动力电池组SOC在线估计模型与方法研究
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摘要
随着近几年国民经济的快速发展和人民生活水平的日益提高,汽车工业在全世界得到了迅猛发展。汽车的大量使用在给人们生活带来便利的同时,也带来了能源消耗、环境污染等诸多负面影响。面对日益严重的石油过快消耗和环保问题,电动汽车作为一种新的绿色交通工具,是各国政府不约而同所提出的一种富有成效的应对措施。在电动汽车中,电池直接作为主动能量供给部件,其工作状态的好坏直接关系到整个汽车的行驶安全性和运行可靠性。为确保电动汽车中的电池组性能良好,延长电池组使用寿命,须及时、准确地了解电池的各种运行状态,其中尤以电池荷电状态(State of Charge, SOC)的精确估算最为关键。本文针对动力锂电池组的SOC在线精确估计这一问题,从锂电池动态模型、估计算法等方面开展了广泛深入的研究。论文主要研究工作如下：
(1)针对动力电池这一动态非线性系统,提出了一种锂电池过程模型的具体改进方法,并给出了相应的模型参数在线估计算法。该方法使用放电速率比例系数对不同放电速率对动力电池SOC的影响进行建模,采用二阶多项式拟合模型完成实际放电速率下的放电容量到标称容量的折算；另一方面,使用温度比例系数对不同温度条件对动力电池SOC的影响进行建模,采用二阶多项式拟合模型完成实际温度下的放电容量到标称容量的折算。放电速率比例系数和温度比例系数的有机结合可以更加客观地描述锂电池在实际放电速率和温度工作条件下的放电特性,从而有效提高单体锂电池SOC的估计精度。
(2)利用扩展卡尔曼滤波、Unscented卡尔曼滤波以及粒子滤波等贝叶斯滤波方法,给出了电池SOC估计的具体算法和步骤,并在多种典型电动汽车运行工况下对相关算法进行了仿真,对比分析了它们在SOC估计精度、收敛速度、算法复杂度及鲁棒性等方面的性能。
(3)针对动力锂电池由于生产、使用过程中所存在的个体差异,以及锂电池使用过程中不可避免的老化现象,提出了一种电池模型参数与SOC联合在线估计方法。该方法在进行锂电池SOC在线估计的同时,可以对电池模型参数尤其是内阻参数进行在线估计和更新,从而可以进一步提高电池SOC估计的精确度。
(4)实现了一个由4只容量为50Ah的动力电池组嵌入式SOC在线估计原型演示系统,开发了相应的系统硬件和软件。实验结果表明,采用文中所提出的基于采样点卡尔曼滤波的锂电池内阻与SOC联合估计算法可以快速地完成动力电池组的SOC精确估计,其最大估计误差为5%,平均估计误差为3%,一次估计时间约为3～4s；所开发的电池组嵌入式SOC在线估计原型系统符合预期效果,为后续现场应用提供了较为可靠的依据。
With the rapid development of the national economy and increasing living standards in recent years, the automotive industry has been developing rapidly in the world. A large number of cars bring convenience to people's lives, but also has brought many negative effects of energy consumption, and environmental pollution. With these growing excessive oil consumption and environmental issues, Governments in the world coincidently develop electric vehicles (EVs) as a new green transport. In electric vehicles, batteries are used directly as the active energy supply, their working states are very important to driving safety and operational reliability of the whole car. To ensure good performance of the battery pack in an EV so as to extend its service life, a timely and accurate understanding of the various operating status, especially the state of charge (SOC) of the battery pack, are very critical to the whole system. In this paper, the problem of online accurate estimate of the SOC for a lithium power battery pack is considered. Extensive researches on the dynamic lithium battery models and the algorithms for SOC estimation are carried out. The main research works of the thesis are as follows:
(1)For the power battery dynamic nonlinear system, specific improvements of the process model of lithium batteries are proposed, and the corresponding model parameters estimation algorithms are given. A discharge rate proportional coefficient is used to model the relationship between different discharge rates and the SOC of the battery, which is a second order polynomial; on the other hand, a temperature proportional coefficient is utilized to model the relationship between different temperature conditions and the SOC, which is another second-order polynomial. The combination of the discharge rate proportional coefficient and the temperature proportional coefficient can effectively simulate the discharge characteristics of lithium batteries in real operating conditions, and can effectively improve the estimated accuracy of the SOC of a single battery.
(2)Bayesian filtering methods such as the extended Kalman filter, the Unscented Kalman filter and the particle filter, are used for battery SOC estimation. Algorithms and specific steps are given in detail. Several typical operating conditions are simulated to verify the effectiveness of the proposed methods. Comparative analysis for the SOC estimation performances of the SOC estimatin algorithms such as the accuracy, convergence speed, complexity and robustness are also shown.
(3)Individual differences exist inevitablely during production processe or using process. On the other hand, aging effect exist commonly for batteries. All these make the battery model parameters variate timely. In this paper, an online joint estimation method of the lithium battery SOC and the battery model parameters, especially the internal resistance, are proposed, which can further improve the estimation accuracy of the battery SOC.
(4)A prototype demonstration system of SOC online estimation for battery pack is developed. The battery pack is composed of four lithium-ion batteries with a nominal capacity of50Ampere hours and a nominal voltage of3.2volts. Both the system hardware and software are built. Experimental results show that the sigma point Kalman filter based internal resistance and SOC joint estimation algorithm can be used for a precise estimation of the SOC of the battery pack with a maximum estimation error of5%, while the average estimation error is3%. The time consumed for one step estimation is3～4seconds. The developed embedded prototype system verifies that the proposed methods can be applied for real applications.
(1)针对动力电池这一动态非线性系统,提出了一种锂电池过程模型的具体改进方法,并给出了相应的模型参数在线估计算法。该方法使用放电速率比例系数对不同放电速率对动力电池SOC的影响进行建模,采用二阶多项式拟合模型完成实际放电速率下的放电容量到标称容量的折算；另一方面,使用温度比例系数对不同温度条件对动力电池SOC的影响进行建模,采用二阶多项式拟合模型完成实际温度下的放电容量到标称容量的折算。放电速率比例系数和温度比例系数的有机结合可以更加客观地描述锂电池在实际放电速率和温度工作条件下的放电特性,从而有效提高单体锂电池SOC的估计精度。
(2)利用扩展卡尔曼滤波、Unscented卡尔曼滤波以及粒子滤波等贝叶斯滤波方法,给出了电池SOC估计的具体算法和步骤,并在多种典型电动汽车运行工况下对相关算法进行了仿真,对比分析了它们在SOC估计精度、收敛速度、算法复杂度及鲁棒性等方面的性能。
(3)针对动力锂电池由于生产、使用过程中所存在的个体差异,以及锂电池使用过程中不可避免的老化现象,提出了一种电池模型参数与SOC联合在线估计方法。该方法在进行锂电池SOC在线估计的同时,可以对电池模型参数尤其是内阻参数进行在线估计和更新,从而可以进一步提高电池SOC估计的精确度。
(4)实现了一个由4只容量为50Ah的动力电池组嵌入式SOC在线估计原型演示系统,开发了相应的系统硬件和软件。实验结果表明,采用文中所提出的基于采样点卡尔曼滤波的锂电池内阻与SOC联合估计算法可以快速地完成动力电池组的SOC精确估计,其最大估计误差为5%,平均估计误差为3%,一次估计时间约为3～4s；所开发的电池组嵌入式SOC在线估计原型系统符合预期效果,为后续现场应用提供了较为可靠的依据。
With the rapid development of the national economy and increasing living standards in recent years, the automotive industry has been developing rapidly in the world. A large number of cars bring convenience to people's lives, but also has brought many negative effects of energy consumption, and environmental pollution. With these growing excessive oil consumption and environmental issues, Governments in the world coincidently develop electric vehicles (EVs) as a new green transport. In electric vehicles, batteries are used directly as the active energy supply, their working states are very important to driving safety and operational reliability of the whole car. To ensure good performance of the battery pack in an EV so as to extend its service life, a timely and accurate understanding of the various operating status, especially the state of charge (SOC) of the battery pack, are very critical to the whole system. In this paper, the problem of online accurate estimate of the SOC for a lithium power battery pack is considered. Extensive researches on the dynamic lithium battery models and the algorithms for SOC estimation are carried out. The main research works of the thesis are as follows:
(1)For the power battery dynamic nonlinear system, specific improvements of the process model of lithium batteries are proposed, and the corresponding model parameters estimation algorithms are given. A discharge rate proportional coefficient is used to model the relationship between different discharge rates and the SOC of the battery, which is a second order polynomial; on the other hand, a temperature proportional coefficient is utilized to model the relationship between different temperature conditions and the SOC, which is another second-order polynomial. The combination of the discharge rate proportional coefficient and the temperature proportional coefficient can effectively simulate the discharge characteristics of lithium batteries in real operating conditions, and can effectively improve the estimated accuracy of the SOC of a single battery.
(2)Bayesian filtering methods such as the extended Kalman filter, the Unscented Kalman filter and the particle filter, are used for battery SOC estimation. Algorithms and specific steps are given in detail. Several typical operating conditions are simulated to verify the effectiveness of the proposed methods. Comparative analysis for the SOC estimation performances of the SOC estimatin algorithms such as the accuracy, convergence speed, complexity and robustness are also shown.
(3)Individual differences exist inevitablely during production processe or using process. On the other hand, aging effect exist commonly for batteries. All these make the battery model parameters variate timely. In this paper, an online joint estimation method of the lithium battery SOC and the battery model parameters, especially the internal resistance, are proposed, which can further improve the estimation accuracy of the battery SOC.
(4)A prototype demonstration system of SOC online estimation for battery pack is developed. The battery pack is composed of four lithium-ion batteries with a nominal capacity of50Ampere hours and a nominal voltage of3.2volts. Both the system hardware and software are built. Experimental results show that the sigma point Kalman filter based internal resistance and SOC joint estimation algorithm can be used for a precise estimation of the SOC of the battery pack with a maximum estimation error of5%, while the average estimation error is3%. The time consumed for one step estimation is3～4seconds. The developed embedded prototype system verifies that the proposed methods can be applied for real applications.
引文
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