MH-Ni动力电池的建模与SOC估算

发布时间：2019-02-28 15:49

【摘要】：随着能源危机的加重和燃油汽车污染气体的排放所引发的环境问题,电动汽车以其零污染、高效率、低噪音等优势日益得到了世界各国的关注和重视。镍氢动力电池作为一种比能量高、比功率高、循环寿命长和污染小的储能设备,在新能源电动汽车以及混合动力电动汽车的动力能源选择上,成为了主要的选择对象之一。本文以镍氢动力电池为研究对象,探索提高SOC估算精度的途径。针对镍氢电池开路电压受极化影响较大的特点,提出了一种基于开路电压自调整的扩展卡尔曼滤波荷电状态SOC估算算法,取得了较好的效果。首先简述了 MH-Ni电池的工作原理,通过设计实验对电池进行测试得到电池的基本特性并分析影响电池SOC的因素。综合考虑电池特性等因素选取一阶Thevenin模型建立MH-Ni电池的等效电路模型,设计HPPC循环实验,用系统辨识的递推最小二乘法辨识出电池模型的参数,在Matlab里建立电池模型,仿真验证了电池模型的可行性,针对模型的开路电压参数进行修正,修正后的模型对于变电流工况测试,端电压具有较好的跟随性。提出了基于开路电压自调整的扩展卡尔曼滤波荷电状态SOC估算算法,在开路电压的选取上更为精确,进而提高了整个算法的精确度。用HPPC实验和放电HPPC实验辨识出了充电模型参数和放电模型参数,针对于充电循环和放电循环实验用扩展卡尔曼滤波算法做了 SOC估算验证,结果得出用充电模型参数用于充电循环实验时精确度较高,放电模型参数用于放电循环实验时精确度较高,而对于变电流工况,两种模型参数都不高。对于DST工况,改进后的基于开路电压自调整的扩展卡尔曼滤波算法比充电模型参数、放电模型参数的扩展卡尔曼滤波算法精度高。
[Abstract]:With the aggravation of the energy crisis and the environmental problems caused by the emission of pollution gases from fuel vehicles, electric vehicles have been paid more and more attention by many countries for their advantages of zero pollution, high efficiency and low noise. As a kind of energy storage equipment with high specific energy, high specific power, long cycle life and low pollution, Ni-MH battery has become one of the main choice objects in the power energy selection of new energy electric vehicles and hybrid electric vehicles. In this paper, nickel-hydrogen power battery as the research object, explore the way to improve the accuracy of SOC estimation. In view of the fact that the open circuit voltage of Ni-MH battery is greatly affected by polarization, an extended Kalman filter (EKF) algorithm based on open-circuit voltage self-adjustment is proposed to estimate the state of charge (SOC) of Ni-MH battery, and good results are obtained. Firstly, the working principle of MH-Ni battery is introduced. The basic characteristics of the battery are obtained by the design experiment, and the factors affecting the SOC of the battery are analyzed. The equivalent circuit model of MH-Ni battery is established by selecting the first-order Thevenin model considering the battery characteristics and other factors. The HPPC cycle experiment is designed. The parameters of the battery model are identified by the recursive least square method of system identification, and the battery model is established in Matlab. The simulation verifies the feasibility of the battery model and modifies the open-circuit voltage parameters of the model. The modified model has a good follow-up to the test of the variable current condition and the terminal voltage. An extended Kalman filter (EKF) state-of-charge SOC estimation algorithm based on open-circuit voltage self-adjustment is proposed, which is more accurate in selecting open-circuit voltage and thus improves the accuracy of the whole algorithm. The charging model parameters and discharge model parameters are identified by HPPC experiment and discharge HPPC experiment. The SOC estimation is verified by extended Kalman filter algorithm for charging cycle and discharge cycle experiments. The results show that the accuracy of charging model parameters in charging cycle experiments is higher than that of discharge model parameters in discharge cycle experiments, but for variable current conditions, both model parameters are not high. For DST, the improved extended Kalman filter algorithm based on open-circuit voltage self-tuning is more accurate than that of charging model parameters and discharge model parameters.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM912

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