Adaptive Fading Extended Kalman Filter Research Articles

ARWLS-AFEKE: SOC Estimation and Capacity Correction of Lithium Batteries Based on a Fusion Algorithm

Accuracy of battery charge status (SOC) estimation plays a significant role in the management of electric vehicle power batteries. However, recently, abrupt changes from SOC data often occurs in the actual operation of electric vehicles and some errors appear in the establishment of battery models and noise models, which give rise to the poorly adaptive and robust performance of traditional algorithms in the process of SOC estimation. The fusion algorithm proposed in this paper can effectively improve the accuracy of models and SOC estimation of lithium-ion batteries. Based on the second-order R-C network model, this method optimizes the accuracy of parameter identification by adopting the adaptive recursive weighted least square algorithm (ARWLS). In addition, the adaptive fading extended Kalman filter algorithm (AFEKF) is applied to estimate the SOC of lithium-ion batteries. Additionally, via introducing a fading factor, the optimal Kalman gain is updated in real-time, which can reduce the impact of data mutation on battery modeling. Compared with the offline AEKF algorithm and the EKF algorithm, the adaptive recursive weighted least square-adaptive fading extended Kalman filter (ARWLS-AFEKF) fusion algorithm had higher accuracy and adaptability, which can be adapted to the variable noise environment.

Sea current relative navigation using interacting multiple model filter with adaptive fading technique

AbstractIn this paper, we propose a sea current relative navigation method using an interacting multiple model (IMM) filter with adaptive fading technique that can compensate an inaccurate sea current dynamics model. Due to the marine environment, the underwater vehicles largely depend on inertial navigation. Unfortunately, since its performance deteriorates with time, it is usually aided by another sensor. An electromagnetic-log (EM-log) and a Doppler velocity log (DVL), which are mainly used in marine navigation, provide relative velocity measurements to the sea currents, and hence require an accurate sea current dynamics model to fully utilise them. However, it is difficult to reflect the actual sea current changes with just a single fixed model, resulting in degraded overall navigation performance. Therefore, this paper proposes an IMM filter that can use multiple sea current dynamics models and has sub-filters designed with adaptive fading extended Kalman filter (AFEKF) to compensate for the mismodelling of sea current dynamics. The method is verified by simulation and shows a performance improvement comparable to the optimal filter.

Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is [0.0245 m0.0224 m0.0039 rad]T and the average localization errors using the conventional EKF are [1.0405 m2.2700 m0.1782 rad]T, [0.4963 m0.3482 m0.0254 rad]T and [0.2774 m0.3897 m0.0353 rad]T, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.

Real-time Topography and Hamaker Constant Estimation in Atomic Force Microscopy Based on Adaptive Fading Extended Kalman Filter

In this study, a novel technique based on adaptive fading extended Kalman filter for atomic force microscopy is proposed to directly estimate the topography of a sample surface without needing any control system. While in conventional imaging techniques, the scanning speed or the bandwidth is limited due to a relatively large settling time, the method proposed in this research is able to address this issue and estimate the topography throughout transient oscillation of the microcantilever. With this aim, an estimation process using an adaptive fading extended Kalman filter (augmented with forgetting factor) as the system observer is designed and coupled with the system dynamics to obtain sample topography. Obtained results demonstrate that the sample height is estimated by this algorithm with high accuracy and a relatively high scanning speed. Moreover, the observer is able to identify the topography and Hamaker constant simultaneously. Therefore, the presented approach can compensate for the low scanning speed of the classical imaging method as well as eliminate the need for a closed-loop controller.

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

Nowadays, the global trend is towards reducing CO2 emissions and one solution is to replace internal combustion vehicles with electric vehicles. To this end, electric drive system, the most crucial part of an electric vehicle, has gained importance and has become a major research field. The induction motor (IM) is one of the best candidates for electric vehicle applications due to its advantages such as having simple and robust design, its low cost maintenance requirements and the ability to operate in harsh environments. However, it has a highly nonlinear model with timevarying electrical and mechanical parameters making them difficult to control. Finite control set-predictive torque control (FCS-PTC) is an inherently suitable and a promising control method for the IM because FCS-PTC is easy to implement and has the ability to handle nonlinearities with the inclusion of constraints. In addition, the elimination of speed sensors increases the reliability of electric motor drives while reducing cost and hardware complexity. In this paper, a speed-sensorless FCS-PTC based IM drive system is designed in order to combine the aforementioned advantages. Unlike the current literature, to improve the torque response of conventional FCS-PTC, the load torque is also estimated by an adaptive fading extended Kalman filter and is fed back into the torque control loop. The results show that improved control performance is achieved.

Improved speed and load torque estimations with adaptive fading extended Kalman filter

Improved speed and load torque estimations with adaptive fading extended Kalman filter

The Adaptive Fading Extended Kalman Filter SOC Estimation Method for Lithium-ion Batteries

The adaptive fading extended Kalman filter (AFEKF) is proposed for the accuracy and convergent speed problem of state of charge (SOC) estimation. This filter algorithm combines the adaptive extended Kalman filter and fading extended Kalman filter, which solves the problem of uncertainty of system noise and over-reliance on old data. The equivalent circuit model is utilized and the variable forgetting factor recursive least square(VFFRLS) is adopted to identify the model parameters. Experimental test platform is established to validate the method. Results show that the method is able to effectively improve the convergent speed and accuracy of SOC estimation, and the SOC error is less than 2%.

The Estimation of State of Charge for Power Battery Packs used in Hybrid Electric Vehicle

The estimation of state of charge (SOC) for power battery packs is significant for a hybrid electric vehicle with providing data supports for the efficient and fine energy managements. In this brief, extended Kalman filter (EKF), multi-model extended Kalman filter (MMEKF) and adaptive fading extended Kalman filter (AFEKF) are used to estimate SOC respectively, then they are combined with a switching strategy to match their own features with that of SOC in different working areas. The estimation error of SOC is below 2.5 percent. And the strategy is verified to have good initialization stabilities and convergence behaviors.

Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

AbstractIn this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.The importance of the theoretical results and the contribution to estimation performance of the adaptation method are demonstrated interactively with the standard extended Kalman filter in the simulation part.

Research the estimated algorithm of the Doppler frequency shift based on adaptive fading EKF with the innovation consequence

The high-speed movement of missiles signal tracking system is strongly nonlinear systems. So while the system model can not be accurately established or the model error changes with time, the traditional extended Kalman filter (EKF) and its improved algorithm have large estimated errors and even bring filter divergence for estimating signal Doppler shift (frequency offset). In order to solve the above problem, a new adaptive fading extended Kalman filtering (AFEKF) method is put for ward based on the real time estimation of the covariance matrix of system noise using the innovation sequences. However, existing solver fading factor method too many constraints and the cumbersome calculation process, so it is not conducive to the missile signal tracking application. The small amount of calculation, and improve the reliability of the filtering algorithm, simulation results show the effectiveness of the method.

Stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

The extended Kalman filter is extensively used in nonlinear state estimation problems. As long as the system characteristics are correctly known, the extended Kalman filter gives the best performance. However, when the system information is partially known or incorrect, the extended Kalman filter may diverge or give biased estimates. An extensive number of works has been published to improve the performance of the extended Kalman filter. Many researchers have proposed the introduction of a forgetting factor, both into the Kalman filter and the extended Kalman filter, to improve the performance. However, there are 2 fundamental problems with this approach: the incorporation of the optimal forgetting factor into the (extended) Kalman filter and the selection of the optimal forgetting factor. These problems have not yet been fully resolved and are still open problems in the field. In this study, we propose a new adaptive fading extended Kalman filter with a matrix forgetting factor, and 2 methods are analyzed for the selection of the optimal forgetting factor. The stability properties of the proposed filter are also investigated. Results of the stability analysis show that the proposed filter is an exponential observer for nonlinear deterministic systems. Additionally, the convergence speed of the filter is simulated.

The stability analysis of the adaptive fading extended Kalman filter using the innovation covariance

The well-known conventional Kalman filter gives the optimal solution but to do so, it requires an accurate system model and exact stochastic information. However, in a number of practical situations, the system model and the stochastic information are incomplete. The Kalman filter with incomplete information may be degraded or even diverged. To solve this problem, a new adaptive fading filter using a forgetting factor has recently been proposed by Kim and co-authors. This paper analyzes the stability of the adaptive fading extended Kalman filter (AFEKF), which is a nonlinear filter form of the adaptive fading filter. The stability analysis of the AFEKF is based on the analysis result of Reif and co-authors for the EKF. From the analysis results, this paper shows the upper bounded condition of the error covariance for the filter stability and the bounded value of the estimation error. Keywords: Adaptive Kalman filter, forgetting factor, nonlinear filter, stability analysis.

Adaptive Two-Stage Extended Kalman Filter for a Fault-Tolerant INS-GPS Loosely Coupled System

The well-known conventional Kalman filter requires an accurate system model and exact stochastic information. But in a number of situations, the system model has an unknown bias, which may degrade the performance of the Kalman filter or may cause the filter to diverge. The effect of the unknown bias may be more pronounced on the extended Kalman filter (EKF), which is a nonlinear filter. The two-stage extended Kalman filter (TEKF) with respect to this problem has been receiving considerable attention for a long time. Recently, the optimal two-stage Kalman filter (TKF) for linear stochastic systems with a constant bias or a random bias has been proposed by several researchers. A TEKF can also be similarly derived as the optimal TKF. In the case of a random bias, the TEKF assumes that the information of a random bias is known. But the information of a random bias is unknown or partially known in general. To solve this problem, this paper proposes an adaptive two-stage extended Kalman filter (ATEKF) using an adaptive fading EKF. To verify the performance of the proposed ATEKF, the ATEKF is applied to the INS-GPS (inertial navigation system-Global Positioning System) loosely coupled system with an unknown fault bias. The proposed ATEKF tracked/estimated the unknown bias effectively although the information about the random bias was unknown.

Adaptive Two-Stage EKF for INS-GPS Loosely Coupled System with Unknown Fault Bias

This paper proposes an adaptive two-stage extended Kalman filter (ATEKF) for estimation of unknown fault bias in an INS-GPS loosely coupled system. The Kalman filtering technique requires complete specifications of both dynamical and statistical model parameters of the system. However, in a number of practical situations, these models may contain parameters, which may deviate from their nominal values by unknown random bias. This unknown random bias may seriously degrade the performance of the filter or cause a divergence of the filter. The two-stage extended Kalman filter (TEKF), which considers this problem in nonlinear system, has received considerable attention for a long time. The TEKF suggested until now assumes that the information of a random bias is known. But the information of a random bias is unknown or partially known in general. To solve this problem, this paper firstly proposes a new adaptive fading extended Kalman filter (AFEKF) that can be used for nonlinear system with incomplete information. Secondly, it proposes the ATEKF that can estimate unknown random bias by using the AFEKF. The proposed ATEKF is more effective than the TEKF for the estimation of the unknown random bias. The ATEKF is applied to the INS-GPS loosely coupled system with unknown fault bias.