Estimation Of Internal States Research Articles

Integrated model for accurate internal state estimation of polymer electrolyte membrane fuel cells

This study unveils a refined methodology for fuel cell behavior simulation. On the basis of three multiphysics models for the polymer membrane, catalyst layer, and mass transport, a new type of integrated model was developed. Using this model, the performance curves for various fuel cell states, charge transfer coefficient of reaction kinetics, concentration gradient inside the catalyst, and ion conductivity of the separator in the thickness direction for various fuel cell states were estimated. The proposed method can estimate microscale to macroscale fuel cell behavior with minimal computational effort while keeping the error relative to experimental data small by combining the most appropriate models to simulate each part of the fuel cell as needed.

A Review of Capacity Decay Studies of All-vanadium Redox Flow Batteries: Mechanism and State Estimation.

As a promising large-scale energy storage technology, all-vanadium redox flow battery has garnered considerable attention. However, the issue of capacity decay significantly hinders its further development, and thus the problem remains to be systematically sorted out and further explored. This review provides comprehensive insights into the multiple factors contributing to capacity decay, encompassing vanadium cross-over, self-discharge reactions, water molecules migration, gas evolution reactions, and vanadium precipitation. Subsequently, it analyzes the impact of various battery parameters on capacity. Based on this foundation, the article expounds upon the significance of battery internal state estimation technology. Additionally, the review also summarizes domestic and international mathematical models utilized for simulating capacity decay, serving as a valuable reference for future research endeavors. Finally, through the comparison of traditional experimental methods and mathematical modeling methods, this article offers effective guidance for the future development direction of battery state monitoring. This review generally overview the problems related to the capacity attenuation of all-vanadium flow batteries, which is of great significance for understanding the mechanism behind capacity decay and state monitoring technology of all-vanadium redox flow battery.

Robust Particle Filter Design With an Application to Power System State Estimation

This paper develops statistically robust particle filters that exhibit high statistical efficiency and good robustness to outliers. Three types of outliers, namely, observation outliers, structural outliers and innovation outliers, are considered. Two robust particle filters are developed and their performance are assessed. Firstly, a weight reconstruction particle filter is proposed that makes use of the projection statistics to derive non-zero particle weights in response to observation outliers. Secondly, a fitting-based particle filter is developed that generalizes the former filter to the case of structural and innovation outliers. This filter approximates the next estimated values using both the projection statistics and the linear fitting technique. The designed filters are applied to the state estimation of decentralized machines' internal state estimation and local bus frequency estimation in power systems. Simulation results performed on a four-machine two-area power system and the 39-bus <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">New England</i> power system demonstrate the excellent performance of the proposed robust particle filters in dealing with various types of outliers.

Estimation of internal states in a Li-ion battery using BiLSTM with Bayesian hyperparameter optimization

An accurate state estimator is critical to Battery Management Systems (BMSs). In more advanced BMSs, state estimators based on electrochemical-thermal battery models are preferred because they can predict the internal states of the battery. However, these physics-based models are computationally costly and time-demanding, preventing them from being directly used for real-time estimation. As an alternative approach, data-driven models, like Deep Neural Networks (DNNs), can be trained on datasets generated by electrochemical models. Increasing the accuracy of data-driven models can be achieved by applying more complex architectures of DNN. However, such DNNs are computationally time-demanding, which makes them inefficient for online state estimation algorithms. To address this issue, we have proposed a BiLSTM model as a state estimator whose hyperparameters are automatically optimized using a Bayesian Optimization (BO) framework. We demonstrate that by applying Bayesian inference, one can have a highly accurate state estimator while using a less complex DNN architecture which allows for being computationally efficient as well. The high accuracy of the BiLSTM model with the optimized combination of hyperparameters is demonstrated by the low Root Mean Squared Errors (RMSE) levels on the test data set.

State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks

Online estimation of unobservable internal states is significant for safe operation of Li-ion batteries, and it constitutes one of the main functions of battery management system (BMS). The next-generation BMS expects model-based state estimation, especially with electrochemical models, which are accurate but often costly for solving. Therefore, it is required to build more easily executable state-space representation of electrochemical models for online state estimation. However, the traditional numerical methods for time discretization are relatively complicated, and the discretized system is not very flexible in modifying predictive time intervals. To address such issues, we introduce the concept of physics-informed operator learning for state-space modeling. Specifically, we propose an architecture, termed the physics-informed multiple-input operator network (PI-MIONet), to reformulate the state-space representation of the extended single particle (eSP) model. In this work, the PI-MIONet takes the Li-ion concentration of the whole electrode particle and current densities at the current time as the input functions, and predicts Li-ion concentration at any spatial-temporal location, which means that the forward predictions can be realized with user-defined step size. In addition, due to the capability of taking discretized functions as inputs, the PI-MIONet can be used for estimating states in the form of long vectors, and it can be conducted very efficiently, which makes it highly suitable for online applications in BMS. We verify the predictive performance of PI-MIONet through several synthetic experiments, and successfully apply it to the estimation of Li-ion concentration across the full particle with unscented Kalman filter algorithms.

Enhanced Model-Based Algorithm for Early Internal Short Circuit Detection in Lithium-Ion Batteries

Electric vehicles (EVs) are becoming more popular due to concerns about fuel shortages and environmental pollution. Lithium-ion batteries are the preferred power source for EVs because they have high energy and power densities. Ensuring the efficient, safe, and reliable operation of these batteries has been a significant focus of research in recent decades. One major concern that can affect Li-ion battery performance is thermal runaway, which can cause dangerous battery fires. Internal short circuits (ISCs) are believed to be the root cause of thermal runaway incidents in batteries, making early detection of spontaneous ISCs a critical diagnostic task. This study presents a new and simple early ISC detection method for a Li-ion cell based on the augmentation of the state space of an Extended Kalman Filter (EKF) that includes voltage and surface temperature observations. The framework allows for an estimation of the cell's internal ISC state while remaining computationally efficient. The proposed approach is demonstrated in a simulated environment using dynamic stress tests that reflect a practical battery working cycle. The results demonstrate that the method can promptly detect ISC occurrences.

Active disturbance rejection control strategy for PEMFC oxygen excess ratio based on adaptive internal state estimation using unscented Kalman filter

The effectiveness of gas flow control will significantly affect the output performance of the PEMFC, however, the gas state inside the cathode is difficult to observe due to the tightness and safety requirements. To achieve effective control of PEMFC in the presence of parameter bias, an improved active disturbance rejection control (IADRC) strategy based on the adaptive unscented Kalman filter (AUKF) is proposed in this paper. A dynamic model of the air supply system with seven state variables was developed, capable of describing the non-linear characteristics of the system, and a semi-empirical air compressor model was designed to match the actual operating characteristics based on the experimental data set. Based on the standard UKF algorithm, an adaptive process was designed using the moving window method to dynamically adjust the noise characteristics to reduce the state estimation bias caused by changes in system structural parameters during service. Finally, the algorithmic structure of the ADRC was optimized to achieve an accurate estimation of the total system perturbation using cascaded extended state observers (ESO), thus achieving a control effect with the advantages of less overshoot and response speed based on fewer tuning parameters. Simulation results show that the proposed strategy can effectively improve the system response and robustness to structural parameters, and has great control effects under real road conditions.

An Insula-Enriched Regulator of Retinoic Acid Marks a New Intersection in the Neural Circuitry of Mouse Social Behavior

An Insula-Enriched Regulator of Retinoic Acid Marks a New Intersection in the Neural Circuitry of Mouse Social Behavior

Investigating re-parametrization of electrochemical model-based battery management using real-world driving data

Li-ion batteries in electric vehicles must be utilized more efficiently to lower their economic and environmental cost. To achieve this increase in efficiency, it is of large interest to develop more thorough battery management that can predict internal states in online settings and update usage and control accordingly. Electrochemical models are an important tool in achieving this, and their implementation in battery management systems is the topic of ongoing research. However, electrochemical battery management relies on accurate parametrization and thus requires re-parametrization as a battery ages. We therefore studied viability of re-parametrization for electrochemical model-based battery management. To this end, we performed global sensitivity analysis on selected Doyle-Fuller-Newman model parameters using on-board current measurements. Representative driving data was collected from several types of heavy-duty vehicles. This elucidated which model parameters should be updated periodically to conserve model accuracy and which parameters are sensitive enough to be estimated from the on-board data. Additionally, we studied how parameter uncertainty affects estimation of internal states and highlight how model-based state estimation relying on a beginning-of-life parametrization degrades as electrochemical parameters change with aging.

A novel Co-estimation framework of state-of-charge, state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method

A battery management system can intelligently manage and maintain battery systems by effectively estimating and predicting battery internal states. Owing to battery nonlinear characteristics related to various influence factors, the estimation of battery internal states should consider the available capacity and ohmic internal resistance. This paper proposes a co-estimation framework for state of charge (SOC), state of power (SOP) and battery available capacity. Firstly, the first-order equivalent model method is used to identify the battery parameters by recursive least squares algorithm with variable forgetting factor, and the SOC-OCV curve of the battery is obtained by combining the ampere-time integration method. Secondly, three Kalman filters are utilized to estimate battery SOCs and the maximum available capacity and internal resistance are estimated by a forgetting factor recursive least square algorithm. Then peak current and power are estimated under the composite constraints of the estimated capacity and internal resistance. Finally, the experimental data are collected at temperatures 25 °C and 40 °C to verify and analyze the proposed method. The results of battery state estimation indicate that the proposed framework can accurate estimation battery internal states and also provide an effective reference for the driving of powered vehicles.

State of health estimation and remaining useful life assessment of lithium-ion batteries: A comparative study

Lithium-ion batteries are widely used due to their attractive features. They have emerged as the primary storage system for electric cars, solar power, and marine vehicles. Consequently, their internal health state estimation has attracted extensive attention. An accurate health assessment results in a safer and more efficient battery management system (BMS), which estimates the battery's state and predicts premature failure. Recurrent neural network (RNN) has been extensively utilized to diagnose and prognosis lithium-ion batteries, as it has demonstrated superior performance. Improving the proficiency of Machine Learning (ML) algorithms has always been a subject of research. Among these attempts, various studies have proposed the combination of RNN and the convolutional neural network (CNN). In this paper, the CNN is combined with the long short-term memory (LSTM) network for the state of health estimation and remaining useful life assessment of lithium-ion batteries. To facilitate the features exploitation procedure for the ML algorithm, a K-means clustering algorithm is proposed for data classification. A comparative study of LSTM and the hybrid CNN-LSTM method is conducted to show the superiority of the proposed method.

A Comparative Study of Equivalent Circuit Models for Electro-Chemical Impedance Spectroscopy Analysis of Proton Exchange Membrane Fuel Cells

Electrochemical impedance spectroscopy is one of the important tools for the performance analysis and diagnosis of proton exchange membrane fuel cells. The equivalent circuit model is an effective method for electrochemical impedance spectroscopy resolution. In this paper, four typical equivalent circuit models are selected to comprehensively compare and analyze the difference in the fitting results of the models for the electrochemical impedance spectroscopy under different working conditions (inlet pressure, stoichiometry, and humidity) from the perspective of the fitting accuracy, change trend of the model parameters, and the goodness of fit. The results show that the fitting accuracy of the model with the Warburg element is the best for all under each working condition. When considering the goodness of fit, the model with constant phase components is the best choice for fitting electrochemical impedance spectroscopy under different inlet pressure and air stoichiometry. However, under different air humidity, the model with the Warburg element is best. This work can help to promote the development of internal state analysis, estimation, and diagnosis of the fuel cell based on the equivalent circuit modeling of electrochemical impedance spectroscopy.

Simultaneous and sequential state and parameter estimation using receding-horizon nonlinear Kalman filter

Online estimation of internal states and parameters is often required for process monitoring, fault diagnosis and real time optimization. The conventional approach to tracking drifting parameters is to model their variations as a random walk process and estimate them simultaneously with the states. However, tuning of the random walk model is not a trivial exercise. Recently, moving window based simultaneous state and parameter estimation schemes have been proposed under the assumption that the parameters change slowly and remain constant within the window. These formulations make use of the conventional recursive Bayesian estimators or moving horizon estimator (MHE) for state estimation. While simultaneous smoothing of states along with filtering gives an edge to MHE over the conventional recursive estimators, MHE is computation intensive scheme. Recently, developed Receding-horizon Nonlinear Kalman (RNK) filter combines advantages of MHE (simultaneous smoothing) and recursive estimators (less computation time). In this work, we extend the RNK formulation to carry out simultaneous state and parameter estimation schemes under the assumption that parameters change at a slow rate and remain constant within the RNK window. Two maximum likelihood (ML) based parameter estimation schemes are developed by constructing partial likelihood and complete likelihood functions. Both formulations are further extended under the Bayesian framework to derive their maximum a-posteriori (MAP) versions by incorporating prior information of the parameters. The only tuning parameter in the proposed parameter estimation schemes is the window size, which is relatively easy to select. The efficacies of the proposed formulations are evaluated using simulation studies on two reactor systems and experimental data obtained from the benchmark quadruple tank system. Simulation studies reveal that the complete likelihood based estimation schemes have an edge over the partial likelihood based estimation schemes. Also, the proposed estimation schemes generate reasonably accurate state and parameter estimates with a significant reduction in the average computation time.

Event-triggered adaptive approximately optimal tracking control of a class of non-affine SISO nonlinear systems via output feedback

In this paper, an event-triggered adaptive approximately optimal tracking control approach is proposed for a class of non-affine nonlinear single-input single-output (SISO) systems via output feedback. With the help of fuzzy logic systems (FLSs), a fuzzy state observer is designed for the estimation of internal states by approximating unknown nonlinear functions, where a low-pass filter (LPF) is added for the algebraic loop problem. Then, the output feedback control approach, in the backstepping framework and the event-triggered mechanism, is presented. It contains the adaptive backstepping control and the approximately optimal control via adaptive dynamic programming (ADP) technology, and the computation cost and transmission load are reduced while guaranteeing that the performance index of the system is approximately minimised. Finally, two simulation examples are provided to verify the effectiveness of the proposed approach.

Consideration of Cyber-Physical System using internal state estimation of soil ground for advanced hazard map

Consideration of Cyber-Physical System using internal state estimation of soil ground for advanced hazard map

Everything Leaves Footprints: Hardware Accelerated Intermittent Deep Inference

Current peripheral execution approaches for intermittently powered systems require full access to the internal hardware state for checkpointing or rely on application-level energy estimation for task partitioning to make correct forward progress. Both requirements present significant practical challenges for energy-harvesting, intelligent edge Internet-of-Things devices, which perform hardware-accelerated deep neural network (DNN) inference. Sophisticated compute peripherals may have an inaccessible internal state, and the complexity of DNN models makes it difficult for programmers to partition the application into suitably sized tasks that fit within an estimated energy budget. This article presents the concept of inference footprinting for intermittent DNN inference, where accelerator progress is accumulatively preserved across power cycles. Our middleware stack, HAWAII, tracks and restores inference footprints efficiently and transparently to make inference forward progress, without requiring access to the accelerator internal state and application-level energy estimation. Evaluations were carried out on a Texas Instruments device, under varied energy budgets and network workloads. Compared to a variety of task-based intermittent approaches, HAWAII improves the inference throughput by 5.7%-95.7%, particularly achieving higher performance on heavily accelerated DNNs.

Fast Adaptive Observers for Battery Management Systems

The underlying models of lithium-ion batteries are spatiotemporal and, therefore, consist of partial differential equations (PDEs), as they have to capture spatiotemporal electrochemical mechanisms. As parametric uncertainty increases, often PDE models become inadequate as modeling errors due to parameter uncertainty are much larger than those due to model reduction to ordinary differential equations (ODEs). In this paper, we focus on a linear ODE model derived from the PDE combined with algebraic nonlinearities to carry out parameter identification. The ODE model captures the diffusion dynamics while the nonlinearities capture the overpotential and open-circuit potential aspects. The parameter identification method consists of a matrix-regressor adaptive observer, whose regressors are composed using both the states of the linear dynamic model and suitably constructed basis functions of the algebraic nonlinearities. Under conditions of persistent excitation, the parameter and state estimation errors are shown to converge to zero if the overpotential is known, and to a compact set if the overpotential is unknown. A PDE-based single-particle model is used as the truth model to evaluate the accuracy of the proposed adaptive observer. It is shown that the adaptive observer is able to carry out fast estimation of internal states for a battery management system, such as the state of charge and state of health, under a range of operating conditions.

Event-trigger-based adaptive output feedback approximately optimal tracking control of a class of MIMO non-affine nonlinear systems

An event-trigger-based adaptive output feedback approximately optimal tracking control strategy is presented for a class of multiple-input multiple-output (MIMO) non-affine nonlinear systems. A fuzzy state observer is designed for estimation of internal states, where fuzzy logic systems (FLSs) are employed to approximate unknown nonlinear dynamics. In the event-triggered and backstepping control framework, an output feedback control strategy, consisting of the adaptive backstepping control and the approximately optimal control via the adaptive dynamic programming (ADP) method, is presented. This strategy not only reduces the number of executions from the actuator but also guarantees that the performance index of the system is approximately minimized. Finally, two simulation examples are provided to verify the effectiveness of the proposed strategy.

Development of landslide sign detection using longitudinal vibration mode of elastic bar is subjected to ground soil elastic force

In this paper, the landslide sign detection sensing method is proposed. First, the requirement definition is conducted based on survey result of conventional detection technology. As the result, importance of internal state estimation of soil status is pointed out. Furthermore, active sensing method based on longitudinal vibration mode of elastic bar subjected to ground soil elastic force is proposed in order to realize the internal state estimation of soil status. Moreover, the prototyping of active sensing system of elastic bar is conducted. The system is composed from elastic bar, vibration speaker, acceleration sensor and data logger. The laboratory experiment of vibration mode visualization is conducted in order to decision of vibration diagnosis mode. As the result, the vibration diagnosis mode is selected. Mode shape of the vibration diagnosis mode is derived from longitudinal mode of elastic bar.

Battery Adaptive Observer for a Single-Particle Model With Intercalation-Induced Stress

Battery electrode particle fracture due to stress generation is a critical mechanism causing capacity fade, and thus reducing battery life. This paper develops a nonlinear adaptive observer for lithium-ion battery state of charge (SOC), electrode particle stress, and solid phase diffusivity estimation using a high-fidelity coupled single particle—mechanical stress model, where the stress submodel captures stress development during lithium-ion intercalation and deintercalation. Simultaneous state and parameter estimation based on coupled single particle and mechanical stress model is extremely challenging because the coupled model is given by highly nonlinear partial differential equations. We address this problem by reducing the coupled model to a nonlinear finite dimensional system. The key novelty of this paper is a nonlinear internal state and parameter estimation methodology, from which the internal stress and the state of health-related parameters are monitored from real-time electric current and terminal voltage measurements. Numerical studies on simulation and experimental data have been conducted to illustrate the performance of the proposed estimation scheme.