Application of Kalman Filter to the uncertainty of Model Resistance Data obtained from experiment

Standard deviation is the correct way to characterise the spread of the data and as the uncertainty associated with measurement the value of the standard deviation may be refined. The aim is to quantify the level of uncertainty in the resistance data of a model tanker obtained from towing tank tests. Kalman Filter (KF) was used to correct the standard deviation of the data, which is composed of the state-space model and least-squares method. Results of the simulations showed that KF could decrease the standard deviation of the resistance for a range of speeds (1,029-1.543 m/s). The standard deviation of filtered data is much smaller (1.3%-4.2%) than that of unfiltered data (14.7%-28.4%). The proposed filter method can therefore reduce the uncertainty of the model experimen

Sensorless speed control of DC motor using EKF estimator and TSK fuzzy logic controller

In this article, sensorless speed control of DC motor has been proposed using the extended Kalman filter (EKF) estimator and Takagi–Sugeno-Kang (TSK) fuzzy logic controller (FLC). In the industry, high-cost measurement systems/sensors are necessary for better controlling and monitoring, which can be replaced by a sensorless control technique to reduce the cost, size and increase system reliability and robustness. EKF has been used to perform the sensorless speed control by estimating the speed of the DC motor using the armature current only and TSK-FLC is used to reduce the effect of motor parameter variation and load torque nonlinearity in close loop speed control for various speed references. The performance of EKF-based TSK-FLC is compared with EKF-based PID controller. The time-domain specification and absolute error performance indices indicate that EKF-based TSK-FLC is superior to the EKF-based PID under similar conditions. The proposed system is executed in the MATLAB/Simulink environment, and sensorless speed control of DC motor prototype model has been developed for validating the proposed technique with the help of a micro-controller

Fault Detection in Offshore Structures: Influence of Sensor Number, Placement and Quality

Within the Space@Sea project floating offshore islands, designed as an assembly of platforms, are used to create space in offshore environments. Offshore structures are exposed to harsh environment conditions. High wind speeds, heavy rainfall, ice and wave forces lead to highly stressed structures. The platforms at the Space@Sea project are connected by ropes and fenders. There exists the risk of a rope failing which is therefore investigated subsequently. To ensure the safety of the structure, the rope parameters are monitored by the Extended Kalman Filter (EKF). For platform arrangements, a large number of sensors is required for accurate fault diagnosis of these ropes, leading to high investment costs. This paper presents a strategy to optimize the number and placement of acceleration sensors attached to the floating platforms. There are also high demands on the sensors due to the harsh offshore conditions. Material deterioration and overloading may lead to decayed sensor performance or sensor defects. Maintenance of offshore sensors is difficult, expensive and often not feasible within a short time. Therefore, sensor measurement deviations must not affect reliable structure fault detection. The influence of defect sensors on the rope fault detection is examined in this study: Types, intensities, number, place of occurrence of defect sensors and the distance between defect sensors and rope faults are varied

A Self-Reconfigurable and Fault-Tolerant Induction Motor Control Architecture for Hybrid Electric Vehicles

International audienceThis paper describes an adaptive control system for an induction motor drive that propels a Hybrid Electrical Vehicle (HEV). It has been designed to comply with the major requirements of HEVs electric propulsion. The fault tolerant controller is based on a Field Oriented Control with 4 IP regulators, a speed sensor and two observers (Extended Kalman Filter (EKF) and an Adaptive Observer (AO)) to guarantee the best dynamic performances required by the application and also to improve the reliability in the event of sensor loss or sensor recovery. The tuning of the observers is based on extensive simulations, experimental results and optimization procedure within an open-loop type approach. The fault tolerant controller reorganization is based on a control decision block implemented with a Maximum Likelihood voting algorithm. The results of the control system show the effectiveness of the approach. Indeed experimental results of the EKF used in closed loop confirm the validity of the sensorless controller and the fault tolerant controller simulation results in the event of speed sensor loss and recovery are very promising even in case of stator resistance variation

Industrial applications of the Kalman filter:a review

International audienc

SoC estimation for lithium-ion batteries : review and future challenges

ABSTRACT: Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence

Non-linear kalman filters for battery state of charge estimation and control

In this paper, two different non-linear Kalman Filters for lithium-ion battery state of charge estimation are presented and compared. Nowadays, lithium-ion batteries are extensively used for hybrid and electric vehicles; in such applications, cells are assembled in module and pack to achieve high performance. At this scope, a Battery Management Systems BMS is required to control each cell and improve the battery pack performance, safety, reliability, and lifecycle. One of the major tasks a BMS must fulfill is an accurate online estimation of the State Of Charge (SOC) of the battery pack. In this paper, the Extended Kalman Filter and Sigma Points Kalman filter are developed and compared. A battery equivalent circuit model has been chosen to have a good compromise between complexity and accuracy and model parameters have been identified from Hybrid Pulse Power Characterization (HPPC) tests carried out at different temperatures and current rates to obtain a model valid for a wide range of operating conditions. The SOC estimation strategies are developed starting from the experimental results and it is validated through different driving cycling simulations. The results show that the Sigma Points Kalman filter produces a better estimate of SOC with respect to the Extended Kalman Filter, due to its better capability to deal with system non-linearities, with comparable computational complexity

Rotor Position Estimation of a Pseudo Direct Drive PM machine using Extended Kalman Filter

The paper describes an improved method to control a Pseudo Direct Drive (PDD) permanent magnet machine with only one sensor on the low-speed rotor (LSR). Due to the magnetic coupling between the two rotors, the PDD machine exhibits low stiffness and non-linear torque transmission characteristics, and hence, the position of the high-speed rotor (HSR) cannot be determined using a simple gear ratio relationship. An extended kalman filter is proposed to accurately estimate the position of the HSR which is used to provide electronic commutation for the drive. The technique has been implemented on a prototype PDD subjected to various speed and load torque profiles