基于双自适应卡尔曼滤波的锂电池状态估算

精准的锂电池建模是保证电池储能系统可靠性至关重要的手段.荷电状态(state of charge,SOC)的准确估计保证了特定应用程序的安全高效运行.为了提高SOC的估计精度,首先建立等效电路模型,利用遗忘因子的偏差补偿最小二乘法(bias compensation recursive least squares,BC...     

基于PNGV模型与自适应卡尔曼滤波的铅炭电池荷电状态评估

储能电池应用广泛,准确估计储能电池的荷电状态(state of charge,SOC)对提高电池健康状态有重要意义。铅炭电池作为一种高性能、低成本、高安全性的新型储能电池,在储能电站等场景受到广泛关注,而目前尚缺少铅炭电池SOC估计相关研究。本工作首先通过静流间歇滴定技术探究铅炭电池的荷电状态与开路电压关系,后通过混合脉冲功率性能试验得到铅炭电池的伏安特征数据,建立一阶Thevenin和一阶PNGV等效电路模型,利用基于代理模型和灵敏度分析的随机算法(surrogate optimization algorithm,SOA)对两种等效电路模型进行参数辨识。在此基础上,利用扩展卡尔曼滤波算法(extended Kalman filter,EKF)估计铅炭电池SOC,估算过程考虑噪声干扰。另外,在铅炭电池SOC初值未知的情况下,EKF算法不能准确估计铅炭电池SOC。因此,本工作提出采用自适应扩展卡尔曼滤波算法(adaptive extended Kalman filter,AEKF)对铅炭电池进行状态估计,来弥补EKF的不足。结果表明,在存在噪声且SOC初值未知的情况下,AEKF算法较EK...     

基于并行卡尔曼滤波器的锂离子电池荷电状态估计

针对新能源电动汽车的电量显示与安全管理问题,对其锂离子电池的荷电状态展开研究,提出了基于并行卡尔曼滤波器的全寿命下的电池荷电状态(state of charge,SOC)估计算法.建立了电池Thevenin一阶RC等效电路模型,通过开路实验的数据处理获取静态OCV-SOC关系表达式,并利用具有动态遗忘因子的最小二乘法对模型参数进行了辨识.以安时积分法为状态传递方程,在扩展卡尔曼滤波的基础上利用最大似然估计准则使模型噪声协方差具有自学习能力.考虑模型参数随电池寿命衰减而改变的问题设计并行结构的滤波器来分别进行电池状态估计和参数修正,保证了数据传递中的纯洁性和独立性,从而实现了全寿命下的SOC估计.经过仿真实验验证算法的快速收敛性与实时性,估计精度在2％以内.     

基于IBAS-NARX神经网络的锂电池荷电状态估计

在使用神经网络方法估计锂电池荷电状态时,传统荷电状态适应度评价函数存在仅考虑均方误差等网络权值参数的缺点,忽略了拓扑参数对模型的影响.故本文提出在适应度评价函数设计中综合考虑输入/输出时序相关性、隐层神经元数量等模型拓扑参数和网络权值参数的加权影响,并将其引入带外部输入非线性自回归神经网络建模方法的锂电池荷电状态估计中,进而基于改进天牛须搜索算法实现了上述模型拓扑参数与网络权值参数的协同辨识优化.仿真结果表明,本文所提出方法能够提高多种复杂工况下的锂电池荷电状态估计精度,在DST标准工况和WLTC标准工况下锂电池荷电状态的均方根误差分别达到3.38×10?3和8.75×10?4,相比于未经改进的天牛须搜索算法优化NARX神经网络在均方根误差上估计精度分别提升了42.4％和20.5％.     

基于区域极点配置电池荷电状态的估计方法

电池管理系统中蓄电池荷电状态的测量是非常重要的。在光伏系统中,蓄电池荷电状态决定了电池管理系统的充放电策略。根据一类电池等效电路模型,利用蓄电池荷电状态观测器误差系统的极点分布,给出蓄电池荷电状态观测器的设计方法。该方法可以根据圆盘极点位置的变化调整观测器荷电状态观测值的误差。通过仿真可以看到,基于极点配置的荷电状态观测器的有效性。     

锂离子电池参数辨识及荷电状态的估算北大核心CSCD

实现对锂电池的荷电状态(state ofcharge,SOC)的准确估算对电动汽车电池管理系统具有重要意义。采用了二阶RC等效电路模型对电池进行精确建模,并分别利用离线参数辨识和带遗忘因子的递推最小二乘法的在线参数辨识方法对等效电路中的参数进行辨识,在确保模型精度满足要求后,利用扩展卡尔曼滤波(extended Kalman filter,EKF)算法来实现对电池SOC的准确估算。以美国联邦城市运行工况(federal urbandriving schedule,FUDS)和城市道路循环工况(urban dynamometerdrivingschedule,UDDS)进行仿真实验,并将实验中标准SOC值与离线辨识和在线辨识的SOC估计值进行对比分析。实验结果表明,在FUDS工况和UDDS工况下利用EKF算法估算SOC的平均误差都在2.5%以下,且在线参数辨识模型比离线参数辨识模型的平均误差分别降低了0.7%和0.9%。证明了EKF算法能实现对电池SOC的准确估算,且在线参数辨识方法下的电池模型具有更高的估算精度。     

基于改进概率神经网络的储能电池荷电状态估计

锂离子电池荷电状态（SOC）估计技术是储能电站电池管理系统重要组成部分。为了实现对SOC的准确估算,提出一种改进概率神经网络（MPNN）用于储能电池荷电状态估计。相较于传统神经网络,结合概率函数和补偿机制的MPNN,不仅可避免陷入局部最优,而且具有更优秀的拟合能力,可进一步提高SOC估计精度。仿真实验表明,所提MPNN方法的SOC估计值平均绝对误差和均方误差均低于1%,获得了满意的性能。     

基于扩展卡尔曼滤波法的锂电池荷电状态估计

锂离子电池在储能电站具有广阔的应用前景,它是储能电站的能量储存单元,精确地对锂离子电池荷电状态(SOC)进行预测是至关重要的,这可以在很大程度上提高电站系统的安全性能。通过多种等效电路模型的对比,选择二阶戴维南等效电路模型。选择三洋18650型动力锂电池作为电池充放电实验对象,设计测试实验步骤,得出电池的参考容量为3.4A·h,库伦效率为98.48%。对电池模型进行精确的参数辨识,通过曲线拟合得到UOC-SOC关系表达式。在仿真软件MATLAB/Simulink中运用扩展卡尔曼滤波法(EKF)对SOC进行仿真预测,通过SOC误差曲线对比可以发现,EKF估计器在起始状态略有误差,但1min内即快速收敛到SOC参考值附近,并且稳定状态下观测误差可维持在0.1%以内,能够满足工程实际应用的要求,证明了扩展卡尔曼滤波法对电池SOC预测的准确性。     

基于简单循环单元的储能锂离子电池SOC和SOH联合估计方法

锂离子电池的荷电状态(State of charge,SOC)和健康状态(State of health,SOH)是电池储能系统在运维过程中所需要估算的重要参数。为了能够对电池状态进行可靠估计,采用深度学习方法中的简单循环单元(Simple recurrent unit,SRU)来实现对电池SOC和SOH的联合估计。首先,通过利用SRU在处理时序问题上的优势,建立了基于SRU的电池SOC估计模型;接着,给模型引入了数据单元的输入形式,并使用含有电池老化信息的样本数据来对模型进行训练,使得训练好的模型能够实现任意电池老化程度下的SOC估计;最后,通过对该模型输出的SOC估计值中所隐含的老化信息进行挖掘,实现对电池SOH的估计。试验结果表明,该联合估计方法可以实现电池SOC与SOH的准确估计,并且对不同种类的电池也有较好的适用能力。     

Active equalization control strategy of Li-ion battery based on state of charge estimation of an electrochemical-thermal coupling model

Aiming at solving the problem of poor battery cell consistency caused by excessive decay of cell capacity or increased internal resistance during the operation of lithium-ion battery packs for vehicles, the paper proposes an active equalization control with 12-V power supply as an equalization energy source, which achieves efficient energy replenishment of individual cells with low power. The electrochemical-thermal coupling model of lithium-ion battery is built, and the order reduction of large-scale system theory ensures that the model had higher accuracy and lower amount of calculation, which is suitable for vehicle battery management system (BMS). Then the extended Kalman filter algorithm is used to calculate the real-time state of charge (SOC) of each cell and set as an equalization variable. The equalization simulation circuit is built with MATLAB/Simulink, the experimental platform of active equalization system for battery packs is constructed, and the battery packs are tested for equalization in static state. The simulation and experimental results show that the proposed active equalization control strategy can rapidly improve the voltage inconsistency between single cells, and the energy transfer efficiency can reach about 85% during the equalization process.     

State of charge estimation for lithium-ion battery based on an intelligent adaptive unscented Kalman filter

Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium-ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time-varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance.     

Research on parameter identification and state of charge estimation of improved equivalent circuit model of Li-ion battery based on temperature effects for battery thermal management

The performance and parameters of Li-ion battery are greatly affected by temperature. As a significant battery parameter, state of charge (SOC) is affected by temperature during the estimation process. In this paper, an improved equivalent circuit model (IECM) considering the influence of ambient temperatures and battery surface temperature (BST) on battery parameters based on second-order RC model have been proposed. The exponential function fitting (EFF) method was used to identify battery model parameters at 5 ambient temperatures including −10°C, 0°C, 10°C, 25°C and 40°C, fitting the relationship between internal resistance and BST. Then, the SOC of the IECM was estimated based on the extended Kalman filter (EKF) algorithm. Using the result calculated by the Ampere-hour integration method as the standard, the data of battery under open circuit voltage (OCV) test profile and dynamic stress test (DST) profile at different ambient temperatures has been compared with the ordinary second-order RC model, and the advantages of the SOC estimation accuracy with IECM was verified. The numerical results showed that the IECM can improve the estimation accuracy of battery SOC under different operating conditions.     

State of charge estimation method based on the extended Kalman filter algorithm with consideration of time-varying battery parameters

In developing battery management systems, estimating state-of-charge (SOC) is important yet challenging. Compared with traditional SOC estimation methods (eg, the ampere-hour integration method), extended Kalman filter (EKF) algorithm does not depend on the initial value of SOC and has no accumulated error, which is suitable for the actual working condition of electric vehicles. EKF is a model-based algorithm; the accuracy of SOC estimated by this algorithm was greatly influenced by the accuracy of battery model and model parameters. The parameters of battery change with many factors and exhibit strong nonlinearity and time variance. Typical EKF algorithm approximates battery as a linear, time-invariant system; however, this approach introduces estimation errors. To minimize such errors, previous studies have focused on improving the accuracy of identifying battery parameters. Although studies on battery model with time-varying parameters have been carried out, few have studied the combination of time-varying battery parameters and EKF algorithm. A SOC estimation method that combines time-varying battery parameters with EKF algorithm is proposed to improve the accuracy of SOC estimation. Battery parameter data were obtained experimentally under different temperatures, SOC levels, and discharge rates. The results of parameter identification are made into a data table, and the battery parameters in the EKF system matrix are updated by looking up the data in the table. Simulation and experimental results shown that, average error of SOC estimated by the proposed algorithm is 2.39% under 0.9 C constant current discharge and 2.4% under 1.3 C, which is 1.91% and 2.35% lower than that of EKF algorithm with fixed battery parameters. Under intermittent discharge with constant current (1.1 C) and capacity (10%), the average error of SOC estimated by the proposed algorithm is 1.4%, which is 0.3% lower than that of EKF algorithm with fixed battery parameters. The average error of SOC estimated by the proposed algorithm under the New European Driving Cycle (NEDC) is 1.6%, which is 0.2% lower than that of EKF algorithm with fixed battery parameters. Relative to the EKF algorithm with fixed battery parameters, the proposed EFK algorithm with time-varying battery parameters yields higher accuracy.     

Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering

The extended Kalman filter (EKF) method for SOC estimation has some problems such as the lack of an accurate model, and model errors due to the variation in the parameters of the model due to the nonlinear behavior of a battery. To solve the aforementioned issues, this paper proposes a reduced order EKF including the measurement noise model and data rejection. In order to do so, the model of a battery in the EKF is simplified into the type of reduced order to decrease the calculation time. Additionally, to compensate the model errors caused by the reduced order model and variation in parameters, a measurement noise model and data rejection are implemented because the model accuracy is critical in the EKF algorithm in order to obtain a good estimation. Finally, the proposed algorithm is verified by short and long term experiments.     

Towards impedance-based temperature estimation for Li-ion battery packs

In order to meet the required power and energy demand of battery-powered applications, battery packs are constructed from a multitude of battery cells. For safety and control purposes, an accurate estimate of the temperature of each battery cell is of vital importance. Using electrochemical impedance spectroscopy (EIS), the battery temperature can be inferred from the impedance. However, performing EIS measurements simultaneously at the same frequency on each cell in a battery pack introduces crosstalk interference in surrounding cells, which may cause EIS measurements in battery packs to be inaccurate. Also, currents flowing through the pack interfere with impedance measurements on the cell level. In this paper, we propose, analyse, and validate a method for estimating the battery temperature in a battery pack in the presence of these disturbances. First, we extend an existing and effective estimation framework for impedance-based temperature estimation towards estimating the temperature of each cell in a pack in the presence of crosstalk and (dis)charge currents. Second, the proposed method is analysed and validated on a two-cell battery pack, which is the first step towards development of this method for a full-size battery pack. Monte Carlo simulations are used to find suitable measurement settings that yield small estimation errors and it is demonstrated experimentally that, over a range of temperatures, the method yields an accuracy of ±1°C in terms of bias, in the presence of both disturbances.     

Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge (SOC) estimation is an important part of the design of electric vehicle battery management systems. Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries, thereby improving discharge efficiency and extending cycle life. In this study, the key lithium-ion battery SOC estimation technologies are summarized. First, the research status of lithium-ion battery modeling is introduced. Second, the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed. Third, the development status and advantages and disadvantages of SOC estimation methods are summarized. Finally, the current research problems and prospects for development trends are summarized.     

基于在线参数辨识和改进2RC-PNGV模型的锂离子电池建模与SOC估算研究

锂电池荷电状态(state of charge,SOC)的准确估计对电池安全监测与能量的高效利用具有重要意义.提出一种新的验证模型,首先对电池新一代汽车合作伙伴(PNGV)模型进行改进,考虑电池充放电的差异,加入了二极管电阻的并联网络来代替传统PNGV模型的内阻,在此基础上,增加了一个RC的并联网络来表征电池的动静态特性.以三元锂电池为研究对象,通过遗忘因子最小二乘法(forgetting factor recursive least square,FFRLS)对改进模型进行在线参数辨识,并提出了主充电、放电实验对锂电池工作特性进行仿真分析,通过FFRLS-EKF算法在DST工况下对SOC进行估算.实验结果表明,改进的2RC-PNGV模型能够较好地反映锂电池工作特性,HPPC实验的平均电压误差为0.17％,模型具有较高的精度.主充电过程SOC平均估算误差为0.957％,最大估算误差为5.03％;主放电过程SOC平均估算误差为0.807％,最大估算误差为3.38％,表明改进的2RC-PNGV模型与联合估计算法均可用于SOC实际估算.