Nonlinear Stochastic Filtering for Online State of Charge and Remaining Useful Life Estimation of Lithium-ion Battery

Battery state monitoring is one of the key techniques in Battery Management System (BMS). Accurate estimation can help to improve the system performance and to prolong the battery lifetime. The main challenges for the state online estimation of Li-ion batteries are the flat characteristic of open circuit voltage (OCV) with the function of the state of charge. Hence, the focus of this thesis study is to estimation of the state of charge (SOC) of Li-ion with high accuracy, more robustness. A 2nd order RC equivalent circuit model is adapted to battery model for simulation, mathematical model analysis, and dynamics characteristic of battery study. Model parameters are identified with MATLAB battery model simulation. Although with more lumped RC loaders, the model is more accurate, high computation with a higher nonlinear function of output will be. So, a discrete state space model for the battery is developed. For a complex battery model with strong nonlinearity, Sequential Monte Carlo (SMC) method can be utilized to perform the on-line SOC estimation. An SMC integrates the Bayesian learning methods with sequential importance sampling. SMC approximate the posterior density function by a set of particles with associated weights, which is developed in MATLAB environment to estimate on-line SOC. A comparison is presented with Kalman Filtering and Extended Kalman Filtering to validated estimation results with SMC. Finally, the comparison results provide that SMC method is more accurate and robust then KF and EKF. Accurately prediction of Remaining Useful Life of Li-ion batteries is necessary to reliable system operation and monitoring the BMS. An empirical model for capacity degradation has been developed based on experimentally obtained capacity fade data. A nonlinear, non-Gaussian state space model is developed for empirical model. The obtained empirical model used in Sequential Monte Carlo (SMC) framework is to update the on-line state and model parameters to make a prediction of remaining useful life of a Li-ion battery at various lifecycle

Estimation of Vehicle Roll Angle

Haldex Traction can with their Active Yaw Control prevent unwanted handling by applying an extra yaw torque with their all wheel drive system. To be able to calculate when or when not, yaw torque should be applied, it is important to accurate know the different state information about the vehicle. When driving on banked roads, vehicle dynamics and sensor measurements are changed compared to driving on a flat surface. Because of this it is desirable to know the degree of banking the vehicle is exposed to. The estimation of the banking is made with sensors already present in modern production cars; lateral accelerometer, yaw rate gyro, steering wheel angle and longitudinal velocity. This by isolating the part of the measured lateral acceleration that is derived from the normal forces due to gravity. To be able to make a good and stable estimation it is necessary to also estimate the vehicle's lateral velocity, and especially its derivative. This is done by estimating vehicle states with a single track bicycle model. The model has been used in former thesis works, but it was in this thesis extended with the angle of the road as a parameter. Two different observers have been evaluated for measurement update of the model; Extended Kalman filter (EKF) and an Averaging observer. Evaluation of the algorithms have been done in Haldex simulator VehSim running in Matlab/Simulink and real measurements from a test vehicle

Modelling and estimation in lithium-ion batteries: a literature review

Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications.This research received support from the Spanish Ministry of Science and Innovation under projects MAFALDA (PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033/ ERDF,EU) and MASHED (TED2021-129927B-I00), and by FI Joan Oró grant (code 2023 FI-1 00827), cofinanced by the European Union.Peer ReviewedPostprint (published version

An Integrity Framework for Image-Based Navigation Systems

This work first examines fundamental differences between measurement models established for GPS and those of proposed image-based navigation systems. In contrast to single value per satellite GPS pseudorange measurements, image measurements are inherently angle-based and represent pixel coordinate pairs for each mapped target. Thus, in the image-based case, special consideration must be given to the units of the transformations between the states and measurements, and also to the fact that multiple rows of the observation matrix relate to particular error states. An algorithm is developed to instantiate a framework for image-based integrity analogous to that of GPS RAIM. The algorithm is applied cases where the navigation system is estimating position only and then extended to cases where both position and attitude estimation is required. Detailed analysis demonstrates the impact of angular error on a single pixel pair measurement and comparisons from both estimation scenario results show that, from an integrity perspective, there is significant benefit in having known attitude information. Additional work demonstrates the impact of pixel pair measurement relative geometries on system integrity, showing potential improvement in image-based integrity through screening and adding measurements, when available, to the navigation system solution

Improved results on fuzzy H ∞ filter design for T-S fuzzy systems

The fuzzy H ∞ filter design problem for T-S fuzzy systems with interval time-varying delay is investigated. The delay is considered as the time-varying delay being either differentiable uniformly bounded with delay derivative in bounded interval or fast varying (with no restrictions on the delay derivative). A novel Lyapunov-Krasovskii functional is employed and a tighter upper bound of its derivative is obtained. The resulting criterion thus has advantages over the existing ones since we estimate the upper bound of the derivative of Lyapunov-Krasovskii functional without ignoring some useful terms. A fuzzy H ∞ filter is designed to ensure that the filter error system is asymptotically stable and has a prescribed H ∞ performance level. An improved delay-derivative-dependent condition for the existence of such a filter is derived in the form of linear matrix inequalities (LMIs). Finally, numerical examples are given to show the effectiveness of the proposed method. © 2010 Jiyao An et al

DYNAMIC ORIGIN-DESTINATION DEMAND ESTIMATION AND PREDICTION FOR OFF-LINE AND ON-LINE DYNAMIC TRAFFIC ASSIGNMENT OPERATION

Time-dependent Origin-Destination (OD) demand information is a fundamental input for Dynamic Traffic Assignment (DTA) models to describe and predict time-varying traffic network flow patterns, as well as to generate anticipatory and coordinated control and information supply strategies for intelligent traffic network management. This dissertation addresses a series of critical and challenging issues in estimating and predicting dynamic OD demand for off-line and on-line DTA operation in a large-scale traffic network with various information sources. Based on an iterative bi-level estimation framework, this dissertation aims to enhance the quality of OD demand estimates by combining available historical static demand information and time-varying traffic measurements into a multi-objective optimization framework that minimizes the overall sum of squared deviations. The multi-day link traffic counts are also utilized to estimate the variation in traffic demand over multiple days. To circumvent the difficulties of obtaining sampling rates in a demand population, this research proposes a novel OD demand estimation formulation to effectively exploit OD demand distribution information provided by emerging Automatic Vehicle Identification (AVI) sensor data, and presents several robust formulations to accommodate possible deviations from idealized conditions in the demand estimation process. A structural real-time OD demand estimation and prediction model and a polynomial trend filter are developed to systematically model regular demand pattern information, structural deviations and random fluctuations, so as to provide reliable prediction and capture the structural changes in time-varying demand. Based on a Kalman filtering framework, an optimal adaptive updating procedure is further presented to use the real-time demand estimates to obtain a priori estimates of the mean and variance of regular demand patterns. To maintain a representation of the network states which consistent with that of the real-world traffic system in a real-time operational environment, this research proposes a dynamic OD demand optimal adjustment model and efficient sub-optimal feedback controllers to regulate the demand input for the real-time DTA simulator while reducing the adjustment magnitude

Augmented State Linear Covariance Applications for Nonlinear Missile Engagements

Sustained actuator saturation is a common occurrence for missile engagements. The saturation nonlinearity creates some difficulty for high-fidelity linear analysis methods. This dissertation investigates three methods of modeling actuator saturation in an advanced linear analysis. The linear covariance tools from this dissertation run extremely fast and provide several advantages over other linear missile engagement analysis methods. First, a simulation is developed and validated for a target engagement scenario without actuator saturation. Next, saturations are introduced to the problem, along with the first analysis method: statistical linear covariance analysis. This method combines the augmented state linear covariance framework with the statistical linearization technique. The second method considered is tunable linear covariance analysis. Tunable linear covariance analysis utilizes a switching parameter to determine when to switch the dynamics of the problem. The final method is called event trigger linear covariance analysis. This method involves switching GN&C modes using a constraint equation and a covariance shaping matrix. All three analysis methods are validated using Monte Carlo methods, and statistical linear covariance analysis is found to be the most robust and accurate of the three methods. This method is utilized to rapidly analyze missile engagement performance under varying levels of saturation. The parameters of the analysis include guidance laws, sensor accuracy levels, target evasive maneuvers, and actuator responsiveness

Utilization of IGS Information for Improved Real-time GPS Positioning

The estimation of a precise user\u27s position is a difficult and complex problem. In addition, the use of geodetic grade position instruments is often not possible for Small Unmanned Aerial Vehicle (SUAV) systems. However, the availability of the global navigation satellite system (GNSS) and International GNSS Service (IGS) predicted product data allows an attempt to increase the precision of a navigation algorithm, which is the aim in this thesis.;The utilization of this information within an algorithm work environment is a complex problem, requiring the development of multiple tools in order to use and access the IGS raw and product data. Therefore, the overall goal of this research project was the development of these tools using MATLAB RTM. The IGS information provided by these tools allows access to a particular set of product and raw data files. The available predicted product data is used to increase the precision of the position estimate for a real-time application. Within this, the conversion from a long time interval to a fast update rate was determined. The use of this information requires these tools to also include important orbit determinations of the GPS satellites.;The use of only precise satellite position information from the developed MATLAB tools is evaluated by a comparison of a position estimation algorithm using recorded satellite position information and the developed satellite position information from the IGS predicted data. The results show an increase in performance of position estimation with the use of the created MATLAB tools. A discussion in how the use of the created tools could further be expanded to increase the accuracy and precision of a position estimation algorithm is presented

Aircraft performance monitoring from flight data

Poznavanje stvarnih performansi zrakoplova bitno je za učinkovitu eksploataciju i pravovremeno održavanje. Performanse su određene fizikalnim karakteristikama zrakoplova. U Priručniku za letenje opisane su teorijske performanse određene od proizvođača nakon proizvodnje zrakoplova i testiranja u letu. Komercijalni zrakoplovi su tijekom svog operativnog ciklusa uglavnom izloženi predviđenim uvjetima eksploatacije. Unatoč predviđenim uvjetima eksploatacije i redovnom održavanju, starenje materijala i velika opterećenja na strukturu zrakoplova kod leta visokim podzvučnim Machovim brojem, mogu dovesti do promjene temeljnih fizikalnih faktora koji određuju performanse. Zbog toga se stvarne performanse zrakoplova nerijetko razlikuju od teorijskih. Zračni prijevoznici prate stanje zrakoplova i njegove stvarne performanse tijekom korištenja. U ovom radu prikazan je pregled dosadašnjih metoda praćenja performansi i mogućnosti istraživanja na području određivanja fizikalnih parametara zrakoplova u eksploataciji prema podacima iz leta.To ensure timely maintenance and efficient aircraft operations, it is necessary to know and keep track of aircraft’s actual performance. Flight performance is determined by aircraft\u27s physical characteristics. Theoretical aircraft performance, obtained after manufacturing and flight testing, are described in flight manual. Transport aircraft in operation is usually exposed to standard operational conditions. Despite the standard operational conditions and regular aircraft maintenance, structure aging and high dynamic loads due to high subsonic Mach number could lead to changes of main physical factors that determine flight performance. For this reason actual aircraft performance often differs from theoretical. Commercial airlines monitor true performance of aircraft in operation. This paper presents an overview of existing performance monitoring methods as well as first indications for new research possibilities regarding physical characteristics determination for aircraft in operation using flight data