Satadru Dey

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

A Comparative Study of Three Fault Diagnosis Schemes for Wind Turbines

In wind turbine systems, early diagnosis and accommodation of faults are crucial for the reliable and cost effective operation of wind turbines and their success as viable renewable energy conversion solutions. This paper proposes and compares three different diagnostic schemes that address the issue of fault detection and isolation for the drivetrain and generator-converter subsystems of a wind turbine. The first diagnostic scheme is based on a cascade of two Kalman filters intended to alleviate the effect of the nonlinear aerodynamic torque generation in the drivetrain dynamics. The second scheme uses a bank of dedicated observers, each of which exploits Thaus argument for systems featuring nonlinear static feedback. The third scheme is a secondary H∞ filtering mechanism constructed from parity equations by treating the nonlinearity as bounded uncertainty. The performance of each scheme is demonstrated using simulations of the wind turbine system. Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions. A detailed comparison is also presented pointing to the positive and negative aspects of each scheme.

Nonlinear Adaptive Observer for a Lithium-Ion Battery Cell Based on Coupled Electrochemical-Thermal Model

Real-time estimation of battery internal states and physical parameters is of the utmost importance for intelligent battery management systems (BMS). Electrochemical models, derived from the principles of electrochemistry, are arguably more accurate in capturing the physical mechanism of the battery cells than their counterpart data-driven or equivalent circuit models (ECM). Moreover, the electrochemical phenomena inside the battery cells are coupled with the thermal dynamics of the cells. Therefore, consideration of the coupling between electrochemical and thermal dynamics inside the battery cell can be potentially advantageous for improving the accuracy of the estimation. In this paper, a nonlinear adaptive observer scheme is developed based on a coupled electrochemical–thermal model of a Li-ion battery cell. The proposed adaptive observer scheme estimates the distributed Li-ion concentration and temperature states inside the electrode, and some of the electrochemical model parameters, simultaneously. These states and parameters determine the state of charge (SOC) and state of health (SOH) of the battery cell. The adaptive scheme is split into two separate but coupled observers, which simplifies the design and gain tuning procedures. The design relies on a Lyapunovs stability analysis of the observers, which guarantees the convergence of the combined state-parameter estimates. To validate the effectiveness of the scheme, both simulation and experimental studies are performed. The results show that the adaptive scheme is able to estimate the desired variables with reasonable accuracy. Finally, some scenarios are described where the performance of the scheme degrades.

Nonlinear observer designs for state-of-charge estimation of Lithium-ion batteries

State-of-Charge (SOC) information is very crucial for the control, diagnostics and monitoring of Li-ion cells/batteries. Compared to conventional data-driven or equivalent circuit models often employed in battery management systems, electrochemical battery models have the potential to give physically accurate the SOC information by tracking the Li-ion concentration in each electrode. In this paper, two nonlinear observer designs are presented to estimate Li-ion battery State-of-Charge based on reductions of an electrochemical model. The first observer design uses a constant gain Luenberger structure whereas the second one improves it by weighing the gain with the output Jacobian. For both observer designs, Lyapunovs direct method is applied and the design problems are converted to solving LMIs. Simulation results are included to demonstrate the effectiveness of both observer designs.

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

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.

Sensor Fault Detection, Isolation, and Estimation in Lithium-Ion Batteries

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.

Combined estimation of State-of-Charge and State-of-Health of Li-ion battery cells using SMO on electrochemical model

Advanced battery management systems require accurate information of battery State-of-Charge (SOC) and State-of-Health (SOH) for diagnostics and prognostics as well as for efficient capacity utilization. In this paper, an integrated SOC and SOH estimation scheme is presented that applies sliding modes on an electrochemical model for Li-ion battery cell. The electrochemical model is selected and progressively reduced to sufficiently describe the relevant temporal and spatial evolution of Li-ion concentration in each electrode. The proposed estimation scheme is comprised of three sub-estimators which work jointly: two separate adaptive sliding mode observers (SMO) for estimation of Li-ion concentration and film resistance, and a separate parameter estimator for the solid state diffusion coefficient of negative electrode. Convergence of the observers has been proven using Lyapunovs stability theory. Simulation results are included to demonstrate the effectiveness of the overall scheme.

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

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 Lyapunovs 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.

Online state and parameter estimation of Battery-Double Layer Capacitor Hybrid Energy Storage System

Hybrid Energy Storage Systems (HESS) are gaining popularity due to their ability to compensate for the deficiencies of the conventional single energy storage solution. Battery-Double Layer Capacitor (DLC) is one of such HESS that is being adopted for different applications such as vehicle propulsion, auxiliary power unit and renewable energy storage. Real-time estimation of the states and parameters of such HESS is crucial for safe, efficient and optimal operation. In this paper, an online state-parameter estimation scheme is presented based on the electrical and thermal dynamics of the battery and DLC. The estimation scheme consists of two separate state-parameter estimators for battery and DLC each of which exploits a cascaded observer-based structure. The observers are designed based on sliding mode methodology. Theoretical verification of the overall state-parameter estimation is provided using Lyapunovs argument. Effectiveness of the scheme is verified via simulation studies.

A Diagnostic Scheme for Detection, Isolation and Estimation of Electrochemical Faults in Lithium-Ion Cells

Improvement of the safety and reliability of the Lithium-ion (Li-ion) battery operation is one of the key tasks for advanced Battery Management Systems (BMSs). It is critical for BMSs to be able to diagnose battery electrochemical faults that can potentially lead to catastrophic failures. In this paper, an observer-based fault diagnosis scheme is presented that can detect, isolate and estimate some internal electrochemical faults. The scheme uses a reduced-order electrochemical-thermal model for a Li-ion battery cell. The paper first presents a modeling framework where the electrochemical faults are modeled as parametric faults. Then, multiple sliding mode observers are incorporated in the diagnostic scheme. The design and selection of the observer gains as well as the convergence of the observers are verified theoretically via Lyapunov’s direct method. Finally, the performance of the observer-based diagnostic scheme is illustrated via simulation studies.Copyright