A General Energy Balance for Battery Systems

Bernardi, Dawn M.; Pawlikowski, Ellen M.; Newman, John

doi:10.1149/1.2113792

Cited by 1,512 publications

(613 citation statements)

References 4 publications

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“…Excessive heat generation may lead to performance degradation and, in extreme cases, thermal runaway of the cell. 30,31 In order to address these possible safety issues, thermal dynamics are included with the set of conservation equations describing the system. The thermal equations include different source terms, which are the ohmic, reversible, and reaction generation rates Q ohm , Q rev , and Q rxn , respectively.…”

Section: Battery Modelmentioning

confidence: 99%

LIONSIMBA: A Matlab Framework Based on a Finite Volume Model Suitable for Li-Ion Battery Design, Simulation, and Control

Torchio

Magni

Gopaluni

et al. 2016

J. Electrochem. Soc.

207

171

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Consumer electronics, wearable and personal health devices, power networks, microgrids, and hybrid electric vehicles (HEVs) are some of the many applications of lithium-ion batteries. Their optimal design and management are important for safe and profitable operations. The use of accurate mathematical models can help in achieving the best performance. This article provides a detailed description of a finite volume method (FVM) for a pseudo-two-dimensional (P2D) Li-ion battery model suitable for the development of model-based advanced battery management systems. The objectives of this work are to provide: (i) a detailed description of the model formulation, (ii) a parametrizable Matlab framework for battery design, simulation, and control of Li-ion cells or battery packs, (iii) a validation of the proposed numerical implementation with respect to the COMSOL MultiPhysics commercial software and the Newman's DUALFOIL code, and (iv) some demonstrative simulations involving thermal dynamics, a hybrid charge-discharge cycle emulating the throttle of an HEV, a model predictive control of state of charge, and a battery pack simulation. The increasing demand for portable devices (e.g., smartphones) and hybrid electric vehicles (HEVs) calls for the design and management of storage devices of high power density and reduced size and weight. During the many decades of research, different chemistries of batteries have been developed, such as Nickel Cadmium (NiCd), Nickel Metal Hydride (NiMH), Lead Acid and Lithium ion (Li-ion) and Lithium ion Polymer (Li-Poly) (e.g., see Refs. 1-4). Among electrochemical accumulators, Li-ion batteries provide one of the best tradeoff in terms of power density, low weight, cell voltage, and low self-discharge. 5 Mathematical models can support the design of new batteries as well as the development of new advanced battery management systems (ABMS). [6][7][8] According to the literature, mathematical models for Li-ion battery dynamics fall within two main categories: Equivalent Circuit Models (ECMs) and Electrochemical Models (EMs). ECMs use only electrical components to model the dynamic behavior of the battery. ECMs include (i) the R int model where only a resistance and a voltage source are used to model the battery, (ii) the RC model (introduced by the company SAFT 9 ) where capacitor dynamics have been added to the R int model, 10 and (iii) the Thevenin model, which is an extension of the RC model (e.g., see Refs. 11, 12 and references therein). In contrast, EMs explicitly represent the chemical processes that take place in the battery. While ECMs have the advantage of simplicity, EMs are more accurate due to their ability to describe detailed physical phenomena. 13 The most widely used EM in the literature is the porous electrode theory-based pseudo-two-dimensional (P2D) model, 14 which is described by a set of tightly coupled and highly nonlinear partial differential-algebraic equations (PDAEs). In order to exploit the model for simulation and design purposes, the set of PDAEs are reformu...

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Section: Battery Modelmentioning

confidence: 99%

LIONSIMBA: A Matlab Framework Based on a Finite Volume Model Suitable for Li-Ion Battery Design, Simulation, and Control

Torchio

Magni

Gopaluni

et al. 2016

J. Electrochem. Soc.

207

171

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show abstract

“…The modeling approaches are based on transport equations for Li ions and charges in the electrolyte as well as in the active particles of cathode and anode under isothermal conditions. Most modeling approaches for the thermal behavior of batteries are concentrating on overall thermal balance equations for a whole cell [9,10,11] [12, 13,14] by combining phenomenologically thermodynamic considerations on entropy or enthalpy changes within a cell with reasonable assumptions on out of equilibrium processes like Joule heating, heat of mixing, Peltier effect and Soret effect. An approach based on local continuity equations for the temperature was presented in [15], extending previous work on species and charge transport in batteries [16].…”

Section: Introductionmentioning

confidence: 99%

Thermodynamic consistent transport theory of Li-ion batteries

Latz

Zausch

2011

Journal of Power Sources

178

220

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Most Li ion insertion batteries consist of a porous cathode, a separator filled with electrolyte and an anode, which very often also has some porous structure. The solid part especially in the cathode is usually produced by mixing a powder of the actual active particles, in which Li ions will be intercalated, binder and carbon black to enhance the electronic conductivity of the electrode. As a result the porous structure of the electrodes is very complex, leading to complex potential, ion and temperature distributions within the electrodes. The intercalation and deintercalation of ions cannot be expected to be homogeneously distributed over the electrode due to the different transport properties of electrolyte and active particles in the electrode and the complex three-dimensional pore structure of the electrode. The influence of the final microstructure on the distribution of temperature, electric potential and ions within the electrodes is not known in detail, but may influence strongly the onset of degradation mechanisms. For being able to numerically simulate the transport phenomena, the equations and interface conditions for ion, charge and heat transport within the complex structure of the electrodes and through the electrolyte filled separator are needed. We will present a rigorous derivation of these equations based exclusively on general principles of nonequilibrium thermodynamics. The theory is thermodynamically consistent i.e. it guarantees strictly positive entropy production. The irreversible and reversible sources of heat are derived within the theory. Especially the various contribution to the Peltier heat due to the intercalation of ions are obtained as a result of the theory

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“…Repeating the above operation at the other five temperatures of 10,20,30,40, and 50 °C, we acquire six pairs of , . Then, the correlations of -and -can be approximated by polynomials as…”

Section: Peukert Coefficient Identification Testmentioning

confidence: 99%

“…Assuming that the temperature gradient across the battery is uniform, the thermal balance [20] can be expressed as…”

Section: Thermal Effect Modelingmentioning

confidence: 99%

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Xie

2018

Energies

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To achieve accurate state-of-charge (SoC) estimation for LiFePO 4 (lithium iron phosphate) batteries under harsh conditions, this paper resorts to the Peukert's law to accommodate different temperatures and load excitations. By analyzing battery heat generation and dissipation, a thermal evolution model (TEM) is elaborated and exploited for on-line parameter identification of the equivalent circuit model (ECM). Then, a SoC estimation framework is proposed based on the Adaptive Extended Kalman Filter (AEKF) algorithm. Experimental results on a LiFePO 4 pack subject to the Federal Urban Driving Schedule (FUDS) profile under different temperatures and initial states suggest that the proposed SoC estimator provides good robustness and accuracy against changing temperature and highly dynamic loads.

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A General Energy Balance for Battery Systems

Cited by 1,512 publications

References 4 publications

LIONSIMBA: A Matlab Framework Based on a Finite Volume Model Suitable for Li-Ion Battery Design, Simulation, and Control

LIONSIMBA: A Matlab Framework Based on a Finite Volume Model Suitable for Li-Ion Battery Design, Simulation, and Control

Thermodynamic consistent transport theory of Li-ion batteries

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

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