State of Charge Estimation for Rechargeable Batteries Based on the Nonlinear Double-Capacitor Model

State of charge (SOC) estimation plays a foundational role in advanced battery management systems, having attracted much attention in the past decade. It is widely acknowledged that the accuracy of SOC estimation largely depends on the accuracy of the selected model. In this thesis, SOC estimation methods are developed based on the nonlinear double-capacitor (NDC) model, a novel equivalent circuit model that is distinctly capable of simulating the charge diffusion inside an electrode of a battery and capturing the battery’s nonlinear voltage behavior simultaneously. With improved predictive accuracy, the NDC model provides a new opportunity for enabling more accurate SOC estimation. With this motivation, the well-known extended Kalman filter (EKF) and unscented Kalman filter (UKF) are utilized to perform SOC estimationbased on the NDC model. The EKF is desirable here as it leads to efficient computation, straightforward implementation, and good convergence in its application to the NDC model, which is low-dimensional and governed by linear dynamics along with nonlinear output. The UKF is another popular version of the Kalman filter that belongs to the sigma-point filter family, and provably offers second-order accuracy under certain conditions, contrasting with the first-order accuracy of the EKF. The proposed SOC estimation methods are validated through simulations and experimental data under various conditions, showing significant accuracy as well as robustness to different levels of initialization error and noise

Target Tracking in Non-Gaussian Environment

Masreliez filter which is a Kalman type of recursive filter is implemented and validated. The main computation in Masreliez filter is to evaluate the score function which directly influences the estimates of the target states. Scalar approximation for score function evaluation is extended to vector observations, implemented and validated. The simulation studies have shown that the performance of the Masreliez filter is relatively better than that of the conventional Kalman filter in the presence of significant glint noise in the observation

Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks

Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network

Visualization on colour based flow vector of thermal image for movement detection during interactive session

Recently thermal imaging is exploited in applications such as motion and face detection. It has drawn attention many researchers to build such technology to improve lifestyle. This work proposed a technique to detect and identify a motion in sequence images for the application in security monitoring system or outdoor surveillance. Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. The results obtained could be observed in flow vector parameter and the segmentation colour based image for the time frame from 1 to 10 seconds. The visualization of both the parameters clarified the movement and changes of pixel intensity between two frames by the supportive colour segmentation, either in smooth or rough motion. Thus, this technique may contribute to others application such as biometrics, military system, and surveillance machine