Single Kalman Filter Research Articles

Improved State-of-Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Parameter Estimation and Multi-Innovation Adaptive Robust Unscented Kalman Filter

In this study, an improved adaptive robust unscented Kalman Filter (ARUKF) is proposed for an accurate state-of-charge (SOC) estimation of battery management system (BMS) in electric vehicles (EV). The extended Kalman Filter (EKF) algorithm is first used to achieve online identification of the model parameters. Subsequently, the identified parameters obtained from the EKF are processed to obtain the SOC of the batteries using a multi-innovation adaptive robust unscented Kalman filter (MIARUKF), developed by the ARUKF based on the principle of multi-innovation. Co-estimation of parameters and SOC is ultimately achieved. The co-estimation algorithm EKF-MIARUKF uses a multi-timescale framework with model parameters estimated on a slow timescale and the SOC estimated on a fast timescale. The EKF-MIARUKF integrates the advantages of multiple Kalman filters and eliminates the disadvantages of a single Kalman filter. The proposed algorithm outperforms other algorithms in terms of accuracy because the average root mean square error (RMSE) and the mean absolute error (MAE) of the SOC estimation were the smallest under three dynamic conditions.

A Novel Trigger Mechanism for a Dual-Filter to Improve the State-of-Charge Estimation of Lithium-Ion Batteries

Abstract State-of-charge (SOC) estimation is essential in the energy management of electric vehicles. In the context of SOC estimation, a dual filter based on the equivalent circuit model represents an important research direction. The trigger for parameter filter in a dual filter has a significant influence on the algorithm, despite which it has been studied scarcely. The present paper, therefore, discusses the types and characteristics of triggers reported in the literature and proposes a novel trigger mechanism for improving the accuracy and robustness of SOC estimation. The proposed mechanism is based on an open-loop model, which determines whether to trigger the parameter filter based on the model voltage error. In the present work, particle filter (PF) is used as the state filter and Kalman filter (KF) as the parameter filter. This dual filter is used as a carrier to compare the proposed trigger with three other triggers and single filter algorithms, including PF and unscented Kalman filter (UKF). According to the results, under different dynamic cycles, initial SOC values, and temperatures, the root-mean-square error of the SOC estimated using the proposed algorithm is at least 34.07% lower than the value estimated using other approaches. In terms of computation time, the value is 4.67%. Therefore, the superiority of the proposed mechanism is demonstrated.

An Observer to Detect Infrequently-Occurring Disturbances in Grinding Operations

Changes in ore properties create challenges for the control and optimization of comminution operations because they are generally difficult to measure in real time and have significant impacts on the grinding process and downstream operations. The effectiveness of a multi-model observer to detect and estimate step changes in the particle size distribution of the ore feed to a semi-autogenous grinding (SAG) mill using noisy measurements of the product particle size, is evaluated using a simulation model of the process. The observer maintains multiple hypotheses about the disturbance until their likelihood given the measurements can be determined and used to estimate the disturbance and the true process output. The results demonstrate that the multi-model observer has lower overall estimation errors than a single Kalman filter because it responds to changes in the output quickly without a compromised sensitivity to noise during steady-state. Real-time estimation of changes in ore feed properties in grinding operations could have significant benefits, however, more work is needed to characterize these disturbances, to determine if the process and disturbance models can be identified in practice, and to estimate the potential benefits.

Robust Localization Based on ML-Type, Multi-Stage ML-Type, and Extrapolated Single Propagation UKF Methods Under Mixed LOS/NLOS Conditions

This paper presents robust localization algorithms that use range measurements to estimate the location parameters. The non-line-of-sight (NLOS) propagation of a signal can severely deteriorate the estimation performance in indoor and population-dense urban areas. Therefore, the robust localization algorithms are considered in this paper. In particular, the robust statistics-based localization is dealt with. The maximum likelihood (ML)-type and multi-stage ML-type method-based weighted least squares (WLS) algorithms and robust extrapolated single propagation unscented Kalman filter (ESPUKF) are proposed for mixed line-of-sight (LOS)/NLOS environments. Based on extensive simulations, the positioning accuracies of the proposed methods are found to be superior to those of conventional methods in the mildly and moderately mixed LOS/NLOS conditions. In addition, analyses are conducted on the mean square error (MSE), asymptotical unbiasedness and computational complexity of the proposed algorithms.

An Enhanced Online Temperature Estimation for Lithium-Ion Batteries

This article presents an enhanced internal temperature-estimation method for lithium-ion batteries using a 1-D model and a dual Kalman filter (DKF). The cylindrical battery cell is modeled by a 1-D thermal model with three nodes. This model provides a more accurate representation of the temperature distribution, resulting in more detail of the temperature field. With the newly developed 1-D model, an enhanced temperature-estimation method is developed by including the internal resistance identification and SOC estimation in the temperature-estimation process. Experiments and simulations are conducted to evaluate the robustness and accuracy of the temperature estimation. The estimated temperature using the 1-D model with random initial values is compared with the surface temperature from experiments, which shows excellent robustness against random initial values. High estimation accuracy is demonstrated by the comparison between the estimated temperature field and the simulated temperature field from a high-fidelity 3-D model. Experimental results show that the DKF method provides better stability than the single Kalman filter, and the accuracy of the internal temperature estimation is improved by the equivalent thermal conductivity identification that considers the anisotropy of thermal conductivity in different directions.

Integrated Sidelobe Cancellation and Linear Prediction Kalman Filter for Joint Multi-Microphone Speech Dereverberation, Interfering Speech Cancellation, and Noise Reduction

In multi-microphone speech enhancement, reverberation as well as additive noise and/or interfering speech are commonly suppressed by deconvolution and spatial filtering, e.g., using multi-channel linear prediction (MCLP) on the one hand and beamforming, e.g., a generalized sidelobe canceler (GSC), on the other hand. In this paper, we consider several reverberant speech components, whereof some are to be dereverberated and others to be canceled, as well as a diffuse (e.g., babble) noise component to be suppressed. In order to perform both deconvolution and spatial filtering, we integrate MCLP and the GSC into a novel architecture referred to as integrated sidelobe cancellation and linear prediction (ISCLP), where the sidelobe-cancellation (SC) filter and the linear prediction (LP) filter operate in parallel, but on different microphone signal frames. Within ISCLP, we estimate both filters jointly by means of a single Kalman filter. We further propose a spectral Wiener gain post-processor, which is shown to relate to the Kalman filter's posterior state estimate. The presented ISCLP Kalman filter is benchmarked against two state-of-the-art approaches, namely first a pair of alternating Kalman filters respectively performing dereverberation and noise reduction, and second an MCLP+GSC Kalman filter cascade. While the ISCLP Kalman filter is roughly $M^2$ times less expensive than both reference algorithms, where $M$ denotes the number of microphones, it is shown to perform similarly as compared to the former, and to outperform the latter.

A Method to Identify Lithium Battery Parameters and Estimate SOC Based on Different Temperatures and Driving Conditions

State of charge (SOC) plays a significant role in the battery management system (BMS), since it can contribute to the establishment of energy management for electric vehicles. Unfortunately, SOC cannot be measured directly. Various single Kalman filters, however, are capable of estimating SOC. Under different working conditions, the SOC estimation error will increase because the battery parameters cannot be estimated in real time. In order to obtain a more accurate and applicable SOC estimation than that of a single Kalman filter under different driving conditions and temperatures, a second-order resistor capacitor (RC) equivalent circuit model (ECM) of a battery was established in this paper. Thereafter, a dual filter, i.e., an unscented Kalman filter–extended Kalman filter (UKF–EKF) was developed. With the EKF updating battery parameters and the UKF estimating the SOC, UKF–EKF has the ability to identify parameters and predict the SOC of the battery simultaneously. The dual filter was verified under two different driving conditions and three different temperatures, and the results showed that the dual filter has an improvement on SOC estimation.

Analysis of a federal Kalman filter-based tracking loop for GPS signals

Kalman filter is widely used in modern GPS receivers. A traditional receiver based on Kalman filter (KF) tracks the GPS signals from each satellite separately, and so its tracking performance could be inferior in challenging situations. The vector delay frequency-locked loop (VDFLL) combines all tracking channels into one Kalman filter and so has a better tracking performance. But VDFLL has two primary drawbacks: high computational cost and low robustness. To solve this problem, the federal tracking loop is proposed. The single Kalman filter of the traditional federal loop is separated into a least-squares block and a master filter in consideration of reducing the computational complexity at the cost of a slight performance reduction. In this tracking loop, the GPS signal from each satellite is tracked independently in the subfilters. The master filter works as a navigation processor, which estimates the acceleration of the receiver and feeds it back to the subfilters. The thermal jitter and dynamic stress error of the federal tracking loop are analyzed theoretically and demonstrated with simulation data. The dynamic performance, robustness and computational complexity of the traditional KF loop, the proposed federal tracking loop and the VDFLL are compared. Analysis and simulation results show that the dynamic tracking performance of the federal loop is much better than that of the KF loop and is almost close to that of the VDFLL. Also, the federal loop has advantages over the VDFLL in terms of robustness and the computational complexity. Therefore, the federal tracking loop can be implemented as a new method in the practical implementation for high-performance applications in the future.

Distributed processing of a GPS receiver network for a regional ionosphere map

This paper proposes a distributed processing method applicable to GPS receivers in a network to generate a regional ionosphere map accurately and reliably. For accuracy, the proposed method is operated by multiple local Kalman filters and Kriging estimators. Each local Kalman filter is applied to a dual-frequency receiver to estimate the receiver’s differential code bias and vertical ionospheric delays (VIDs) at different ionospheric pierce points. The Kriging estimator selects and combines several VID estimates provided by the local Kalman filters to generate the VID estimate at each ionospheric grid point. For reliability, the proposed method uses receiver fault detectors and satellite fault detectors. Each receiver fault detector compares the VID estimates of the same local area provided by different local Kalman filters. Each satellite fault detector compares the VID estimate of each local area with that projected from the other local areas. Compared with the traditional centralized processing method, the proposed method is advantageous in that it considerably reduces the computational burden of each single Kalman filter and enables flexible fault detection, isolation, and reconfiguration capability. To evaluate the performance of the proposed method, several experiments with field collected measurements were performed.

Dual estimation of lateral analysis for railway vehicle wheelset to control disturbance for smooth running

The lateral aspects of railway vehicle wheelset have a significant impact on railway wheelset dynamic systems. Lateral analysis usually causes wheelset slippage from the track resulting insufficient adhesion which can lead to creep. Significant disturbances are mostly caused in lateral direction due to speed. This paper uses the dual Kalman filter strategy to deal with noise issues and thus minimizes the error ratio during observation. A single Kalman filter reduces errors to a minimum extent while the second estimator decreases the maximum available chance of error. Using a third estimator provides no further improvement over the dual Kalman filter implementation.

Linear Kalman Filter for Attitude Estimation from Angular Rate and a Single Vector Measurement

In this paper, a new Kalman filtering scheme is designed in order to give the optimal attitude estimation with gyroscopic data and a single vector observation. The quaternion kinematic equation is adopted as the state model while the quaternion of the attitude determination from a strapdown sensor is treated as the measurement. Derivations of the attitude solution from a single vector observation along with its variance analysis are presented. The proposed filter is named as the Single Vector Observation Linear Kalman filter (SVO-LKF). Flexible design of the filter facilitates fast execution speed with respect to other filters with linearization. Simulations and experiments are conducted in the presence of large external acceleration and magnetic distortion. The results show that, compared with representative filtering methods and attitude observers, the SVO-LKF owns the best estimation accuracy and it consumes much less time in the fusion process.

A Novel Channel Estimation Technique of MIMO-OFDM System based on Modified Kalman Filter

Objectives: Novel channel estimation technique of MIMO-OFDM system based on Modified Kalman Filter (MKF).The performance metrics are bit error rate, types of modulation method, size of the antenna, types of the Kalman filter. Methods: The performance methods considered are MKF, Single Kalman Filter (SKF), Double Kalman Filter (DKF).We have evaluated channel effect with respect to QPSK modulation technique and Rayleigh channel. Using MATLAB simulation tool, we have plot the bit error rate versus signal to noise ratio graph for MKF, SKF and DKF method. Results: The proposed channel estimation method improves the bit error rate performance compare to conventional channel estimation method. We have compared the proposed MKF method with the SKF and DKF method. Theoretical analysis and simulation results shows that the proposed channel estimation and tracking method based on MKF offer the excellent improvement in bit error rate compare to conventional channel estimation method. Improvements: We have achieved lowest bit error rate in the proposed MKF method compare to SKF and DKF method. As we increase the number of antenna at transmitter and receiver side we have also achieved the lowest bit error rate in the proposed MKF method.

Tracking Architecture Based on Dual-Filter with State Feedback and Its Application in Ultra-Tight GPS/INS Integration.

If a Kalman Filter (KF) is applied to Global Positioning System (GPS) baseband signal preprocessing, the estimates of signal phase and frequency can have low variance, even in highly dynamic situations. This paper presents a novel preprocessing scheme based on a dual-filter structure. Compared with the traditional model utilizing a single KF, this structure avoids carrier tracking being subjected to code tracking errors. Meanwhile, as the loop filters are completely removed, state feedback values are adopted to generate local carrier and code. Although local carrier frequency has a wide fluctuation, the accuracy of Doppler shift estimation is improved. In the ultra-tight GPS/Inertial Navigation System (INS) integration, the carrier frequency derived from the external navigation information is not viewed as the local carrier frequency directly. That facilitates retaining the design principle of state feedback. However, under harsh conditions, the GPS outputs may still bear large errors which can destroy the estimation of INS errors. Thus, an innovative integrated navigation filter is constructed by modeling the non-negligible errors in the estimated Doppler shifts, to ensure INS is properly calibrated. Finally, field test and semi-physical simulation based on telemetered missile trajectory validate the effectiveness of methods proposed in this paper.

Multiple Model-Based Synchronization Approaches for Time Delayed Slaving Data in a Space Launch Vehicle Tracking System

Due to the inherent characteristics of the flight mission of a space launch vehicle (SLV), which is required to fly over very large distances and have very high fault tolerances, in general, SLV tracking systems (TSs) comprise multiple heterogeneous sensors such as radars, GPS, INS, and electrooptical targeting systems installed over widespread areas. To track an SLV without interruption and to hand over the measurement coverage between TSs properly, the mission control system (MCS) transfers slaving data to each TS through mission networks. When serious network delays occur, however, the slaving data from the MCS can lead to the failure of the TS. To address this problem, in this paper, we propose multiple model-based synchronization (MMS) approaches, which take advantage of the multiple motion models of an SLV. Cubic spline extrapolation, prediction through anα-β-γfilter, and a single model Kalman filter are presented as benchmark approaches. We demonstrate the synchronization accuracy and effectiveness of the proposed MMS approaches using the Monte Carlo simulation with the nominal trajectory data of Korea Space Launch Vehicle-I.

Modified Cascade Kalman Filter for Sensor Data Fusion in Micro Aerial Vehicle∗∗This work is funded by the DRDO/NPMICAV project.

Precise attitude estimation is important for navigation, guidance and control of Micro Aerial Vehicles (MAV) as they are mostly equipped with low integrity sensors due to the constraints on MAV payload and able power. The MAY sensors such as accelerometer and magnetometer are prone to noise as they are placed in proximity to the motor due to constraints on centre of gravity (CG). Data from a single sensor are not reliable for all operating points of flight envelope as the motor vibration and magnetic flux due to the motor vary with RPM. Hence accurate attitude estimation of MAV is achieved through multisensor data fusion. In this paper, a modification to the classical cascade Kalman filter is proposed. Modified Cascade Kalman Filter (MCKF) has better estimation performance as it is a single Kalman filter similar to measurement fusion technique and also it restores the flexibility and computational efficiency of state vector fusion method. A numerical example is presented wherein the derived MCKF is implemented for the attitude estimation of MAV in the 6 DOF simulation model developed in MATLAB/SIMULINK and also for a set of calibrated accelerometer and magnetometer sensor data With motor noise acquired from the autopilot mounted on the rate table of the notion simulator. It was found that MCKF exhibits substantially improved performance compared to extended Kalman filter with only accelerometer data with motor noise. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Weighted Measurement Fusion Kalman Filter for Multisensor Descriptor System

For the multi-sensor descriptor system with correlated measurement noises and same measurement matrix, the reduced-order sub-systems are obtained, applying singular value decomposition method. And measurements of every sensor are transformed to the measurement of one state component. For this new reduced-order normal system, the new fused measurement can be obtained applying the weighted least squares method. Then, the weighted measurement fusion Kalman filter and its filtering error variance are presented, applying a single Kalman filter. This method avoids computing the cross-variances among all local filters, compared with the state fusion Kalman filtering algorithm. And the accuracy of this fused filter is higher than that of local filter and state fusion Kalman filter. A simulation example verifies its effectiveness.

Inpatient Studies of a Kalman-Filter-Based Predictive Pump Shutoff Algorithm

An insulin pump shutoff system can prevent nocturnal hypoglycemia and is a first step on the pathway toward a closed-loop artificial pancreas. In previous pump shutoff studies using a voting algorithm and a 1 min continuous glucose monitor (CGM), 80% of induced hypoglycemic events were prevented. The pump shutoff algorithm used in previous studies was revised to a single Kalman filter to reduce complexity, incorporate CGMs with different sample times, handle sensor signal dropouts, and enforce safety constraints on the allowable pump shutoff time. Retrospective testing of the new algorithm on previous clinical data sets indicated that, for the four cases where the previous algorithm failed (minimum reference glucose less than 60 mg/dl), the mean suspension start time was 30 min earlier than the previous algorithm. Inpatient studies of the new algorithm have been conducted on 16 subjects. The algorithm prevented hypoglycemia in 73% of subjects. Suspension-induced hyperglycemia is not assessed, because this study forced excessive basal insulin infusion rates. The new algorithm functioned well and is flexible enough to handle variable sensor sample times and sensor dropouts. It also provides a framework for handling sensor signal attenuations, which can be challenging, particularly when they occur overnight.

Battery SOC Estimation Based on Multi-Model Adaptive Kalman Filter

This paper introduces multi-model adaptive kalman filter estimation algorithm.Based on the battery thevenin model,the multi-model adaptive kalman filter is applied to the battery SOC(state of charge) estimation, which solute the battery SOC estimation in conditions that the battery model parameters change caused by temperature changing. Simulation results show that compared to the single model kalman filter algorithm, Multi-Model adaptive kalman filter algorithm improves the estimation precision and reliability greatly.

An Improved Adaptive Kalman Filter for Gravity Anomaly Distortion Correction

Combined with the system state equation and the measurement equation, a new method of cascade Kalman filter is proposed and applied to the correction of gravity anomaly distortion. In the signal processing procedure, according to the self-correlation sequences of the measurement gravity signal, the relation of the gain matrix K and the self-correlation sequences could be obtain, and the gravity signal at current time can be calculated by the gain matrix K. Emulations and experiments indicate that both the cascade Kalman filter method and the single inverse Kalman filter method are effective in alleviating the distortion of the gravity anomaly signal, but the performance of the cascade Kalman filter method is better than that of single inverse Kalman filter method.

Robust object tracking under appearance change conditions

We propose a robust visual tracking framework based on particle filter to deal with the object appearance changes due to varying illumination, pose variantions, and occlusions. We mainly improve the observation model and re-sampling process in a particle filter. We use on-line updating appearance model, affine transformation, and M-estimation to construct an adaptive observation model. On-line updating appearance model can adapt to the changes of illumination partially. Affine transformation-based similarity measurement is introduced to tackle pose variantions, and M-estimation is used to handle the occluded object in computing observation likelihood. To take advantage of the most recent observation and produce a suboptimal Gaussian proposal distribution, we incorporate Kalman filter into a particle filter to enhance the performance of the resampling process. To estimate the posterior probability density properly with lower computational complexity, we only employ a single Kalman filter to propagate Gaussian distribution. Experimental results have demonstrated the effectiveness and robustness of the proposed algorithm by tracking visual objects in the recorded video sequences.