Efficient Macro-Micro Scale Coupled Modeling of Batteries

Subramanian, Venkat R.; Diwakar, V.; Tapriyal, Deepak

doi:10.1149/1.2032427

Cited by 344 publications

(257 citation statements)

References 29 publications

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“…If the equation of the solid phase concentration at any time t is observed, it is clear that, it depends on a radial variable, which brings complexity while the overall model is under simulation or under trial for solving. To eradicate this solution complexity, this r dependency is solved in [41]. The equations adopted from [41] are provided below:…”

Section: Governing Equationsmentioning

confidence: 99%

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Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery

Rahman¹,

Anwar²,

Izadian³

2016

Adv Automob Eng

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Section: Governing Equationsmentioning

confidence: 99%

“…To eradicate this solution complexity, this r dependency is solved in [41]. The equations adopted from [41] are provided below:…”

Section: Governing Equationsmentioning

confidence: 99%

Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery

Rahman¹,

Anwar²,

Izadian³

2016

Adv Automob Eng

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“…Battery models are typically solved efficiently using the method of lines in which discretization of the spatial derivatives results in a system of first-order differential algebraic equations (DAEs) 33,34 that can be solved using optimized solvers for initial value problems. DAEs can be difficult to solve because the initial conditions must be consistent with the algebraic equations, which causes many solvers to fail if inconsistent conditions are provided, especially when nonlinear algebraic equations are considered.…”

Section: Current Approach and The Role Of Efficient Battery Simulationmentioning

confidence: 99%

“…Using the parabolic profile approximation for the concentration profile in the radial direction, the number of DAEs can be significantly reduced, thereby improving computational efficiency. 34 For the case with 50 node points across each electrode and 25 node points for the separator, roughly 500 DAEs must be solved, much less than the 2000 for a full finite difference approach. The parabolic profile approximation is valid for long times and low rates, but has inaccuracies when there is a large gradient in the solid phase particles, which become significant for rates greater than about 4C.…”

Section: Current Approach and The Role Of Efficient Battery Simulationmentioning

confidence: 99%

Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation

Northrop

Suthar

Ramadesigan

et al. 2014

J. Electrochem. Soc.

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Improving the efficiency and utilization of battery systems can increase the viability and cost-effectiveness of existing technologies for electric vehicles (EVs). Developing smarter battery management systems and advanced sensing technologies can circumvent problems arising due to capacity fade and safety concerns. This paper describes how efficient simulation techniques and improved algorithms can alleviate some of these problems to help electrify the transportation industry by improving the range of variables that are predictable and controllable in a battery in real-time within an electric vehicle. The use of battery models in a battery management system (BMS) is reviewed. The effect of different simulation techniques on computational cost and accuracy are also compared, and the validity of implementation in a microcontroller environment for model predictive control (MPC) is addressed. Using mathematical techniques to add more physics without losing efficiency is also discussed. Behavioral predictions can be made using mathematical models without the need to directly observe the states using expensive and time consuming physical experiments. Such predictions allow for more intelligent design of new systems, which is generally limited by the mathematical techniques used and the computational resources available. An improved modeling and simulation approach can achieve the following goals when applied to engineering systems: r More accurate predictions by using more meaningful models r Faster simulation with fewer computational resources r Optimization of design parameters r Better control, allowing aggressive performance while maintaining safetyHere we focus on the application of such principles to the use of physics-based battery models in battery management systems in electric vehicles.In recent years, battery electric vehicles (BEV) have increased in popularity to reduce the dependence on fossil fuels. Lithium-ion batteries are a popular choice as an energy storage medium for high demand applications due to their large energy density but are not utilized to their full capacity in BEV applications; operating a Li-ion battery too aggressively can lead to reduced cycle life and unpredictable thermal runaway reactions. These challenges reduce the functional capacity of the battery available for propulsion.The consumer expects the vehicle's performance and capabilities to remain uniform regardless of the state of charge or age of the battery, as they have become accustomed to internal combustion engines. When the battery is nearly depleted, it is difficult or impossible to satisfy high power demand, which is aggravated as the battery ages. To avoid these difficulties, the BMS shuts off the battery with a large amount of energy unused, so that Li-ion batteries for EVs are greatly overdesigned and carry extra weight and volume, reducing efficiency and increasing cost.1 Research is underway to better understand the internal limitations of Li-ion batteries including SEI layer growth, side * Electrochemical Society ...

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“…In [10,11], Subramanian et al used perturbation techniques. Model reduction and Chebyshev polynomial methods were used by Bhikkaji and Söderström in [12].…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Robust Numerical Solution of the Full-Order Multiscale Model of Lithium-Ion Battery

Beneš

Fučík

Havlena

et al. 2018

Mathematical Problems in Engineering

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We propose a novel and efficient numerical approach for solving the pseudo two-dimensional multiscale model of the Li-ion cell dynamics based on first principles, describing the ion diffusion through the electrolyte and the porous electrodes, electric potential distribution, and Butler-Volmer kinetics. The numerical solution is obtained by the finite difference discretization of the diffusion equations combined with an original iterative scheme for solving the integral formulation of the laws of electrochemical interactions. We demonstrate that our implementation is fast and stable over the expected lifetime of the cell. In contrast to some simplified models, it provides physically consistent results for a wide range of applied currents including high loads. The algorithm forms a solid basis for simulations of cells and battery packs in hybrid electric vehicles, with possible straightforward extensions by aging and heat effects.

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Efficient Macro-Micro Scale Coupled Modeling of Batteries

Cited by 344 publications

References 29 publications

Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery

Electrochemical Model Based Fault Diagnosis of Lithium Ion Battery

Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation

An Efficient and Robust Numerical Solution of the Full-Order Multiscale Model of Lithium-Ion Battery

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