Project 2 Thermal modeling of battery pack

1.AIM

Using MATLAB, to simulate the life cycle performance of a 10 cell series lithium ion battery model at various temperatures, charge, and discharge rates.

2.Objective

10 cell series battery pack temperature life cycle parameters analysis
Connect the series analyze of 10 cells to the source of cyclic charge and discharge.
Alter the ambient temperatures, compare the battery pack's performance.
Include battery parameters and evaluate performance after each charge and discharge cycle.

3.Simulink Modelling of Battery pack

3.1 Need of Battery Modelling

a precise electric vehicle (EV) driving range to avoid range anxiety.Vehicle battery recharge management system batteries need to be replaced for drivers who travel.The battery's full capacity is utilized in the charge calculation.A conventional vehicle's fuel gauge or battery pack's state of charge (SOC).

3.2 Battery Modelling

The Battery modeling in the MATLAB is explained below available from the library given below.

Battery (Table-Based)

Library:

Simscape / Electrical / Sources

The Battery (Table-Based) block high-fidelity battery model block calculates no-load voltage function of charge level optional temperature using lookup tables and includes several modeling options:

• Self-discharge
• Battery fade
• Charge dynamics

By right-clicking the Battery (Table-Based) block in your block diagram and selecting the appropriate option from the context menu, under Simscape > Block choices, you can access its four modeling variants:

The basic model is uninstrumented and does not have a thermal port. The battery charge level does not simulate a fixed temperature.The default modeling option is this one.

The model's exposed thermal port outputs the battery's internal charge level through an uninstrumented thermal port.
Model exposed charge output port | Instrumented | No thermal port.This model simulated at a fixed temperature.

Model that permits you to output the battery's internal charge level. Instrumented | Show thermal portBoth the warm port and charge yield port are uncovered.

The instrumented variants an extra physical signal port outputs internal state of charge. Use this functionality change load behavior a function of state of charge, without the complexity of building a charge state estimator.

The thermal port variants expose a thermal port, which represents the battery thermal mass.

The battery equivalent circuit is fundamental battery model self-discharge resistance RSD, charge dynamics model series resistance R0.

3.3 Battery Modeling Procedure followed by MATLAB

Equivalent circuit modeling battery numerical analysis.lithium cells, a one or two RC block model with no parasitic branch is a common choice. It has the advantage of being computationally simple and is easily combined with other methods coulomb counting OCV / SOC correlation periodic recalibration during rest.

Figure 1: A general equivalent circuit model of an electrochemical cell.

There is a compromise between fidelity and complexity regarding the number of equivalent circuit elements.For cells with high columbic efficiencies, the parasitic branch can be ignored.

The number of RC blocks typically ranges from one to two, depending on the characteristics of the analyzed problem, as larger numbers increase computational effort without significantly improving model accuracy.

Figure 2: Equivalent circuit of the cell used in this model

• Thermal modeling

The temperature of the SOC inner cell affects the value of equivalent circuit components.The cell's average temperature is assumed to be constant for T.This cell's temperature was calculated by resolving the heat equation for a homogenous body that exchanges heat with the environment.

Where CT = heat capacitance (J m-3 K-1)

T = Inner cell temperature (ºC)

Ta = ambient temperature (ºC)

RT = convection resistance (W1 m-2 K-1)

Ps = power dissipated inside the cell (W)

Taking Laplace transform we get,

• Cell capacity and state of charge

The cell capacity (extractable charge) depends number of factors, including:

1. average cell discharge current and discharge time
2. Inner cell temperature
3. Value of the end-of-discharge voltage
4. Storage time (self-discharge)
5. Number of charge-discharge cycles that the cell has undergone (aging)

short periods of time restrict list above average cell discharge current, discharge time, inner cell temperature.

Hence, cell capacity,

the cell fully charged at time t=0, the extracted charge, Qe as:

Im = Current in main branch (A)

Then, the state-of-charge (SOC) is:

where CQ is capacity of cell at temperature and discharge current Consequently SOC definition conditions under cell is discharged current and temperature SOC has evaluated.

• Equivalent circuit model parameters

Each element of the equivalent circuit of Figure 2 is a function of SOC and temperature. Specifically:

R0= R0(SOC,T)

R1=R1(SOC,T)

C1=C1(SOC,T)

Em= Em(SOC,T)

The parameter estimation routine run a range of discharge experiments at different temperatures. The results provide two-dimensional look-up tables each of these four equivalent circuit elements.

3.4 MODELING AND SIMULATION

The simulation phase of numerical analysis came after the parameter estimation and validation phases.An experimental data ECM was built using SimscapeTM blocks and the SimscapeTM programming language, and a discharge profile was simulated during parameter estimation.The single RC block circuit element subsystem shown in Figure 4 consisted of custom electrical blocks used to calculate the element's properties.

Figure 3:The resistive circuit elements in the Simscape equivalent circuit model, which are variable resistors, are depicted in Figure 4. This prevents the Ohm's Law minimum resistance value differential equation solver from entering a bad state while parameter estimation or simulation are taking place.In addition, the calculated power of the resistive elements was used in the thermal dynamics simulation later on.The value of the resistance was provided by a lookup table with either one or two inputs of SOC and temperature.

Figure:Simscape, a numerical tool that combines the parameter estimation capabilities of Simulink Design Optimization with the modeling of electrochemical systems equivalent circuits, was used to validate the model using the resistive circuit element and the Simscape language.inner heat generated by the cell, the corresponding thermal buildup, the predicted voltage of the cell, and the current profile of the SOC.

3.4.1 Modeling Battery Fade The lithium deposition electrolyte decomposition of the active material, phase transitions within the materials of the insertion electrodes, and passive film formation on the electrode current collectors all have the potential to influence the extent to which lithium-ion batteries lose capacity.Quantifying these degradation processes will improve battery models' predictive ability and capacity fade mechanism.

Quantified were the three primary causes of capacity fade: loss of secondary material, loss of primary material, and difference in rate capability.The loss of secondary material always dominated the cell's capacity loss as a whole.In contrast to the 1C-discharge-rate-cycled battery, the limiting secondary material in the 3Cdischarge-rate-cycled cell was carbon.The cell as a whole experienced a capacity fade of 16.9%, while the carbon electrode material alone experienced a capacity loss of 10.6%. The rapid capacity loss of the carbon material is connected to an increase in internal resistance.Quantitatively, fitting impedance spectra of the carbon half-cell showed that the bulk carbon material's resistance and the surface film's resistance rise faster in cells discharged at a higher rate than in cells discharged at a lower rate.

The battery fade is modeled in MATLAB as follows.

• No load voltage change

The no load voltage across fundamental battery is modeled using the equation below.

V0,fade = V0 ( 1+ (dV0 * n / 100*N))

Where,

N is the reference number of discharge cycles over which we specify percent change of various battery parameters.

dV0 is the change in no load voltage after N discharge cycles.

• Battery Capacity change

Qnom,fade = Qnom (1+ dAH* sqrt(n/N)/100)

Where, Qnom is the rating of the battery

dAH is the change in AH rating after N cycles

• Change in Resistance

Ri,fade = Ri (1+ (dRi * sqrt (n/N)/100))

Where, Ri is the series resistance of the battery

dRi is the change in series resistance after N cycles

3.5 Selection of values for Battery pack

All the cell parameters depend upon Temperature and SOC and the values of them are given below.

Temperature_LUT(1*3) = [278.15 293.15 313.15]

SOC_LUT(7*1) = 0

0.1

0.25

0.50

0.75

0.90

1

The other parameters like No-load voltage(Em_LUT), Terminal resistance (R0_LUT), First polarization resistance (R1_LUT), and time constant(TC) are extracted from MATLAB example[5].

Figure 5: Main battery parameters

Since battery model is table based with temperature dependent, all the values have entered depending upon temperature and SOC.

Figure 6: Dynamics of battery

For simplicity of analysis, only one time constant has considered.

1. Output Analysis

4.1 Temperature Analysis

The battery performance parameters like Voltage, SOC has to be analysed at different temperatures.

Figure 7 shows the battery pack model 10 cells connected in series. All these cells are connected it transfer heat conduction and convection. The ambient temperature changed to different values analyse battery pack performance at different temperatures.

Figure 7: Simulink model of 10 cell connected in series for temperature analysis

Figure 8: Cyclic charge/discharge profile

An AC source has been connected to analyse the cyclic charge and discharge performance. The positive cycle will charge the battery and negative cycle will discharge the battery. Peak Amplitude has given 3A so that the charging/ discharging doesn’t go beyond 1C rate.

9 Figure:Battery pack terminal voltage at various temperatures For a more precise comparison, the simulation was run three times with different ambient temperatures—273K (-degree Celsius), 298K (-25°C), and 313K (-40°C).These result results put away in work area in out_T0, out_T1, out_T2 separately as displayed in figure 10.

Figure 9 depicts a plot of the battery pack's terminal voltage.We can see from the graph above that the voltage in the battery pack will reach its maximum at 273K (-degree Celsius).The voltage of the battery pack decreases as the temperature rises.

At 273K, the battery performs so well that it can go from peak voltage to lowest voltage.Figure 11's SOC plot reveals that terminal voltage falls below the cut-off voltage at zero SOC, which is undesirable.

Figure 10: Workspace variables

Figure 11: SOC of Cell1 at different ambient temperatures

For comparison purpose SOC cell1 are connected in series, there will be some difference in charging/ discharging of cell.

In figure12, cell 1 temperature plotted at different ambient temperatures. We see 10,000sec, temperatures settling ambient temperature.

Figure 12: Temperature of cell1 at different ambient temperatures

4.2 Life cycle Analysis

Figure 13 Simulink model battery pack for lifecycle analysis From [2] and [3] terminal voltage and capacity of battery will decrease, internal resistance will increase after specified number of cycles.

Figure 13: Simulink model of battery pack to analyse battery fading

Figure 14: Fade parameters of battery

For the fade data I’ve just given some arbitrary values for the purpose of simulation analysis. In actual case, battery fading happens after 2000-5000 cycles depending upon battery type. But to analyse the performance in simulation, 100 cycles have given.

1. Change in no-load voltage = -2% (This indicates that the no load voltage will reduce 2% after N cycles)
2. Change in terminal resistance = 2% (The terminal resistance will increase by 2%)
3. Change in AH rating = -3% (Ampere hour decrease by 3% after N cycles)
4. Change in first polarization resistance = 3% (First polarization resistance increase by 3%

Figure 15: Making the fade parameters to zero to compare performance

For comparison of analysis, simulation ran 2 times saved the data in ‘out_fade’ and ‘out_nofade’ variables. In first all the cells entered fade parameters saved the output in out_fade variable in WS. In second simulation, all cells fade parameters made to zero as shown in figure 15 and saved the output in out_nofade variable in WS.

Figure 16: SOC of cell1 comparison plot

Figure 16 shows that the SOC of a battery with fading effect (blue plot) is slightly higher than that of a battery without fading effect (pink plot).

This is because a battery with a lower capacity was added to the fade parameters.As can be seen in the graph above, battery maximum charge and discharge change over time.
17 Figure:Battery pack terminal voltage with no fading effect when the fading parameters are set to zero and the output simulation is saved in WS. Battery pack terminal voltage with no fading effect.

The battery pack's terminal voltage decreases as the figure's cycles continue.This is because the battery has a fading effect; without it, the terminal voltage stays the same.
5. In conclusion, the parameters of the MATLAB library model the life cycle and the thermal effects.A battery with ten series-connected cells is also a source for the cycle of charging and discharging.

Analyzed and plotted the thermal effects of different ambient temperatures on the battery pack during cycling charging and discharging.Additionally, the battery pack's life cycle performance evaluated each individual cell.

6. The future scope of this battery pack consists of individual cells connected in series. Individual cells' thermal life cycle performance differs from that of a battery pack.

In future the battery pack boundaries to examine the exhibition.
Chargers, connectors, wire resistances, and other components all contribute to the rise in temperature.In the future, thermal behavior analysis parameters