超级电容器及其复合储能系统荷电状态研究

发布时间：2018-01-08 02:24

本文关键词：超级电容器及其复合储能系统荷电状态研究　出处：《大连理工大学》2015年硕士论文　论文类型：学位论文

【摘要】：近年来随着能源危机和环境污染问题日益严峻,燃油交通工具面临着挑战,电动汽车成为解决此类危机的潜在选择,但目前由于车载能量存储技术远未成熟,其动力性能以及续航里程无法满足市场消费需求。基于此,本文提出一种石墨烯超级电容器的制备方法,并对复合储能系统中超级电容器和锂电池的荷电状态(State-of-Charge, SOC)估算展开研究。论文首先阐明了超级电容器的工作原理,根据其储能特性提出一种氮掺杂石墨烯电极材料微波固相制备方法。并对所制备电极材料进行物理与电气性能研究,验证了其较高的相对表面积、循环稳定性以及储能密度；进一步基于该电极材料制作出实验超级电容器,其比电容在电流密度0.5A/g和5A/g时分别为208.17F/g和157.31F/g,容量保持率为75.57%,在0.5A/g电流密度下进行5000次循环充放电实验,其比电容存留约为98.56%,等效串联电阻约为0.32Ω。其次,根据电动汽车复合储能系统的储能原理,分别建立超级电容器和锂电池的电路模型,利用最小二乘法和卡尔曼滤波算法对模型参数进行在线辨识,然后根据无迹卡尔曼滤波算法完成其荷电状态在线估算。最后,搭建荷电状态估算系统实验平台,利用NI PCI 6221高速数据采集卡与LabVIEW建立数据采集系统,然后采用Matlab进行计算分析,分别对超级电容器和锂电池进行荷电状态完成在线估算。实验结果表明：超级电容器估算结果达到较高精确度,全程估算误差变动区间为[-0.94%,0.34%]；而对于锂电池,综合考虑准确性和计算量,最小二乘法与2RC模型搭配获得最优结果,其估算误差在[-1.16%,0.85%]区间内,且系统鲁棒性能突出,可靠性高。实验结果验证了荷电状态估算系统的有效性,可为后续混合储能系统的控制策略提供了准确的荷电状态信息。
[Abstract]:In recent years, with the energy crisis and environmental pollution problems have become increasingly serious, fuel transportation is facing challenges, the electric car has become a potential solution to such crises, but because of the current vehicle energy storage technology is far from mature, its dynamic performance and mileage can not meet the market demand. Based on this, this paper proposes a method for preparing graphene the super capacitor, and the composite energy storage system in super capacitors and lithium battery state of charge (State-of-Charge, SOC) estimation is researched. Firstly the thesis introduces the working principle of the super capacitor, the energy storage characteristics of a nitrogen doped graphene electrode material microwave solid preparation method and. Study on the physical and electrical properties of the prepared electrode materials, to verify the relative surface area of its high energy density, cycle stability and further based on the electrode materials; To the super capacitor, the capacitance in the current density of 0.5A/g and 5A/g were 208.17F/g and 157.31F/g, the capacity retention rate was 75.57%, 5000 cycles of charge discharge experiment in the current density of 0.5A/g, the specific capacitance retained about 98.56%, the equivalent series resistance of approximately 0.32. Secondly, according to the electric vehicle storage composite the system of the principle of energy storage circuit models of super capacitor and lithium battery, online identification of the model parameters using the least square method and Calman filtering algorithm, unscented filtering algorithm and then according to Calman completed its charged state estimation online. Finally, build a SOC estimation system platform, the establishment of data acquisition system and LabVIEW card with NI PCI 6221 high speed data acquisition, and then the Matlab were calculated and analyzed respectively for super capacitor and lithium battery state of charge in line estimation. The experimental results show that the super capacitor estimation results to achieve the high accuracy, the whole error estimates of changes in the range of [-0.94% and 0.34%]; and for the lithium battery, considering the accuracy and computation, least square method and 2RC collocation model for optimal results, the estimation error in the interval [-1.16%, 0.85%], and the robust performance is outstanding, high reliability. The experimental results verify the validity of SOC estimation system, control strategy for the hybrid energy storage system provides charging state information accurately.

【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM53

【相似文献】