一种基于预测开路电压的SOC估算方法

估算锂离子动力电池荷电状态（SOC）是电池管理的一个难点,通过对电池放电曲线及恢复曲线分析,拟合出电池开路电压的计算公式,解决了动态情况下预测电池开路电压的问题,使开路电压估算SOC在电动车上使用成为可能。本文采用建立电池模型的方法,通过实验所得数据对模型进行曲线拟合,得到最优参数,并通过另外几组数据进行验证。实验结果...     

阀控密封铅蓄电池的开路电压变化规律

起动型铅蓄电池的开路电压跟荷电态之间存在线性关系,因而可以根据开路电压来推断电池的放电容量或荷电态。阀控密封铅蓄电池的开路电压跟电极表面附近液层中的电解液的密度有关,但它的放电容量不仅跟电池中参与电极反应的电解液的量以及活性物质的量有关,而且还深受反应粒子的扩散过程迟缓性的影响。因而不能直接用开路电压来推断阀控密封铅蓄电池的放电容量或荷电态。     

一种基于预测开路电压估算SOC初值的方法

为解决电动汽车动力电池 SOC初值估算问题,文章以锂离子动力电池为对象,进行了脉冲放电实验,拟合了锂离子动力电池开路电压与 SOC函数关系式。对七阶Thevenin等效电池模型进行了参数辨识,预测了锂离子电池开路电压,将预测的开路电压代入开路电压与 SOC函数关系式进行了 SOC初值的估计。通过仿真实验,得出 SOC 初值估计误差为0.1321%。文中 SOC初值估算精度优于市场上通用的电池容量检测仪精度,验证了预测开路电压估算 SOC初值方法的可行性。     

基于恢复电压的铅酸蓄电池容量研究

为了快速准确地预测铅酸蓄电池的剩余容量,设计了电池在一定温度下以不同条件放电的实验。研究电池放出一定容量后,恢复稳定过程中各参数的变化。通过测量不同荷电状态下的恢复电压并进行归一化处理,拟合归一化恢复电压和蓄电池当前剩余容量关系,得到函数关系式。结果表明,测量蓄电池放电后的恢复电压,进行相应函数运算,可以在短时间内较准确预测蓄电池的荷电状态,其测量误差小于5%;较于核对性容量试验测量蓄电池容量,时间短、操作方便,适合实际应用。     

一种改进型锂离子电池SOC估算方法

为提高锂离子电池荷电状态(SOC)的估算精度，首先建立锂离子电池的较精确的二阶 RC等效模型，根据模型建立 状态空间方程，其次介绍电池充放电时电压电流采集方法，根据采集的数据，运用扩展卡尔曼滤波算法(EKF)，对锂离子电池的荷电状态进行估算。结果表明，该数据的采集方法具有很高的精度，用扩展卡尔曼滤波算法估算的结果与真实值较为接近，精确度可达4％，对噪声有很强的抑制作用。     

装甲车辆铅酸蓄电池容量在线检测的研究

针对装甲车辆铅酸蓄电池特殊变电流工况,文中在对开路电压法和内阻法深入分析的基础上提出了一种新的荷电状态辨识方法,并对其进行了硬件电路设计。该辨识方法适合装甲车辆复杂的变电流工况,可实现电池荷电状态与性能状态的在线准确评估,实现蓄电池高效管理。     

储能用电芯及模组恒流和恒功率充放电对比研究

储能电站在电力系统的实际应用中都以恒定的功率来工作,一般以kWh和MWh等作为单位参数。但现阶段锂离子电池在研发、设计、生产和试验过程中通常用电量作为容量参数,以Ah作为单位参数。分别对锂离子电池的电芯和模组层级,以不同的充放电工况（恒流恒压充电/恒流放电和恒功率充放电）进行容量和能量测试,研究这两种不同的充放电工况对电芯、模组的容量和能量的影响,同时研究这两种不同充放电工况下,电芯、模组在充放电过程中的电流和电压变化情况。     

退役动力电池双层均衡方法研究

针对如何提高退役电池组均衡速度,提出了一种双层均衡电路,底层电路采用基于电感的Buck-Boost均衡电路结构,顶层电路采用可重构电路结构。根据开路电压(OCV)和电池荷电状态(SOC)曲线关系采用分段均衡控制策略。设计双层均衡电路的仿真实验,仿真实验结果表明：在电池参数相同情况下,与传统Buck-Boost均衡电路相比,双层电路均衡时间减少25%,有效提高了电池组均衡速度。在电池状态和均衡电路结构相同的情况下,与单一变量的均衡策略相比,SOC-电压分段均衡策略时间减少11%,验证了该均衡方案可行性。     

电动汽车锂离子电池模型参数辨识和荷电状态估算

针对电动汽车锂离子电池荷电状态在线估算准确率低、实时性差等问题,文中建立一种精确在线估算荷电状态的有效方法,采用MAFF-RLS和EKF对荷电状态进行估算。建立锂离子电池的等效电路模型,将MAFF-RLS应用在电池等效电路模型的参数辨识上,可以有效在线辨识模型参数。在模型参数辨识的基础上,将辨识出的模型参数作为荷电状态估算的输入,采用EKF估算动力电池实时荷电状态。经过实验仿真发现,采用MAFF-RLS和EKF联合估算荷电状态能够提高估算精确度,估算误差仅在2%以内。     

基于主成分分析的液流电池状态估计策略研究

为克服放电后期电压拐点对锌溴液流电池（ZBFB）荷电状态（SOC）辨识精度的影响,研究基于主成分分析（PCA）的锌溴液流电池荷电状态估计方法。以提高锌溴液流电池SOC参数辨识精度为目标,特别是解决锌溴液流电池在放电后期特有的电压拐点非线性特性,在兼顾实际算力限制与运行速率要求的条件下,对锌溴液流电池SOC参数辨识算法进行分析。研究结果表明,所提方法兼顾空间维度特征提取以及系统降维建模,可有效改善电压拐点处的电池SOC的辨识精度,为锌溴液流电池的长时储能应用提供理论与算法分析基础。     

基于回跳电压的锂离子电池SOC模型的建立与应用

通过对高低温下锂离子电池进行充放电实验,对得到的回跳电压等实验数据进行分析,建立了自适应神经模糊推理系统（ANFIS）的锂离子电池剩余容量预测模型,其中以回跳电压和温度为预测系统的输入,剩余容量为输出。在MATLAB平台上,通过用大量的实验数据对ANFIS模型进行训练、校验,将此模型用于不同电池组的剩余容量预测和验证,证明了该模型的可靠性和适用性。     

基于联合算法的锂电池SOC与SOH协同在线预测

以18650型锂电池为研究对象,建立双极化Thevenin(DP-Thevenin)等效电路模型描述其动静态特征。分别以恒流脉冲放电实验和带遗忘因子的递推最小二乘法完成电池电动势及模型参数的辨识;在Simulink中搭建等效电路模型,以脉冲电流作为激励进行验证,得出模型响应电压与实际端电压契合度较好,平均误差为1.836%;构建电池实验硬件电路,编写算法程序完成了锂电池实验系统的构建。最后,在随机测试工况下借助Matlab分析了基于联合算法的锂电池荷电状态(SOC)与健康状态(SOH)在预测精确度、错误初值时算法收敛性等方面的性能。实验结果表明,算法可精确估计出电池SOC和内阻大小,最大误差不超过3.5%;且在初值相差15%时,算法可在319 s内收敛至真值附近,鲁棒性较好     

基于模糊逻辑的变电站蓄电池在线健康状态评估

为了保证变电站直流电源设备运行的安全可靠性,提高铅酸蓄电池健康状态(State of Health,SOH)的评估能力至关重要。提出了一种基于模糊逻辑预测SOH的在线检测法,利用蓄电池在充放电状态下电荷量与开路电压之间的线性关系分别在线、离线检测蓄电池的SOH。实验结果表明,传统的离线法与在线法评估的结果一致,误差均接近2%左右。与SOC在线检测方法相比,SOH对蓄电池健康评估具有灵敏度高、收敛快等特点。     

State-of-Charge Determination From EMF Voltage Estimation: Using Impedance, Terminal Voltage, and Current for Lead-Acid and Lithium-Ion Batteries

State-of-charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process, which can be employed to increase battery life. This reduces the risk of overvoltage and gassing, which degrade the chemical composition of the electrolyte and plates. The proposed model in this paper determines the SOC by incorporating the changes occurring due to terminal voltage, current load, and internal resistance, which mitigate the disadvantages of using impedance only. Electromotive force (EMF) voltage is predicted while the battery is under load conditions; from the estimated EMF voltage, the SOC is then determined. The method divides the battery voltage curve into two regions: 1) the linear region for full to partial SOC and 2) the hyperbolic region from partial to low SOC. Algorithms are developed to correspond to the different characteristic changes occurring within each region. In the hyperbolic region, the rate of change in impedance and terminal voltage is greater than that in the linear region. The magnitude of current discharge causes varying rates of change to the terminal voltage and impedance. Experimental tests and results are presented to validate the new models.     

VRLA battery state-of-charge estimation in telecommunication powersystems

This paper presents the logical analysis of valve-regulated lead-acid battery discharge behavior and suggests a model for obtaining estimates of the state of charge (SOC) and reserve time throughout discharge. The basis of the model is the relationship between the discharge voltage and SOC. This relationship is valid for a wide range of discharge rates and ambient temperatures as related to the telecommunications backup power supply application. Due to the robust nature of this relationship, only a single discharge characteristic under nominal operating conditions is required by the model. Case studies reveal that the model enables accuracy in estimation of SOC of better than 10% of actual SOC after discharging 10% of the rated capacity. As the discharge proceeds, the error reduces substantially. A feature of the model is that it is easily adaptable to changes in battery characteristics which occur as a result of extreme stress     

Modeling of Zinc Bromide Energy Storage for Vehicular Applications

Energy storage devices such as lithium-ion and nickel-metal hydrate batteries and ultracapacitors have been considered for utilization in plug-in hybrid electric vehicles (HEVs) and HEVs to improve efficiency and performance and reduce gas mileage. In this paper, we analyze and model an advanced energy storage device, namely, zinc bromide, for vehicular applications. This system has high energy and power density, high efficiency, and long life. A series of tests has been conducted on the storage to create an electrical model of the system. The modeling results show that the open-circuit voltage of the battery is a direct function of the battery's state of charge (SOC). In addition, the battery internal resistance is also a function of SOC at constant temperature. A Kalman filtering technique is also designed to adjust the estimated SOC according to battery current.      

Prediction of Battery Behavior Subject to High-Rate Partial State of Charge

An online optimization procedure provides the parameters of a nonlinear battery model by taking into account a few minutes of measured current–voltage data. Within a defined range in terms of charge current, state of charge (SOC), and duration of charge and discharge events, the model is able to capture the relevant battery dynamics and predict the behavior for the next few minutes. From the battery behavior during specific events, the state of the battery can be revealed, which is defined as the state of function. Validation, which is carried out on measured current–voltage profiles, shows the accuracy of prediction during the high-rate partial SOC operation. Even with the data measured during a city drive within a microhybrid electrical vehicle, the method is able to predict the voltage level during high-rate discharge pulses (cranking).      

基于SOC估算的锂电池组复合型均衡拓扑设计

锂电池的均衡管理可以提高锂电池的使用寿命和续航里程。针对磷酸铁锂电池串联电池组中,电池组中各个单体电池之间存在电量不一致的问题,提出一种复合式均衡拓扑结构,通过对单体电池之间的电感或电池组间的变压器选择性放电均衡,实现电池组内的各个单体电池的电量均衡,并测量实际的锂电池放电曲线,拟合锂电池开路电压与SOC的曲线。此外,建立了对应的磷酸铁锂电池Simulink模型,使用SOC估算值作为判断均衡的条件,以提高启动或停止均衡子电路的准确性。在Matlab/Simulink的软件仿真下证明,所提出的复合式均衡方案均衡效果良好,易于实现,控制简易。