优化结果表明:通过增加最大不超过60 W·;h的超级电容组能够延长电池组一倍的寿命里程,改变凸优化问题的权重可以快速获取所需参数。

关键词: 参数匹配, 复合电源, 能量管理, 凸优化

Abstract: A fast parameter matching method for hybrid energy storage system applied to electric vehicle is proposed, optimizing HESS parameters and corresponding energy management strategy simultaneously. In order to avoid computation burden/suboptimal problems when solving this optimization problem with dynamic programming(DP) method or evolutionary algorithms, convex optimization theory is applied to solve this problem. Firstly, nonlinear relationship of HESS model is approximated into linear relationship, then the objective function and constraints are converted to convex functions by introducing new variables; the original optimization problem is thus transformed into convex optimization problem, finally the problem is solved using Matlab/cvx toolbox. The same problem is solved using DP method and optimization results of both methods are compared:there are only 4% difference between optimization results, which means convex optimization ensures results to be global optimal; additionally, convex optimization shows favourable engineering application potential for its computation time never exceeds 100 s, which is far less than DP method. The optimization result shows that:battery pack life mileage is doubled by introducing an ultracpacitor pack with no more than 60 Wh, and convex optimization weights can be tuned to obtain desired HESS parameters rapidly.

Key words: convex optimization, energy management, hybrid energy storage system, parameter matching

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