用于寒地的电动汽车锂电池荷电状态估计及均衡策略研究

发布时间：2018-01-16 09:04

本文关键词：用于寒地的电动汽车锂电池荷电状态估计及均衡策略研究　出处：《哈尔滨工业大学》2017年博士论文　论文类型：学位论文

【摘要】：目前,电动汽车在气温温和地区的示范运行和市场推广都取得了很好的效果,但是在寒冷地区的应用却存在着很大障碍,这由于该地区的温度环境比较恶劣。在低温环境下,动力锂电池的性能将会急剧下降。因此,在低温环境下对电池组进行高效、合理的管理是十分必要的。电池的荷电状态(State of Charge,SOC)是电池管理系统中的重要参数之一。环境温度会对电池特性产生严重影响,使得现有SOC估计算法无法精确估计。随着电动汽车在寒冷地区的普及,电池SOC估计算法迫切需要解决高精度、宽温度范围和多温度工况的兼顾性问题。均衡策略是均衡管理技术中重要的组成部分之一。然而,电池组参数的不一致性将会给现有均衡判据带来误差,进而导致过均衡的问题。随着主动均衡技术的发展,降低过均衡的程度,提高均衡策略的精确性是亟待解决问题。本课题旨在解决宽温度范围下电池SOC的精确估计问题,以及参数不一致条件下电池组均衡策略的精确性问题,为电动汽车在寒冷地区的应用推广提供关键技术和理论基础。本课题主要研究内容如下:为了在不同温度下提高电池模型的精度,对锂电池的温度特性进行研究。设计了电池实验系统和相应的实验内容。定性分析了电池工作温度和电流对容量和库伦效率的影响,为拓宽安时积分法的温度适用范围提供依据。开路电压(Open-circuit Voltage,OCV)是电池SOC估计中的重要特性。通过对比恒温和变温实验下得到的SOC-OCV曲线,分析得出不同温度路径下导致OCV偏移的原因,为实现不同温度工况下SOC的精确估计提供理论基础。对电池组模型参数与温度的关系进行分析,结果表明温度会影响模型参数的离散性,进而增加均衡策略的误判性。针对宽范围变温条件下电池SOC的精确估计问题,提出了一种基于多温度路径OCV-扩展安时的电池SOC估计方法。通过建立多温度路径下SOC-OCV映射模型,实现了不同温度条件下SOC初始值的精确估计。通过建立容量、库伦效率与温度的模型,构建不同温度条件间SOC的转换方程,扩展了传统安时积分法的温度适用范围。实验表明,该方法在变温条件下SOC估计精度较高。温度从-30℃升高至40℃的变温条件下,钛酸锂电池SOC估计最大误差为2.3%,均方根误差为1.0%,与传统的OCV-安时积分方法相比,最大误差降低了8.0%,均方根误差降低了3.3%。针对宽范围恒温条件下电池模型参数及SOC的精确估计问题,提出了一种基于参数估计OCV(Parametric Estimation-based OCV,OCVPE)的多温度电池SOC估计方法。利用新息序列对系统和量测噪声协方差进行实时更新,提高了基于联合扩展卡尔曼滤波的电池模型状态和参数在线估计的精度和稳定性。通过建立OCVPE、SOC和温度的映射模型,减小基于OCVPE的SOC估计系统误差。实验表明,该方法在恒温条件下SOC估计精度较高。在-30℃恒温条件下,钛酸锂电池SOC估计最大误差为3.9%,均方根误差为1.2%,与传统的基于库伦滴定OCV-SOC方法相比,最大误差降低了9.7%,均方根误差降低了8.0%。针对电池组参数不一致条件下均衡策略的精确性问题,提出了一种基于模糊化热力学SOC的电池组均衡策略。通过建立基于热力学SOC的均衡判断依据,实现了电池真实状态达到一致的均衡目标。基于热力学SOC估计误差模型,利用模糊理论实现了消减过均衡的控制策略。实验表明,对于5节串联电池组,在初始最大电压差0.059V条件下,经过均衡电池组最大电压差减小至0.008V,与基于电压的均衡策略相比,最大电压差减小了0.024V。
[Abstract]:At present, the electric car in the warm area demonstration of operation and marketing have achieved very good results, but the application in cold regions but there is a big obstacle, because this temperature environment in this area is relatively poor. Under the low temperature environment, the performance of power lithium battery will be decreased rapidly. Therefore, in low temperature environment under the battery pack for efficient and reasonable management is very necessary. The battery state of charge (State of, Charge, SOC) is one of the most important parameters in battery management system. The environmental temperature will have a serious impact on the characteristics of battery, making the existing SOC estimation algorithm can not be accurately estimated. With the popularization of electric vehicles in the cold area and the battery SOC estimation algorithm is an urgent need to solve the problem of both high precision and wide temperature range and temperature conditions. The equilibrium strategies are an important part of a balanced management technology. However, the battery No consistent set of parameters will bring errors to the existing equalization criterion, and then lead to equilibrium problems. With the development of active balancing technology, reduce the degree of balance, improve the accuracy of balancing strategy is a problem to be solved. This paper aims to solve the problem of accurate estimation of battery SOC wide temperature range, accuracy under the condition of battery equalization policies and parameters, provide key technology and theoretical basis for the popularization and application of electric vehicles in cold areas. The main contents of this paper are as follows: in order to improve the accuracy of the battery model under different temperature conditions, studied the temperature characteristics of the lithium battery. The battery design experiment system and corresponding experiment the content was analyzed qualitatively. Effects of temperature and current of battery capacity and efficiency in Kulun, provide the basis for the temperature to widen the scope of application of current time integral method. The open circuit voltage (Open-circuit Voltage OCV) is an important characteristic of battery SOC estimation. The SOC-OCV curve can be obtained by comparing the constant and variable temperature experiments under different temperature, analyzed the cause of the OCV migration path, and provide a theoretical basis for the estimation of different temperature conditions of SOC. By analyzing the relationship between the model parameters and battery temperature the results show that the discrete temperature will affect the parameters of the model, and then increase the equilibrium strategy. For the miscarriage of the problem of precise estimation of a wide range of temperature conditions under the battery SOC, proposes an extended ah based on multi path OCV- SOC battery temperature estimation method. By using SOC-OCV mapping model to realize the accurate temperature path. Estimation of SOC under different temperature conditions of the initial value. Through the establishment of the Kulun capacity, efficiency and temperature conversion model, construction of different temperature conditions of SOC equation, and extends the traditional The temperature scope of current time integral method. Experimental results show that this method under the conditions of variable temperature SOC high estimation accuracy. Temperature temperature to 40 DEG C from -30 deg.c, lithium titanate battery SOC estimation of the maximum error is 2.3%, the root mean square error is 1%, compared with the traditional OCV- current time integral method, the maximum error decreases 8%, the RMS error is reduced for the problem of precise estimation of parameters and SOC cell model of 3.3%. under the condition of constant temperature and wide range, proposes a parameter estimation based on OCV (Parametric Estimation-based OCV, OCVPE) the temperature of the battery SOC estimation method. The real-time update of system noise and measurement noise covariance by the innovation sequence, improve the estimation of battery state model of the extended Calman filter and parameters of on-line precision and stability. Based on the mapping model through the establishment of OCVPE, SOC and temperature, the decrease of OCVPE system based on SOC estimation System error. Experimental results show that this method under the condition of constant temperature SOC estimation accuracy is high. At -30 DEG C under the condition of constant temperature, the lithium titanate battery SOC estimation of the maximum error is 3.9%, the root mean square error is 1.2%, and the traditional Kulun OCV-SOC titration method based on comparison, the maximum error is reduced by 9.7%, root mean square error decreases the accuracy problem 8.0%. according to the battery parameter of inconsistent condition equilibrium strategy, put forward a fuzzy thermodynamic SOC battery equalization method based on balanced judgment. By establishing the basis of thermodynamics based on SOC, realize the true state of equilibrium of a target cell induced. The error model of SOC estimation based on thermodynamics, using fuzzy theory to realize to abate balanced control strategy. Experimental results show that for the 5 day series battery, poor 0.059V conditions in the initial maximum voltage, battery maximum voltage difference after equilibrium is reduced to 0.008V, and based on the Compared to the voltage balancing strategy, the maximum voltage difference is reduced by 0.024V.

【学位授予单位】：哈尔滨工业大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：U469.72

【相似文献】