Distributed parameter cyber-physical systems (DPCPSs) are characterized by two features: 1) they exhibit spatio-temporal (distributed parameter) dynamics and 2) there exist cyber components in the ...form of communication channels between the physical plant and control system. DPCPSs are vulnerable to anomalies such as nonmalicious physical faults and malicious cyber-attacks. Although the safety and security aspects of CPSs modeled by ordinary differential equations (ODEs) have been extensively explored during the past decade, security of DPCPSs has not received its due attention despite its safety-critical nature. In this work, we explore anomaly diagnostics in DPCPSs from a system theoretic viewpoint. Specifically, we focus on DPCPSs modeled by linear parabolic partial differential equations (PDEs) subject to physical faults and cyber-attacks in the actuation channel. First, we explore the detectability of malicious anomalies (i.e., cyber-attacks) and derive conditions for stealthy attacks in the context of output-feedback-based monitoring. Such stealthy attacks essentially illustrate the fundamental limitations of feedback-based monitoring algorithms given perfect adversarial knowledge. Subsequently, focusing on the anomalies that are detectable, we develop a design framework for anomaly detection algorithms based on output injection observers. Such detection algorithm explicitly considers the conflicting objectives of robustness to uncertainties and sensitivity to anomalies in their design. Finally, theoretical analysis and simulation studies are performed to illustrate the effectiveness of the proposed approach.
Human route choice is undeniably one of the key contributing factors towards traffic dynamics. However, most existing macroscopic traffic models are typically concerned with driving behavior and do ...not incorporate human route choice behavior models in their formulation. In this paper, we propose a socio-technical macroscopic traffic model that characterizes the traffic states using human route choice attributes. Essentially, such model provides a framework for capturing the Cyber-Physical-Social coupling in smart transportation systems. To derive this model, we first use Cumulative Prospect Theory (CPT) to model the human passengers' route choice under bounded rationality. These choices are assumed to be influenced by traffic alerts and other incomplete traffic information. Next, we assume that the vehicles are operating under a non-cooperative cruise control scenario. Accordingly, human route choice segregates the traffic into multiple classes where each class corresponds to a specific route between an origin-destination pair. Thereafter, we derive a Mean Field Game (MFG) limit of this non-cooperative game to obtain a macroscopic model which embeds the human route choice attribute. Finally, we analyze the mathematical characteristics of the proposed model, present simulation studies to illustrate the model behavior and show comparison with existing traffic model.
Advanced battery management systems rely on accurate cell- or module-level state-of-charge (SOC) information for effective control, monitoring, and diagnostics. Electrochemical models provide ...arguably the most accurate and detailed information about the SOC of lithium-ion cells. In this brief, two nonlinear observer designs are presented based on a reduced order electrochemical model. Both observers consist of a Luenberger term acting on nominal errors and a variable structure term for handling model uncertainty. Using Lyapunov's direct method, the design of the Luenberger term in each observer is formulated as a linear matrix inequality problem, whereas the variable structure term is designed assuming uncertainty bounds. Simulation and experimental studies are included to demonstrate the performance of the proposed observers.
Supercapacitors are receiving significant interest in wireless sensor network applications due to their high power density and longer lifespan. In such applications, consideration of the leakage ...effect is crucial for online control and management of supercapacitors. Motivated by this fact, in this paper we propose an online scheme to estimate the state-of-charge (SOC) of supercapacitors, taking the leakage effect into account. First, we propose a completely observable equivalent circuit model (ECM) of a supercapacitor, which captures the leakage effect. Then, we design an unscented-Kalman-filter-based SOC estimator on the proposed ECM. To illustrate the proposed scheme, we consider a commercially available 5 F supercapacitor (Maxwell BCAP0005) and conduct experimental studies to identify the ECM parameters. We also perform simulation and experimental studies to test the effectiveness of the proposed estimation scheme, in this paper. Finally, we evaluate the robustness of the proposed scheme under parametric and measurement uncertainties.
Safety against thermal failures is crucial in battery systems. Real-time thermal diagnostics can be a key enabler of such safer batteries. Thermal fault diagnostics in large format pouch or prismatic ...cells pose additional challenges compared to cylindrical cells. These challenges arise from the fact that the temperature distribution in large format cells is at least two-dimensional in nature (along length and breadth) while such distribution can be reasonably approximated in one dimension (along radial direction) in cylindrical cells. This difference makes the placement of temperature sensor(s) non-trivial and the design of detection algorithm challenging. In this work, we address these issues by proposing a framework that (i) optimizes the sensor locations to improve detectability and isolability of thermal faults, and (ii) designs a filtering scheme for fault detection and localization based on a two dimensional thermal model. The proposed framework is illustrated by experimental and simulation studies on a commercial battery cell.
•Thermal Fault diagnostics in large format batteries.•Sensor placement to improve detectability and isolability of thermal faults.•Fault detection and localization using Kalman Filtering framework.
Accurate information of supercapacitor (SC), also called electric double layer capacitor, leakage current is vital for effective State-of-Charge (SOC) estimation in Wireless Sensor Network (WSN) ...applications having long rest phase. In addition to improving accuracy of SOC estimation, real-time information on leakage current is highly beneficial for SC health monitoring. On the other hand, accurate mapping of SC open circuit voltage (OCV) vs. SOC significantly contributes towards accurate SOC estimation. Inaccuracies in either of these two information, i.e. leakage and OCV-SOC map, lead to inaccuracies in estimated SOC. In this paper, we propose a real-time estimation framework for accurate estimation of SOC under uncertain leakage and OCV-SOC map. Specifically, the proposed approach co-estimates leakage and part of OCV-SOC map in real-time along with SOC. The estimation framework utilizes Unscented Kalman Filter (UKF) along with an Equivalent Circuit Model (ECM) which captures SC leakage phenomenon. We identify the ECM parameters based on a Maxwell 25 F commercial SC. The experimentally identified ECM is subsequently used to perform simulation and experimental studies to validate the proposed framework. Finally, the robustness of the proposed framework with respect to parametric and measurement uncertainties is verified.
The popularity of supercapacitors (SC) as an energy storage device is growing along with the development of the electrical and electronics applications demanding high operating power. Consequently, ...the number of retired SC cells is increasing leading to the recycling problem. Supercapacitor reaches its end-of-life (EOL) when its nominal capacitance is decreased by 20% and the initial equivalent series resistance (ESR) is increased by 100%. Under this scenario, the SC is no-longer suitable for its original purpose as a source of high power. However, the aged supercapacitors often retain enough capacity to serve the applications demanding low operating power. This paper investigates the possibility to re-purpose the aged supercapacitors for low power second-life applications. Specifically, we propose a capacitance prognosis model as a function of the operating power of second-life applications. We consider three aged Maxwell supercapacitors to illustrate the proposed scheme. Subsequently, we identify their effective capacitance considering MICAz wireless sensor module as the low power second-life application. Next, we evaluate the sensitivity and robustness of the proposed model. Finally, we study the second-life aging of the aged supercapacitors under low-power charge–discharge cycling.
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•Proposed second-life application power-based capacitance prognosis model.•The model predicts remaining effective capacitance of retired supercapacitor cells.•Experimentally demonstrated the applicability of retired Maxwell Supercapacitors.•Analyzed second-life capacitance dynamics of retired supercapacitor cells.•Experimentally illustrated the second-life aging of retired supercapacitor cells.
High power operation in extreme fast charging significantly increases the risk of internal faults in Electric Vehicle batteries which can lead to accelerated battery failure. Early detection of these ...faults is crucial for battery safety and widespread deployment of fast charging. In this setting, we propose a real-time detection framework for battery voltage and thermal faults. A major challenge in battery fault detection arises from the effect of uncertainties originating from sensor inaccuracies, nominal aging, or unmodelled dynamics. Inspired by physics-based learning, we explore a detection paradigm that combines physics-based models, model-based detection observers, and data-driven learning techniques to address this challenge. Specifically, we construct the detection observers based on an experimentally identified electrochemical-thermal model, and subsequently design the observer tuning parameters following Lyapunov's stability theory. Furthermore, we utilize Gaussian Process Regression technique to learn the model and measurement uncertainties which in turn aid the detection observers in distinguishing faults and uncertainties. Such uncertainty learning essentially helps suppressing their effects, potentially enabling early detection of faults. We perform simulation and experimental case studies on the proposed fault detection scheme verifying the potential of physics-based learning in early detection of battery faults.
Fast and safe charging protocols are crucial for enhancing the practicality of batteries, especially for mobile applications, such as smartphones and electric vehicles. This paper proposes an ...innovative approach to devising optimally health-conscious fast-safe charge protocols. A multiobjective optimal control problem is mathematically formulated via a coupled electro-thermal-aging battery model, where electrical and aging submodels depend upon the core temperature captured by a two-state thermal submodel. The Legendre-Gauss-Radau pseudospectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting highly nonlinear six-state optimal control problem. Charge time and health degradation are, therefore, optimally traded off, subject to both electrical and thermal constraints. Minimum-time, minimum-aging, and balanced charge scenarios are examined in detail. Sensitivities to the upper voltage bound, ambient temperature, and cooling convection resistance are investigated as well. Experimental results are provided to compare the tradeoffs between a balanced and traditional charge protocol.
In battery management systems (BMSs), real-time diagnosis of sensor faults is critical for ensuring the safety and reliability of the battery. For example, a current sensor fault leads to erroneous ...estimates of state of charge and other parameters, which in turn affects the control actions in the BMS. A temperature sensor fault may lead to ineffective thermal management. In this brief, a model-based diagnostic scheme is presented that uses sliding mode observers designed based on the electrical and thermal dynamics of the battery. It is analytically shown how the extraction of the equivalent output error injection signals on the sliding manifolds enables the detection, the isolation, as well as the estimation of the temperature, voltage, and current sensor faults. This brief includes simulation and experimental studies to demonstrate and evaluate the effectiveness of the proposed scheme. Discussions are also included on the effects of uncertainty and on threshold design.
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