Adaptive Unscented Kalman Filter Research Articles

A Dual Kalman Filtering Algorithm for Estimating the SOC of Lithium-Ion Batteries with LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 Cathode Based on Multi-Innovation and Schmidt Orthogonal Transformation

Accurately estimating the state of charge (SOC) is imperative for ensuring safe and dependable battery utilization. However, accurately calculating SOC for LiMn0.6Fe0.4PO4/LiNi0.5Co0.2Mn0.3O2 (LMFP/NCM) batteries can be challenging due to their two flat voltage platforms and significant temperature dependence. To improve estimation accuracy, a battery SOC estimation method based on a dual Kalman filter (DKF) was proposed. The adaptive unscented Kalman filter (AUKF) process starts with the introduction of Schmidt orthogonal transform, which is subsequently employed in the algorithm’s sampling point selection procedure to mitigate computational complexity. Moreover, the utilization of the multi-innovation theory serves to enhance the accuracy of algorithmic estimation. The extended Kalman filter is used to identify the parameters of the equivalent circuit model online while simultaneously carrying out battery SOC estimation. This approach mitigates the impact of variations in battery model parameters during charging and discharging processes. Under complex conditions, the algorithm’s average error is less than 0.53%, demonstrating its effectiveness in improving SOC estimation accuracy as evidenced by comparison between experiment and simulation results. It has reference significance for optimizing LMFP/NCM battery SOC estimation.

Active disturbance rejection control strategy for PEMFC oxygen excess ratio based on adaptive internal state estimation using unscented Kalman filter

The effectiveness of gas flow control will significantly affect the output performance of the PEMFC, however, the gas state inside the cathode is difficult to observe due to the tightness and safety requirements. To achieve effective control of PEMFC in the presence of parameter bias, an improved active disturbance rejection control (IADRC) strategy based on the adaptive unscented Kalman filter (AUKF) is proposed in this paper. A dynamic model of the air supply system with seven state variables was developed, capable of describing the non-linear characteristics of the system, and a semi-empirical air compressor model was designed to match the actual operating characteristics based on the experimental data set. Based on the standard UKF algorithm, an adaptive process was designed using the moving window method to dynamically adjust the noise characteristics to reduce the state estimation bias caused by changes in system structural parameters during service. Finally, the algorithmic structure of the ADRC was optimized to achieve an accurate estimation of the total system perturbation using cascaded extended state observers (ESO), thus achieving a control effect with the advantages of less overshoot and response speed based on fewer tuning parameters. Simulation results show that the proposed strategy can effectively improve the system response and robustness to structural parameters, and has great control effects under real road conditions.

An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

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.

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

SummaryAccurate state of charge (SOC) estimation is based on a precise battery model and is the focus of the battery management system (BMS). First, based on the second‐order RC equivalent circuit model and Grunwald–Letnikov (G‐L) definition, the high‐precision fractional‐order hysteresis‐equivalent circuit model (FH‐ECM) is established considering the open‐circuit voltage hysteresis effect. Then, the global parameters of the battery model are identified using a particle swarm algorithm optimized by the genetic algorithm (GA‐PSO). Third, a fractional‐order adaptive unscented Kalman filter (FOAUKF) algorithm is derived to estimate the SOC of lithium‐ion batteries. Finally, the feasibility of the model and algorithm is verified under complex working conditions. Under the dynamic stress test (DST) condition, the accuracy of model terminal voltage has been improved by 37.83%, and the error of SOC estimation has been reduced by 11.28%. Under Beijing bus dynamic stress test (BBDST) condition, the model terminal voltage accuracy has been improved by 51.44%, and the SOC estimation error has been reduced by 35.71%. The experimental results fully confirm the accuracy of the fractional‐order hysteresis‐equivalent circuit modeling method.

Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter

This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.

Robust adaptive Unscented Kalman Filter with gross error detection and identification for power system forecasting-aided state estimation

Owing to the effect of measurement noise and sudden changes in the power system, the robustness of state estimation for power system becomes very important. The Unscented Kalman Filter (UKF) is widely used for state estimation. However, it does not consider the influence of different kinds of gross errors. To better deal with gross errors, a robust adaptive UKF with gross error detection and identification (RAUKF-GEDI) is proposed, which uses the robust generalized correntropy loss in the UKF framework. The RAUKF-GEDI detects gross errors by hypothesis testing, and then uses the moving window to identify and classify three kinds of gross errors. Subsequently, the RAUKF-GEDI estimates the magnitudes of the gross errors to further compensate the measurements, and finally uses the compensated measurements to re-estimate the state to obtain precise estimated states. In addition, RAUKF-GEDI also introduces adaptive covariance matching method for state estimation. The RAUKF-GEDI is applied to the state estimation for power systems where the measurements are contaminated by three kinds of gross errors. Finally, the RAUKF-GEDI is also applied to the practical power system of Zhejiang Juchuang Smart Technology Company Park. The results show that the RAUKF-GEDI can detect and identify gross errors and enhance the robustness of UKF.

Hybrid Indoor Positioning System Based on Acoustic Ranging and Wi-Fi Fingerprinting under NLOS Environments

An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and time-varying measurement errors of different location sources. This paper introduces a hybrid indoor positioning system (H-IPS) that combines acoustic ranging, Wi-Fi fingerprinting, and low-cost sensors. This system is designed specifically for large-scale indoor environments with non-line-of-sight (NLOS) conditions. To improve the accuracy in estimating pedestrian motion trajectory, a data and model dual-driven (DMDD) model is proposed to integrate the inertial navigation system (INS) mechanization and the deep learning-based speed estimator. Additionally, a double-weighted K-nearest neighbor matching algorithm enhanced the accuracy of Wi-Fi fingerprinting and scene recognition. The detected scene results were then utilized for NLOS detection and estimation of acoustic ranging results. Finally, an adaptive unscented Kalman filter (AUKF) was developed to provide universal positioning performance, which further improved by the Wi-Fi accuracy indicator and acoustic drift estimator. The experimental results demonstrate that the presented H-IPS achieves precise positioning under NLOS scenes, with meter-level accuracy attainable within the coverage range of acoustic signals.

Dynamic state estimation with additive noise for load frequency control using bilateral fuzzy adaptive unscented Kalman filter

Dynamic state estimation with additive noise for load frequency control using bilateral fuzzy adaptive unscented Kalman filter

Vehicle State Estimation Based on Sage–Husa Adaptive Unscented Kalman Filtering

To combat the impacts of uncertain noise on the estimation of vehicle state parameters and the high cost of sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The design equation of the state observer is derived by establishing the vehicle’s three degrees-of-freedom (DOF) model. On this basis, the Sage–Husa algorithm and unscented Kalman filter (UKF) are combined to form the AUKF algorithm to adaptively update the statistical feature estimation of measurement noise. Finally, a co-simulation using Carsim and Matlab/Simulink confirms the algorithm is effective and reasonable. The simulation results demonstrate that the proposed algorithm, compared with the UKF algorithm, increases estimation accuracy by 19.13%, 32.8%, and 39.46% in yaw rate, side-slip angle, and longitudinal velocity, respectively. This is because the proposed algorithm adaptively adjusts the measurement noise covariance matrix, which can estimate the state parameters of the vehicle more accurately.

Initial value compensation of an adaptive fractional-order unscented Kalman filter for estimating the state of charge of lithium-ion batteries

This study indicates an adaptive fractional-order unscented Kalman filter (UKF) with unknown parameters and order, which provides a higher accuracy of SOC estimation. In order to solve the problem of reduced estimation accuracy caused by the low order in the adaptive estimation, this paper adopts the augmented vector method for the initial value compensation (IVC) to reduce the impact of initial values on the estimation accuracy. Because the parameters of the LIB change with the charge and discharge of the battery use and the working conditions, and to increase the ability of the adaptive SOC estimation, this paper studies an adaptive unscented Kalman filter (AUKF) algorithm for the joint estimation of state, parameters, and order of model based on IVC. The algorithm can achieve the adaptive estimation of SOC under different working conditions. Finally, several sets of experiments are conducted under different working conditions to verify that the AUKF algorithm with IVC can achieve the effective estimation of SOC, and the adaptive effect and the estimation error of IVC on the algorithm are better than those without IVC.

USV Parameter Estimation: Adaptive Unscented Kalman Filter-Based Approach

In this article, a new kind of adaptive unscented Kalman filter (AUKF) is proposed to deal with the parameter estimation problem for a class of nonlinear unmanned surface vessel (USV) models with unknown statistical characteristics of process noises. Specifically, the considered parameter estimation problem is first transformed into the state estimation problem by extending eighteen parameters and three unknown inputs into augmented states for the USVs. With the help of such a transformation, the unknown inputs including disturbances and modeling errors are estimated effectively, and employed to construct the estimators such that the effect of these unknown inputs on parameter estimation can be significantly suppressed. Under the condition that the structure of the covariance matrix of the process noise is available, an adaptive law is further designed to estimate such a high-dimensional covariance matrix where the covariance estimation errors can be reduced. Finally, the proposed estimation approach is verified via performing the practical experiment as well as numerical simulations.

Vehicle state estimation using a maximum likelihood based robust adaptive extended kalman filter considering unknown white Gaussian process and measurement noise signal

The stochastic nature of noise signals affects the vehicle’s internal states and the outputs, resulting in the difficulty in estimation. The unknown or time-varying nature of noise signals if not taken into account for estimation, the results will diverge and be highly deteriorated. In this paper, a maximum likelihood principle (MLP) based adaptive robust extended kalman filter for estimating the states of the adopted non-linear vehicle model is proposed. An observability test is done for the purpose of estimation. A covariance matching (CM) based robust adaptive high forgetting factor based fault tolerant technique is also employed on the robust adaptive extended and unscented kalman filters for comparison purpose. The Robustness of the filter is analyzed by varying the parameter of the vehicle through a local sensitivity analysis. The results show that the MLP based approach to the extended kalman filter performs well in three simulations for sinusoidal steering, Double Lance Change, J-Turn, Fishhook, Slalom maneuver in comparison to robust adaptive unscented kalman filter. Friction coefficient of 0.8 (dry road) and 0.4 (wet road) is chosen for the simulation. The sideslip angle RMSE value for MLP based estimation is obtained as 2.62e-05, 4.545e-06 for Sine and DLC maneuver.

A novel approach to estimate the state of charge for lithium-ion battery under different temperatures incorporating open circuit voltage online identification

The open circuit voltage (OCV) is inherently related to the state of charge (SoC) and their relationships under different temperatures are crucial for accurate SoC estimation for the lithium-ion battery based on the equivalent circuit model (ECM), which requires long time-consuming offline OCV tests. In this research, an online closed-loop SoC estimation without conducting OCV tests is put forward. Firstly, the parameters of the Thevenin model for the lithium-ion battery are identified online through the adaptive recursive least square with forgetting factor (AFFRLS). Afterwards, the adaptive unscented Kalman filter (AUKF) is applied to achieve the online closed-loop SoC estimation with the identified parameters. Subsequently, the relationships between the OCV and SoC under different temperatures have been reconstructed online. The proposed method is validated under different temperatures. The research reveals this method can accurately estimate the SoC and is robust to the initial SoC values in wide temperature range.

A tracking method for GEO space targets based on adaptive memory attenuation UKF

Aiming at the problem that the nonlinearity of the tracking state equation and the problem of strong noise in the measurement process in the process of tracking the target, a non-cooperative target tracking method in geostationary earth orbit based on adaptive memory attenuation UKF is proposed. Firstly, the relative motion model of the non-cooperative target is constructed, and the state equation and measurement equation are established. Use the memory attenuation adjustment factor to enhance the correction effect of the new measurement on the state estimation, improve the adaptability of the filtering method to nonlinearity and noise, and finally the relative motion trajectory of the space non-cooperative target is obtained. Finally, the standard UKF algorithm and improved UKF algorithm are applied to target tracking and compared by simulation. The deviation of the three coordinate axis components of the actual motion trajectory and the predicted trajectory of the space target is used as the evaluation index. The simulation results show that the filtering accuracy of the improved UKF algorithm is significantly improved, while taking into account the real-time performance of on-orbit tracking.

State estimation of radar tracking system using a robust adaptive unscented Kalman filter

State estimation of radar tracking system using a robust adaptive unscented Kalman filter

Co-estimation of lithium-ion battery state-of-charge and state-of-health based on fractional-order model

Accurate estimation of lithium-ion battery state of charge and state of health has been the focus of research in battery management systems. Effectively improving the estimation accuracy of battery state parameters is crucial for the safe and stable driving of electric vehicles. In this paper, based on the fractional-order model, a multi-scale cooperative estimation method based on the dual adaptive Unscented Kalman filter is proposed. Firstly, according to the fast and slow time-varying characteristics of the measured parameters, the dual filters perform state estimation of SOC and SOH according to different time scales, alternately updating the information parameters by nesting them with each other and optimizing the noise terms with an adaptive algorithm. Meanwhile, the influence of temperature on the battery parameters is considered, and experimental analysis of the battery is carried out using a variety of different temperatures and operating conditions, as well as the accuracy and stability of the algorithm are verified by simulation. The experimental and simulation results show that the co-estimation method has good accuracy and robustness in both high and low-temperature environments.

Noise-immune state of charge estimation for lithium-ion batteries based on optimized dynamic model and improved adaptive unscented Kalman filter under wide temperature range

Due to diverse vehicle driving conditions, it is difficult to ensure that lithium-ion batteries operate at a fixed ambient temperature. Meanwhile, the environmental noise accompanying vehicle driving can also affect the data sampling accuracy of batteries. To achieve a high-robustness state of charge (SOC) estimation for lithium-ion batteries at different ambient temperatures with noise effects, a noise-immune SOC estimation method under a wide ambient temperature range is proposed. First, based on the moving window principle, a high-precision fitting model of the open-circuit voltage is established. Second, based on the Multi-Verse Optimizer, a new dynamic model parameter identification method is proposed, while the complete optimized dynamic battery model is established relying on the data of Dynamic Stress Test at different temperatures. Third, to enhance the SOC estimation accuracy and stability, based on adaptive theory and matrix diagonalization theory, the unscented Kalman filter is improved. Finally, the effectiveness and robustness of the proposed method are validated under two other working conditions with random noise added at various temperatures. Under every set of working conditions at all temperatures, the SOC estimation results can maintain stability after converging to the reference SOC, while root mean square errors and mean absolute errors under all cases do not exceed 1.5 %.

State Parameter Estimation of Intelligent Vehicles Based on an Adaptive Unscented Kalman Filter

The premise of vehicle intelligent decision making is to obtain vehicle motion state parameters accurately and in real-time. Several state parameters cannot be measured directly by vehicle sensors, so estimation algorithms based on filtering are effective solutions. The most representative algorithm is the Kalman filter, especially the standard unscented Kalman filter (UKF) that has been widely used in vehicle state estimation because of its superiority in dealing with nonlinear filtering problems. However, although the UKF assumes that the noise statistics of the system are known, due to the complex and changeable operating conditions, sensor aging and other factors, these noises vary. In order to realize high-precision vehicle state estimation, a noise-adaptive UKF algorithm is proposed in this article. The maximum a posteriori (MAP) algorithm is used to dynamically update the noise of the vehicle system, and it is embedded into the update step of the UKF to form an adaptive unscented Kalman filter (AUKF). The system will dynamically update the noise when noise statistics are unknown and prevent filter divergence by adjusting the mean and covariance of the estimated noise to improve accuracy. On this basis, the proposed method is verified by the joint simulation of CarSim and Matlab/Simulink, confirming that the AUKF performs better than the standard UKF in estimation accuracy and stability under different degrees of noise disturbance, and the estimation accuracy for the yaw rate, side slip angle and longitudinal velocity is improved by 20.08%, 40.98% and 89.91%, respectively.

Leveraging neural network uncertainty in adaptive unscented Kalman Filter for spacecraft pose estimation

This paper introduces an adaptive Convolutional Neural Network (CNN)-based Unscented Kalman Filter for the pose estimation of uncooperative spacecraft. The validation is carried out at Stanford’s robotic Testbed for Rendezvous and Optical Navigation on the Satellite Hardware-In-the-loop Rendezvous Trajectories (SHIRT) dataset, which simulates vision-based rendezvous trajectories of a servicer spacecraft to PRISMA’s Tango spacecraft. The proposed navigation system is stress-tested on synthetic as well as realistic lab imagery by simulating space-like illumination conditions on-ground. The validation is performed at different levels of the navigation system by first training and testing the adopted CNN on SPEED+, Stanford’s spacecraft pose estimation dataset with specific emphasis on domain shift between a synthetic domain and an Hardware-In-the-Loop domain. A novel data augmentation scheme based on light randomization is proposed to improve the CNN robustness under adverse viewing conditions, reaching centimeter-level and 10 degree-level pose errors in 80% of the SPEED+ lab images. Next, the entire navigation system is tested on the SHIRT dataset. Results indicate that the inclusion of a new scheme to adaptively scale the heatmaps-based measurement error covariance based on filter innovations improves filter robustness by returning centimeter-level position errors and moderate attitude accuracies, suggesting that a proper representation of the measurements uncertainty combined with an adaptive measurement error covariance is key in improving the navigation robustness.

State of energy estimation of the echelon-use lithium-ion battery based on Takagi–Sugeno fuzzy optimization

Owing to the degradation of an echelon-use lithium-ion battery (EULIB), the Ohmic internal resistance (OIR) and actual capacity (AE) have both changed greatly, and the state of energy (SOE) can more accurately represent the state of a EULIB than the state of charge (SOC) because of the working voltage. To improve the accuracy and adaptability of SOE estimation, in the paper, we study the energy state estimation of a EULIB. First, the four-order resistor–capacitance equivalent model of a EULIB is established, and an unscented transformation is introduced to further improve the estimation accuracy of the SOE. Second, a EULIB’s SOE is estimated based on adaptive unscented Kalman filter (AUKF), and the OIR and AE of a EULIB are estimated based on the AUKF. Third, a Takagi–Sugeno fuzzy model is introduced to optimize the OIR and AE of the EULIB, and the SOE estimation method is established based on an adaptive dual unscented Kalman filter (ADUKF). Through simulation experiments, verification, and comparison, energy decayed to 80%, 60%, and 40% of the rated energy, respectively, even with a large initial error; with the initial value of the SOE starting at 100%, 60%, or 20%, the estimated SOE can track the actual value. It can be seen that the method has a strong adaptive ability, and the estimation accuracy error is less than 1.0%, indicating that the algorithm has high accuracy. The method presented in this paper provides a new perspective for SOE estimation of EULIBs.