An improved theoretical electrochemical-thermal modelling of lithium-ion battery packs in electric vehicles

Amiribavandpour, Parisa; Shen, Weixiang; Mu, Daobin; Kapoor, Ajay

doi:10.1016/j.jpowsour.2015.03.022

Cited by 82 publications

(47 citation statements)

References 18 publications

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“…Hence in spite of the positive Qn the temperature falls as the Qs is more prevalent. This entropic heat generation behaviour has also been shown in other newer analysis [28].…”

Section: Adaptive Thermal Modelsupporting

confidence: 82%

“…For example many studies have been focused on vehicular applications specifically electric vehicles (EV) and hybrid electric vehicles (HEV) with many of the recent theoretical and experimental research efforts also have been aimed towards EV battery operation [28][29][30]. The high charge and discharge rates of batteries used in these applications warrant a well-designed thermal management systems [31].…”

Section: Introductionmentioning

confidence: 99%

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Theoretical Modelling Methods for Thermal Management of Batteries

Shabani¹,

Biju²

2015

Energies

108

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Abstract:The main challenge associated with renewable energy generation is the intermittency of the renewable source of power. Because of this, back-up generation sources fuelled by fossil fuels are required. In stationary applications whether it is a back-up diesel generator or connection to the grid, these systems are yet to be truly emissions-free. One solution to the problem is the utilisation of electrochemical energy storage systems (ESS) to store the excess renewable energy and then reusing this energy when the renewable energy source is insufficient to meet the demand. The performance of an ESS amongst other things is affected by the design, materials used and the operating temperature of the system. The operating temperature is critical since operating an ESS at low ambient temperatures affects its capacity and charge acceptance while operating the ESS at high ambient temperatures affects its lifetime and suggests safety risks. Safety risks are magnified in renewable energy storage applications given the scale of the ESS required to meet the energy demand. This necessity has propelled significant effort to model the thermal behaviour of ESS. Understanding and modelling the thermal behaviour of these systems is a crucial consideration before designing an efficient thermal management system that would operate safely and extend the lifetime of the ESS. This is vital in order to eliminate intermittency and add value to renewable sources of power. This paper concentrates on reviewing theoretical approaches used to simulate the operating temperatures of ESS and the subsequent endeavours of modelling thermal management systems for these systems. The intent of this review is to present some of the different methods of modelling the thermal behaviour of ESS highlighting the advantages and disadvantages of each approach. OPEN ACCESSEnergies 2015, 8 10154

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“…Hence in spite of the positive Qn the temperature falls as the Qs is more prevalent. This entropic heat generation behaviour has also been shown in other newer analysis [28].…”

Section: Adaptive Thermal Modelsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Theoretical Modelling Methods for Thermal Management of Batteries

Shabani¹,

Biju²

2015

Energies

108

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“…The diffusivitity of lithium ions and anion in the electrolyte, D 0 L and D 0 A , are obtained by simultaneously solving (10) and (11) given known values of the effective diffusivity D 0 e and the transference number θ 0 at ambient temperature. [22] xp (m) 70×10 −6 [22] xn (m) 95×10 −6 [22] L (m) 129×10 −6 [22] λ cc p (m) 1.0 × 10 −6 [22] λ cc n (m) 6.2 × 10 −6 [22] A cell (m 2 ) 16.94×10 −2 [22] F (C mol −1 ) 96487 [66] R (J mol −1 K −1 ) 8.314 [66] Ta (K) 298.15 [22] Iapp (A) 2.3 [69] i 0 (A m −2 ) 13.6 [64] [22] 3.9 ×10 −14 [22] θ 0 0.363 [22,65] where = [(φ a,p ν a,n G n )/(φ a,n ν a,p G p )] 1/2 1, C = C p /C n 1, and ε n = G n ν a,n /φ a,n 1. where the equivalent resistances are defined in (72). Note that (B.8) also applies to the separator.…”

Section: Discussionmentioning

confidence: 99%

“…In each of the phases, conservation of mass, charge, and thermal energy is imposed. A one-dimensional model is employed because of the large aspect ratio between the height and length of a cell, consistent with a number of previous studies [22,40,64,65].…”

Section: Cell-scale Modellingmentioning

confidence: 99%

Asymptotic reduction and homogenization of a thermo-electrochemical model for a lithium-ion battery

Hennessy

Moyles

2020

Applied Mathematical Modelling

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We study two thermo-electrochemical models for lithium-ion batteries. The first is based on volume averaging the electrode microstructure whereas the second is based on the pseudo-two-dimensional (P2D) approach which treats the electrode as a collection of spherical particles. A scaling analysis is used to reduce the volume-averaged model and show that the electrochemical reactions are the dominant source of heat. Matched asymptotic expansions are used to compute solutions of the volume-averaged model for the cases of constant applied current, oscillating applied current, and constant cell potential. The asymptotic and numerical solutions of the volume-averaged model are in remarkable agreement with numerical solutions of the thermal P2D model for (dis)charge rates up to 2C, and reasonable agreement is found at 4C. Homogenisation is then used to derive a thermal model for a battery consisting of several connected lithium-ion cells. Despite accounting for the Arrhenius dependence of the reaction coefficients, we show that thermal runaway does not occur in the model. Instead, the cell potential is simply pushedcloser to the open-circuit potential. We also show that in many cases, the homogenised battery model can be solved analytically, making it ideal for use in on-board thermal management systems.

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“…In a 3-level full factorial design, each factor is varied over three levels within the determined ranges, as summarised in Table 1. The range of variation of the design variables for LFP lithium ion battery is determined based on the data from literature [11,28,29,33,[47][48][49], which is: r p : 30-100 nm, L pos : 20-100 µm, ε s,pos : 0.3-0.7, C-rate: 1-5. The number of required simulations in a 3 level full factorial design for four design variables is equal to 3 4 , i.e., 81 numerical case studies.…”

Section: Full Factorial Designmentioning

confidence: 99%

Electrochemical-Thermal Modelling and Optimisation of Lithium-Ion Battery Design Parameters Using Analysis of Variance

2017

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A 1D electrochemical-thermal model of an electrode pair of a lithium ion battery is developed in Comsol Multiphysics. The mathematical model is validated against the literature data for a 10 Ah lithium phosphate (LFP) pouch cell operating under 1 C to 5 C electrical load at 25 • C ambient temperature. The validated model is used to conduct statistical analysis of the most influential parameters that dictate cell performance; i.e., particle radius (r p ); electrode thickness (L pos ); volume fraction of the active material (ε s,pos ) and C-rate; and their interaction on the two main responses; namely; specific energy and specific power. To achieve an optimised window for energy and power within the defined range of design variables; the range of variation of the variables is determined based on literature data and includes: r p : 30-100 nm; L pos : 20-100 µm; ε s,pos : 0.3-0.7; C-rate: 1-5. By investigating the main effect and the interaction effect of the design variables on energy and power; it is observed that the optimum energy can be achieved when (r p < 40 nm); (75 µm < L pos < 100 µm); (0.4 < ε s,pos < 0.6) and while the C-rate is below 4C. Conversely; the optimum power is achieved for a thin electrode (L pos < 30 µm); with high porosity and high C-rate (5 C).

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An improved theoretical electrochemical-thermal modelling of lithium-ion battery packs in electric vehicles

Cited by 82 publications

References 18 publications

Theoretical Modelling Methods for Thermal Management of Batteries

Theoretical Modelling Methods for Thermal Management of Batteries

Asymptotic reduction and homogenization of a thermo-electrochemical model for a lithium-ion battery

Electrochemical-Thermal Modelling and Optimisation of Lithium-Ion Battery Design Parameters Using Analysis of Variance

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