Week 7 State of charge estimation

TASK:

1) Simulate the 3 test cases from harness dashboard and write a detailed report on the results

Now,at first we have to simulate 3 test cases from harness dashboard. We need to, select the harness dashboard from project file which we downloaded with the reference link https://in.mathworks.com/matlabcentral/fileexchange/72865-design-and-test-lithium-ion-battery-management-algorithms . The battery model harness dash board is represented below.

1)Click on the Test Sequence Varient Block and select test case as 1

Now by clicking on the BMS Sequence block we get the chart

When simulation will start, the model will be in driving condition. After 3000 seconds it will be in standby mode and after 1000 seconds it will be in charging mode. Now by Running the simulation.

Results:-

Here all the indicators are green. So we can see no errors and we check the plot for this model and is shown below.

From the plot we can see the vehicle is initially in driving state and charging was done till 90%. After charging it reaches to standby mode. It can be seen in BMS state plot.Battery temperature increases in driving and charging. Battery voltage was increased to 4.2V while charging. Current plot shows the value of current supplied to vehicle while driving. SOC decreased up to 50% in driving condition and it was increased while charging.

2)Click on the Test Sequence Varient Block and select test case as 2

Now by clicking on the BMS state request block

The Driving stage done by time 0 to 10000

then charging stage starts.

Result:

From the above graph , a fault condition occured in second case. So even after charging was started, the soc didnt increase. It continue to decrease.

3)Click on the Test Sequence Varient Block and select test case as 3

This condition has only one phase and that is charging.Thus 0 to 20000 seconds simulation runs.

Result:

• Here we can see that the soc reached 100% in charging and remained in stand by mode. Cell temperature also decrased when stsndby mode was acheived.

From the 3 cases we observer that only charging takes place thus SOC was 80% at first , then SOC increases to 100% in 2000 seconds then SOC remains constant at 100%.

Task :2 coulomb counting:

The Coulomb counting method measures the discharging current of a battery and integrates the discharging current over time in order to estimate SOC . Coulomb counting method is done to estimate the , which is estimated from the discharging current, , and previously estimated SOC values, . SOC is calculated by the following equation:

But there are several factors that affect the accuracy of Coulomb counting method including temperature, battery history, discharge current, and cycle life.

Modified Coulomb Counting Method

To improve the Coulomb counting method, a new technique called modified Coulomb counting method is proposed. The modified Coulomb counting method uses the corrected current to improve the accuracy of estimation.

The corrected current is the function of discharging current. There is a quadratic relationship between the corrected current and discharging current of battery. By practice of experimental data, corrected current is calculated by the following form:

where k2,k1andk0k2,k1andk0are constant values obtained from the practice experimental data.

In modified Coulomb counting method, SOC is calculated by the following equation:

The experimental results show that the accuracy of the modified Coulomb counting method is superior to the conventional Coulomb counting method.

But there are several factors that affect the accuracy of Coulomb counting method including temperature, battery history, discharge current, and cycle life .

BMS algorithm models , protects , reports the measurement from battery pack

By clicking on the SOC estimation block we get

From above figure we observe the Coulomb Counting block.

A subsystem opens when we click on the Coulomb Counting

• This system does the estimation by coulomb counting equation mentioned above.
• Advantage of coulomb counting method is its simplicity and low computational cost.
• Temperature and current from the battery is given as inputs.
• Current signal is given to a gain block and temperature is given to capacity since it varies according to temperature.
• These inputs are given for integration where current will be integrated with respect to time.
• This outout is given to another block where it is divided by capacity.
• Hence the SOC is estimated.

Conclussion:

Thus,simulation of 3 test cases from harness dashboard presented and coulomb counting with BMS implements coulomb counting for SOC estimation is presented.