Ternary-material lithium-ion battery SOC estimation under various ambient temperature

Ambient temperature produces great effects on battery state-of-charge (SOC) estimation, due to the unstable estimation algorithm, the weakened traceability of battery model, and variable model parameters at various temperatures, especially lower temperatures. The widely used method based on the equivalent circuit model (ECM) offline in using different algorithm, like current integral, the extended Kalman filter (EKF), or the unscented Kalman filter (UKF), can obtain an accurate SOC estimation at room temperature, but it is difficult to guarantee the high precision at lower temperatures. To address this problem, the battery model is investigated at different temperature, and an offset item is proposed to develop the observer equation in the estimated model. Then, the square root of the Sigma points Kalman filter (SR-UKF) is applied, and on the basis of the individual model parameter-temperature table and the developed model, the high accuracy of SOC estimation is achieved. Additionally, considering the burden of original parameter modification (all model parameters modified) at various temperature which will increase the product cost and computational complexity of the battery management system (BMS), the relationship between individual model parameter and the error of SOC estimation is built, which is helpful for the simplification of parameter modification. The results indicate that the proposed method based on the developed estimated model and the simplified parameter modification can achieve an accurate, stable, and efficient SOC 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。     

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Simulink的锂电池建模仿真

针对锂离子电池SOC(荷电状态)难以估算的问题,通过对电池建立等效的Thevenin电路模型,对不同时刻的SOC的模型参数进行拟合得到动态的模型参数,在Matlab中借助Simulink建立仿真模型,采用模块化结构,建立基于卡尔曼滤波算法的电池SOC估算系统;利用测得的电池电压电流,仿真系统可直接估算出实时的电池SOC,与实际的电池SOC对比,误差保持在2.5%以内,表明该方法可以有效地估计电池的SOC,对于锂离子电池在实际应用的容量估算有着重要意义。     

最优控制在车载惯性平台稳定回路中的应用

在分析车载惯性平台数学模型的基础上,针对平台的扰动特性，提出了稳定伺服回路的一种改进型线性二次高斯 (LQG) 控制方法。该方法在反馈中加入了积分项，可以消除稳态偏差，并且依据滤波器收敛性的判据，分别利用Sage Husa自适应滤波算法和强跟踪Kalman滤波器进行状态估计,既保证了估计精度，又具有跟踪突变状态的能力。仿真和实验表明：该方法在一定程度上降低了对系统模型误差和噪声统计特性误差的要求。     

自动引导车电池SOC估算方法研究

自动引导车(AGV车)工况特殊,电流积分法估算电池剩余容量(SOC)误差较大,而且存在累积误差。为了提高AGV车电池剩余容量估算的准确度,对扩展卡尔曼滤波法估算AGV车电池剩余容量进行了研究,分析了AGV车特殊工况,提出将扩展卡尔曼滤波法的滤波增益改进为动态调整滤波增益,有效提高扩展卡尔曼滤波法的跟踪效果。实验表明使用扩展卡尔曼滤波法估算AGV车电池剩余容量精度较高,采用动态校正的滤波增益提高了估算过程的跟踪效果,解决了AGV车电池剩余容量估算不准确的问题。     

卡尔曼滤波在激光跟踪测量系统中的应用

激光跟踪测量系统对于测量运动目标空间位置是行之有效的,但在测量过程中,各种干扰噪声的影响会降低测量精度。采用卡尔曼滤波来减小噪声的影响以提高测量精度。介绍了激光跟踪测量系统,建立了状态方程和测量方程,给出了卡尔曼滤波算法,仿真结果表明,运用卡尔曼滤波大大提高了测量系统的精度。     

基于Huber的高阶容积卡尔曼跟踪算法

为改善高阶容积卡尔曼滤波算法的滤波精度和鲁棒性, 提出了一种新的基于Huber的高阶容积卡尔曼滤波算法. 在采用统计线性回归模型近似非线性量测模型的基础上, 利用Huber M 估计算法实现状态的量测更新. 进一步结合高阶球面-径向容积准则的状态预测模块构成基于 Huber的高阶容积卡尔曼跟踪算法. 重点分析了Huber代价函数的调节因子对算法跟踪性能的影响. 通过对纯方位目标跟踪和再入飞行器跟踪两个实例验证了所提算法的跟踪性能优于传统高阶容积卡尔曼滤波算法.     

基于路况预测的PHEV能量管理策略

为了提高插电式混合动力汽车(plug-in hybrid electric vehicle, PHEV)的燃油经济性,减少排放,提出了基于路况预测的PHEV能量管理策略;首先,建立PHEV系统结构并在此基础上依据动力电池SOC(State of charge)变化规律定义了3种PHEV基本工作模式;然后,设计路况识别模糊控制器对当前行驶路况进行识别并预测;最后,根据预测的路况类型结合合理规划的动力电池SOC的曲线约束,制定PHEV能量管理策略;仿真结果表明,该能量管理策略能够较好的使动力电池SOC保持在设定的参考轨迹附近,提高燃油经济性,减少排放。     

基于改进雁群PSO算法的模糊自适应扩展卡尔曼滤波的SLAM算法

针对扩展卡尔曼滤波同时定位与地图创建算法中难以建立准确的先验噪声模型的问题, 提出一种基于改进雁群粒子群算法的模糊自适应卡尔曼滤波算法. 利用分数阶微积分改进粒子进化速度, 利用混沌来改进粒子的初始化和发生早熟时的处理. 改进后的雁群粒子群算法在收敛速度与避免早熟方面有了很大改进, 并将改进的雁群粒子群算法用于模糊自适应扩展卡尔曼滤波同时定位与地图创建算法的训练, 并与用雁群粒子群算法训练的模糊自适应扩展卡尔曼滤波同时定位与地图创建算法进行对比, 其在定位与构图方面有很大的提高. 关键词： 同时定位与地图创建 雁群粒子群算法 分数阶微积分 混沌     

基于简化模型与自适应滤波的车载SINS静基座快速对准

为了实现捷联惯性导航系统(Strap-down Inertial Navigation System,SINS)快速初始对准,根据已有可观测性分析结果,通过理论分析和计算得到了扩展观测量时初始对准系统最优可观测状态量组合,在此基础上简化了对准模型,建立了新的系统方程。针对载车发动机启动或其他情况导致系统噪声无法精确统计,提出了运用基于强跟踪滤波原理的自适应卡尔曼滤波(Kalman Filter,KF)算法抑制滤波发散,加快收敛速度。仿真结果表明运用简化模型和自适应滤波在系统噪声不匹配时具有更快的收敛速度和更高的对准精度,车载实验结果也表明运用简化模型和自适应滤波可以实现快速对准。     

利用粒子滤波从雷达回波实时跟踪反演大气波导

粒子滤波(particle filter,PF)是利用蒙特卡洛仿真方法处理递推估计问题的非线性滤波算法,这种方法不受模型线性和高斯假设的约束,是处理非线性非高斯动态系统状态估计的有效算法,适用于雷达回波反演大气波导(RFC)这类非线性非高斯问题.文中分别介绍了PF的基本思想和具体算法实现步骤,最后导出PF反演算法的迭代求解格式.数值试验结果表明,与扩展卡尔曼滤波(extended kalman filter,EKF)和不敏卡尔曼滤波(unscented kalman filter,UKF)相比,PF更适用于RFC这类高度非线性反演问题,可有效提高反演结果的稳定性和精度.     

Infrared/laser multi-sensor fusion and tracking based on the multi-scale model

The state estimation problem of targets detected by infrared/laser composite detection system with different sampling rates was studied in this paper. An effective state estimation algorithm based on data fusion is presented. Because sampling rate of infrared detection system is much higher than that of the laser detection system, the theory of multi-scale analysis is used to establish multi-scale model in this algorithm. At the fine scale, angle information provided by infrared detection system is used to estimate the target state through the unscented Kalman filter. It makes full use of the high frequency characteristic of infrared detection system to improve target state estimation accuracy. At the coarse scale, due to the sampling ratio of infrared and laser detection systems is an integer multiple, the angle information can be fused directly with the distance information of laser detection system to determine the target location. The fused information is served as observation, while the converted measurement Kalman filter (CMKF) is used to estimate the target state, which greatly reduces the complexity of filtering process and gets the optimal fusion estimation. The simulation results of tracking a target in 3-D space by infrared and laser detection systems demonstrate that the proposed algorithm in this paper is efficient and can obtain better performance than traditional algorithm.     

一种新的卫星钟差Kalman滤波噪声协方差估计方法

采用Kalman滤波方法进行钟差参数计算和预报时, 需确定Kalman滤波噪声协方差矩阵. 针对这一问题, 提出了一种新的卫星钟差Kalman滤波噪声协方差估计方法, 通过建立新息的相关函数序列与未知的噪声参数间的线性函数模型, 采用最小二乘法进行噪声参数估计. 采用精密钟差数据进行钟差参数估计和预报分析, 结果表明, 该方法具有较好的收敛性, 并与顾及随机噪声模型的开窗分类因子自适应抗差估计方法进行对比分析, 验证了新方法的正确性和有效性.     

基于滚动时域估计的飞行器姿态估计及三轴磁强计在线校正

针对飞行器姿态估计以及三轴磁强计在线校正问题, 提出了一种实时滚动时域估计算法. 首先, 为了解决在卡尔曼滤波框架下系统约束不能显式求解的问题, 设计了滚动时域估计滤波算法. 该算法将飞行器姿态估计问题转化为优化问题, 显式求解四元数归一化性质, 缩小搜索空间的同时提高了搜索效率和精度. 其次, 滤波时域窗内应用高斯-牛顿迭代法求解最优状态估计值, 满足了实时性要求. 最后, 在没有增加系统状态维数的情况下, 在线求解了三轴磁强计校正参数, 保证了磁强计量测值以矢量形式输入系统. 仿真结果表明, 由于合理地利用了历史信息, 该方法精度较高, 且对初始误差、系统误差均不敏感, 具有一定鲁棒性.     

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks(WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter(CKPF) is proposed in this paper.We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter(CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter(UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.     

逼近积分点数下限的五阶容积卡尔曼滤波定轨算法

为了在保持滤波定轨精度不变的条件下提高定轨计算的实时性,提出一种新的逼近积分点个数下限的五阶容积卡尔曼滤波定轨算法.首先,采用一种数值容积准则对非线性函数的高斯加权积分进行近似,该准则所需的积分点个数仅比五阶代数精度容积准则积分点个数的理论下限多一个积分点,并在贝叶斯滤波算法框架下推导出本文算法的更新步骤.然后,给出实时定轨所需的状态方程和量测方程,在状态方程中考虑了J2项引力摄动和大气阻力摄动,在量测方程中利用坐标系转换推导了轨道状态与测量元素之间的非线性关系.仿真实验结果表明,本文所提算法在定轨精度方面与已有的五阶滤波算法相当,但所需的积分点个数最少,计算实时性最高,从而验证了本文算法的有效性.     

An innovation based random weighting estimation mechanism for denoising fiber optic gyro drift signal

In Interferometric Fiber Optic Gyroscope (IFOG), the diminution of random noise and drift error is a critical task. These errors degrade the performance of IFOG. In this paper, a modified adaptive Kalman gain correction (AKFG) algorithm is proposed to denoise IFOG signal. The covariance matrix of innovation sequence is estimated using weighted average window method in which the weights are randomly generated in the range [0, 1]. Innovation based random weighted estimation (IRWE)-AKFG is applied to denoise the IFOG drift signal. The Kalman gain is adaptively updated using the covariance matrix of innovation sequence. The proposed algorithm is applied for denoising IFOG signal under static and dynamic environment. Allan variance method is used to analyze and quantify the stochastic errors in IFOG sensor. The performance of the proposed algorithm is compared with Conventional Kalman filter (CKF) and the simulation results reveal that the proposed algorithm is an efficient algorithm for denoising the IFOG signal.     

A robust adaptive hybrid feedback cancellation scheme for hearing aids in the presence of outliers

It has been demonstrated that the Filtered-x Wilcoxon LMS (FxWLMS) based adaptive filter mitigates the effect of the outliers acquired by the microphone signal of hearing aids by minimizing the Wilcoxon norm and hence shows better cancellation performance than the existing Filtered-x LMS (FxLMS) algorithm. The prediction error method based adaptive feedback canceller (PEMAFC) reduces the bias present in the estimate of the feedback path due to the continuous adaptive filtering (CAF). However, the impulse response of the measured feedback path is close to zero for the first many samples due to the delay introduced by ADC converters and then contains few significant values, which results in slow convergence rate when an adaptive filter is used to model the same. To overcome this limitation, we propose a proportionate normalized WLMS (PNWLMS) algorithm based PEMAFC (P-PNWLMS) for feedback cancellation in hearing aid in the presence of outliers. Further, with an objective to improve the convergence rate and performance accuracy simultaneously, this paper proposes a novel convex PNWLMS (CPNWLMS) algorithm which incorporates convex combination of PNWLMS and WLMS algorithms. The weight update equations are derived for PEMAFC trained by PNWLMS (P-PNWLMS) and CPNWLMS (P-CPNWLMS) algorithms respectively. The results of the simulation study show improved performance of the proposed CPNWLMS based adaptive filter over its component filters.     

A Novel Real-Time State-of-Health and State-of-Charge Co-Estimation Method for LiFePO_4 Battery

The state of charge(SOC) and state of health(SOH) are two of the most important parameters of Li-ion batteries in industrial production and in practical applications.The real-time estimation for these two parameters is crucial to realize a safe and reliable battery application.However,this is a great problem for LiFePO_4 batteries due to the large constant potential plateau in the charge/discharge process.Here we propose a combined SOC and SOH co-estimation method based on the experimental test under the simulating electric vehicle working condition.A first-order resistance-capacitance equivalent circuit is used to model the battery cell,and three parameter values,ohmic resistance(R_s),parallel resistance(R_p) and parallel capacity(C_p),are identified from a real-time experimental test.Finally we find that R_p and C_p could be utilized to make a judgement on the SOH.More importantly,the linear relationship between C_p and the SOC is established to make the estimation of the SOC for the first time.