This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre-road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre-road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10821337 | PMC |
| http://dx.doi.org/10.3390/s24020436 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles.
Sensors (Basel)
January 2025
College of Mechatronics Engineering, North University of China, Taiyuan 030051, China.
To enhance the positioning accuracy of autonomous underwater vehicles (AUVs), a new adaptive filtering algorithm (RHAUKF) is proposed. The most widely used filtering algorithm is the traditional Unscented Kalman Filter or the Adaptive Robust UKF (ARUKF). Excessive noise interference may cause a decrease in filtering accuracy and is highly likely to result in divergence by means of the traditional Unscented Kalman Filter, resulting in an increase in uncertainty factors during submersible mission execution.
View Article and Find Full Text PDFOptimizing papermaking wastewater treatment by predicting effluent quality with node-level capsule graph neural networks.
Environ Monit Assess
January 2025
Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India.
Papermaking wastewater consists of a sizable amount of industrial wastewater; hence, real-time access to precise and trustworthy effluent indices is crucial. Because wastewater treatment processes are complicated, nonlinear, and time-varying, it is essential to adequately monitor critical quality indices, especially chemical oxygen demand (COD). Traditional models for predicting COD often struggle with sensitivity to parameter tuning and lack interpretability, underscoring the need for improvement in industrial wastewater treatment.
View Article and Find Full Text PDFDataset of noise signals generated by smart attackers for disrupting state of health and state of charge estimations of battery energy storage systems.
Data Brief
February 2025
School of Engineering and Technology, University of New South Wales, Canberra, Australia.
This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF).
View Article and Find Full Text PDFReal time SOC estimation for Li-ion batteries in Electric vehicles using UKBF with online parameter identification.
Sci Rep
January 2025
School of Electrical Engineering, VIT University, Tamilnadu, 632014, India.
Article Synopsis
- Lithium-ion batteries are crucial for the electric vehicle (EV) industry due to their high energy density, low discharge rate, and long lifespan, making accurate State of Charge (SOC) estimation important for performance improvement.
- The proposed method combines the Thevenin 2RC battery model to capture the battery's non-linear dynamics with the Unscented Kalman Bucy Filter (UKBF) to enhance SOC estimation by dealing with measurement noise and nonlinearities.
- A simulation in Matlab Simulink reveals that the UKBF outperforms other estimation methods like EKF and UKF, achieving a notably lower Root Mean Square Error (RMSE) of 0.003276 for SOC estimation.
Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells.
Sensors (Basel)
December 2024
Institute of Autmatic Control, University of Kaiserslautern-Landau, 67653 Kaiserslautern, Germany.
Harsh operating conditions imposed by vehicular applications significantly limit the utilization of proton exchange membrane fuel cells (PEMFCs) in electric propulsion systems. Improper/poor management and supervision of rapidly varying current demands can lead to undesired electrochemical reactions and critical cell failures. Among other failures, flooding and catalytic degradation are failure mechanisms that directly impact the composition of the membrane electrode assembly and can cause irreversible cell performance deterioration.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!