Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models

Chen, Chang-Hui; Planella, Ferran Brosa; O’Regan, Kieran; Gastol, Dominika; Widanage, Widanalage Dhammika; Kendrick, Emma

doi:10.1149/1945-7111/ab9050

Cited by 342 publications

(385 citation statements)

References 77 publications

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“…This simulation was used to further validate DandeLiion. The voltage is compared with both experiment and simulation results from [26] and Figure 6 shows that good agreement is obtained.…”

Section: Illustrative Examplesmentioning

confidence: 76%

“…Hence the finite element discretisation retains its second order convergence rate despite the additional approximations. The integrals on the right-hand side of equations (25)- (26) have integrands that depend solely upon the basis functions and their derivatives, and so can be computed exactly (for details see Appendix A). This observation leaves us in a position to write down the DAE system arising from the spatial discretisation of the macroscopic PDEs (1)-(5) of the Doyle-Fuller-Newman model.…”

Section: Finite Element Discretisation Of the Macroscopic Equationsmentioning

confidence: 99%

“…and b i+1/2 = b| x=x i+1/2 . The first equation in (32) is required to set a reference value for the potential, and we select the value of zero at x = L 1 for convenience and without loss of generality. The algebraic equations for the potential in anode Φ a , which result from discretisation of equation (20) and the boundary conditions (7a) and (8), are…”

Section: Finite Element Implementationmentioning

confidence: 99%

“…LG M50 battery cell chemistry [26] and a simulation of a charge/discharge current profile applied to the cell at different (dis)charge rates. The DFN model implemented in DandeLiion was fully parametrised according to [26]. All the parameters, including functions (e.g.…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…The computational grid can be defined by the user as well, and for the purposes of this demonstration we set up 50 grid points in the electrolyte in each electrode, 30 points across the separator, and 100 nodes in each solid particle. The authors in [26] test their parametrisation using 0.5C, 1C, and 1.5C constant discharge currents followed by a relaxation period. As a first example, we simulate a full 1C discharge with two-hour relaxation.…”

Section: Illustrative Examplesmentioning

confidence: 99%

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Extremely Fast Solvers for Newman-Type Models of Li-Ion Cell (dis)Charge

et al. 2020

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DandeLiion (available at dandeliion.com) is a robust and extremely fast solver for the Doyle Fuller Newman (DFN) model, the standard electrochemical model for (dis)charge of a planar lithium-ion cell. DandeLiion conserves lithium, uses a second order spatial discretisation method (enabling accurate computations using relatively coarse discretisations) and is many times faster than its competitors. The code can be used 'in the cloud' and does not require installation before use. The difference in compute time between DandeLiion and its commercial counterparts is roughly a factor of 100 for the moderately-sized test case of the discharge of a single cell. Its linear scaling property means that the disparity in performance is even more pronounced for bigger systems, making it particularly suitable for applications involving multiple coupled cells. The model is characterised by a number of phenomenological parameters and functions, which may either be provided by the user or chosen from DandeLiion's library. This library contains data for the most commonly used electrolyte (LiPF 6) and

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“…This simulation was used to further validate DandeLiion. The voltage is compared with both experiment and simulation results from [26] and Figure 6 shows that good agreement is obtained.…”

Section: Illustrative Examplesmentioning

confidence: 76%

Section: Finite Element Discretisation Of the Macroscopic Equationsmentioning

confidence: 99%

Section: Finite Element Implementationmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

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Extremely Fast Solvers for Newman-Type Models of Li-Ion Cell (dis)Charge

et al. 2020

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A Solid‐State Battery Cathode with a Polymer Composite Electrolyte and Low Tortuosity Microstructure by Directional Freezing and Polymerization

Huang

Leung

et al. 2020

Advanced Energy Materials

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Rechargeable batteries that provide increased specific energy and improved safety over commercial Li ion batteries (LIBs) are in demand for applications such as electric vehicles (EVs), all-electric aircraft and the grid-scale storage of electricity from renewable but intermittent electricial generation. [1] Most commercial LIBs use a graphite anode (theoretical capacity 372 mAh g −1 , electrochemical potential −0.43 V versus standard hydrogen electrode). [2] Although graphite is relatively low cost and easy to process into electrodes at large scale, a switch to a Li metal anode would provide a theoretical capacity of 3860 mAh g −1 and a lower electrochemical potential (−3.04 V versus standard hydrogen electrode). [3] When a Li metal anode is coupled with a high capacity cathode (e.g., LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811)), the resulting battery would approximately double specific energy from 250 to 300 Wh kg −1 for commercial LIBs to ≈500 Wh kg −1 . [4][5][6] However, a Li metal anode is unstable with conventional liquid electrolytes (which are also flammable) and Li dendrites formed during cycling may readily penetrate through standard porous olefin separators and cause short circuits, rapid discharge, and a range of subsequent safety hazards. [7] Solid-state Li metal batteries (SLMBs) use a solid-state electrolyte (SSE) to replace the liquid electrolyte and separator, and alongside capacity improvements, also have the potential to enable safer cycling, although achieving practical current densities remains a significant challenge. [8] Compared with the high ionic conductivity of liquid electrolytes (10 −3 -10 −2 S cm −1 at room temperature, RT), SSEs usually have much lower intrinsic ionic conductivities at RT, [9] and most SSLMB research has therefore focused on increasing the ionic conductivity of SSEs. The SSEs divide into two main families: inorganic electrolytes and polymer electrolytes. [10] Inorganic electrolyte types include NASICON, [11][12][13] garnet, [14][15][16] perovskite, [17][18][19] LISICON, [20] sulfide, [21][22][23][24] argyrodite, [25][26][27] glassy, [9] etc. Inorganic electrolytes typically have ionic conductivities of 2 × 10 −5 to 2 × 10 −3 S cm −1 at RT. Practical applications have been limited by manufacturing difficulties (fragility over large areas), poor electrode/ SSE interfacial contact, and risk of Li dendrite growth along grain boundaries, although steady progress is being made. [10] In most polymer electrolytes, Li + ionic conductivity is achieved by Solid-state Li metal batteries (SSLMBs) combine improved safety and high specific energy that can surpass current Li ion batteries. However, the Li + ion diffusivity in a composite cathode-a combination of active material and solidstate electrolyte (SSE)-is at least an order of magnitude lower than that of the SSE alone because of the highly tortuous ion transport pathways in the cathode. This lowers the realizable capacity and mandates relatively thin (30-300 μm) cathodes, and hence low overall energy storage. Here, a thick (600 μm...

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Fast Charging of Lithium‐Ion Batteries: A Review of Materials Aspects

Weiß

Rueß

Kasnatscheew

et al. 2021

Advanced Energy Materials

603

348

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Fast charging is considered to be a key requirement for widespread economic success of electric vehicles. Current lithium‐ion batteries (LIBs) offer high energy density enabling sufficient driving range, but take considerably longer to recharge than traditional vehicles. Multiple properties of the applied anode, cathode, and electrolyte materials influence the fast‐charging ability of a battery cell. In this review, the physicochemical basics of different material combinations are considered in detail, identifying the transport of lithium inside the electrodes as the crucial rate‐limiting steps for fast‐charging. Lithium diffusion within the active materials inherently slows down the charging process and causes high overpotentials. In addition, concentration polarization by slow lithium‐ion transport within the electrolyte phase in the porous electrodes also limits the charging rate. Both kinetic effects are responsible for lithium plating observed on graphite anodes. Conclusions drawn from potential and concentration profiles within LIB cells are complemented by extensive literature surveys on anode, cathode, and electrolyte materials—including solid‐state batteries. The advantages and disadvantages of typical LIB materials are analyzed, resulting in suggestions for optimum properties on the material and electrode level for fast‐charging applications. Finally, limitations on the cell level are discussed briefly as well.

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Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models

Cited by 342 publications

References 77 publications

Extremely Fast Solvers for Newman-Type Models of Li-Ion Cell (dis)Charge

Extremely Fast Solvers for Newman-Type Models of Li-Ion Cell (dis)Charge

A Solid‐State Battery Cathode with a Polymer Composite Electrolyte and Low Tortuosity Microstructure by Directional Freezing and Polymerization

Fast Charging of Lithium‐Ion Batteries: A Review of Materials Aspects

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