基于交叉熵算法的电动车辆复合电源参数优化

戴朝华; 刘洋; 黄晨曦; 赵舵; 郭爱; 陈维荣; 刘楠

doi:10.3969/j.issn.0258-2724.20190442

基于交叉熵算法的电动车辆复合电源参数优化

doi: 10.3969/j.issn.0258-2724.20190442

戴朝华^1,,
刘洋¹,
黄晨曦¹,
赵舵^1, ,,
郭爱¹,
陈维荣¹,
刘楠²

基金项目: 国家重点研发计划（2017YFB1201003，2017YFB1201005）

详细信息

作者简介:
戴朝华（1973—），男，副教授，研究方向为新能源车辆，E-mail：daichaohua@swjtu.edu.cn

通讯作者:
赵舵（1975—），男，副教授，研究方向为智能优化算法，E-mail：zhaoduo@swjtu.edu.cn

中图分类号: V221.3
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出版历程
- 收稿日期: 2019-05-17
- 修回日期: 2019-10-18
- 网络出版日期: 2020-01-21
- 刊出日期: 2020-08-01

Parameters Optimization for Hybrid Energy Storage System of Electric Vehicles Based on Cross-Entropy Algorithm

摘要

摘要: 为了提升电动汽车动力性能、降低车辆成本，以复合电源成本和车辆电耗最小为目标，通过交叉熵（cross- entropy，CE）算法对车载复合电源的参数优化进行了研究. 首先，以某款纯电动汽车为研究对象，根据能量与功率性能指标确定锂离子电池和超级电容的容量范围；其次，选取复合电源成本和车辆电耗建立多目标优化函数，并在ADVISOR环境中搭建车辆仿真模型；接着，采用CE算法，通过种群的不断迭代，更新高斯概率密度函数的均值和方差，找到复合电源参数的Pareto最优解集；最后，从最优Pareto解集中选取典型的匹配参数，分析复合电源成本、车辆电耗和整车性能. 研究结果表明：在满足基本约束的前提下，得到了由100个解组成的Pareto最优解集. 与第二代非劣排序遗传算法（non-dominated sorting genetic algorithm-Ⅱ，NSGA-Ⅱ）比较，CE算法有更好的收敛性与分布性；复合电源成本平均降低了9.49%，车辆电耗平均降低了22.81%；此外，城市道路循环工况（urban dynamometer driving schedule，UDDS）下车速误差最大值降低16.15%，整车动力性也有显著提升，百公里加速时间缩短7.81%，最高车速提升1.98%.
- 复合电源 /
- CE算法 /
- 多目标优化 /
- 参数匹配
Abstract: In order to improve the dynamic performance of electric vehicles and reduce costs, a parameter optimization method for vehicle-mounted hybrid power supply based on cross-entropy (CE) algorithm is explored with the intent of minimizing the hybrid power supply cost and power consumption. Firstly, a hybrid electric vehicle is used as the object, and the capacity ranges of its lithium-ion batteries and super-capacitors are determined according to the energy and power performance indexes. Secondly, the multi-objective optimization function of minimizing power supply cost and power consumption and the vehicle simulation model are established in ADVISOR. Subsequently, with CE algorithm, the mean and variance of the Gaussian probability density function are updated by the continuous iterations of populations to find out the optimal Pareto solution set. Finally, the typical solutions are selected to analyze the cost, power consumption and vehicle performance. The results show that under the basic requirements, 100 optimal solutions are found, which constitute an optimal Pareto solution set. Compared with the results of (non-dominated sorting genetic algorithm-Ⅱ) NSGA-Ⅱ, the convergence and distribution of CE algorithm are better, the cost of hybrid power supply is reduced by 9.49% and the vehicle power consumption by 22.81% on average. Furthermore, the maximum error of vehicle speed is reduced by 16.15% under UDDS cycle condition, and the vehicle dynamic performance is improved significantly with the acceleration time of 100 km reduced by 7.81% and the maximum speed increased by 1.98%.
- hybrid power supply /
- cross-entropy algorithm /
- multi-objective optimization /
- parameter matching

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图 1 整车拓扑结构

Figure 1. Vehicle topology

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图 2 工况车速需求

Figure 2. Speed requirements under working conditions

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图 3 Pareto前沿面

Figure 3. Pareto front

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图 4 不同优化算法的车速匹配

Figure 4. Speed matching of different optimization algorithms

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图 5 不同算法的锂离子电池SOC、温度及超级电容SOC

Figure 5. Lithium-ion battery SOC, supercapacitor SOC and temperature curve of different algorithms

下载: 全尺寸图片幻灯片

表 1 整车参数

Table 1. Vehicle parameters

部件	参数	数值
整车	总质量/kg	1 191
	长，宽，高/mm	4 410，1 750，1 000
	轴距/mm	2 600
电机	最大功率/kW	75
	电压等级/V	320
	最大转速/（r•min⁻¹）	10 000
	最大转矩/（N•m）	200
锂离子电池	单体电池额定电压/V	3.2
	单体电池额定容量/（A•h）	10～60
	能量密度/（W•h•kg⁻¹）	120
超级电容	额定电压/V	2.5
	额定容量/F	1 000～3 500
	能量密度/（W•h•kg⁻¹）	6

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表 2 整车循环工况功率需求

Table 2. Power requirements in driving cycle

项目	正（驱动）需求	负（制动）需求
总能量需求/kJ	6 799	−440.2
循环中所占时间/h	21.50	2.683
平均功率需求/kW	5.270	−2.734
峰值功率需求/kW	39.70	−8.942

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表 3 不同算法优化结果比较

Table 3. Comparison of different algorithm optimization results

项目	CE	NSGA-II	优化前
最小车辆电耗/（kW•h）	2.241 8	2.125 1	7.140 1
平均车辆电耗/（kW•h）	3.254 0	3.562 8	7.140 1
最低电源成本/万元	8.311 1	9.432 8	16.080
平均电源成本/万元	10.937 1	14.169 2	16.080
锂离子电池DOD	0.061 0	0.086 0	0.154 0
超级电容DOD	0.426 1	0.465 2
制动能量回收/kJ	−6 198.6	−5 469.1	−5 331.2
放电效率	0.995 2	0.989 5	0.922 0

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表 4 整车动力性能比较

Table 4. Comparison of vehicle dynamic performance

项目	CE 算法优化	NSGA-II 算法优化	未优化
最高车速/（km•h⁻¹）	157.55 （提高 14.64%）	154.49 （提高 12.41%）	137.43
15 km/h 最大爬坡度/%	35（提高 25.00%）	33（提高 17.85%）	28
5 s 能达到的最远距离/m	195.8 （提高 3.87%）	188.9 （提高 0.21%）	188.5
0～50 km/h 加速时间/s	5.0 （提高 5.66%）	5.2 （提高 1.88%）	5.3
50～80 km/h 加速时间/s	5.8 （提高 48.67%）	6.0 （提高 46.9%）	11.3
0～100 km/h 加速时间/s	17.7 （提高 11.94%）	19.2 （提高 4.47%）	20.1

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