Satadru Dey

Bio: Satadru Dey is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Battery (electricity) & Fault (power engineering). The author has an hindex of 18, co-authored 54 publications receiving 1072 citations. Previous affiliations of Satadru Dey include Center for Automotive Research & University of Colorado Denver.

Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures

Abstract: Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

Optimal Charging of Li-Ion Batteries With Coupled Electro-Thermal-Aging Dynamics

Abstract: 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.

Real-Time Detection and Estimation of Denial of Service Attack in Connected Vehicle Systems

Abstract: Advanced connectivity features in today’s smart vehicles are giving rise to several promising intelligent transportation technologies. Connected vehicle system is one among such technologies, where a set of vehicles can communicate with each other and the infrastructure via communication networks. Connected vehicles have the potential to improve the traffic throughput, minimize the risk of accidents and reduce vehicle energy consumption. Despite these promising features, connected vehicles suffer from the safety and security issues. Especially, vehicle-to-vehicle and vehicle-to-infrastructure communication make the connected vehicles vulnerable to cyber attacks. In order to improve safety and security, advanced vehicular control systems must be designed to be resilient to such cyber attacks. The first step of designing such attack-resilient control system is detection of the occurrence of the cyber attack. In this paper, we address this need and propose a real-time scheme that can potentially 1) detect the occurrence of a particular cyber attack, namely denial of service; and 2) estimate the effect of the attack on the connected vehicle system. The scheme consists of a set of observers, which are designed using sliding mode and adaptive estimation theory. The mathematical convergence properties of the observers are analyzed via Lyapunov’s stability theory. Finally, simulation demonstrates the performance of the approach and the robustness of the scheme under several forms of uncertainties.

Nonlinear Robust Observers for State-of-Charge Estimation of Lithium-Ion Cells Based on a Reduced Electrochemical Model

Abstract: 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.

Nonlinear Fractional-Order Estimator With Guaranteed Robustness and Stability for Lithium-Ion Batteries

Abstract: This paper proposes a new estimator design algorithm for state-of-charge (SoC) indication of lithium-ion batteries. A fractional-order model-based nonlinear estimator is first framed including a Luenberger term and a sliding mode term. The estimator gains are designed by Lyapunov's direct method, providing a guarantee for stability and robustness of the error system under certain assumptions. This generic estimation algorithm is then applied to lithium-ion batteries. A fractional-order circuit model is adopted to predict battery dynamic behaviours. Assumptions based on which the estimation algorithm is developed are justified and remarked. Experiments corresponding to electric vehicle applications are conducted to parameterize the battery model and demonstrate the estimation performance. It shows that the proposed approach is able to estimate SoC with errors less than 0.03 in the presence of initial deviation and persistent noise. Furthermore, the benefits of using the proposed estimator relative to other estimators are calculated over different cycles and conditions.

Ultracapacitors: Why, How, and Where is the Technology

Abstract: The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

A review of supercapacitor modeling, estimation, and applications: A control/management perspective

Abstract: Supercapacitors (SCs) have high power density and exceptional durability. Progress has been made in their materials and chemistries, while extensive research has been carried out to address challenges of SC management. The potential engineering applications of SCs are being continually explored. This paper presents a review of SC modeling, state estimation, and industrial applications reported in the literature, with the overarching goal to summarize recent research progress and stimulate innovative thoughts for SC control/management. For SC modeling, the state-of-the-art models for electrical, self-discharge, and thermal behaviors are systematically reviewed, where electrochemical, equivalent circuit, intelligent, and fractional-order models for electrical behavior simulation are highlighted. For SC state estimation, methods for State-of-Charge (SOC) estimation and State-of-Health (SOH) monitoring are covered, together with an underlying analysis of aging mechanism and its influencing factors. Finally, a wide range of potential SC applications is summarized. Particularly, co-working with high energy-density devices constitutes hybrid energy storage for renewable energy systems and electric vehicles (EVs), sufficiently reaping synergistic benefits of multiple energy-storage units.

Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Abstract: Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems

Abstract: With the rapid development of new energy electric vehicles and smart grids, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. This paper presents a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs. The models include the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models. The state estimation approaches are analyzed from the perspectives of remaining capacity and energy estimation, power capability prediction, lifespan and health prognoses, and other crucial indexes in BMS. This present paper, through the analysis of literature, includes almost all states in the BMS. The estimation approaches of state-of-charge (SOC), state-of-energy (SOE), state-of-power (SOP), state-of-function (SOF), state-of-health (SOH), remaining useful life (RUL), remaining discharge time (RDT), state-of-balance (SOB), and state-of-temperature (SOT) are reviewed and discussed in a systematical way. Moreover, the challenges and outlooks of the research on future battery management are disclosed, in the hope of providing some inspirations to the development and design of the next-generation BMSs.