电动汽车电池状态估计及均衡管理研究

发布时间：2018-07-03 19:52

本文选题：电动汽车 + 锂离子动力电池　；参考：《天津大学》2014年博士论文

【摘要】：电动汽车发展过程中，储能元件一直是阻碍电动汽车发展的瓶颈。动力电池作为汽车的储能元件，在汽车行驶过程中，需要时刻知道电池的核电状态(state ofcharge, SOC)。由于电池是一种非线性动力系统，使用过程中，电池模型参数又受温度和老化等因素的影响而发生变化，因此实时估计电池SOC具有很大的难度。本文采用自适应卡尔曼滤波算法估计电池状态，即可以实时的辨识出电池模型的参数，又提高了估计精度。电动汽车中电池是成组使用的，为了提高其使用效率，延长使用寿命，必须对电池组进行均衡管理。本文采用基于专家系统的均衡控制策略，可以减小系统的均衡损耗，提高系统的均衡速度。通过研究工作，本文取得了以下研究成果： 1．统计电池外部电流和端电压信号的方法估计电池健康状态(state of health,SOH)。电池外部电流与端电压信号获取方便，本文从三个角度对其做统计分析，发现电池外部信号关于SOH具有统计规律，为电池SOH估计提供了一种新的思路。 2．多模型自适应的方法估计电池SOC。采用卡尔曼滤波器估计电池SOC时，估计精度受电池模型准确性的影响较大，电池模型参数随着电池的老化和温度变化而变化，因此传统的卡尔曼滤波算法估计误差较大。针对上述问题，本文采用了多模型自适应的估计方法，提高了估计精度。多模型卡尔曼滤波算法是在电池的SOH分布范围内，选取几种不同SOH电池建立模型，，分别基于每个模型设计卡尔曼滤波器，利用各个滤波器并行估计电池SOC，计算各个单一模型的权值，所有单一模型SOC估计的加权和即为最终SOC估计值。 3．自适应无迹卡尔曼滤波算法估计电池SOC与欧姆内阻。无迹卡尔曼滤波器不需要对系统模型做线性化处理，这样既减小了计算量又提高了估计精度。本文利用无迹卡尔曼滤波器估计电池SOC，利用扩展的卡尔曼滤波器辨识电池欧姆内阻，两个滤波器联立构成循环迭代算法，可以实时更新电池模型参数，提高了模型的准确性，进而提高了电池SOC的估计精度。由于电池欧姆内阻可以表征电池SOH，因此可以进一步估计出电池的SOH。 4．基于专家系统的电池组非能耗型电压均衡控制策略。电池组电压均衡控制的目标是电池组工作过程中保持各单体电压一致，均衡原则是减小均衡过程中的能耗，提高均衡速度。本文以开关电容均衡电路为例，分析了均衡电路容量，开关频率与电池工作电流之间的关系，为均衡电路设计提供了一种理论分析的方法。
[Abstract]:In the development of electric vehicles, energy storage components have been the bottleneck of the development of electric vehicles. Power battery is the energy storage component of automobile. It is necessary to know the nuclear power state of the battery (state ofcharge, SOC at all times in the driving process of the vehicle. Since the battery is a nonlinear dynamic system, the parameters of the battery model are affected by temperature and aging, so it is very difficult to estimate the SOC of the battery in real time. In this paper, the adaptive Kalman filter algorithm is used to estimate the state of the battery, which can identify the parameters of the battery model in real time and improve the estimation accuracy. Batteries in electric vehicles are used in groups. In order to improve their efficiency and prolong their service life, the battery pack must be balanced management. In this paper, the equalization control strategy based on expert system is adopted, which can reduce the equilibrium loss of the system and improve the equalization speed of the system. The research results are as follows: 1. The method of estimating the healthy state of (state of by the method of estimating the external current and terminal voltage signals of the battery. The external current and terminal voltage signal of the battery is easy to obtain. This paper makes a statistical analysis of the signal from three angles, and finds that the external signal of the battery has the statistical law about SOH. It provides a new idea for SOH estimation of battery. 2. Multi-model adaptive method is used to estimate SOC. When using Kalman filter to estimate battery SOC, the estimation accuracy is greatly affected by the accuracy of the battery model. The parameters of the battery model vary with the aging of the battery and the change of temperature, so the estimation error of the traditional Kalman filter algorithm is large. In order to solve the above problems, a multi-model adaptive estimation method is used to improve the estimation accuracy. In the SOH distribution range of the battery, several different SOH cell models are selected and the Kalman filter is designed based on each model. The weight of each single model is calculated by using each filter to estimate the SOC in parallel. The weighted sum of SOC estimation of all single models is the final SOC estimation. 3. Adaptive unscented Kalman filter algorithm is used to estimate the SOC and ohmic internal resistance of the cell. The unscented Kalman filter does not need to linearize the system model, which not only reduces the computational complexity but also improves the estimation accuracy. In this paper, the unscented Kalman filter is used to estimate the SOCand the extended Kalman filter is used to identify the ohmic internal resistance of the battery. The two filters are combined to form a cyclic iterative algorithm, which can update the parameters of the battery model in real time and improve the accuracy of the model. Furthermore, the estimation accuracy of battery SOC is improved. Because the ohmic internal resistance of the battery can represent the SOH of the battery, the SOH.4 of the battery can be further estimated. The non-energy type voltage equalization control strategy of the battery pack based on expert system can be further estimated. The goal of voltage equalization control is to keep the voltage of each cell consistent during battery operation. The principle of equalization is to reduce the energy consumption and improve the equalization speed. Taking the switched capacitor equalization circuit as an example, this paper analyzes the relationship among the equalization circuit capacity, switching frequency and battery operating current, and provides a theoretical analysis method for the equalization circuit design.
【学位授予单位】：天津大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：U469.72;TM912

【参考文献】