Dual Kalman Filter Algorithm Research Articles

An improved multi innovation adaptive robust dual kalman filter algorithm for estimating battery state

An improved multi innovation adaptive robust dual kalman filter algorithm for estimating battery state

High‐precision state of charge estimation of lithium‐ion batteries based on improved particle swarm optimization‐backpropagation neural network‐dual extended Kalman filtering

SummaryHigh‐precision state of charge (SOC) estimation is essential for battery management systems (BMSs). In this paper, a new SOC estimation method is proposed. The dual Kalman filter algorithm and backpropagation neural network (particle swarm optimization ‐ backpropagation neural network ‐ double extended Kalman filter [PSO‐BPNN‐DEKF]) are combined to estimate and correct the SOC of lithium‐ion batteries, in which the initial weight and threshold of the BPNN are optimized by particle swarm optimization algorithm. Based on the second‐order RC equivalent circuit model, parameter identification is carried out using the adaptive forgetting factor least squares (AFFRLS) method. Online parameter updates and SOC estimation are realized by DEKF algorithm. Then, the trained PSO‐BPNN is used to predict the SOC estimation error in real time, and the SOC estimation value is corrected by adding prediction errors. The SOC estimates before and after correction under Beijing Dynamic Stress Test (BBDST), dynamic stress test (DST), and hybrid pulse power characterization (HPPC) were compared. Under BBDST, DST, and HPPC tests, the maximum errors of the corrected SOC estimates are 0.0107, 0.0090, and 0.0147, respectively. The root mean square error (RMSE) of the corrected SOC estimates decreased by 94.02%, 83.18%, and 88.03%, respectively, compared with the extended Kalman filtering (EKF). The mean absolute error (MAE) of the corrected SOC estimates remained around 0.1% for all the BBDST dynamic operating conditions at different temperatures. The experimental results demonstrate the accuracy, effectiveness, and temperature adaptability of the proposed algorithm for SOC estimation under complex conditions of lithium‐ion batteries.

A new Kalman filter approach for structural parameter tracking: Application to the monitoring of damaging structures tested on shaking-tables

In this paper, a new data assimilation framework for correcting finite element models from datasets acquired on-the-fly in low-frequency dynamics is presented. An Unscented Kalman filter algorithm is coupled with a modified Constitutive Relation Error (mCRE) observer, leading to a Modified Dual Kalman Filter algorithm (MDKF). Built as a Hermitian data-to-model distance written in the frequency domain enriched with a CRE residual accounting for model bias, the mCRE functional has shown interesting assets for model updating purposes, in particular enhanced convexity and robustness to measurement noise. The proposed data assimilation strategy integrates the latter through a metric change in the measurement update equation. It thus differs from classical nonlinear Kalman filtering for parameter estimation as the comparison between measurements and model predictions is achieved through the mCRE functional itself. Besides, the calibration of MDKF internal parameters is facilitated by a set of general guidelines that ensure the performance of the algorithm. The methodology is applied to two earthquake engineering examples. The performances of MDKF are first assessed using synthetic measurements from a plane frame subjected to random ground acceleration. Actual measurements from the SMART2013 benchmark are then assimilated in a real-time context to monitor the eigenfrequency drop of a reinforced-concrete structure submitted to a sequence of gradually damaging shaking-table tests. The nice correlation with (i) data-driven identification results, and (ii) sequential mCRE-based model updating results, illustrates the relevance of this new approach and suggests promising use of MDKF for on-the-fly adaptive control prospects and applications involving data-to-model interaction.

Low-Cost Parameter Estimation Approach for Modular Converters and Reconfigurable Battery Systems Using Dual Kalman Filter

Modular converters or reconfigurable battery energy storage systems are a promising approach to eliminate the dependence on the weakest element in previously hard-wired battery packs and to combine heterogeneous batteries (so-called mixed-battery systems). But their need for expensive sensors and complex monitoring as well as control subsystems hinders their progress. Estimating the parameters of each module can substantially reduce the number of required sensors and/or communication components. However, the existing estimation methods for cascaded modular circuits neglect important parameters such as the internal resistance of the battery, resulting in large systematic errors and bias. This article proposes an online estimator based on a dual Kalman filter (DKF) that exploits the slow dynamics of the battery compared to the load. The DKF algorithm estimates the open-circuit voltage (OCV) and internal resistance of each module by measuring only the output voltage and current of the system. Compared with the state-of-the-art, the proposed method is simpler and cheaper with only two sensors compared to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \geq N + 2$</tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is the number of modules). Furthermore, the proposed algorithm achieves a fast convergence through an optimal learning rate. Simulations and experimental results confirm the ability of the proposed approach, achieving <1.5% and <5% estimation error for OCV and the internal resistance, respectively.

A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter

The control strategy of electric vehicles mainly depends on the power battery state-of-charge estimation. One of the most important issues is the power lithium-ion battery state-of-charge (SOC) estimation. Compare with the extended Kalman filter algorithm, this paper proposed a novel adaptive square root extended Kalman filter together with the Thevenin equivalent circuit model which can solve the problem of filtering divergence caused by computer rounding errors. It uses Sage-Husa adaptive filter to update the noise variable, and performs square root decomposition on the covariance matrix to ensure its non-negative definiteness. Moreover, a multi-scale dual Kalman filter algorithm is used for joint estimation of SOC and capacity; the forgetting factor recursive least-square method is used for parameter identification. To verify the feasibility of the algorithm under complicated operating conditions, different types of dynamic working conditions are performed on the ternary lithium-ion battery. The proposed algorithm has robust and accurate SOC estimation results and can eliminate computer rounding errors to improve adaptability compared to the conventional extended Kalman filter algorithm.

State of Charge Estimation for Lithium-Ion Batteries Based on Temperature-Dependent Second-Order RC Model

Accurate estimation of battery state of charge (SOC) is of great significance for extending battery life, improving battery utilization, and ensuring battery safety. Aiming to improve the accuracy of SOC estimation, in this paper, a temperature-dependent second-order RC equivalent circuit model is established for lithium-ion batteries, based on the battery electrical characteristics at different ambient temperatures. Then, a dual Kalman filter algorithm is proposed to estimate the battery SOC, using the proposed equivalent circuit model. The SOC estimation results are compared with the SOC value obtained from experiments, and the estimation errors under different temperature conditions are found to be within ±0.4%. These results prove that the proposed SOC estimation algorithm, based on a temperature-dependent second-order RC equivalent circuit model, provides accurate SOC estimation performance with high temperature adaptability and robustness.

State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter

Accurate estimation of the battery state of charge (SOC) is of great significance for enhancing its service life and safety. In this study, based on the fractional-order equivalent circuit model of lithium-ion battery, the SOC estimation methods using dual Kalman filter (DKF) and dual extended Kalman filter (DEKF) are simulated and compared, in terms of model accuracy and SOC estimation accuracy. Then, combining the advantages of the DKF and DEKF algorithms, an SOC estimation algorithm based on adaptive double Kalman filter is proposed. This algorithm uses the recursive least squares (RLS) method to update the battery model parameters online in real time, and employs the DKF algorithm to filter the SOC twice to reduce the interferences from the battery model error and the current measurement error. In the experimental studies, the measured SOC values are compared with the estimated SOC values produced by the proposed algorithm. The comparison results show that SOC estimation error of the proposed algorithm is within the range of ±0.01 under most test conditions, and it can automatically correct SOC to true value in the presence of system errors. Thus, the validity, accuracy, robustness and adaptability of the proposed algorithm under different operation conditions are verified.

Mobile robot indoor dual Kalman filter localisation based on inertial measurement and stereo vision

This study presents a novel navigation method designed to support a real-time, efficient, accurate indoor localisation for mobile robot system. It is applicable for inertial measurement units (IMU) consisting of gyroscopes, accelerometers, and magnetic besides stereo vision (SV). The current indoor mobile robot localisation technology adopts traditional active sensing devices such as laser, and ultrasonic method which belongs to the signal of localisation and navigation method which has low efficiency complex structure, and poor anti-interference ability. Through dual Kalman filter (DKF) algorithm, the accumulated error of gyroscope can be reduced, while combining with SV, mobile robot binocular SV orientation of inertial location can be realised under the DKF mechanism, which is introduced. First, high precision posture information of mobile robot can be obtained using fusing Kalman filter algorithm of accelerometer, gyroscope and magnetometer data. Second, inertial measurement precision can be optimised using Kalman filtering algorithm combined with machine vision localisation algorithm. The results indicate that the method achieves the levels of accuracy location comparable with that of the IMU/SV fusion algorithm; <0.0066 static RMS error, <0.0056 dynamic RMS error. The mobile robot using DKF algorithm of inertial navigation and SV indoor localisation is feasible.

On-board identification of tyre cornering stiffness using dual Kalman filter and GPS

Cornering stiffness is one of the important vehicle parameters for steering control of a vehicle. Accurate vehicle parameters are essential for a high performance of vehicle control because vehicle control is significantly affected by variations in vehicle parameters. In this study, a novel identification method is proposed using a dual Kalman filter algorithm and a GPS (global positioning system) measurement system to estimate the cornering stiffness for on-board identification. Performance of the identification method is examined with experiments, and the estimation results show that this identification method is effective on both a flat road and a banked curve road.

The Analysis of Modeling of Dual Kalman Filter in Lithium Battery SOC Estimates

In order to online estimate the state of charge (SOC) of lithium-ion battery pack in this paper. This article establishes a dual Kalman filter (DEKF) algorithms for it. We establish a battery model of state space expression based on Thevenin battery model and Kalman filter algorithms. We use least square method and DEKF algorithms to identify the parameters of battery model in order to improve the accuracy of battery model. It can make battery model reflect true state of internal battery well. It introduces the principle which is dual Kalman filter algorithm online estimate the state of charge. Finally, it verifies DEKF algorithm has better convergence and robustness which can effectively resolve the problems of initial estimates error and cumulative error issue.

1B35 Identification of the yaw moment of inertia of vehicle in real-time using GPS sensor

Advanced traffic systems, for instance automatic platooning control, are required to solve environmental problems and traffic problems. The vehicles under the automatic platooning control can travel automatically with maintaining short distances between vehicles and following preceding vehicles. However, precise control is necessary to ensure driving safety because vehicles controlled by automatic platooning maintain short distances between vehicles. Accurate parameters are important to conduct high performance of automatic control because control system is affected by a change of vehicle parameters. Especially parameters of trucks are changed widely according to the loaded condition. Performance of the automatic platooning control is affected by changing of vehicle parameters. Therefore the accurate parameters is required in real-time. Among the vehicle parameters, the yaw moment of inertia is an important parameter for the vehicle steering control, cornering or lane change. A method is proposed using Dual Kalman filter algorithm, which estimates state of vehicle and parameters of vehicle simultaneously, to identify the yaw moment of inertia of vehicle on travelling. Performance to estimate the yaw moment of inertia was evaluated through the experiment using the GPS sensor. The yaw moment of inertia was estimated by the proposed method. The results of the experiment show the proposed method is effective for the identification of the yaw moment of inertia. Validity of the proposed method is confirmed through the examination.

2205 走行時における車両のコーナリングスティフネスとヨー慣性モメントの同定(OS3-1 自動車の制御,OS3 交通・物流システムの制御,オーガナイズド・セッション(OS))

Accurate parameters are necessary to accomplish precise driving control of vehicle. Identification of vehicle parameters is important to get accurate parameters because vehicle control is affected by the vehicle parameters. An identification method is proposed using Dual Kalman filter algorithm and GPS sensor for the simultaneous estimation of vehicle parameters, i.e. yaw moment of inertia and cornering stiffness. Performance of the proposed identification method is evaluated through examination. Possibility of the proposed identification method is confirmed by examining the estimated results.