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Lithium-Ion Battery Rate Capability
Introduction
A battery’s possible energy and power outputs are critical to consider when deciding in which type of device it can be used.
A cell with high rate capability is able to generate a considerable amount of power, as it suffers from little polarization (voltage loss) even at high current loads. In contrast, a low rate-capability cell has the opposite behavior. The former cell type is said to be power optimized, while the latter type is energy optimized.
Characteristic for energy-optimized cells is that these have more capacity, and are thus able to supply more energy, but only for mild loads. Therefore, energy-optimized batteries are more suitable for portable electronics, for example, cell phones. The power energy-optimized ones fits, for example, power-demanding hybrid-electric vehicles better. The difference between these two types of cells is illustrated in Figure 1. This way of plotting energy versus power (or current if the cell voltage stays fairly constant throughout the load cycle) is also called a Ragone plot.
Figure 1: Comparison of energy outputs. Energy optimized cells (gray) can supply more energy but for lower current loads. Power optimized cells (black) work fine for higher power (current loads) but can only provide a fraction of the energy at low power.
This tutorial performs a rate capability investigation of two lithium-ion battery cell designs using the Lithium-Ion Battery interface.
You can also learn more about how to study rate capability with the Lithium-Ion Battery Internal Resistance tutorial, where the individual contributions to the voltage losses are analyzed more in detail.
Model Definition
The model is set up in 1D for a graphite/NMC battery cell. A more detailed description of the model can be found in Lithium-Ion Battery Base Model in 1D. Discharge curves are simulated for a range of current magnitudes (C-rates) for two different battery designs: an energy-optimized cell and a power-optimized cell. Changing from the energy-optimized case to the power-optimized case is done in the model by lowering the cross-sectional charge capacity parameter from 40 Ah/m2 to 20 Ah/m2, which results in reducing the electrode thickness in the geometry by half.
The volumetric energy (J/m3) and power (W/m3) outputs during the discharge, starting from fully charged conditions, are calculated and investigated in a Ragone plot. A Global ODEs and DAEs interface is used to calculate the energy output according to Equation 1.
(1)
Where the length Lcell of the cell is calculated as
(2)
where Lccs is the sum both thickness of the positive and negative current collector foils in jelly roll. (The factor 1/2 stems from the configuration of a typical jelly roll where each metal foil is being coated on both sides by the same electrode layer.)
The power output is computed by dividing the energy with the total discharge time.
An Event interface is also used for implementing an accurate stop condition in the solver for when the cell voltage goes below a threshold level equal to the open circuit voltage at 0% cell state of charge.
Results and Discussion
Figure 2 shows the discharge curves of the energy optimized cell. A clear increase in polarization (voltage drop) with increased load is observed. Compared to the open-circuit voltage curve, the capacity utilization decreases considerably with increased load as well. At 10C the capacity utilization decrease is substantial; less than 10% of the available capacity has been utilized.
Figure 2: Solid lines: Cell voltages versus SOC during discharge at various C-rates of the energy-optimized cell. Dashed line: Corresponding open circuit voltage (OCV) versus SOC.
To investigate what could be the reason for the large capacity decrease at 10C, we plot the electrolyte salt concentration at the end of this discharge simulation in Figure 3. Toward the right in the figure a noticeable drop in electrolyte concentration is seen, and for the 25 rightmost micrometers, the concentration is close to 0. This region corresponds to the inner parts of the positive electrode in the model, and the reason for the depletion of lithium ions is the prolonged fast lithium intercalation rate, in combination with an insufficient lithium ion transport from the negative electrode through the separator. .
Figure 3: Electrolyte concentration at the end of discharge at 10C of the energy-optimized cell.
The depletion of lithium ions will result in a very low local electrolyte conductivity, and this is manifested in Figure 4 where a corresponding steep potential drop is seen. This steep potential drop will result in the interior of the electrode not being utilized at all toward the end of discharge at high rates for the energy-optimized cell.
Figure 4: Electrolyte phase potential at the end of discharge at 10C of the energy-optimized cell.
Figure 5 shows the discharge curves for the power-optimized case, using half as thick electrodes as in the energy optimized case. These thinner electrodes now allows for utilizing about 60% of the available charge at the highest 10C discharge rate, as opposed to less than 10% for the energy-optimized case.
Figure 5: Solid lines: Cell voltages versus SOC during discharge at various C-rates of the power-optimized cell. Dashed line: Corresponding open circuit voltage (OCV) versus SOC.
Finally, the energy versus power Ragone plot is plotted in Figure 6. For power levels above around 200 W/m3, the power-optimized battery starts to outperform the energy-optimized cell.
Figure 6: Volumetric energy versus power Ragone plot, comparing the energy optimized to the power optimized cell.
Application Library path: Battery_Design_Module/Batteries,_Lithium-Ion/li_battery_rate_capability
Modeling Instructions
Application Libraries
1
From the File menu, choose Application Libraries.
2
In the Application Libraries window, select Battery Design Module>Batteries, Lithium-Ion>lib_base_model_1d in the tree.
3
In this tutorial we will discharge the battery model you just opened at various C-rates from 100% state of charge. The first simulations will be run for an energy-optimized cell, with a large cross-sectional cell capacity. Those results will then be compared with a power-optimized cell, featuring thinner electrodes.
Global Definitions
Parameters 1
1
In the Model Builder window, under Global Definitions click Parameters 1.
2
In the Settings window for Parameters, locate the Parameters section.
3
Component 1 (comp1)
In the Model Builder window, expand the Component 1 (comp1) node.
Lithium-Ion Battery (liion)
Electrode Current Density 1
Modify the cell current density boundary condition as follows:
1
In the Model Builder window, expand the Component 1 (comp1)>Lithium-Ion Battery (liion) node, then click Electrode Current Density 1.
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In the Settings window for Electrode Current Density, locate the Electrode Current Density section.
3
In the in,s text field, type I_app.
Component 1 (comp1)
Add additional physics interfaces for adding an integral equation for accumulated energy, and for setting up an event-based stop condition.
Add Physics
1
In the Home toolbar, click  Add Physics to open the Add Physics window.
2
Go to the Add Physics window.
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In the tree, select Mathematics>ODE and DAE Interfaces>Global ODEs and DAEs (ge).
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Click Add to Component 1 in the window toolbar.
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In the tree, select Mathematics>ODE and DAE Interfaces>Events (ev).
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Click Add to Component 1 in the window toolbar.
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In the Home toolbar, click  Add Physics to close the Add Physics window.
Cumulative Energy
1
In the Model Builder window, under Component 1 (comp1) click Global ODEs and DAEs (ge).
2
In the Settings window for Global ODEs and DAEs, type Cumulative Energy in the Label text field.
Global Equations 1
1
In the Model Builder window, under Component 1 (comp1)>Cumulative Energy (ge) click Global Equations 1.
2
In the Settings window for Global Equations, locate the Global Equations section.
3
The above equation sets the time derivative of the accumulated energy to equal the power density output (current density times voltage) of the cell. The expression is marked in orange, indicating unit issues. The equation is formulated per cell area unit (m^2) of the jelly roll. Set the units of the dependent variable W and the equation expression as follows:
4
Locate the Units section. Click  Define Dependent Variable Unit.
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In the Dependent variable quantity table, enter the following settings:
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Click  Define Source Term Unit.
7
In the Source term quantity table, enter the following settings:
Events (ev)
Add an indicator state and an implicit event which will be used to control when to stop the simulation. (It is also possible to add stop conditions in the time-dependent solver without the using of events, but that would typically be less accurate).
1
In the Model Builder window, under Component 1 (comp1) click Events (ev).
Indicator States 1
1
In the Physics toolbar, click  Global and choose Indicator States.
The indicator state is defined to turn positive when the cell voltage drops below the OCV voltage at 0% SOC.
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In the Settings window for Indicator States, locate the Indicator Variables section.
3
Implicit Event 1
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In the Physics toolbar, click  Global and choose Implicit Event.
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In the Settings window for Implicit Event, locate the Event Conditions section.
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In the Condition text field, type STOP_DCH>0.
Study 1
Add a parametric sweep for a range of C-rates as follows:
Parametric Sweep
1
In the Study toolbar, click  Parametric Sweep.
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In the Settings window for Parametric Sweep, locate the Study Settings section.
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Step 1: Time Dependent
Set the solver maximum solver time to be inversely proportional to the C-rate as follows:
1
In the Model Builder window, click Step 1: Time Dependent.
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In the Settings window for Time Dependent, locate the Study Settings section.
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In the Output times text field, type 0 1/C_rate.
Solution 1 (sol1)
1
In the Study toolbar, click  Show Default Solver.
The time dependent solver will automatically shorten the time step when needed in order to resolve gradients in the model. In order to get a good resolution in the discharge curves, store the solution every 3rd time step taken by the solver. (Solving the solution every time step would require more disk space when saving the solution.)
2
In the Model Builder window, expand the Solution 1 (sol1) node, then click Time-Dependent Solver 1.
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In the Settings window for Time-Dependent Solver, locate the General section.
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From the Times to store list, choose Steps taken by solver.
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In the Store every Nth step text field, type 3.
In order to get the simulation started more easily, set the initial time step to be inversely proportional to the C-rate as follows:
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Click to expand the Time Stepping section.
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Select the Initial step check box. In the associated text field, type 0.001/C_rate.
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Right-click Study 1>Solver Configurations>Solution 1 (sol1)>Time-Dependent Solver 1 and choose Stop Condition.
Finally, add a stop condition based on the event you added previously.
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In the Settings window for Stop Condition, locate the Stop Events section.
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In the table, select the Active check box for Events (ev)/Implicit Event 1.
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Locate the Output at Stop section. Clear the Add warning check box.
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In the Study toolbar, click  Compute.
Results
Add a plot of the cell voltages vs the state of charge as follows:
Cell Voltages vs SOC
1
In the Home toolbar, click  Add Plot Group and choose 1D Plot Group.
2
In the Settings window for 1D Plot Group, type Cell Voltages vs SOC in the Label text field.
3
Locate the Data section. From the Dataset list, choose Study 1/Parametric Solutions 1 (sol2).
Global 1
1
Right-click Cell Voltages vs SOC and choose Global.
2
In the Settings window for Global, click Replace Expression in the upper-right corner of the y-Axis Data section. From the menu, choose Component 1 (comp1)>Definitions>E_cell - Point Probe 1 - V.
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Locate the y-Axis Data section. In the table, enter the following settings:
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Locate the x-Axis Data section. From the Parameter list, choose Expression.
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In the Expression text field, type soc_cell.
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Click to expand the Legends section. From the Legends list, choose Evaluated.
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In the Legend text field, type eval(C_rate) C.
Global 2
1
In the Model Builder window, right-click Cell Voltages vs SOC and choose Global.
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In the Settings window for Global, click Replace Expression in the upper-right corner of the y-Axis Data section. From the menu, choose Component 1 (comp1)>Definitions>Variables>E_ocv_cell - Open-circuit cell voltage - V.
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Locate the Data section. From the Dataset list, choose Study 1/Parametric Solutions 1 (sol2).
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From the Parameter selection (C_rate) list, choose First.
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Locate the x-Axis Data section. From the Parameter list, choose Expression.
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In the Expression text field, type soc_cell.
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Locate the Legends section. From the Legends list, choose Manual.
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Click to expand the Coloring and Style section. Find the Line style subsection. From the Line list, choose Dashed.
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From the Color list, choose Black.
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In the Cell Voltages vs SOC toolbar, click  Plot.
Cell Voltages vs SOC
1
In the Model Builder window, click Cell Voltages vs SOC.
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In the Settings window for 1D Plot Group, click to expand the Title section.
3
From the Title type list, choose None.
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Locate the Plot Settings section.
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Select the y-axis label check box. In the associated text field, type Cell voltage (V).
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Locate the Legend section. From the Position list, choose Upper left.
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In the Cell Voltages vs SOC toolbar, click  Plot.
Global Evaluation 2
To create the Ragone plot (energy density vs average power density), first evaluate the energy variable at the last time point of the simulation.
In the Results toolbar, click  Global Evaluation.
Ragone Plot Data Evaluation
1
In the Model Builder window, under Results>Derived Values click Global Evaluation 1.
2
In the Settings window for Global Evaluation, type Ragone Plot Data Evaluation in the Label text field.
3
Locate the Data section. From the Time selection list, choose Last.
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Locate the Expressions section. In the table, enter the following settings:
Note that we are computing the volumetric values by dividing by the thickness of the cell.
5
Clicknext to  Evaluate, then choose New Table.
Ragone Plot Data Energy Optimized Cell
1
In the Model Builder window, expand the Results>Tables node, then click Table 2.
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In the Settings window for Table, type Ragone Plot Data Energy Optimized Cell in the Label text field.
Now plot the table data you just generated.
Ragone Plots
1
In the Results toolbar, click  1D Plot Group.
2
In the Settings window for 1D Plot Group, type Ragone Plots in the Label text field.
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Locate the Axis section. Select the x-axis log scale check box.
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Locate the Legend section. From the Position list, choose Lower left.
Table Graph 1
1
Right-click Ragone Plots and choose Table Graph.
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In the Settings window for Table Graph, locate the Data section.
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From the Table list, choose Ragone Plot Data Energy Optimized Cell.
4
From the x-axis data list, choose Average Volumetric Power (W/m^3).
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From the Plot columns list, choose Manual.
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In the Columns list, select Volumetric Energy (W*h/m^3).
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Click to expand the Legends section. Select the Show legends check box.
8
From the Legends list, choose Manual.
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In the Ragone Plots toolbar, click  Plot.
The Ragone plot indicates a large decrease in output energy for large power levels (discharge currents). Reviewing the default electrolyte salt (Li ion) concentration plot at the last time point at the highest current level indicates what could be the issue.
Electrolyte Salt Concentration (liion)
1
In the Model Builder window, under Results click Electrolyte Salt Concentration (liion).
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In the Settings window for 1D Plot Group, locate the Data section.
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From the Parameter selection (C_rate) list, choose Last.
4
From the Time selection list, choose Last.
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In the Electrolyte Salt Concentration (liion) toolbar, click  Plot.
The concentration drops to zero in the interior of the positive electrode. Now check out the electrolyte potential plot at the same time.
Electrolyte Potential (liion)
1
In the Model Builder window, click Electrolyte Potential (liion).
2
In the Settings window for 1D Plot Group, locate the Data section.
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From the Parameter selection (C_rate) list, choose Last.
4
From the Time selection list, choose Last.
5
In the Electrolyte Potential (liion) toolbar, click  Plot.
A very large electrolyte potential drop is seen in the positive electrode. This is a result of the electrolyte conductivity dropping to essentially zero when the lithium ions are depleted in the electrolyte. As a result of this, the whole electrode cannot be utilized during discharge at high rates. We will now rerun the simulations for a power-optimized cell using thinner electrodes to see if this improves the rate capability at high currents.
Global Definitions
Parameters 1
Lower the cross-sectional cell capacity. Note that this also automatically changes the values for the electrode thicknesses (L_neg and L_pos).
1
In the Model Builder window, under Global Definitions click Parameters 1.
2
In the Settings window for Parameters, locate the Parameters section.
3
Study 1
Parametric Solutions 1 (sol2)
Before recomputing, make sure you copy the old solution for future reference.
1
In the Model Builder window, under Study 1>Solver Configurations right-click Parametric Solutions 1 (sol2) and choose Solution>Copy.
Parametric Solutions - Energy Optimized Cell
1
In the Model Builder window, under Study 1>Solver Configurations click Parametric Solutions 1 - Copy 1 (sol9).
2
In the Settings window for Solution, type Parametric Solutions - Energy Optimized Cell in the Label text field.
3
In the Home toolbar, click  Compute.
Results
Check out the discharge curves for the power-optimized cell:
Cell Voltages vs SOC
1
In the Model Builder window, under Results click Cell Voltages vs SOC.
2
In the Cell Voltages vs SOC toolbar, click  Plot.
Note that the voltages do not drop as sharply for low SOCs as they did for the energy-optimized cell. (You can do the comparison by changing the Dataset to your stored solution for the energy-optimized case.)
Ragone Plot Data Evaluation
Generate new Ragone table data and plot it as follows:
1
In the Model Builder window, under Results>Derived Values right-click Ragone Plot Data Evaluation and choose Evaluate>New Table.
Ragone Plot Data for Power Optimized Cell
1
In the Model Builder window, under Results>Tables click Table 3.
2
In the Settings window for Table, type Ragone Plot Data for Power Optimized Cell in the Label text field.
Table Graph 2
1
In the Model Builder window, under Results>Ragone Plots right-click Table Graph 1 and choose Duplicate.
2
In the Settings window for Table Graph, locate the Data section.
3
From the Table list, choose Ragone Plot Data for Power Optimized Cell.
4
Locate the Legends section. In the table, enter the following settings:
5
In the Ragone Plots toolbar, click  Plot.
As can be seen, the power-optimized cell is capable of delivering higher energy densities for higher power densities than the energy-optimized cell.
Electrolyte Salt Concentration (liion)
The salt concentration plot for the power-optimized case does not indicate as severe ion depletion as for the energy optimized case:
1
In the Model Builder window, under Results click Electrolyte Salt Concentration (liion).
2
In the Electrolyte Salt Concentration (liion) toolbar, click  Plot.
Electrolyte Potential (liion)
The absence of ion depletion does not result in the huge electrolyte potential drop as was seen for the energy-optimized case:
1
In the Model Builder window, click Electrolyte Potential (liion).
2
In the Electrolyte Potential (liion) toolbar, click  Plot.