Unscented Particle Filter Research Articles

A Novel High-Fidelity Unscented Particle Filtering Method for the Accurate State of Charge Estimation of Lithium-Ion Batteries

Power Li-ion batteries are one of the core three powers systems of new energy vehicles, and its accurate batteries modeling and state prediction have become the core technology of the scientific and technological progress in the industry. This paper takes the ternary Li-ion batteries as the research subject. Aiming at the mathematical expressions of different structural features, innovatively construct a second-order Thevenin equivalent circuit model with autoregressive effect. This model can characterize the internal reaction mechanism of Li-ion batteries and fit the complex electrochemical reactions inside the battery. An improved particle filter model, namely a new high-fidelity unscented particle filter method, is designed and established. By introducing a suitable suggested density function, the model can accurately calculate the mean and variance, solve the particle degradation problem, and find out the Li-ion batteries state of charge, which is suitable for complex charging and discharging conditions. By further improving the theoretical analysis and combining with experiments under different working conditions, this method studies the Li-ion batteries state of charge. The test results show that the average absolute error of the improved equivalent circuit model is reduced by 0.00457 V, and the error rate is stably kept within 1%, which has the ability to describe Li-ion batteries well. When using the high-fidelity unscented particle filter algorithm to estimate the state of charge of the lithium battery, the robustness of the system is improved, the following effect is better, and the estimation error is controlled within 1.5%, which brings good practical value to the power Li-ion batteries.

A novel combined estimation method of online full‐parameter identification and adaptive unscented particle filter for Li ‐ion batteries SOC based on fractional‐order modeling

Accurate estimation of the state of charge (SOC) of Li-ion battery can ensure the reliability of the storage system. A combined estimator of online full-parameter identification and adaptive unscented particle filter for Li-ion battery SOC based on an improved fractional-order model is proposed, which overcomes the shortcomings of the traditional SOC cumulative error and the difficulty of OCV acquisition. The proposed adaptive fractional unscented particle filter algorithm introduces fractional parameters as hidden parameters and reduces the complexity of the algorithm iteration by reducing the number of particles. At the same time, the noise adaptive algorithm based on the residual sequence can solve the divergence problem of the filter and improve the adaptability of the algorithm. To verify the feasibility of the algorithm under complex operating conditions, the urban dynamometer driving schedule dynamic working conditions of Li-ion batteries are verified. The experimental results show that the evaluation index of the algorithm is the best, the RMSE is 0.67%, and the SOC estimation is more accurate. It shows that the algorithm has strong robustness and fast convergence.

Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

A new method of unscented particle filter for high-fidelity lithium-ion battery SOC estimation

A new method of unscented particle filter for high-fidelity lithium-ion battery SOC estimation

Smooth particle filter‐based likelihood approximations for remaining useful life prediction of Lithium‐ion batteries

Accurate prediction of the remaining useful life (RUL) in Lithium-ion batteries (LiBs) is a key aspect of managing its health, in order to promote reliable and secure systems, and to reduce the need for unscheduled maintenance and costs. Recent work on RUL prediction has largely focused on refining the accuracy and reliability of the RUL prediction. The author introduces a new online RUL prediction for LiB using smooth particle filter (SPF)- based likelihood approximation method. The proposed algorithm can accurately estimate the unknown degradation model parameters and predict the degradation state by solving the optimisation problem at each iteration, rather than only taking a gradient step, that tends to lead to rapid convergence, avoids instability issues and improves predictive accuracy. From the experimental datasets published by Prognostics Centre of Excellence (PCoE) of NASA, a second order degradation model was created to explore the degradation of LiB, utilising non-linear characteristics and non-Gaussian capacity degradation. RUL prediction was tested with various predicted starting points to assess whether the amount of data and parameters' uncertainty influenced the accuracy of the prediction. Results show that the proposed prediction approach gives improved prediction accuracy and improves the convergence rate in comparison with the particle filter (PF) and other methods such as unscented particle filter (UPF). Since the maximum error of the SPF predicting approach is relatively small, RUL prediction in the best case at the prediction starting point consisting of 80 cycles is 127 cycles. The prediction relative error was approximately 0.024, and the absolute error of the proposed algorithm is around 2 cycles, which is lower than the PF (around 16 cycles). RUL prediction is close to 108 cycles and relative error is around 0.136, while the absolute error prediction is approximately 16.

A UPF-PS SLAM Algorithm for Indoor Mobile Robot With NonGaussian Detection Model

In this article, we propose a simultaneous localization and mapping (SLAM) framework that combines the methods of the unscented particle filter (UPF) and particle smoothers (PS). The UPF is used to estimate the position and orientation of the mobile robot and PS are used to update the locations of landmarks. We show that UPF is capable of estimating the pose of the robot more consistently and accurately than generic particle filter and unscented Kalman filter (UKF) do, especially when the dynamic models of the robot are highly nonlinear or noises are nonGaussian. Then, the proposed SLAM framework is applied to high-frequency (HF) band radio-frequency identification (RFID) system. Since detection model of the HF band RFID system is highly nonGaussian, the traditional SLAM frameworks with the Gaussian assumption are not suitable and the proposed UPF-PS framework presents the superior performance. The proposed SLAM framework is evaluated through simulated experiments with an HF band RFID system and a range and bearing sensor. The comparisons between the proposed SLAM framework and four other SLAM frameworks, i.e., FastSLAM, UFastSLAM, and P-SLAM 1.0 and 2.0 are also given. Finally, the proposed SLAM framework is verified in a real experimental environment. The experimental results validate the effectiveness and superiority of the proposed SLAM framework.

Vehicle State Estimation Based on Unscented Kalman Filtering and a Genetic-particle Swarm Algorithm

Vehicle state and parameter estimation has gradually become an important way to soft-sense some variables that are difficult to measure directly using general sensors. In the traditional Kalman filtering algorithm, the selection of the process noise covariance matrix and the measurement noise covariance matrix will directly affect the filtering accuracy of the algorithm. In order to improve the filtering accuracy of the filter algorithm to obtain the optimal solution, based on a 7-DOF nonlinear vehicle dynamics model and the Magic formula tire model, a hybrid algorithm containing an unscented Kalman filter (UKF) and a genetic-particle swarm algorithm (genetic-particle swarm UKF) is used to estimate several vehicle key states. Compared with traditional estimator based on UKF and the unscented particle filter (UPF), the simulation and real vehicle test results show that the proposed estimator based on the genetic-particle swarm UKF algorithm has higher accuracy and less computation requirements than the UKF estimator. And also, the proposed hybrid algorithm has superiority on the convergence speed of the optimization than the UPF algorithm. The results of a real-vehicle experiment demonstrate that the proposed hybrid algorithm can be used effectively for solving the vehicle-state estimation problem.

On the Adaptivity of Unscented Particle Filter for GNSS/INS Tightly-Integrated Navigation Unit in Urban Environment

Tight integration algorithms fusing Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have become popular in many high-accuracy positioning and navigation applications. Despite their reliability, common integration architectures can still run into accuracy drops under challenging navigation settings. The growing computational power of low-cost, embedded systems has allowed for the exploitation of several advanced Bayesian state estimation algorithms, such as the Particle Filter (PF) and its hybrid variants, e.g. Unscented Particle Filter (UPF). Although sophisticated, these architectures are not immune from multipath scattering and Non-Line-of-Sight (NLOS) signal receptions, which frequently corrupt satellite measurements and jeopardise GNSS/INS solutions. Hence, a certain level of modelling adaptivity should be granted to avoid severe drifts in the estimated states. Given these premises, the paper presents a novel Adaptive Unscented Particle Filter (AUPF) architecture leveraging two cascading stages to cope with disruptive, biased GNSS input observables in harsh conditions. A INS-based signal processing block is implemented upstream of a Redundant Measurement Noise Covariance Estimation (RMNCE) stage to strengthen the adaptation of observables’ statistics and improve the state estimation. An experimental assessment is provided for the proposed robust AUPF that demonstrates a 10 % average reduction of the horizontal position error above the 75-th percentile. In addition, a comparative analysis both with previous adaptive architectures and a plain UPF is carried out to highlight the improved performance of the proposed methodology.

Estimation Performance for the Cubature Particle Filter under Nonlinear/Non-Gaussian Environments

This paper evaluates the state estimation performance for processing nonlinear/non-Gaussian systems using the cubature particle filter (CPF), which is an estimation algorithm that combines the cubature Kalman filter (CKF) and the particle filter (PF). The CPF is essentially a realization of PF where the third-degree cubature rule based on numerical integration method is adopted to approximate the proposal distribution. It is beneficial where the CKF is used to generate the importance density function in the PF framework for effectively resolving the nonlinear/non-Gaussian problems. Based on the spherical-radial transformation to generate an even number of equally weighted cubature points, the CKF uses cubature points with the same weights through the spherical-radial integration rule and employs an analytical probability density function (pdf) to capture the mean and covariance of the posterior distribution using the total probability theorem and subsequently uses the measurement to update with Bayes’ rule. It is capable of acquiring a maximum a posteriori probability estimate of the nonlinear system, and thus the importance density function can be used to approximate the true posterior density distribution. In Bayesian filtering, the nonlinear filter performs well when all conditional densities are assumed Gaussian. When applied to the nonlinear/non-Gaussian distribution systems, the CPF algorithm can remarkably improve the estimation accuracy as compared to the other particle filter-based approaches, such as the extended particle filter (EPF), and unscented particle filter (UPF), and also the Kalman filter (KF)-type approaches, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF) and CKF. Two illustrative examples are presented showing that the CPF achieves better performance as compared to the other approaches.

Multi‐Scale Prediction of RUL and SOH for Lithium‐Ion Batteries Based on WNN‐UPF Combined Model

The prediction of Remaining useful life (RUL) and the estimation of State of health (SOH) are extremely important issues for operating performance of Lithium-ion (Li-ion) batteries in the Battery management system (BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network (WNN) with Unscented particle filter (UPF) model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform (DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries' lifespan.

Research on underwater target tracking based on Gaussian Hermitian Kalman Particle filter algorithm

The underwater target tracking environment usually has the characteristics of strong nonlinearity and non-Gaussian. The target tracking problem usually uses the nonlinear filtering algorithm, which combines the nonlinear measurement model with the linear system dynamics. The primary goal of target tracking is to extract accurate information about the real-time state of the target from the noise nonlinear observation obtained by the sensor. A new Gaussian Hermitt Kalman particle filter algorithm (GHKF-PF) is used to improve the tracking accuracy of the particle filter algorithm (PF). GHKF-PF uses the GHKF sampling method to sample particles from the posterior distribution of the target, calculates the mean and covariance for each particle, and uses the mean and variance to guide sampling. In the simulation experiment, the uniform linear motion model of underwater 3D target in the environment of Gaussian mixture noise (GMN) is established, and the application of GHKF-PF algorithm in 3D space is realized, and the accuracy is higher than that of PF. In order to further verify the effectiveness of the algorithm, a six-dimensional uniform acceleration motion model is established and compared with the extended particle filter (EPF) algorithm and the unscented particle filter (UPF) algorithm. The simulation results show that the performance of the GHKF-PF algorithm is similar to that of the UPF algorithm and better than the EPF algorithm.

An Improved Unscented Particle Filter Approach for Multi-Sensor Fusion Target Tracking.

In this paper, a new approach of multi-sensor fusion algorithm based on the improved unscented particle filter (IUPF) and a new multi-sensor distributed fusion model are proposed. Additionally, we employ a novel multi-target tracking algorithm that combines the joint probabilistic data association (JPDA) algorithm and the IUPF algorithm. To improve the real-time performance of the UPF algorithm for the maneuvering target, minimum skew simplex unscented transform combined with a scaled unscented transform is utilized, which significantly reduces the calculation of UPF sample selection. Moreover, a self-adaptive gain modification coefficient is defined to solve the low accuracy problem caused by the sigma point reduction, and the problem of particle degradation is solved by modifying the weights calculation method. In addition, a new multi-sensor fusion model is proposed, which better integrates radar and infrared sensors. Simulation results show that IUPF effectively improves real-time performance while ensuring the tracking accuracy compared with other algorithms. Besides, compared with the traditional distributed fusion architecture, the proposed new architecture makes better use of the advantages of radar and an infrared sensor and improves the tracking accuracy.

Self-Localization in Highly Dynamic Environments Based on Dual-Channel Unscented Particle Filter

SUMMARYSelf-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

Robust Interacting Multiple Model Unscented Particle Filter for Navigation

In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.

A hybrid prognostic strategy with unscented particle filter and optimized multiple kernel relevance vector machine for lithium-ion battery

To make up the deficiencies of single methods in lithium-ion battery state of health (SOH) and remaining useful life (RUL) estimation, this paper presents a novel hybrid method using unscented particle filter (UPF) with optimized multiple kernel relevance vector machine (OMKRVM). Firstly, the errors between the initial estimation by UPF and the actual capacity are obtained. After that, the residuals are reconstructed by complementary ensemble empirical mode decomposition (CEEMD) to reduce interference. In addition, OMKRVM is adopted to provide multiple predictive abilities, and kernel parameters and weights of OMKRVM are yielded by the grid search. Finally, the initial estimation is corrected by the predicted residuals using OMKRVM to further improve prediction performance. The new method (UPF-OMKRVM) is compared with existing methods in predicting the degradation process of lithium-ion battery. The experimental results show that the UPF-OMKRVM has high prediction accuracy in lithium-ion battery SOH and RUL estimation.

Decreasing Weight Particle Swarm Optimization Combined with Unscented Particle Filter for the Non-Linear Model for Lithium Battery State of Charge Estimation

Accurate estimation of State of Charge (SOC) of wireless sensor network nodes is of great significance for wireless sensor network layout. A combination strategy method based on unscented particle filter using weight particle swarm optimization (PSO UPF) algorithm is proposed to improve estimation accuracy. The particle filter (PF) algorithm is usually used to deal with nonlinear problems, easily falling into particle degeneration and particle shortage. The unscented particle filter (UPF) algorithm can overcome the shortcomings by using the unscented Kalman filter (UKF) to generate the importance density function. Meanwhile, the particle swarm optimization (PSO) algorithm could improve the resampling process to solve particle shortage. Thus, the combination strategy improves the importance density function and the resampling method simultaneously. With the simulation comparison of PF, UPF and PSO UPF algorithms, the results show that the proposed algorithm has higher estimation accuracy with the root mean square error less than 1%. Furthermore, the proposed algorithm could achieve good accuracy with few particles, which could save running time and improve the estimate efficiency.

A Hybrid Method for the Prediction of the Remaining Useful Life of Lithium-Ion Batteries With Accelerated Capacity Degradation

A hybrid method for the prediction of the remaining useful life (RUL) of Lithium-ion batteries considering error-correction is proposed in respect of capacity diving phenomenon in later capacity degradation. First, an improved empirical capacity degradation model is proposed based on our previous work, with the analytic expression further revised in this paper to enhance its fitting accuracy, parameter identifiability, and applicability to tracking algorithm. Unscented particle filter (UPF) algorithm is then implemented to obtain prognostic results with original error series. Next, to enhance the quality of original error data by reducing the local uncertainty, complete ensemble empirical mode decomposition (CEEMD) algorithm is utilized to reconstruct error series. The fundamental error evolution information is retained by selecting relatively highly correlated intrinsic mode functions (IMFs) of the decomposition results of original error series. Finally, after employing Gaussian process regression (GPR) algorithm, the prognostic error to correct the UPF-based prognostic result is obtained from the reconstructed error series. RUL prediction experiments for batteries with different working conditions have been conducted to verify the improved performance of the proposed model and the hybrid method, with mean absolute percentage errors of battery capacity degradation predicted less than 0.4%.

RUL Prediction and Uncertainty Management for Multisensor System Using an Integrated Data-Level Fusion and UPF Approach

Due to the fact that single sensor data contains only partial information about complex systems, multiple sensors are often embedded to simultaneously monitor the health state and predict the remaining useful life (RUL). This brings new challenges to traditional approaches that focus on single sensor in terms of data fusion, optimization, and uncertainty management. To address these challenges, this article proposes a novel RUL prediction and uncertainty management framework for multisensor systems. In this framework, a composite 1-D health indicator (1-D HI) is obtained from multiple sensors by optimizing some HI characteristics, including monotonicity, robustness, fitting error, and range information, to better describe the underlying degradation process. A multiobjective grasshopper optimization algorithm is used to achieve the optimal weight vector of the fusion model. Then, an unscented particle filter is introduced to predict the RUL by combining the degradation model constructed from HI and the composite 1-D HI as measurement. To manage the uncertainty in prognosis, a probability distribution of failure threshold and noise parameter adjustment are developed. Experimental results on aircraft turbine engine degradation and comparison with state-of-the-art methods are presented to demonstrate the effectiveness of the proposed framework in RUL prediction of multisensor systems.

Direct Yaw-Moment Control of Electric Vehicle With in-Wheel Motor Drive System

With the development of redundant driving system, vehicles' maneuverability and stability have great optimized potential. In this research, an electric off-road vehicle is served as controlled objective and a dynamic direct yaw-moment controller is proposed to realize the smooth accommodation of vehicle longitudinal-lateral motion under various transport conditions via controlling In-wheel motor (IWM) drive system. A data fusion technology is employed to enhance the control accuracy and responsiveness via amending the metrical data of INS and GPS primarily, and the discrete measured information are transmitted to a unscented particle filter with fine resampling (UPF-FR) that estimates the sideslip angle and yaw rate synchronously. Then, a feedforward-feedback control system is constructed to improves the controlled vehicle's maneuverability and stability based on the calculated active yaw moment. It is noteworthy that a nonlinear finite time feedback control system is utilized to acquire minimum error between the actual vehicle status and the desired vehicle status. After that, the IWM drive system execute the active yaw moment through modulating every motor's alternate current independently. In the end, some specific simulation and real tests are conducted to verify the performance of hierarchical control system.

Precise 3-D Indoor Localization Based on Wi-Fi FTM and Built-In Sensors

More and more applications of location-based services lead to the development of indoor positioning technology. As a part of the Internet-of-Things ecosystem, most existing indoor positioning algorithms are applied to specific situations, e.g., pedestrian navigation and target detection. To meet the high-precision indoor localization requirement, IEEE 802.11 included the Wi-Fi fine-time measurement (FTM) protocol in 2016, which provides a novel approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). This article proposes a precise 3-D indoor localization algorithm based on Wi-Fi FTM and smartphone built-in sensors (3D-WFBS). The adaptive extended Kalman filter (AEKF) is used to estimate the pedestrian's real-time heading and walking speed, and the received signal strength indication and round-trip time collected from Wi-Fi APs are combined for proximity detection and providing more accurate ranging results. In addition, the unscented particle filter is applied to fuse the results of AEKF, proximity detection, and Wi-Fi ranging. The experimental results show that compared with the existing dead reckoning method and the other fusion methods, the proposed 3D-WFBS algorithm is proved to achieve meter-level indoor positioning accuracy in typical indoor scenes.