Analysis of Online Parameter Estimation for Electrochemical Li-ion Battery Models via Reduced Sensitivity Equations

Gima, Zachary T.; Kato, Dylan; Klein, Reinhardt; Moura, Scott J.

doi:10.23919/acc45564.2020.9147260

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…Many different combinations of parameter values can lead to the same model output. Even if an optimized set of parameter values fits the experimental input-output data perfectly, the model may not give acceptable results in terms of predictions of cell internal variables [21].…”

Section: Grouping Parametersmentioning

confidence: 97%

“…If the model has been lumped there can be two solutions, the aging model can be reformulated accordingly, or the set of lumped parameters can be freed using additional tests (either cycling or physico-chemical). On the other hand, if some parameters have been discarded, or even if nominal values are used because voltage is insensitive to the parameters, there is a risk that the internal variables are not correctly estimated [21]. This can lead to a poor response of the aging model, since internal variables are used to feed aging models.…”

Section: Grouping Parametersmentioning

confidence: 99%

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Review of computational parameter estimation methods for electrochemical models

Miguel

Plett

Trimboli

et al. 2021

Journal of Energy Storage

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Section: Grouping Parametersmentioning

confidence: 97%

Section: Grouping Parametersmentioning

confidence: 99%

Review of computational parameter estimation methods for electrochemical models

Miguel

Plett

Trimboli

et al. 2021

Journal of Energy Storage

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“…The rapid pace of advancement in microprocessor technology has enabled the implementation of high-fidelity reduced-order models (ROMs) of the Li-ion battery with over 100th system order [24], which has the potential to be incorporated in real-time embedded systems as a "digital twin" of the battery [26]. However, model orders not much higher than that of the prevailing ECMs with 2 to 5 states [27] are most desirable in the design of many BMS functionalities, such as online state estimation [28], available power prediction [29], parameter estimation [30], cell balancing [31], and fault diagnosis [32]. The development of optimal battery control, such as the fast charging for EVs, energy management for HEVs, and power flow control for grid-connected battery energy storage systems, also requires a low-order system to balance the trilemma of high charging/discharging rates, long battery life, and ensured safety, since the complexity of most of those algorithms increases dramatically as the order of the model increases [33].…”

Section: Model Order Reduction Techniquesmentioning

confidence: 99%

Model Order Reduction Techniques for Physics-Based Lithium-Ion Battery Management: A Survey

Karunathilake

Vilathgamuwa

et al. 2022

EEE Ind. Electron. Mag.

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To unlock the promise of electrified transportation and smart grid, emerging advanced battery management systems (BMSs) shall play an important role in health-aware monitoring, diagnosis, and control of widely used lithium-ion (Li-ion) batteries. Sophisticated physics-based battery models incorporated in the advanced BMS can offer valuable battery internal information to achieve improved operational safety, reliability and efficiency, and to extend the lifetime of the batteries. However, developed from the fundamental electrochemical and thermodynamic principles, the rigorous physics-based models are saddled with exceedingly high cognitive and computational complexity for practical applications. This article reviews prevailing order reduction techniques of physics-based Li-ion battery models to facilitate the development of next-generation BMSs. We analyze and comparatively characterize these techniques, mainly from perspectives of model fidelity, computational efficiency, and the scope of applications. By representing many effective and flexible reduced-order models as equivalent circuits, designers and practitioners, who do not have electrochemical expertise but with knowledge of circuit theory, can readily gain insights into multi-physical dynamics as well as their coupling effects inside the batteries. In addition, recommendations are made on how to select appropriate physics-based models for various model-based applications in battery management. Finally, the prospect of physical model-enabled BMSs is discussed, including the potential challenges and future research directions.

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“…Other methods of battery modeling such as online cell parameter estimation 48 or recursive neural networks 47 can be used to improve model accuracy and manage aging, however these methods need extensive sensor instrumentation inside the battery pack, or large operating data sets.…”

Section: Li-ion Battery Modelmentioning

confidence: 99%

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Hemmati

Yadav

Surresh

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

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Analysis of Online Parameter Estimation for Electrochemical Li-ion Battery Models via Reduced Sensitivity Equations

Cited by 6 publications

References 17 publications

Review of computational parameter estimation methods for electrochemical models

Review of computational parameter estimation methods for electrochemical models

Model Order Reduction Techniques for Physics-Based Lithium-Ion Battery Management: A Survey

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

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