Week 7 State of charge estimation
AIM:
1. Simulate the 3 test cases from harness dashboard and write a detailed report on the results
2. What is coulomb counting? Refer to the above model and explain how BMS implements coulomb counting for SOC estimation ?
THEORY:
Lithium - ion battery packs are widely used energy storage systems in electric vehicles. The battery management system (BMS) is responsible for safe operation, performance and battery life under diverse charge - discharge and environmental conditions. When designing a BMS the cell voltage and temperature, SOC, SOH and the charging profile has to be controlled and maintained.
Also, it indicates whether there has been a fault (eg: that any cell has reached an over temperature condition or under voltage condition)
PROCESS:
Download and extract the file for closed loop BMS harness dashboard model using the link provided.
Open the simulink model by selecting Plant--> Battery Model in the software MATLAB-Simulink.
Open the model using the correct source. Click on Battery_System.prj and make sure that all the files have a green tick indicating that the BMS model is ready to start without any interruptions. The command line shows a message "Successfully created search database" meaning the system has no errors.
The following document open up.
Click on the Design and Test BMS Algorithm option.
Now We have to open the BMS system and the closed loop harness dashboard. So, we go back to the MATLAB window and select the BMS System and open the subsystem.
The BMS model consists of a BMS ECU and a plant. The control unit with its variation monitoring and control algorithms is connected to a block with the representation of the battery pack and associated circuitry and peripherals. This model contains 2 versions of the battery pack: a small one with 6 cell series, and a larger 16-module pack, each module containing a 6-cell series string. The architecture is 6s-1p and 96s-1p packs respectively.
BMS ECU Subsystem:
Here, the State_Machine Chart defines the main operating state of the BMS. It has 4 parallel states(can be activated simultaneously):
a.) 1st defines the variable BMS_State which indicates standby, driving, charging and fault. Charging comprises constant current and constant Voltage stages.
b.) 2nd state switches the fault state ON in case a current, voltage or temperature value reaches an unsafe level.
c.) 3rd and 4th states define the contractor ON and OFF switching sequence for the charger and inverter. This is needed to avoid an excessively large current inrush at the beginning of the charging stage.
After opening the BMS system, open harness dashboard from the tests folder from the extracted files. It consists of the test sequence variant subsystem which holds 3 different test cases. This dashboard also helps in determining the fault states and other parameters as shown. Green colour indicates that the system is working without any error while red indicates there is fault.
Run the simulation and the scope opens up upon successful completion. The scope consists of the measurements of cell voltage, cell temperatures, SOC, Pack current, BMS state.
To check the faults in the system open the harness dashboard and select the test case as per the requiremnt.
After applying the test case, run the simulation. the dashboard shows if there are any errors and faults.
Now Click on the "BMS_StateRequest" the following system opens up. Now select "BMS_TestSequence" as per the requirement.
FOR THE TEST CASE 1:
As shown above, there are no errors and faults found after simulation of test case 1 as all show green light.
There are 3 stages of driving conditions, namely: *Driving, *Charging, * Standby.
When the simulation begins, initially the vehicle is in driving state and the SOC will decrease for this time period. After 3000 seconds the next step is balancing and the vehicle is in standby condition. After, another 1000 seconds, the vehicle is in charging phase and the battery's SOC increases almost to 100%. The next phase is the post charging phase in which the vehicle again goes for the standby mode.
RESULT/ SCOPE of Test Case 1:
We can observe that:
* Initially, the SOC of battery module is 80%.
* The input cycle can be seen in the BMS_State plot in which the vehicle initially undergoes driving, then charging SOC = 90% and then later in standby state.
* During charging, the battery pack voltage is increased up to 4.2V.
* The cell temperature increases during the driving and charging cycle and starts falling off in standby mode.
* The SOC drops to less than 50% after the driving cycle and then later put into charging until SOC reaches 100%.
* The current supplied to the EV during phase can be seen in the current plot.
* In SOC plot: The Yellow plot represents the estimation done using the coulomb counting technique. Blue represents the estimation done using Unscented Kalman filter technique. Red represents gauging using Extended Kalman filter technique.
FOR THE TEST CASE 2:
The result shows that there is no fault condition in the vehicle.
Select the "TestSequence2" in the "BMS_TestSequence" block.
This test case has 2 phases for the battery pack: *Driving and * Charging
RESULT/ SCOPE of Test Case 2:
* We can observe that SOC reduces from 80% to 0% in entire drive mode, and at standby modes in between them the SOC remains constant as shown.
* Balancing is not being performed in this test case.
FOR THE TEST CASE 3:
The dashboard shows no errors and faults.
Select the "TestSequence3" in the "BMS_TestSequence" block.
There is only a single phase which is charging.
RESULT/ SCOPE of Test Case 2:
We can observe that the SOC increases from 80 to 100% and remains in standby mode. The actual SOC is around 93% shown as full charge shown by UKF and EKF techniques.
CONCLUSION:
Hence, from the above 3 test cases the performance of the BMS on various factors are simulated.
B) Coulomb Counting:
SOC is defined as the rate of the available capacity to its maximum capacity when a battery is fully charged and describes the remaining percentage of the battery capacity.
To find this SOC% we are using coulomb counting method. This method measures the discharging current of the battery and integrates the discharging current over time in order to estimate SOC. Which can be estimated from discharging current (i[t]), previously estimated SOC values(SOC[t-1]). The relation can be given as, 
Further, to increase the accuracy of the SOC estimated the function which is the modified is used:
Substituting we get:
From BMS model:
To check the coulomb counting technique in BMS model click the subsystem "SOC_Estimation" and select coulomb counting.
Here the discharge current from battery given to gain as shown to convert current to Ahr.
The value is divided by capacity due to temperature change. Then integrated with gain value of 1 with sample time -1 as we are using discrete integrator.
The saturation limits are from 0 to 1.
We will get the SOC values from 0 to 1 multiplied with 100 to get in the SOC%.
This Coulomb counting method is simple and require less computational compared to other 2 methods.