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Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2021年 210卷

作者： Li, Sai Fang, Huajing Shi, Bing Wuhan Inst Technol Sch Elect & Informat Engn Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China

Lithium-ion batteries have become an integral part of our lives, and it is important to find a reliable and accurate long-term prognostic scheme to supervise the performance degradation and predict the remaining useful life of batteries. In the perspective of information fusion methodology, an interacting multiple model framework with particle filter and support vector regression is developed to realize multi-step-ahead estimation of the capacity and remaining useful life of batteries. During the multi-step-ahead prediction period, the support vector regression model with sliding windows is used to compensate the future measurements online. Thus, the interacting multiple model with particle filter can relocate the particles and update the capacity estimation. The probability distribution of the remaining useful life is also obtained. Finally, the proposed method is compared and validated with particle filter model using the benchmark data. The experimental results prove that the proposed model yields stable forecasting performance and narrows the uncertainty in remaining useful life estimation.

关键词： Remaining useful life interacting multiple model Particle filter Support vector regression

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State Observers for Suspension Systems with interacting multiple model Unscented Kalman Filter Subject to Markovian Switching

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2021年第6期22卷 1459-1473页

作者： Zhang, Zipeng Xu, Nan Chen, Hong Wang, Zhenfeng Li, Fei Wang, Xinyu Jilin Univ State Key Lab Automot Simulat & Control Changchun 130022 Peoples R China China Automot Technol & Res Ctr Automot Engn Res Inst 68 East Xianfeng Rd Tianjin 300300 Peoples R China CATARC Tianjin Automot Engn Res Inst Co Ltd 68 East Xianfeng Rd Tianjin 300300 Peoples R China

This paper presents a novel model-based observer algorithm to address issues associated with nonlinear suspension system state estimation using interacting multiple model unscented Kalman Filters (IMMUKF) under various road excitation. Due to the fact that practical working condition is complex for the suspension system, e.g. additional load. Meanwhile, the changed sprung mass parameter will induce model changed of suspension system, and it can lead to state transition between various models. To tackle the mentioned issue, the models of road profile and suspension system are first established to describe the nonlinear suspension dynamics. Then, considering the variation of sprung mass under various movement conditions, an unscented Kalman Filter (UKF) algorithm is proposed to identify the sprung mass. Based on the interacting multiple model (IMM) and Markov Chain Monte Carlo (MCMC) theory, a novel IMMUKF observer is developed to estimate the movement state of suspension system. The stability conditions for the proposed observer is calculated using the stochastic stability theory. Finally, simulations and validations are performed on a quarter vehicle suspension system under various ISO road excitations, to validate the UKF and IMMUKF algorithms for acquiring suspension system states, and results illustrate that the maximum root mean square error of state estimation for the proposed algorithm is less than 7.5 %.

关键词： A state observer Nonlinear suspension Unscented kalman filter interacting multiple model Markov chain monte carlo

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Tracking Human Motion With Multichannel interacting multiple model

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2013年第3期9卷 1751-1763页

作者： Lee, Suk Jin Motai, Yuichi Choi, Hongsik Virginia Commonwealth Univ Dept Elect & Comp Engn Richmond VA 23284 USA Georgia Gwinnett Coll Dept Informat Technol Lawrenceville GA 30043 USA

Tracking human motion with multiple body sensors has the potential to promote a large number of applications such as detecting patient motion, and monitoring for home-based applications. With multiple sensors, the tracking system architecture and data processing cannot perform the expected outcomes because of the limitations of data association. For the collaborative and intelligent applications of motion tracking (Polhemus Liberty AC magnetic tracker), we propose a human motion tracking system with multichannel interacting multiple model estimator (MC-IMME). To figure out interactive relationships among distributed sensors, we used a Gaussian mixture model (GMM) for clustering. With a collaborative grouping method based on GMM and expectation-maximization algorithm for distributed sensors, we can estimate the interactive relationship with multiple body sensors and achieve the efficient target estimation to employ a tracking relationship within a cluster. Using multiple models with filter divergence, the proposed MC-IMME can achieve the efficient estimation of the measurement and the velocity from measured datasets of human sensory data. We have newly developed MC-IMME to improve overall performance with a Markov switch probability and a proper grouping method. The experiment results shows that the prediction overshoot error can be improved on average by 19.31% by employing a tracking relationship.

关键词： Cluster number selection expectation-maximization (EM) Gaussian mixture model (GMM) interacting multiple model motion tracking

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A novel interacting multiple model algorithm

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SIGNAL PROCESSING 2009年第11期89卷 2171-2177页

作者： Qu HongQuan Pang LiPing Li ShaoHong N China Univ Technol Coll Informat Engn Beijing 100144 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

For maneuvering target tracking, the interacting multiple model (IMM) algorithm employs a fixed model set. The performance of this algorithm depends on the model set adopted. The result of using too many models is as bad as the case of too few models. Therefore, a variable structure IMM (VSIMM) was presented and applied to ground target tracking. This algorithm improves performance and reduces computational load with using auxiliary information. But it is difficult to extend the VSIMM to other scenario (for example, aerial target), where there is not auxiliary information such as a map. A novel interacting multiple model (Novel-IMM) algorithm was presented to solve the problem of model set adaptation without auxiliary information. The Novel-IMM algorithm consists of N independent IMM filters operating in parallel, and each independent IMM filter also consists of multiple sub-filters, which operate interactively. In every time index, only one IMM Output of a certain model set is used;but for a long time, the algorithm will alternatively choose an output of the model set to be the optimum final output. The Novel-IMM approach was illustrated in detail with an aerial complex maneuvering target tracking example. (C) 2009 Elsevier B.V. All rights reserved.

关键词： interacting multiple model Variable structure IMM model set adaptation Novel-IMM

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Automatic Modulation Classification Using interacting multiple model Kalman Filter for Channel Estimation

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第9期68卷 8928-8939页

作者： Salam, Ahmed O. Abdul Sheriff, Ray E. Hue, Yim-Fun Al-Araji, Saleh R. Mezher, Kahtan Univ Bradford Fac Engn & Informat Bradford BD7 1DP W Yorkshire England Univ Bolton Sch Engn Bolton BL3 5AB England Khalifa Univ Coll Engn Abu Dhabi 127788 U Arab Emirates

A rigorous model for automatic modulation classification (AMC) in cognitive radio systems is proposed in this paper. This is achieved by exploiting the Kalman filter (KF) integrated with an adaptive interacting multiple model (IMM) for resilient estimation of the channel state information. A novel approach is proposed, in adding up the square root singular values of the decomposed channel using the singular value decompositions algorithm. This new scheme, termed Frobenius eigenmode transmission, is chiefly intended to maintain the total power of all individual effective eigenmodes, as opposed to keeping only the dominant one. The analysis is applied over multiple-input multiple-output (MIMO) antennas in combination with a Rayleigh fading channel using a quasi-likelihood ratio test algorithm for AMC. The expectation-maximization is employed for recursive computation of the underlying estimation and classification algorithms. Novel simulations demonstrate the advantages of the combined IMM-KF structure when compared to the perfectly known channel and maximum likelihood estimate, in terms of achieving the targeted optimal performance with the desirable benefit of less computational complexity loads.

关键词： Automatic modulation classification Kalman filter interacting multiple model channel estimation

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Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2007年第8期8卷 1277-1282页

作者： DU Shi-chuan SHI Zhi-guo ZANG Wei CHEN Kang-sheng Department of Information and Electronie Engineering Zhejiang University Hangzhou 310027 China

In airborne tracking,the blind Doppler makes the target undetectable,resulting in tracking difficulties. In this paper,we studied most possible blind-Doppler cases and summed them up into two types:targets' intentional tangential flying to radar and unintentional flying with large tangential speed. We proposed an interacting multiple model(IMM) particle filter which combines a constant velocity model and an acceleration model to handle maneuvering motions. We compared the IMM particle filter with a previous particle filter solution. Simulation results showed that the IMM particle filter outperforms the method in previous works in terms of tracking accuracy and continuity.

关键词： interacting multiple model Particle filter Blind Doppler

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interacting multiple model Algorithm with the Unscented Particle Filter (UPF)

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Chinese Journal of Aeronautics 2005年第4期18卷 366-371页

作者：邓小龙谢剑英倪宏伟 Department of Automation Shanghai Jiaotong University Shanghai 200030 China First Research Institute of Corps of Engineers General Armaments Department PLA Wuxi 214035 China

Combining interacting multiple model （IMM） and unscented particle filter （UPF）, a new multiple model filtering algorithm is presented. multiple models can be adapted to targets＇ high maneu- vering. Particle filter can be used to deal with the nonlinear or non-Gaussian problems and the unscented Kalman filter （UKF） can improve the approximate accuracy. Compared with other interacting multiple model algorithms in the simulations, the results demonstrate the validity of the new filtering method.

关键词： interacting multiple model UPF UKF nonlinear/non-Gaussian

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Use of the interacting multiple model algorithm with multiple sensors

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MATHEMATICAL AND COMPUTER modelLING 2006年第3-4期44卷 332-341页

作者： Ruan, Yanhua Hong, Lang Wright State Univ Dept Elect Engn Dayton OH 45435 USA

In a tracking system with multiple sensors, fusion usually plays a critical role in combining information. There exist many fusion techniques and most of them fall into two categories - measurement fusion and track fusion, depending on what kind of information is to be shared among sensors. These two techniques are typically applied to centralized or distributed systems, respectively. In this paper, our main interest lies in the centralized systems which generally have a central processor where measurement fusion is performed. A common question arising in measurement fusion is the following: is it necessary for the system to process all the measurements, or does more data always mean better estimates? Blair et al. have shown that the use of multiple sensor data can sometimes degrade performance when a single model filter is used, and that the use of a multiple model filter, such as the interacting multiple model (IMM) algorithm, is preferred in order to best utilize the multiple sensor data. Since the IMM algorithm is the state-of-the-art technique for tracking maneuvering targets, we investigate the IMM algorithm for multiple sensor tracking in more detail. We have found that, even with the IMM, more data may not give us a better estimate and, in some cases, it may give us a worse estimate. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： target tracking multi-sensor fusion interacting multiple model Kalman filter performance prediction

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Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization

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ROBOTICS AND AUTONOMOUS SYSTEMS 2012年第12期60卷 1592-1606页

作者： Lee, Hyoungki Jung, Jongdae Choi, Kiwan Park, Jiyoung Myung, Hyun Korea Adv Inst Sci & Technol Urban Robot Lab Taejon 305701 South Korea Samsung Elect Microsyst Lab SAIT Yongin South Korea

Improvement of dead reckoning accuracy is essential for robotic localization systems and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose a fuzzy-logic-assisted interacting multiple model (FLAIMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a fuzzy inference system (FIS) model for slip estimation is constructed using an adaptive neuro-fuzzy inference system (ANFIS). The trained model is utilized along with the two EKFs in the FLAIMM framework. The approach is evaluated using real data sets acquired with a robot driving in an indoor environment. The experimental results show that our approach improves position accuracy and works better in slip detection and compensation compared to the conventional multiple model approach. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Dead reckoning Slip detection Fuzzy inference system interacting multiple model

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Shipborne radar maneuvering target tracking based on the variable structure adaptive grid interacting multiple model

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第9期14卷 733-742页

作者： Zheng-wei ZHU School of Information Engineering Southwest University of Science and Technology Mianyang 621010 China

The trajectory of a shipbome radar target has a certain complexity, randomness, and diversity. Tracking a strong maneuvering target timely, accurately, and effectively is a key technology for a shipbome radar tracking system. Combining a variable structure interacting multiple model with an adaptive grid algorithm, we present a variable structure adaptive grid inter- acting multiple model maneuvering target tracking method. Tracking experiments are performed using the proposed method for five maneuvering targets, including a uniform motion - uniform acceleration motion target, a uniform acceleration motion - uni- form motion target, a serpentine locomotion target, and two variable acceleration motion targets. Experimental results show that the target position, velocity, and acceleration tracking errors for the five typical target trajectories are small. The method has high tracking precision, good stability, and flexible adaptability.

关键词： Shipbome radar Target tracking Variable structure interacting multiple model Adaptive grid algorithm

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