Project 2 Thermal modeling of battery pack

For a 10 cell series lithium ion battery model, simulate the thermal effects and compare life cycle performance at various temperatures, charge & discharge rates using MATLAB.

Answer:

Theory-

Now a days lithium ion battery are mostly used in electric vehicles we must know its charge and discharge rates to operate this batterys in efficient manner and with out damage when this batterys in operation this temperature of it must be kept in limit we simulate the thermal effect of this cell , by knowing the how much temp rise take place we can provide efficient cooling to the batterys so that the life cycle of the battery increases we will see a model on this issue below and explaination as follows

cells connected in series and one of the cell is faulty so what will be thermal effect . This example shows how to simulate a battery pack consisting of multiple series-connected cells in an efficient manner. It also shows how a fault can be introduced into one of the cells to see the impact on battery performance and cell temperatures. For efficiency, identical series-connected cells are not just simply modeled by connecting cell models in series. Instead a single cell is used, and the terminal voltage scaled up by the number of cells. The fault is represented by changing the parameters for the Cell 10 Fault subsystem, reducing both capacity and open-circuit voltage, and increasing the resistance values.

Simulink Modelling of Battery pack 3.1 Need of Battery Modelling

An accurate forecast of the remaining driving range of an electric vehicle (EV) is crucial to avoid range anxiety. Drivers need to know how much further they would travel before their vehicle batteries require a recharge. In addition, the battery management system should predict when batteries need replacement. The remaining charge calculation must be precise to utilize the battery's full capability. The state of charge (SOC) of a battery or pack of batteries is analogous to a fuel gauge of a conventional vehicle.

Battery Modelling

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

Battery (Table-Based)

Library:

Simscape / Electrical / Sources

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

• Self-discharge
• Battery fade
• Charge dynamics

The Battery (Table-Based) block has four modeling variants, accessible by right-clicking the block in your block diagram and then selecting the appropriate option from the context menu, under Simscape > Block choices:

• Uninstrumented I No thermal port — Basic model that does not output battery charge level and simulates at a

• fixed temperature. This modeling variant is the default.

  • Uninstrumented I Show thermal port — Model with exposed thermal port. This model does not output internal charge level of the battery.
  • Instrumented I No thermal port — Model with exposed charge output port. This model uses a fixed temperature throughout the simulation.
  • Instrumented I Show thermal port — Model that lets you output internal charge level of the battery. Both the thermal port and the charge output port are exposed.

  The instrumented variants have an extra physical signal port that outputs the internal state of charge. Use this functionality to change load behavior as 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 made up of the fundamental battery model, the self-discharge resistance RSD, the charge dynamics model, and the series resistance RO.

  3.3 Battery Modeling Procedure followed by MATLAB

  Equivalent circuit modeling is the most common approach for battery numerical analysis. For 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 such as coulomb counting with an OCV / SOC correlation for
• 

• The number of equivalent circuit elements results in a trade-off between fidelity and complexity. The parasitic branch can be neglected for cells with high columbic efficiencies.

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

  

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

• Thermal modeling

  The value of the equivalent circuit components depends on SOC and inner cell temperature. The inner cell temperature is assumed to be uniform, and taken as the average temperature inside the cell. This cell temperature can be computed by solving the heat equation of a homogeneous body exchanging heat with the environment.

  

  Where CT = heat capacitance (J m-3 K-1) T = Inner cell temperature (°C)

  Ta = ambient temperature (^°C)

  RT = convection resistance (W1 m-2 K-1) Ps = power dissipated inside the cell (W)

  • Cell capacity and state of charge

• The cell capacity (extractable charge) depends upon a 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)

  For short periods of time we can restrict the list above to average cell discharge current, discharge time, and inner cell temperature.

  Hence, cell capacity,

  

  Assuming the cell to be fully charged at time t=0, the extracted charge, Qe is defined as:

• 

  Im = Current in main branch (A)

• Then, the state-of-charge (SOC) is:

• 

• where CQ is the capacity of the cell at the temperature and discharge current considered. Consequently, any SOC definition should consider the conditions under which a cell is discharged and refer to the particular discharge current and temperature under which the SOC has been evaluated.

  • Equivalent circuit model parameters

  Each element of the equivalent circuit of Figure 2 is a function of SOC and temperature. Specifically: RO= RO(SOC,T)

  R1=R1(SOC,T)

• Each element of the equivalent circuit of Figure 2 is a function of SOC and temperature. Specifically: RO= RO(SOC,T)

  RI=R1(SOC,T)

  C1=CI(SOC,T)

  Em= Em(SOC,T)

  The parameter estimation routine was run for 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 numerical analysis proposed here consisted of a parameter estimation / validation stage, and a simulation stage. During parameter estimation, a discharge profile was iteratively simulated and its results compared with experimental data. The ECM was created using SimscapeTM blocks and SimscapeTM language. The drawing shown in Figure 4 represented the circuit diagram with a single RC block. Each of the circuit elements was a subsystem consisting of custom electrical blocks, and blocks to calculate the properties of the circuit element.

circuit construction and working -in this ckt we use li-ion cell 1 to 10 , temperature sensors ,ambient temp block , state of charge estimation scope, charge discharge profile given to ckt ,from & goto block block matlab simulink
Cell 01 to 04 Subsystem:

Cell subsystem basically consist of a Thermal Model and Lithium Cell 1RC block which calculate the temperature and thermal effect for cells and overall calculate for the battery pack.

This block basically consists of capacitance value (c) table which depends on an external physical signal inputs SOC and T. It is assumed that the capacitance value is slowly varying with time, and hence the equation i = C*dv/dt holds. Resistance value (R) table which depends on an external physical signal inputs SOC and T. Voltage value (EM) which implements the cell's main branch voltage source, and determines values for capacity (C) and state of charge (SOC). The defining equations depend on cell temperature, T.

output of one cell is connected with the SOC estimation whereas other output is connected with AC current source for cyclic charge/discharge profile.

he peak amplitudes set to 50 A for cyclic charge and discharge. A basic parameter of AC current source is as

other block which are used in lithium ion battery pack are as

Convective Heat Transfer:

This block models heat transfer in a thermal network by convection due to fluid motion. The rate of heat transfer is proportional to the temperature difference, heat transfer coefficient, and surface area in contact with the fluid.

this used for 1-4 cells and 6-10 cell a from and goto blocks

The Goto block passes its input to its corresponding From blocks. The input can be a real- or complex-valued signal or vector of any data type. From and Goto blocks allow you to pass a signal from one block to another without actually connecting them.

The From block accepts a signal from a corresponding Goto block, then passes it as output. The data type of the output is the same as that of the input from the Goto block. From and Goto blocks allow you to pass a signal from one block to another without actually connecting them.

The Coulomb counting method measures the discharging current of a battery and integrates the discharging current over time in order to estimate SOC

This subsystem takes current as input and gives the battery SOC % as output. A current sensor block is used and connected for soc estimation

This ckt estimates the battery SOC % using coulomb counting method. It takes current as the input and gives SOC% as output. Initial SOC of the battery is assumed to be 100% (1)

example shows how to simulate a battery pack consisting of multiple series-connected cells in an efficient manner. It also shows how a fault can be introduced into one of the cells to see the impact on battery performance and cell temperatures

Results:

1) results at 300 k ie 26.85°C

The Coulomb counting method measures the discharging current of a battery and integrates the discharging current over time in order to estimate SOC

This subsystem takes current as input and gives the battery SOC % as output. A current sensor block is used and connected for soc estimation

This ckt estimates the battery SOC % using coulomb counting method. It takes current as the input and gives SOC% as output. Initial SOC of the battery is assumed to be 100% (1)

example shows how to simulate a battery pack consisting of multiple series-connected cells in an efficient manner. It also shows how a fault can be introduced into one of the cells to see the impact on battery performance and cell temperatures

results

1) results at 300 k ie 26.85°C

2) results at 310 k ie 36.85°C

3) results at 324k ie 50.85°C

Result explanation:

For a 10 cell series lithium ion battery model we simulate the thermal effects and compare life cycle performance at various temperatures, charge & discharge rates using MATLAB. results are as follows

1-4 cells are normal cell 5 th cell is faulty 6-10 cell is normal cell as we are simulating thermal effect on we

give Cyclic Charge/ Discharge Profile to circuit the waveform you can see above results all conditions it is same but when ckt is run on this drive cycle profile as 5th cell was faulty it will show temperature rise high than any other cell and the life cycle of cell get reduced and cell even can get damage

so as we give temp of results at 300 k ie 26.85°C the 1-4 and 6-10 cell temp is near to 316k

but 5th faulty cell temp goes to 330k ie 56.85°C and the drive cycle is shown which is Cyclic Charge/ Discharge drive cycle

in condition two we give temp 310 k ie 36.85°C and 1-4 and 6-10 cell temp is near to 326 k but 5th faulty cell temp goes to 340k ie 66.85°C and the drive cycle is shown which is Cyclic Charge/ Discharge drive cycle

in condition two we give temp 324 k ie 50.85°C and 1-4 and 6-10 cell temp is near to 340 k but 5th faulty cell temp goes to 354k ie 80.85°C and the drive cycle is shown which is Cyclic Charge/ Discharge drive cycle

conclusion:

we simulated the thermal effect and compared at various temperature ,faulty cell temp rise more than normal cell charge and discharge cycle given and due to temp rise it can damage the cell and reduce the performance of the battery.