Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC

Lithium-ion batteries are widely used as power sources in various portable electronics, hybrid electric vehicles, aeronautic and aerospace engineering, etc. To ensure an uninterruptible power supply, the remaining useful life (RUL) prediction of lithium-ion batteries has attracted extensive attention in recent years. This paper proposed an improved unscented particle filter (IUPF) method for lithium-ion battery RUL prediction based on Markov chain Monte Carlo (MCMC). The method uses the MCMC to solve the problem of sample impoverishment in UPF algorithm. Additionally, the IUPF method is proposed on the basis of UPF, so it can also suppress the particle degradation existing in the standard PF algorithm. In this work, the IUPF method is introduced firstly. Then, the capacity data of lithium-ion batteries are collected and the empirical capacity degradation model is established. The proposed method is used to estimate the RUL of lithium-ion battery. The RUL prediction results demonstrate the effectiveness and advantage.     

Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery

Electrical power system (EPS) is one of the most critical sub-systems of the spacecraft. Lithium-ion battery is the vital component is the EPS. Remaining useful life (RUL) prediction is an effective mean to evaluate the battery reliability. Autoregressive model (AR) and particle filter (PF) are two traditional approaches in battery prognosis. However, the parameters in a trained AR model cannot be updated which will cause the under-fitting in the long term prediction and further decrease the RUL prediction accuracy. On the other hand, the measurement function in the PF algorithm cannot be obtained in the long term prediction process. To address these two challenges, a hybrid method of IND-AR model and PF algorithm are proposed in this work. Compared with basic AR model, a nonlinear degradation factor and an iterative parameter updating method are utilized to improve the long term prediction performance. The capacity prediction results are applied as the measurement function for the PF algorithm. The nonlinear degradation factor can make the linear AR model suitable for nonlinear degradation estimation. And once the capacity is predicted, the state-space model in the PF is activated to obtain an optimized result. Optimized capacity prediction result of each cycle is utilized to re-train the regression model and update the parameters. The predictor keeps working iteratively until the capacity hit the failure threshold to calculate the RUL value. The uncertainty involved in the RUL prediction result is presented by PF algorithm as well. Experiments are conducted based on commercial lithium-ion batteries and real-applied satellite lithium-ion batteries. The results have high accuracy in capacity fade prediction and RUL prediction of the proposed method. The real applied lithium-ion battery can meet the requirement of spacecraft. All the experiments results show great potential of the proposed framework.     

Prognostics of Lithium-ion batteries based on state space modeling with heterogeneous noise variances

Prognostics and health management of lithium-ion batteries, especially their remaining useful life (RUL) prediction, has attracted much attention in recent years because accurate battery RUL prediction is beneficial to ensuring high reliability of lithium-ion batteries for providing power sources for many electronic products. In the common state space modeling of battery RUL prediction, noise variances are usually assumed to be fixed. However, noise variances have great influence on state space modeling. If noise variances are too small, it takes long time for initial guess states to approach true states, and thus estimated states and measurements are biased. If noise variances are too large, state space modeling based filtering will suffer divergence. Besides, even though a same type of lithium-ion batteries are investigated, their degradation paths vary quite differently in practice due to unit-to-unit variation, ambient temperature and other working conditions. Consequently, heterogeneity of noise variances should be taken into consideration in state space modeling so as to improve RUL prediction accuracy. More importantly, noise variances should be posteriorly updated by using up-to-date battery capacity degradation measurements. In this paper, an efficient prognostic method for battery RUL prediction is proposed based on state space modeling with heterogeneity of noise variances. 26 lithium-ion batteries degradation data are used to illustrate how the proposed prognostic method works. Some comparisons with other common prognostic methods are conducted to show the superiority of the proposed prognostic method.     

Remaining useful life prediction of lithium-ion batteries using a fusion method based on Wasserstein GAN

Lithium-ion batteries are the main power supply equipment in many fields due to their advantages of no memory, high energy density, long cycle life and no pollution to the environment. Accurate prediction for the remaining useful life (RUL) of lithium-ion batteries can avoid serious economic and safety problems such as spontaneous combustion. At present, most of the RUL prediction studies ignore the lithium-ion battery capacity recovery phenomenon caused by the rest time between the charge and discharge cycles. In this paper, a fusion method based on Wasserstein generative adversarial network (GAN) is proposed. This method achieves a more reliable and accurate RUL prediction of lithium-ion batteries by combining the artificial neural network (ANN) model which takes the rest time between battery charging cycles into account and the empirical degradation models which provide the correct degradation trend. The weight of each model is calculated by the discriminator in the Wasserstein GAN model. Four data sets of lithium-ion battery provided by the National Aeronautics and Space Administration (NASA) Ames Research Center are used to prove the feasibility and accuracy of the proposed method.     

Interacting multiple model particle filter for prognostics of lithium-ion batteries

We propose a new data-driven prognostic method based on the interacting multiple model particle filter (IMMPF) for determining the remaining useful life (RUL) of lithium-ion (Li-ion) batteries and the probability distribution function (PDF) of the associated uncertainty. The method applies the IMMPF to different state equations. Modeling the battery capacity degradation is very important for predicting the RUL of Li-ion batteries. In this study, improvements are made on various Li-ion battery capacity models (i.e., polynomial, exponential, and Verhulst models). Further, three different one-step state transition equations are developed, and the IMMPF method is applied to estimate the RUL of Li-ion batteries with the use of the three improved models. The PDF of the predicted RUL is obtained by combining the PDFs obtained with each individual model. We conduct four case studies to validate the proposed method. The results are as follows: (1) the three improved models require fewer parameters than the original models, (2) the proposed prognostic method shows stable and high prediction accuracy, and (3) the proposed method narrows the uncertainty PDF of the predicted RUL of Li-ion batteries.     

Adaptive and robust prediction for the remaining useful life of electrolytic capacitors

In integrated avionics systems, ensuring the high reliability and lengthening the life cycle of the avionics circuits become more and more important. This paper proposes an adaptive and robust prediction method to estimate the state of health and predict the remaining useful life (RUL) of electrolytic capacitors, which is one of the most significant components in avionics circuits. Based on an accelerated aging experiment performed by NASA, the degradation mechanism of electrolytic capacitors is analyzed. According to the capacitance loss data, a combination of the Verhulst model and the exponential model is adopted as the empirical model, and the unscented Kalman filter is applied to generate the proposal distribution of the particle filter to track the degradation path. Regarding the particle impoverishment, a particle swarm optimization algorithm is adopted to optimize the residual resampling step to improve the prediction accuracy. Also, adaptively adjusting the number of particles is introduced to make the algorithm more computationally efficient. Compared with the conventional particle filter algorithms, the experiment on the electrolytic capacitors degradation data indicates that the proposed novel method is able to provide a higher accuracy for the remaining useful life evaluation.     

Fault prognostic of electronics based on optimal multi-order particle filter

The accurate fault prediction is of great importance in electronics high reliability applications for condition based maintenance. Traditional Particle filter (TPF) used for fault prognostic mainly uses the first-order state equation which represents the relationship between the current state and one-step-before state without considering the relation with multi-step-before states. This paper presents an optimal multi-order particle filter method to improve the prediction accuracy. The multiple τth-order state equation is established by training Least Squares Support Vector Regression (LSSVR) via electronics historical failure data, the τ value and LSSVR parameters are optimized through Genetic Algorithm (GA). The optimal τth-order state equation which can really reflect electronics degradation process is used in particle filter to predict the electronics status, remaining useful life (RUL) or other performances. An online update scheme is developed to adapt the optimal τth-order state transformation model to dynamic electronics. The performance of the proposed method is evaluated by using the testing data from CG36A transistor degradation and lithium-ion battery data. Results show that it surpasses classical prediction methods, such as LSSVR, TPF.     

Heuristic Kalman optimized particle filter for remaining useful life prediction of lithium-ion battery

Accurate prediction of the remaining useful life of a faulty component is important to the prognosis and health management of any engineering system. In recent times, the particle filter algorithm and several variants of it have been used as an effective method for this purpose. However, particle filter suffers from sample degeneracy and impoverishment. In this study, we introduce the Heuristic Kalman algorithm, a metaheuristic optimization approach, in combination with particle filtering to tackle sample degeneracy and impoverishment. Our proposed method is compared with the particle swarm optimized particle filtering technique, another popular metaheuristic approach for improvement of particle filtering. The prediction accuracy and precision of our proposed method is validated using several Lithium ion battery data sets from NASA® Ames research center.     

Prognostics of Li(NiMnCo)O2-based lithium-ion batteries using a novel battery degradation model

Some lithium-ion battery materials show two-phase degradation behavior with evident inflection points, such as lithium nickel manganese cobalt oxide (Li(NiMnCo)O₂ or NMC) cells. A model-based Bayesian approach is proposed in this paper to predict remaining useful life (RUL) for these types of batteries. First, a two-term logarithmic model is developed to capture the degradation trends of NMC batteries. By fitting the battery degradation data, it is experimentally demonstrated that the developed model is superior to existing empirical battery degradation models. A particle filtering–based prognostic method is then incorporated into the model to estimate the batteries' possible degradation trajectories. Correspondingly, the RUL values of NMC batteries are expressed in terms of probability density function. The effectiveness of the developed method is verified with our collected experimental data. The results indicate that the proposed prognostic method can achieve higher predictive accuracy than the existing two-term exponential model.     

多核相关向量机优化模型的锂电池剩余寿命预测方法

基于相关向量机的剩余寿命预测方法,核函数是影响相关向量机模型预测性能的重要因素.目前的相关向量机预测模型以单核为主,且核函数的选择存在较大主观性,导致所构建的预测模型性能有限.本文提出一种融合多个核函数构建相关向量机预测模型的方法,通过果蝇算法优化多个核函数优化组合的线性方程系数,提高了模型的预测性能,并将该方法应用于预测锂离子电池的循环剩余寿命.分别采用美国NASA和马里兰大学的电池退化数据集,对本文的方法进行了实验验证.实验结果表明：多核相关向量机预测方法的平均绝对误差和均方根误差都小于最优的单核相关向量机预测方法.     

一种改进重采样的粒子滤波算法

粒子滤波算法中重采样是解决粒子退化的一种重要方法,但重采样会导致粒子多样性的损失。针对这一问题,对基本重采样算法进行了改进。改进算法首先按基本重采样思想找到权值大的粒子进行复制,然后借鉴遗传算法进行交叉和变异操作,其中变异由变异尺度因子和粒子集的均值来实现。利用改进重采样的粒子滤波算法对经典纯方位目标跟踪问题进行了仿真,仿真结果表明,改进算法具有更好的跟踪精度。     

裂变自举粒子滤波

自举粒子滤波(BPF:Bootstrap Particle Filtering)是一种经典而应用广泛的粒子滤波算法,但其重采样后常会引起严重的样本枯竭问题.本文提出在权值蜕化较为严重时,在原先的重采样前增加SFN预处理,即权值排序、裂变繁殖(fission)和权值归一,得到裂变BPF(FBPF)算法.针对一个典型的后验密度为双峰的强非线性滤波估计问题,通过Monte Carlo仿真表明,FBPF算法在保持与BPF算法相当的估计精度和运算时间的条件下,克服了样本枯竭问题,算法的鲁棒性更强.     

量子进化粒子滤波算法及其在说话人跟踪中的应用

以说话人跟踪问题作为应用背景,针对非线性、非高斯随机系统的状态估计问题,本文将量子进化算法和粒子滤波相结合,提出了一种量子进化粒子滤波算法.该算法采用量子编码来表示粒子;通过模拟量子坍塌产生粒子集,同时引入量子变异与量子交叉操作来保持粒子的多样性,从而提高了算法的滤波性能.计算机仿真与说话人跟踪实验结果表明,量子进化粒子滤波算法比通常的粒子滤波算法具有更好的滤波性能.     

MCMC粒子滤波算法研究及应用

粒子滤波算法是一种基于贝叶斯估计的蒙特卡罗方法,适用于非线性非高斯系统的分析,被广泛应用于跟踪、定位等问题的研究中。为了解决粒子滤波算法在重采样后,丧失粒子多样性的问题,本文在粒子滤波算法的重采样步骤后,加入了马尔可夫链蒙特卡罗（Markov Chain Monte Carlo,简称MCMC）移动步骤,增加粒子的多样性。利用粒子滤波算法和MCMC粒子滤波算法对目标跟踪问题进行了仿真,并且通过分析仿真实验结果,比较了两种算法的性能,结果说明加入MCMC粒子滤波算法的性能优于粒子滤波算法。     

Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model

Prediction of lithium-ion batteries remaining useful life (RUL) plays an important role in battery management system (BMS) used in electric vehicles. A novel approach which combines empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA) model is proposed for RUL prognostic in this paper. At first, EMD is utilized to decouple global deterioration trend and capacity regeneration from state-of-health (SOH) time series, which are then used in ARIMA model to predict the global deterioration trend and capacity regeneration, respectively. Next, all the separate prediction results are added up to obtain a comprehensive SOH prediction from which the RUL is acquired. The proposed method is validated through lithium-ion batteries aging test data. By comparison with relevance vector machine, monotonic echo sate networks and ARIMA methods, EMD-ARIMA approach gives a more satisfying and accurate prediction result.     

Improved particle filter based on fine resampling algorithm

In order to solve particle degeneracy phenomenon and simultaneously avoid sample impoverishment,this paper proposed an improved particle filter based on fine resampling algorithm for general case,calle...     

基于遗传粒子滤波的GPS定位数据处理算法

针对粒子滤波算法中的权值退化问题,提出了一种遗传算法辅助下的粒子滤波算法。利用遗传算法在选择继承性上的优势,将遗传算法中的选择、交叉和变异操作引入到粒子滤波算法中。在此基础上,将改进的粒子滤波算法与建立的全球定位系统(GPS)非线性动态状态空间模型结合应用于GPS定位数据处理问题,通过采集实测GPS数据将改进粒子滤波算法与基本粒子滤波算法做了比较。结果表明,遗传算法辅助下的粒子滤波可以增加有效粒子数目,有效解决粒子退化问题,可提高GPS定位数据处理精确度。     

基于ARIMA和PF的锂电池剩余使用寿命预测方法

有效的电池剩余使用寿命(RUL)预测方法能够极大地提高系统的可靠性。提出一种基于自回归集成滑动平均模型(ARIMA)和粒子滤波(PF)融合预测框架,该框架由ARIMA方法和PF方法构成,ARIMA 应用于短期预测,而粒子滤波应用于长期预测。首先在线对锂离子电池进行监测,然后根据短期预测或长期预测要求执行相应的算法,得出横纵坐标分别为周期和容量的 RUL 预测图。实验结果表明,该预测框架能够快速准确地预测锂离子电池 RUL。     

基于最优采样函数的粒子滤波算法与贝叶斯估计

传统粒子滤波器(PF)直接根据状态演化方程产生新的粒子,由于没有考虑新近观测对状态估计的影响,这种滤波器性能较差,即便在粒子数目很大的情况也是如此。为此,本文提出一种基于序贯重要采样(SIS)的改进粒子滤波算法,该算法采用集成了新近观测量的最优采样(或重要密度)函数指导粒子的生成,使粒子权值的方差最小化,能有效减轻粒子退化问题;同时。在粒子重采样之后增加了马尔科夫链蒙特卡洛(MCMC)过程,消除了重采样引起的粒子贫化的负面影响,从而使粒子的多样性得以保持。对非线性系统的状态估计和只测角跟踪的仿真实例均表明,本文所提出的算法比传统估计算法如EKF,UKF具有更高的精度和更强的鲁棒性;与标准PF相比,其性能也有较大的提高,并可以在相同的估计精度下大大减少所需的粒子数目,是一种有效的非线性滤波算法。     

非均匀稀疏采样环境的改进高斯粒子滤波方法

粒子滤波（PF）技术的研究一直是非线性滤波领域的热点和难点问题,针对非均匀稀疏采样环境下传感器观测的滤波估计问题,提出了一种结合目标运动特性的改进型高斯粒子滤波方法。在该方法中,首先深入分析了传统粒子滤波不能有效对非均匀稀疏采样观测数据进行有效处理的原因,通过引入目标观测、目标观测的有效时间间隔、目标速度等目标特性,综合改善高斯粒子滤波器在时间更新阶段预测粒子和预测协方差估计的准确性,从而提高观测更新阶段重要性密度函数的估计精度,实现对目标状态的精确估计。实验结果表明,对于一维非线性非高斯例子,提出方法要稍好于传统的PF、辅助粒子滤波（APF）和高斯粒子滤波（GPF）;而对于实际的非均匀稀疏采样观测样本,提出方法要远好于PF、APF和GPF,能够有效对目标进行状态估计。