Extending explicit and linearly implicit ODE solvers for index-1 DAEs

Lawder, Matthew T; Ramadesigan, Venkatasailanathan; Suthar, Bharatkumar; Subramanian, Venkat R.

doi:10.1016/j.compchemeng.2015.07.002

Cited by 27 publications

(23 citation statements)

References 40 publications

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“…A robust initialization strategy was used to find consistent initial conditions 21 . The same set of equations were also solved in IDA which enables order of magnitude faster computation compared to Maple.…”

Section: Model Developmentmentioning

confidence: 99%

An Efficient Electrochemical Tanks-in-Series Model for Lithium-Sulfur Batteries

Parke¹,

Subramaniam²,

Kolluri³

et al. 2020

Preprint

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This article applies and efficiently implements the Tanks-in-Series methodology (J. Electrochem. Soc., 167, 013534 (2020)) to generate a computationally efficient electrochemical model for Lithium Sulfur batteries. The original Tank model approach for Lithium-ion batteries is modified to account for porosity changes with time. In addition, exponential scaling is introduced that enables efficient simulation of the model equations to address a wide range of time constants present for different reactions in Lithium-sulfur system. The tank model achieves acceptable voltage error even for relatively aggressive conditions of discharge. Predictions of internal electrochemical variables are examined, and electrochemical implications of the approximations discussed. This suggests significant potential for real-time applications such as optimal charging, cell-balancing and estimation, and represents a step forward in efforts to incorporate detailed electrochemical models in advanced Battery Management Systems for Lithium-Sulfur batteries.

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Section: Model Developmentmentioning

confidence: 99%

An Efficient Electrochemical Tanks-in-Series Model for Lithium-Sulfur Batteries

Parke¹,

Subramaniam²,

Kolluri³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…This system can cause difficultly in determining consistent ICs [111]. When deviating from the consistent ICs, many initialization routines and solvers fail to obtain a solution [112], which shows the range of possible ICs for the algebraic variable that provide a solution for different solvers and approaches including the proposed single-step approach.…”

Section: Advantagesmentioning

confidence: 99%

State of the Art and Future Research Needs for Multiscale Analysis of Li-Ion Cells

Shah

Balsara

Banerjee

et al. 2017

Journal of Electrochemical Energy Conversion and Storage

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The performance, safety, and reliability of Li-ion batteries are determined by a complex set of multiphysics, multiscale phenomena that must be holistically studied and optimized. This paper provides a summary of the state of the art in a variety of research fields related to Li-ion battery materials, processes, and systems. The material presented here is based on a series of discussions at a recently concluded bilateral workshop in which researchers and students from India and the U.S. participated. It is expected that this summary will help understand the complex nature of Li-ion batteries and help highlight the critical directions for future research.

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“…The advantage of implementing control through GMC is that the variables are only to be integrated in time. We implemented the control scheme using the direct iteration-free DAE numerical solvers previously reported by Lawder et al 30 This makes the approach more robust and fail-proof, as standard optimizers fail to find the optimal solution often times because of their inability to find consistent initial conditions for algebraic variables. It should be noted that for simple first order or second order processes, the time constants of the processes can be related to the tuning of the GMC parameters.…”

Section: Generic Model Control For Battery Modelsmentioning

confidence: 99%

Generic Model Control for Lithium-Ion Batteries

Pathak

Kolluri

Subramanian

2017

J. Electrochem. Soc.

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Battery Management Systems (BMS) are critical to safe and efficient operation of lithium-ion batteries and accurate prediction of the internal states. Smarter BMS that can estimate and implement optimal charging profiles in real-time are important for advancement of the Li-ion battery technology. Estimating optimal profiles using physics-based models is computationally expensive because of the non-linear and stiff nature of the model equations, involving the need for constrained nonlinear optimization. In this work, we present an alternative approach to control batteries called as Generic Model Control, or Reference System Synthesis. This work enables robust stabilization and control of battery models to set-point as an alternative approach, eliminating the need to perform optimization of nonlinear models. As compared to the generic model control approaches implemented by previous researchers, we implement the same concept using direct DAE numerical solvers. The results are presented for single input single objective problems, and for constrained problems for various battery models. Lithium-ion batteries are used in a wide range of applications ranging from cell phones to electric vehicles (EVs) to electric grids. With the expected decline in the price of batteries over the next few years, the market for EVs and grids is growing rapidly. Long recharging times, capacity fade and thermal runaway are some of the main issues that need to be addressed in order to use batteries safely and efficiently for a longer time. This necessitates the use of smarter battery management systems that can derive optimal use strategies by exploiting the dynamics of the battery.Battery models based on transport, physical, electrochemical and thermodynamics principles can be used to monitor the internal states of the battery and to obtain optimal control strategies. Such models are computationally expensive which limits their use in control applications in real-time. Various issues, such as the onset of dynamics of some internal states only during use of batteries at high charge/discharge rates, and several other states which are active only while charging or discharging, adds to the challenges of the observability of these physics-based electrochemical models. Hence, various approximated and reduced order models have been proposed in the past, to make the models strongly observable in local intervals. 1,[2][3][4][5] These models are also highly non-linear in nature, which adds significant challenges to implement control strategies along with the abovementioned issues. Past researchers have shown multivariable control techniques like Dynamic Matrix Control (DMC), 6 Internal Model Control (IMC), 7 and Model Predictive Heuristic Control (MPHC) 8 for a variety of systems. However, these models suffer from their reliability on the linear approximations of the experimentally obtained step-response data and do not directly consider the full nonlinear model explicitly. Several researchers have been working to design optimal charging profiles ...

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Extending explicit and linearly implicit ODE solvers for index-1 DAEs

Cited by 27 publications

References 40 publications

An Efficient Electrochemical Tanks-in-Series Model for Lithium-Sulfur Batteries

An Efficient Electrochemical Tanks-in-Series Model for Lithium-Sulfur Batteries

State of the Art and Future Research Needs for Multiscale Analysis of Li-Ion Cells

Generic Model Control for Lithium-Ion Batteries

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