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This study presents a preliminary investigation into robust vehicle mass estimation to enhance braking distance calculations for autonomous emergency braking systems. While active research has focused on headway distance estimation, primarily using computer vision systems such as cameras or light detection and ranging, there remains considerable room for improvement in reliable autonomous emergency braking systems. In this study, we propose a novel approach to vehicle mass estimation that leverages vehicle longitudinal dynamics, considering that vehicle mass influences braking distance as well as vehicle velocity. We employ an adaptive extended Kalman filter that combines the capabilities of the extended Kalman filter with a fading factor. This algorithm aims to estimate the time-varying vehicle mass using measurements of vehicle longitudinal velocity and driving torque inputs. Subsequently, the estimated vehicle mass serves as the basis for calculating more accurate braking distances. The proposed vehicle mass estimation algorithm is rigorously simulated using MATLAB/SIMULINK, and it undergoes an in-vehicle test.

With the increase in the amount of actual operating data on electric vehicles, how to analyze and process useful information from existing battery charging and discharging data and apply it to subsequent state estimation is worthy of in-depth thinking and practice by researchers. This article proposes a collaborative estimation architecture for SOC and SOH based on the 1RC equivalent circuit model, recursive least squares, and adaptive extended Kalman filtering algorithms (AEKF), which combine offline data processing with online applications. By applying offline data processing, OCV–SOC polynomial fitting and average polarization resistance were determined, which reduced the time required for basic data measurement and improved the accuracy of model parameter identification, while a recursive estimation combining micro- and macro-time-scales of AEKF was used for the online real-time estimation of the SOC and actual available capacity of batteries, in order to eliminate interference from measurement and process noise. The results of the simulated and experimental data validation indicate that the proposed algorithm is applicable to the lithium-ion batteries studied in this paper, the average SOC deviation is less than 1.5%, the maximum deviation is less than 2.02%, and the SOH estimation deviation is less than 1% under different driving conditions in the multi-temperature range. This study lays the foundation for further utilizing offline data and improving SOC and SOH collaborative estimation algorithms.

In this paper, a method for improving the performance of Complementary filter in the Attitude and Heading Reference System for estimating the orientation in accelerated movements is presented. Although existing complementary filters have advantages such as low computational volume, stability in different dynamic conditions, effectiveness at low sampling rates, and simplicity in the parameter setting process; But in the situation where the mobile device is exposed to non-gravitational accelerations, they show inappropriate performance. The proposed algorithm is designed based on the threshold-based path selection method and by adjusting the gain of complementary filters according to the size of the external acceleration, it improves the estimation of angles. In the following, the proposed algorithm is compared with the Extended Kalman filter and its three adaptive versions. The simulation and evaluation results of the proposed method show that the improved complementary filters achieve good performance in accelerated movements compared to the AEKF in the direction and alignment reference system.

Aiming at the problem that the noise information is fixed when the extended Kalman filter(EKF) algorithm estimates the state of charge(SOC) of lithium-ion battery, resulting in low estimation accuracy, an adaptive extended Kalman filter(AEKF) algorithm with automatic matching of noise information covariance is proposed. Firstly, the parameters are identified based on the dual polarization (DP)equivalent circuit model of the battery, and an accurate equivalent model is established. Then, the variation of noise covariance matrix of EKF filtering algorithm and AEKF filtering algorithm and the estimation effect of battery SOC are compared under dynamic stress test(DST) conditions. The results show that AEKF filtering algorithm has higher estimation accuracy. Finally, several groups of different SOC initial deviations are set to verify the strong robustness of AEKF filtering algorithm in estimating battery SOC.

The intermittent non-dispatchable power produced by Renewable Energy Sources (RESs) in distribution networks caused additional challenges in load forecasting due to the introduced uncertainties. Therefore, high-quality load forecasting is essential for distribution network planning and operation. Most of the work presented in literature focusing on Short-Term Load Forecasting (STLF) has paid little consideration to the intrinsic uncertainty associated with the load dataset. A few research studies focused on developing data filtering algorithm for the load forecasting process using approaches such as Kalman filter, which has good tracking capability in the presence of noise in the data collection process. To avoid the divergence of conventional Kalman filter and improve the system stability, Adaptive Extended Kalman Filter (AEKF) is introduced through incorporating a moving-window method with the Extended Kalman Filter (EKF). Nonetheless, the moving window adds an extra computational burden. In this regard, this paper employs the concept of Forgetting Factor AEKF (FFAEKF) for STLF in distribution networks to avoid the computational burden introduced by the AEKF. The forgetting factor improves the estimation accuracy and increases the system convergence when compared with the AEKF. In this paper, the AEKF and FFAEKF are compared in terms of their performance using Maximum Absolute Error (MaxAE) and Root Mean Square Error (RMSE). Matlab/Simulink platform is used to apply the AEKF and FFAEKF algorithms on the load dataset. Results have demonstrated that the FFAEKF improves the forecasting performance through providing less MaxAE and less RMSE. In which, the FFAEKF MaxAE and RMSE are reduced by two and three times, respectively, compared to the AEKF MaxAE and RMSE.

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.

Forward collision warning (FCW) is a critical technology to improve road safety and reduce traffic accidents. However, the existing multi-sensor fusion methods for FCW suffer from a high false alarm rate and missed alarm rate in complex weather and road environments. For these issues, this paper proposes a decision-level fusion collision warning strategy. The vision algorithm and radar tracking algorithm are improved in order to reduce the false alarm rate and omission rate of forward collision warning. Firstly, this paper proposes an information entropy-based memory index for an adaptive Kalman filter for radar target tracking that can adaptively adjust the noise model in a variety of complex environments. Then, for visual detection, the YOLOv5s model is enhanced in conjunction with the SKBAM (Selective Kernel and Bottleneck Attention Mechanism) designed in this paper to improve the accuracy of vehicle target detection. Finally, a decision-level fusion warning fusion strategy for millimeter-wave radar and vision fusion is proposed. The strategy effectively fuses the detection results of radar and vision and employs a minimum safe distance model to determine the potential danger ahead. Experiments are conducted under various weather and road conditions, and the experimental results show that the proposed algorithm reduces the false alarm rate by 11.619% and the missed alarm rate by 15.672% compared with the traditional algorithm.

State-of-charge (SoC) estimation is one of the core functions of battery energy management systems. An accurate SoC estimation can guarantee the safe and reliable operation of the batteries system. In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm in multiple time scales for 18,650 ternary lithium-ion batteries. Based on the slowly varying characteristics of lithium-ion batteries’ parameters and the quickly varying characteristics of the SoC parameter, firstly, the AEKF algorithm was used to online identify the parameters of the model of batteries with a macroscopic time scale. Secondly, the identified parameters were applied to the AUKF algorithm for SoC estimation of lithium-ion batteries with a microscopic time scale. Finally, the comparative simulation experiments were implemented, and the experimental results show the proposed joint algorithm has higher accuracy, adaptivity, robustness, and self-correction capability compared with the conventional algorithm.

Star images from star trackers are usually defocused to capture stars over an exposure time for better centroid measurements. While a satellite is maneuvering, the star point on the screen of the camera is affected by the satellite, which results in the degradation of centroid measurement accuracy. Additionally, this could result in a worse star vector outcome. For geostationary satellites, onboard thrusters are used to maintain or change orbit parameters under orbit disturbances. Since there is misalignment in the thruster and torque is generated by an impulsive shape signal from the torque command, it is difficult to generate target torque; in addition, it also impacts the star image because the impulsive torque creates a sudden change in the angular velocity in the satellite dynamics. This makes the noise of the star image non-Gaussian, which may require introducing a method for dealing with non-Gaussian measurement noise. To meet this goal, in this study, an adaptive extended Kalman filter is implemented to predict measurement vectors with predicted states. The GMM (Gaussian mixture model) is connected in this sequence, giving weighting parameters to each Gaussian density and resulting in the better prediction of measurement vectors. Simulation results show that the GMM-EKF exhibits a better performance than the EKF for attitude estimation, with 30% improvement in performance. Therefore, the GMM-EKF could be a more attractive approach for use with geostationary satellites during station-keeping maneuvers.

Cooperative localization (CL) is a popular research topic in the area of localization. Research is becoming more focused on Unmanned Aerial Vehicles (UAVs) and robots and less on pedestrians. This is because UAVs and robots can work in formation, but pedestrians cannot. In this study, we develop an adaptive decentralized cooperative localization (DCL) algorithm for a group of firefighters. Every member maintains a local filter and estimates the position and the relative measurement noise covariance is estimated rather than a fixed value. We derived the explicit expressions for the inter-member collaboration instead of using approximations. This method reduces the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) ranging on the CL, eliminating the need for fixed UWB anchors. The proposed algorithm was validated by two experiments designed in the building and forest environments. The experimental results demonstrate that the proposed algorithm improved the accuracy of localization, and the proposed algorithm suppressed the localization errors by 14.23% and 47.01% compared to the decentralized cooperative localization extended Kalman filter (DCLEKF) algorithm, respectively.

Load currents of batteries undergo real-time change depending on the operations of battery-powered devices, such as acceleration, deceleration, and sudden braking of electric vehicles. With the variation in current magnitude, parameters in an equivalent circuit model of the battery change depending on its chemical characteristics. Therefore, accurately identifying model parameters offline for various sizes and patterns of load currents with few experiments is difficult. Besides, there is a model uncertainty due to the difference between the linearity of the model structure and the nonlinearity of the real battery; hence, an experimentally obtained open-circuit voltage (OCV)-state-of-charge (SOC) curve has difficulty in providing an accurate OCV value for the model. Consequently, these difficulties lead to inaccuracies in the model and SOC estimation. In this article, novel methods are proposed to reduce these inaccuracies under time-varying current conditions, where the magnitude of the load current changes in real time. These are (1) an OCV-SOC curve estimation for reducing the model uncertainty and (2) a current-adaptive extended Kalman filter algorithm for decreasing the effect of inaccurately identified model parameters. The experimental results show that the maximum-absolute-errors of the proposed SOC estimation were reduced to

In this study, we present an alternative solution for detecting crack damages in rotating shafts under torque fluctuation by directly estimating the reduction in torsional shaft stiffness using the adaptive extended Kalman filter (AEKF) algorithm. A dynamic system model of a rotating shaft for designing AEKF was derived and implemented. An AEKF with a forgetting factor (λ) update was then designed to effectively estimate the time-varying parameter (torsional shaft stiffness) owing to cracks. Both simulation and experimental results demonstrated that the proposed estimation method could not only estimate the decrease in stiffness caused by a crack, but also quantitatively evaluate the fatigue crack growth by directly estimating the shaft torsional stiffness. Another advantage of the proposed approach is that it uses only two cost-effective rotational speed sensors and can be readily implemented in structural health monitoring systems of rotating machinery.

The most popular filtering method used for solving a Simultaneous Localization and Mapping is the Extended Kalman Filter. Essentially, it requires prior stochastic knowledge both the process and measurement noise statistic. In order to avoid this requirement, these noise statistics have been defined at the beginning and kept to be fixed for the whole process. Indeed, it will satisfy the desired robustness in the case of simulation. Oppositely, due to the continuous uncertainty affected by the dynamic system under time integration, this manner is strongly not recommended. The reason is, improperly defined noise will not only degrade the filter performance but also might lead the filter to divergence condition. For this reason, there has been a strong manner well-termed as an adaptive-based strategy that commonly used to equip the classical filter for having an ability to approximate the noise statistic. Of course, by knowing the closely responsive noise statistic, the robustness and accuracy of an EKF can increase. However, most of the existed Adaptive-EKF only considered that the process and measurement noise statistic are characteristically zero-mean and responsive covariances. Accordingly, the robustness of EKF can still be enhanced. This paper presents a proposed method named as a MAPAEKF-SLAM algorithm used for solving the SLAM problem of a mobile robot, Turtlebot2. Sequentially, a classical EKF was estimated using Maximum a Posteriori. However, due to the existence of unobserved value, EKF was also smoothed one time based on the fixed-interval smoothing method. This smoothing step aims to keep-up the derivation process under MAP creation. Realistically, this proposed method was simulated and compared to the conventional one. Finally, it has been showing better accuracy in terms of Root Mean Square Error (RMSE) of both Estimated Map Coordinate (EMC) and Estimated Path Coordinate (EPC).

Lithium-ion power batteries are widely used in the electric vehicle (EV) industry due to their high working voltage, high energy density, long cycle life, low self-discharge rate, and environmental protection. A multi-algorithm fusion method is proposed in this paper to estimate the battery state of charge (SOC), establishing the Thevenin model and collecting the terminal voltage residuals when the extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF), and H infinite filter (HIF) estimate the SOC separately. The residuals are fused by Bayesian probability and the weight is obtained, and then the SOC estimated value of the fusion algorithm is obtained from the weight. A comparative analysis of the estimation accuracy of a single algorithm and a fusion algorithm under two different working conditions is made. Experimental results show that the fusion algorithm is more robust in the whole process of SOC estimation, and its estimation accuracy is better than the EKF algorithm. The estimation result for the fusion algorithm under a Dynamic Stress Test (DST) is better than that under a Hybrid Pulse Power Characterization (HPPC) test. With the emergence of cloud batteries, the fusion algorithm is expected to realize real vehicle online application.

The limited lifetime of proton exchange membrane fuel cell (PEMFC) inhibits the further development of the fuel cell industry. Prediction is one of the most effective means for managing the lifetime of a fuel cell because it can assist in the implementation of mitigation actions before a vehicular fuel cell fails by estimating the residual lifetime. Therefore, this study aimed to develop a PEMFC lifetime prediction method for online applications. This paper presents the online prediction method developed for the residual lifetime of a vehicular fuel cell, which utilises data processing with an adaptive extended Kalman filter and a prediction formula. The formula considers different operating conditions and the external environment, which is in accord with the actual operating conditions of fuel cell vehicles. This method realises the online prediction of the residual lifetime of a vehicular fuel cell by updating weight coefficients for the operating conditions and environmental factors. This prediction method was validated and analysed using a simulation. The influences of key parameters on the stability and prediction accuracy of the algorithm were evaluated. The prediction method proposed in this paper can provide a reference for studies on fuel cell lifetime prediction.

To achieve frequency regulation, energy-storage systems (ESSs) are systems that monitor and maintain the grid frequency. In South Korea, the total installed capacity of battery ESSs (BESSs) is 376 MW, and these have been employed to achieve frequency regulation since 2015. When the frequency of a power grid is input, accurately estimating the state of charge (SOC) of a battery is difficult because it charges or discharges quickly according to the frequency regulation algorithm. If the SOC of a battery cannot be estimated, the battery can be used in either a high SOC or low SOC. This makes the battery unstable and reduces the safety of the ESS system. Therefore, it is important to precisely estimate the SOC. This paper proposes a technique to estimate the SOC in the test pattern of a frequency regulation ESS using extended Kalman filters. In addition, unlike the conventional extended Kalman filter input with a fixed-error covariance, the SOC is estimated using an adaptive extended Kalman filter (AEKF) whose error covariance is updated according to the input data. Noise is likely to exist in the environment of frequency regulation ESSs, and this makes battery-state estimation more difficult. Therefore, significant noise has been added to the frequency regulation test pattern, and this study compares and verifies the estimation performance of the proposed AEKF and a conventional extended Kalman filter using measurement data with severe noise.

This paper presents an adaptive information fusion method to improve the accuracy and reliability of the altitude measurement information for small unmanned aerial rotorcraft during the landing process. Focusing on the low measurement performance of sensors mounted on small unmanned aerial rotorcraft, a wavelet filter is applied as a pre-filter to attenuate the high frequency noises in the sensor output. Furthermore, to improve altitude information, an adaptive extended Kalman filter based on a maximum a posteriori criterion is proposed to estimate measurement noise covariance matrix in real time. Finally, the effectiveness of the proposed method is proved by static tests, hovering flight and autonomous landing flight tests.

The traditional speed prediction generally utilizes the Global Position System (GPS) and video images, and thus, the prediction precision mainly depends on environmental factors (i.e., weather, ionosphere, troposphere, air, and electromagnetic waves). We study the Radio Frequency Identification (RFID) data-driven vehicle speed prediction and proposed an improved extended kalman filter (i.e., the adaptive extended kalman filter, AEKF) algorithm. Firstly, the on-board RFID reader equipped in the vehicle reads the information (i.e., current speed and time) from the tag deployed on the road. Secondly, the received information is transmitted to the on-board information processing unit, and it is demodulated and decoded into available information. Finally, based on the vehicle state space model, the AEKF algorithm is proposed to predict vehicle speed and improve the prediction results, so that the vehicle speed gradually approaches the actual vehicle speed. The simulation results show that compared with the conventional extended kalman filter (EKF) algorithm, our proposed AEKF algorithm improves the dynamic performance of the filtering and better suppresses the filtering divergence process. Moreover, the AEKF algorithm also improves the precision of the Mean Square Error (MSE) and Mean Absolute Error (MAE) by 57.4% and 32.4%, respectively.

To achieve accurate state-of-charge (SoC) estimation for LiFePO4 (lithium iron phosphate) batteries under harsh conditions, this paper resorts to the Peukert’s law to accommodate different temperatures and load excitations. By analyzing battery heat generation and dissipation, a thermal evolution model (TEM) is elaborated and exploited for on-line parameter identification of the equivalent circuit model (ECM). Then, a SoC estimation framework is proposed based on the Adaptive Extended Kalman Filter (AEKF) algorithm. Experimental results on a LiFePO4 pack subject to the Federal Urban Driving Schedule (FUDS) profile under different temperatures and initial states suggest that the proposed SoC estimator provides good robustness and accuracy against changing temperature and highly dynamic loads.

In practical electric vehicle applications, the noise of original discharging/charging voltage (DCV) signals are inevitable, which comes from electromagnetic interference and the measurement noise of the sensors. To solve such problems, the Discrete Wavelet Transform (DWT) based state of charge (SOC) estimation method is proposed in this paper. Through a multi-resolution analysis, the original DCV signals with noise are decomposed into different frequency sub-bands. The desired de-noised DCV signals are then reconstructed by utilizing the inverse discrete wavelet transform, based on the sure rule. With the de-noised DCV signal, the SOC and the parameters are obtained using the adaptive extended Kalman Filter algorithm, and the adaptive forgetting factor recursive least square method. Simulation and experimental results show that the SOC estimation error is less than 1%, which indicates an effective improvement in SOC estimation accuracy.

Abstract: Non-uniformity of Lithium-ion cells in a battery pack is inevitable and has become the bottleneck to the pack capacity, especially in the fast charging process. Therefore, a balancing approach is essentially required. This paper proposes an active online cell balancing approach in a fast charging process using the state of charge (SOC) as balancing criterion. The goal of this approach is to complete pack balancing within the limited charging time. An adaptive extended Kalman filter (AEKF) is applied to estimate the pack cell SOC during the charging process to obtain accurate results under modeling errors and measurement noises. To implement the proposed AEKF, only one additional current sensor is required to obtain the current of each cell required for the SOC estimation. An experimental platform is established to verify the effectiveness of the proposed approach. The results show that the proposed balancing approach with the SOC as a balancing criterion can overcome the challenges of non-uniformity and flat voltage plateau and charge more capacity into a LiFePO4 battery pack than those with the terminal voltage as a balancing criterion in the fast charging process.