无人机锂电池电路等效和荷电状态智能计算估计

锂电池电池管理的核心是电池荷电状态(SOC)的实时准确估算。为精确实时估算SOC值,以无人机(UAV)锂电池为研究对象,建立戴维南等效电路模型,对电池进行试验测量、研究分析。首先,运用开路电压法标定锂电池的估算初值,在卡尔曼滤波算法的基础上进一步改良优化得到扩展卡尔曼滤波(EKF)算法。然后,将该算法运用到SOC估算中,即可在较短时间内高精度的估算出无人机锂电池的实时SOC值。在MATLAB/Simulink中搭建对应电池模型输入算法进行运行,并对得到的结果与实际数据进行比较、论证。试验表明,基于戴维南模型的EKF算法能很好地对无人机锂电池SOC进行估算,收敛效果好而且估算精度高于98.5%。扩展卡尔曼算法可以很准确地估算出无人机锂电池的实时SOC值。     

基于电动汽车磷酸铁锂动力电池荷电状态 估计方法研究

近几年,磷酸铁锂动力电池逐渐成为电动汽车动力电池首选.但是由于材料本身特性,使得磷酸铁锂电池的荷电状态难以精确估算.当电动汽车处于复杂工作环境时,荷电状态估计在保证电动汽车电池操作中的安全性和可靠性方面起到了至关重要的作用.文章采用戴维宁等效电路模型,验证无迹卡尔曼滤波和粒子滤波两种方法的估算效果,并分别与扩展卡尔曼滤波方法作对比,结果证明无迹卡尔曼滤波和粒子滤波都具有更好的估算精度.     

基于DSP的电动汽车锂电池荷电状态估算的研究与实现

锂电池荷电状态(SOC)的准确估算是电动汽车能源管理的关键技术。为了提高锂电池SOC的估算精度,将无迹卡尔曼滤波(UKF)应用于锂电池SOC估算,以减小拓展卡尔曼滤波(EKF)简单线性化带来的误差。搭建电池检测系统的硬件平台,以TMS320F28335型数字信号处理器(DSP)为主控芯片(MCU),实现电压、电流、温度的检测及UKF算法,并设计了相关的电池测试实验。实验结果表明,UKF可以实时估算锂电池SOC,估算误差在4%以内,高于传统的拓展卡尔曼滤波(EKF)。     

基于Vmin-EKF的动力锂电池组SOC估计

动力电池组的荷电状态(SOC)是电动汽车能量控制的重要参数.针对串联锂电池组的SOC估计问题,建立电池组的Vmin状态空间模型,电池组内单体电池负载电压的最小值Vmin和电池组的SOC分别作为模型的观测变量和状态变量.应用扩展卡尔曼滤波算法,实现对SOC的动态估计.对模拟电动汽车的实际工况进行电池组放电实验,结果表明,该方法能实时准确地估计电池组SOC.     

基于双扩展卡尔曼滤波的电池荷电状态估计

以磷酸铁锂动力电池为研究对象,以精确估算电动汽车动力锂电池组在实际运行工况中的SOC为目的,基于Thevenin等效电路模型和扩展卡尔曼滤波算法,结合脉冲功率特性实验（HPPC Test）对模型参数进行辨识,采用双扩展卡尔曼滤波对SOC和模型参数进行在线估算,并分析算法在不同温度下的适应性和不同SOC初始值条件下的收敛特性。仿真结果表明,在不同的工况下,相比于单扩展卡尔曼滤波该算法具有更高的精度、更好的环境适应度和对初始误差的收敛性。     

基于RLS与EKF算法的锂电池SOC估计

准确估计荷电状态是电池管理系统高效和安全运行的关键因素之一.以Thevenin模型为基础,运用递推最小二乘法,对模型参数进行估计并且定期更新.采用扩展卡尔曼滤波算法实现了对锂电池荷电状态的估算.仿真结果表明,该估算策略能保持很高的精度,并对观测噪声有很强的抑制作用.     

基于Thevenin模型和UKF的锂电池SOC估算方法研究

为解决在多种工况下锂电池实时估算困难、估算精度不高等问题,以三元锂电池为研究对象,建立Thevenin模型,对电池的工作特性进行表征。综合多种工况对锂电池工作特性进行研究分析,避免了依据电池内部复杂结构建立等效模型的困难。考虑到估算初期荷电状态(SOC)准确性对于后期估算的重要性,首先用开路电压法标定初值,然后运用无迹卡尔曼滤波(UKF)算法进行估算跟踪。UKF算法基于无迹变换,没有忽略高阶项,对于非线性分布具有较高的计算精度。在Matlab/Simulink中搭建仿真模型并结合多种工况数据进行分析。试验结果表明,Thevenin模型能够较好地对锂电池SOC进行估算,收敛速度快、跟踪效果好且能将估算误差控制在0.8%以内,验证了UKF在对锂电池进行SOC估算时具有较高的精度。     

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation (MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter-multiple model adaptive estimation unscented Kalman filter (MMAE-UKF) rather than conventional Kalman filter methods, like the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters, which the improved filtering method can overcome. Meanwhile, this algorithm is used for integrated navigation system of strapdown inertial navigation system (SINS) and celestial navigation system (CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.      

基于自适应UKF算法的小型水下机器人导航系统

针对海流扰动及姿态、航向误差角引起的无法确知的导航系统模型误差, 设计了一种带模型误差的自适应无迹卡尔曼滤波器(Adaptive unscented Kalman filter, AUKF)用于小型水下机器人(Small autonomous underwater vehicle, SAUV)推位导航系统. 首先提出了小型水下机器人三维运动连续时间模型; 然后针对该模型特点, 基于极大后验估值原理推导了AUKF算法. 仿真结果说明该算法能够克服海流扰动及姿态和航向误差引起的模型误差. 对比经典无迹卡尔曼滤波器算法, 采用该算法的小型水下机器人推位导航系统在复杂海况下的滤波精度显著提高.     

基于Thevenin模型和自适应卡尔曼的SOC估算研究

精确的荷电状态(SOC)值在电池的应用开发中具有重要的意义.选择合适的滤波算法是精确估算的前提.由于扩展卡尔曼滤波(EKF)中噪声的给定值与实际工况下噪声的统计特性不符,导致估算精度低.为提高SOC估算精度,构建能准确反映锂电池工作特性的Thevenin电路模型.在此基础上,构建状态方程和观测方程,提出自适应卡尔曼滤波...     

Comparison between two model-based algorithms for Li-ion battery SOC estimation in electric vehicles

Accurate battery State of Charge (SOC) estimation is of great significance for safe and efficient energy utilization for electric vehicles. This paper presents a comparison between a novel robust extended Kalman filter (REKF) and a standard extended Kalman filter (EKF) for Li-ion battery SOC indication. The REKF-based method is formulated to explicitly compensate for the battery modeling uncertainty and linearization error often involved in EKF, as well as to provide robustness against the battery system noise to some extent. Evaluation results indicate that both filters have a good average performance, given appropriate noise covariances, owing to a small average modeling error. However, in contrast, the REKF-based SOC estimation method possesses slightly smaller root-mean-square (RMS) error. In the worst case, the robustness characteristics of the REKF result in an obviously smaller error bound (around by 1%). Additionally, the REKF-based approach shows superior robustness against the noise statistics, leading to a better tolerance to inappropriate tuning of the process and measurement noise covariances.     

基于UKF的电动汽车锂电池SOC估计方法

准确估计电池的荷电状态(SOC,state of charge)是电动汽车电池管理系统研究的关键技术。基于Thevenin模型建立了状态空间方程组,采用无色卡尔曼滤波(UKF,unscented Kalman filtering)算法实现非线性条件下的SOC准确估计。硬件在环仿真试验表明:UKF估计误差小于5%,且当SOC值低于50%时,其估计结果明显优于扩展卡尔曼滤波(EKF,extended Kalman filtering)方法,有较高的实用价值。     

在线参数辨识和扩展卡尔曼算法的锂离子电池SOC估算研究

荷电状态(SOC)的准确估计对锂离子电池的在线实时监测和安全控制具有重要意义。以中航锂电池为研究对象,选择二阶阻容(RC)模型对电池工作特性进行表征,并结合多种工况情形对锂离子电池进行研究分析。考虑到参数辨识的初值对在线辨识修正效果的影响,搭建仿真模型与电池脉冲工况特性比较验证,仿真误差在0.05 V以内。在此基础上,构建含有遗忘因子的递推最小二乘法(FFRLS)的在线参数辨识系统,对电池动态应力测试工况(DST)进行仿真预测,相对误差在1.50%以内。针对离线参数辨识的不足,采用在线参数辨识结合扩展卡尔曼(EKF)算法对工况下电池SOC进行估计。试验结果表明,在线参数辨识下,EKF算法能够有效表征系统SOC估算,相对误差精度在0.3%以内。     

基于全新等效电路模型的电池关键状态在线联合估计器

针对电池三大关键状态(State of Charge–SOC、State of Health-SOH、State of Power-SOP)之间相互耦合的关系,同时考虑到其估计精度受到电池时变的内部参数等因素影响的问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model, AR-ECM)的电池关键状态在线联合估计算法.该方法提出基于AR模型的全新电池ECM,给出同时表征SOC、SOH和电池内部压降的状态空间方程以及区别化参数更新策略.在此基础上,考虑状态方程容易发生不正定的问题,提出采用平方根无迹卡尔曼滤波(square root unscent kalman filter, SR-UKF)算法实现电池状态的联合估计.该算法的优势在于真正实现了电池关键状态以及ECM参数的联合估计,更符合实际工程应用需求.仿真验证表明,在噪声干扰环境下,该联合估计器能够得到较高的精确度和稳定性.     

An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles

As the demand for electric vehicle (EV)'s remaining operation range and power supply life, Lithium-ion (Li-ion) battery state of charge (SOC) and state of health (SOH) estimation are important in battery management system (BMS). In this paper, a proposed adaptive observer based on sliding mode method is used to estimate SOC and SOH of the Li-ion battery. An equivalent circuit model with two resistor and capacitor (RC) networks is established, and the model equations in specific structure with uncertainties are given and analyzed. The proposed adaptive sliding mode observer is applied to estimate SOC and SOH based on the established battery model with uncertainties, and it can avoid the chattering effects and improve the estimation performance. The experiment and simulation estimation results show that the proposed adaptive sliding mode observer has good performance and robustness on battery SOC and SOH estimation.     

基于等效模型和多时间尺度扩展卡尔曼滤波的锂离子电池SOC预测

荷电状态(SOC)和最大可用电量估计是锂离子电池寿命预测中的两个最重要部分;然而与快速时变的SOC比较,最大可用电量的参数变化缓慢;文章提出了一个基于等效模型和多时间尺度的扩展卡尔曼滤波(EKF)预测算法对SOC和最大可用容量分别在不同时间尺度上进行估计,在宏观尺度上利用了SOC估计值作为观测量,更新最大可用电量;针对NCA/C卫星锂离子电池实验数据的仿真结果表明,提出的多时间尺度EKF预测算法与EKF联合估计算法相比,SOC和最大可用电量估计准确度更高,同时提高了计算效率。     

电动汽车动力电池组SOC均衡控制算法仿真

采用当前算法均衡控制电动汽车动力电池组的SOC(电池荷电状态)时,得到电动汽车动力电池组SOC估计值与实际值之间的误差较大,并且存在SOC估计精准度低和控制效果差的问题。提出电动汽车动力电池组SOC均衡控制算法,建立电动汽车动力电池组的Thevenin等效电路模型,在Thevenin等效电路模型的基础上采用扩展卡尔曼滤波算法估算电动汽车动力电池组的SOC,引入标准差判断电动汽车动力电池组的工作状态,根据判断结果对电动汽车动力电池组SOC进行均衡控制。仿真结果表明,所提方法估算SOC的精准度较高、均衡控制效果好,均衡控制后电动汽车动力电池组的容量利用率较高。     

基于RLS和EKF算法的全钒液流电池SOC估计

针对全钒液流电池的荷电状态(SOC)估计精度低、估计成本较高等问题,提出一种基于递推最小二乘算法(RLS)与扩展卡尔曼滤波算法(EKF)相结合的估计方法.该方法通过RLS算法辨识所建立的钒电池数学模型参数,通过EKF算法估计钒电池的SOC,将二者结合实现电池参数发生变化时准确估计钒电池的SOC.以5kW/ 30kWh的钒电池为对象,应用所提出的算法实现钒电池的SOC估计.结果表明,该算法可以准确估计钒电池的SOC,且可节省额外增加单片检测电池测量SOC的费用.     

FPGA implementation of extended Kalman filter for SOC estimation of lithium‐ion battery in electric vehicle

A FPGA implementation for a model‐based state of charge (SOC) estimation is described in this paper. A Thevenin equivalent circuit model is designed for SOC estimation. The extended Kalman filter (EKF) is designed to complete the SOC estimation, and the error is within 1 % . The FPGA is chosen to achieve realtime SOC estimation. A fast matrix method is proposed to improve the calculation speed of the EKF in FPGA because the EKF algorithm requires many matrix operations. In addition, the embedded system based on the FPGA with a system on a programmable chip (SOPC) technique is built using the Qsys platform in Quartus II. Based on the embedded system, an online testing platform is established to monitor the terminal voltage and load current of the experimental battery in real time; experimental results show that the online SOC estimation is successful. The measurement results show that the FPGA embedded scheme of the EKF allows for successful implementation of the SOC estimation with accuracy and speed. The fast matrix method requires 0.00007 s to implement the SOC estimation and is four times faster than the conventional matrix method.     

An adaptive strategy for Li-ion battery internal state estimation

Further developing a study presented in Di Domenico, Prada, and Creff (2011), this paper presents an extended Kalman filter (EKF) based on an electro-thermal model for the estimation of the internal state of a lithium-ion battery, i.e. state of charge and the cell overpotential. In order to compensate for uncertainties in the model parameters and in the measurements, it is first shown that the filter robustness strongly depends on the State of Charge (SOC) range. Then the filter weights are adapted according to the estimated SOC value. This estimation technique is tested using experimental data collected from a commercial A123 Systems lithium iron phosphate/graphite (LiFePO₄/graphite) cell. The filter shows good performance. The estimation of SOC exhibits an average error within 3% range and the overpotential is estimated with a precision higher than 5 mV.