首页> 外文会议>2017 4th International Conference on Electric Vehicular Technology >State of energy (SOE) estimation of LiNiCoAlO2 battery module considering cells unbalance and energy efficiency
【24h】

State of energy (SOE) estimation of LiNiCoAlO2 battery module considering cells unbalance and energy efficiency

机译:考虑电池不平衡和能量效率的LiNiCoAlO2电池模块的能量状态(SOE)估计

获取原文
获取原文并翻译 | 示例

摘要

In this work, we developed the state of energy (SOE) estimation method for battery module while taking into account the unbalance voltage between cells and energy efficiency by using energy counting and support vector machine (SVM). The energy counting is performed by accumulating power coming in and out of the battery module when discharging or charging process occurs. The power is accumulated over time and compared to its nominal energy to obtain the SOE of the battery module. The experiment was conducted in two stages by utilizing Lithium Nickel Cobalt Aluminium Oxide (LiNiCoAlO2) batteries with the nominal voltage 3.6 V and the nominal capacity 3350 mAh, a DC power supply model, a programmable DC electronic load, and sensors for battery monitoring and protection system. In the first experiment, the energy counting is performed on a single cell to obtain individual battery cell characteristics based on C5, C10, and C20 discharging methods. The analytical results show that the relationship energy efficiency of charging and discharging process to discharge rate was 0.8974 × C(exp(-0.033)). The dataset from one battery cell characteristics is arranged into a lookup table that expresses the relationship between voltage, current and SOE of the battery. The lookup table was used as training datasets for SVM to generate model for estimating battery cell and module SOE. The second experiment was conducted to estimate the SOE of battery module consisting of 10 battery cells in series connection with the same discharging method as the single cell experiment. The SOE estimation results by using radial basis function based support vector regression model, with cost function value of 30, and an epsilon value of 0.04, and with kernel parameter value of 1, give the average of root-mean-squared-error value was below 5% which show an acceptable result. Because of cell unbalance will occur continuously during the battery charging and discharging process, the lowest SOE value from battery cells is selected as the reference value for the next cycle's process estimation.
机译:在这项工作中,我们通过使用能量计数和支持向量机(SVM)考虑了电池之间的不平衡电压和能量效率,开发了用于电池模块的能量状态(SOE)估计方法。当发生放电或充电过程时,通过累加进出电池模块的功率来执行能量计数。随时间积累功率,并将其与标称能量进行比较,以获得电池模块的SOE。通过使用标称电压为3.6 V,标称容量为3350 mAh的锂镍钴铝氧化物(LiNiCoAlO2)电池,直流电源模型,可编程直流电子负载以及用于电池监控和保护的传感器,分两个阶段进行了实验系统。在第一个实验中,基于C5,C10和C20放电方法对单个电池进行能量计数以获得单个电池的电池特性。分析结果表明,充放电过程的能量效率与放电率的关系为0.8974×C(exp(-0.033))。来自一个电池单元特性的数据集被安排到一个查找表中,该表表示电池的电压,电流和SOE之间的关系。查找表用作SVM的训练数据集,以生成用于估计电池单元和模块SOE的模型。进行第二个实验,以与单电池实验相同的放电方法估算由10个串联电池组成的电池模块的SOE。使用基于径向基函数的支持向量回归模型对SOE进行估计,成本函数值为30,ε值为0.04,内核参数值为1,得出均方根误差值为低于5%则显示可接受的结果。由于在电池充电和放电过程中会不断发生电池单元不平衡,因此,将来自电池单元的最低SOE值选作下一个周期过程估计的参考值。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号