Parameter identification of reduced-order electrochemical model simplified by spectral methods and state estimation based on square-root cubature Kalman filter

Ding, Qianqian; Wang, Yujie; Chen, Zonghai

doi:10.1016/j.est.2021.103828

Cited by 25 publications

(4 citation statements)

References 29 publications

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“…Several literature works exist on SA for obtaining influential parameters. 30,35 Common ways to perform SA can broadly be classified into two categories, namely analytical and empirical. 13 To know the parameters in an integrated and interacting geometrical system, we have defined the following sensitivity function (SF) directly from the state equations.…”

Section: Selection Of Parametersmentioning

confidence: 99%

Predictive control scheme by integrating event-triggered mechanism and disturbance observer under actuator failure and sensor fault

Panda,

Thirunavukarasu,

Panda

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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Liquid-level control in an interacting storage-tank system exhibits major challenges due to the effect of interaction and the presence of combustible/flammable liquid as it faces huge threats from the unmeasurable disturbances, irregular geometry and unmodelled dynamics of the process plant. A combination of tanks with multiple geometries may be preferred for storing liquids using common pumps for energy conservation. Continuous improvement of risk assessment, predicting hazardous scenarios and implementation of inherently-safer-control systems ensure process safety and, as a result, ease out the safe operation. From the process analysis, it was observed that the dynamics of the interacting tank are very sensitive to effective parameters like valve coefficient and pump gain. To overcome the loss of inventory or production, we investigate the synthesis of a non-linear model predictive control scheme with an outline of simultaneous state and parameter estimation strategies. This mechanism deals with a stochastic event-trigger cubature Kalman filter scheme to estimate process states while performing servo operations. To mitigate plant uncertainty, reduce the impact of large disturbances and nullify the effect of actuator failure/sensor fault, influential model parameters are estimated simultaneously with the model state(s). Predicted values of the model states/parameters/faults are used to derive an effective control effort. An inferential non-linear model predictive control paradigm is proposed, where the primary variable(s) is (are) measured with the help of measurable secondary state(s). To demonstrate the practical utility and effectiveness of the control framework for safe operation and loss prevention, a complex industrial process (quadrupled tank) is considered for control, where the system dynamics have been structured newly. Realistic simulations such as servo-regulatory compliance and elimination of measurement noise with a state-of-the-art simulator ensures the efficacy of the proposed scheme. The results are analysed further for a comparative study with traditional non-linear model predictive control law. To guarantee convergence of the estimator and stability of the closed-loop controller, the Lyapunov theorem is well blended.

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Section: Selection Of Parametersmentioning

confidence: 99%

Predictive control scheme by integrating event-triggered mechanism and disturbance observer under actuator failure and sensor fault

Panda,

Thirunavukarasu,

Panda

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…For the changeable environment of electric vehicles or the technical requirements of smart grid energy storage, battery thermoelectric modeling needs to meet at least three conditions at the same time: first, BMS hardware computing complexity requirements; second, adaptability requirements for complex and changeable environments; third, online real-time estimation requirements for internal temperature and terminal voltage. In view of this, electrochemical thermoelectric models [23][24][25][26][27][28][29] using a large number of partial differential equations (PDEs) do not seem to meet the requirements of embedded system applications. Under these requirements, the battery thermoelectric coupling modeling strategy developed based on mature circuit theory to achieve effective prediction of internal temperature and terminal voltage has become the most potential solution [30][31].…”

Section: Literature Reviewmentioning

confidence: 99%

Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries

et al. 2022

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“…Normally, the PCM must be solved using numerical methods such as the finite-element-method, which prevents it from real-time applications and large-scale simulation studies. To resolve the computational burden issue and meanwhile keep the accuracy loss on an acceptable level, the ROM has been developed by neglecting the less important processes or conducting mathematical simplification [14][15][16][17][18][19][20][21][22]. Compared to the full-order PCM, the computational demand of the ROM can be largely reduced and the accuracy loss can be generally kept to an acceptable level.…”

Section: Parameter Identification In Time Domainmentioning

confidence: 99%

Comparative Study of Parameter Identification with Frequency and Time Domain Fitting Using a Physics-Based Battery Model

Zhao

Jossen

2022

Batteries

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Parameter identification with the pseudo-two-dimensional (p2D) model has been an important research topic in battery engineering because some of the physicochemical parameters used in the model can be measured, while some can only be estimated or calculated based on the measurement data. Various methods, either in the time domain or frequency domain, have been proposed to identify the parameters of the p2D model. While the methods in each domain bring their advantages and disadvantages, a comprehensive comparison regarding parameter identifiability and accuracy is still missing. In this present work, some selected physicochemical parameters of the p2D model are identified in four different cases and with different methods, either only in the time domain or with a combined model. Which parameters are identified in the frequency domain is decided by a comprehensive analysis of the analytical expression for the DRT spectrum. Finally, the parameter identifiability results are analyzed and the validation results with two highly dynamic load profiles are shown and compared. The results indicate that the model with ohmic resistance and the combined method achieves the best performance and the average voltage error is at the level of 12 mV.

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Parameter identification of reduced-order electrochemical model simplified by spectral methods and state estimation based on square-root cubature Kalman filter

Cited by 25 publications

References 29 publications

Predictive control scheme by integrating event-triggered mechanism and disturbance observer under actuator failure and sensor fault

Predictive control scheme by integrating event-triggered mechanism and disturbance observer under actuator failure and sensor fault

Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries

Comparative Study of Parameter Identification with Frequency and Time Domain Fitting Using a Physics-Based Battery Model

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