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.
Downloading and open closedloop BMS harness dashboard model:
To open the Lithium Ion Battery Management algorithm firstly we have to download the zip file from the link given in the task and the link has also been attached below for refrence. Then to get access to the Close Loop BMS harness dashboard we have to check for the following model Test>System>BMS_ClosedLoop_Harness_Dashboard model file.
I have selected to Open Project and Model. ANd after that the whole project will be loaded in the Matlab in current folder and workkspace.
After this the BMS_ClosedLoop_Harness_Dashboard model file is open as:-
CASE STUDY 1
Now we will run the test cases by double-clicking the test sequence variant there are three cases that we will run in sequence. In the above given model, we need to give the test case number in the 'Test Sequence Variant' by double clicking on the sub-system. Once we selected the test case number in the Test Sequence Variant subsystem run the model, now the model will run 20000 seconds
Result :-
Observation:
As the simulation ends we got 6 result graph considering the BMS is an operation for electrical vehicle
Cell voltage :-
Visualizes the cell voltage of all the cells in the entire battery pack, while simulating the model the cell voltages are changing as per the current flow (in and out). In the initial stage of simulation the, the voltages are slightly different because the model simulation was initialized with a slight associate of imbalance in the voltages are deflecting from 3.1V to 3.9V and the time period is 0.3*10^4 seconds. Now we can visualize the balance state of voltages their respective cells until the voltage of cells reach 4.2V, and it will remain till the end of simulation. We can observe a small voltage difference in their voltages and the voltage lies between 4V to 4.2V due the system is on standby mode.
Cell Temperature :-
The temperature starts at 289 degrees and it will increases gradually, all the cells have different temperatures. The temperature traces shows the significant difference between the highest and lowest temperature cells, due to symmetry in the merged layers in terms of their thermal characteristics and behaviour. The 6th cell is high temperature cell and the 1st cell is lowest temperature cell due to it is thermally insulated at one side. In the graph we can visualize that the temperature of the cells will gradually increase till the time of 0.6*10^4 seconds and then the temperatures of all the cells will stars depleting gradually, at the end of the simulation the temperatures of all the cells are 293 degrees, 297 degrees, 302 degrees, 304.95 degrees, 307 degrees and 308 degrees.
SOC :-
Graph visualizes the differnt battery cell performances corresponding to coulomb count method, corresponding to ukf and ekf. The initial SOC is 75%, the starts at 80% to set their capability to recover. It is apparent that coulomb counting never does, because it has no way to get feedback to understand it is wrong due to its absence of voltage information. Both the algorithms recover on the other hand, from the initial error within the first hour of simulated time. With the ekf slightly outperforming than ukf.
Peak current :-
While in the charging state due to constant current phase, the current is lies between in the range of 19A to -70A in the time range of 0.3*10^4 seconds because maximum cell voltage is high. After 0.3*104^4 seconds the current will remain at 0A till 0.4*10^4 seconds and then the current will raise till the current attains 30A and the current will remains at 30A till 0.49*10^4 seconds. Now the current starts depleting gradually to around 2A and again it will depletes to 0A, from 0.7*10^4 seconds to till the end of simulation the current will remains at 0A.
BMS :-
The other two scopes in result output visualizes the BMS (battery management system) and the balancing command signals respectvely.
Cell balancing :-
After 3000 secs cell balancing starts with on cell 6 and cell 2 as they rised to 1 as shown above since its voltage is higher than all other cell voltages. After this stage charging and balancing,post charge balancing are occurs as Cell 6 and cell 2 remains in 1 state till end of simulation. As cell 1(yellow) is in lesser voltage than other cells it remains in 0 state through out the simulation. At the end of the simulation all cell voltages try to balance equally as we seen earlier in single cell voltages plot.
CASE STUDY 2 :-
Now select test case 2 and click apply in test sequence varient dialog box as shown below
The case is altered to second as shown below
SCOPE RESULT :-
Cell voltage :-
The cell voltages varies from maximum 3.9V and reduces to 3.05V as cutoff voltage upto 9000 secs.Entire capacity drained at 9000 secs.The pack current discharges upto maximum 70A and slight charging occurs during the overall drive. Some stand by mode conditions are in between the drive modes.
Cell Temperature :-
The cell temperatures of the battery pack will be different for each individual cell. The cell number six has highest and cell number one has lowest temperatures. The cell one has a thermal insulation at one side, so the cell one temperature is low. The process initializes at 289 degrees and the temperature of the cells gradually increases till the simulation process attains above 6200 seconds and the temperature range is lies between 306 degrees to 323 degrees. After 6200 seconds the temperature graph starts depleting gradually till the end of the simulation. The temperature values are at the 20000 seconds for each cell are 294 degrees, 300 degrees, 304.5 degrees, 308 degrees, 311 degrees and 313 degrees.
SOC :-
Initializes the simulation at 80% and the state of charge percent will deplete gradually till 4500 seconds and at 4500 seconds the state of charge is at 35%. After 4500 seconds the state of charge will starts increasing till SOC reaches to 69%, 70% and 76% until the simulation time attains above 6200 seconds and after the state of charge will remains same till the end of the simulation.
Peak current :-
The simulation process at 0A and the current will increase and decrease in the range between 10A to -70A and the current will be at 0A till the simulation attains 2000 seconds. After 2000 seconds the current will fluctuates up and down in the range between 9A to -65A till the simulation attains to 3900 seconds. After 4200 seconds the current will fluctuates 9A to -60A for 800 seconds and then the current will increases to 30A and it will remains for 1000 seconds.
BMS :-
Entire sequence is performing in drive mode as shown from the above plot. Small duration of standby modes are not plotted in this plot as simulation upto 10000secs command is given as drive mode. But in actual practical situation standby modes are not present present as battery current drains by other small auxilaries of EV's.
Cell balancing :-
Here balancing of cells are not performed as shown as balance states are in 0 for entire drive mode.
CASE 3
Now select test case and click apply in test sequence varient dialog box as shown below
SCOPE RESULT :-
Cell voltage :-
Due to continuous voltage supply the graph is moving like a straight line, and we can visualize a very less amount of deflections in voltage of different cells. At the end of simulation the battery management system balances the voltage deflection in among the battery cells. Cell number six having higher voltage of 4.2V and cell number one having lower voltage of 4.1V and the remaining cells are falling under the range of BMS on charging state throughout the simulation and the voltage graph starts at 3.83V.
Cell Temperature :-
The temperature traces shows the discrepancy in between the highest and lowest temperature cells, due to symmetry in the merged layer in terms of the cell thermal character and behaviour. Cell number six has highest temperature and cell number one has lowest temperature because the cell one has thermal insulation at one end. The active battery managements system will maintains a very less temperature difference between the cells.
SOC :-
The soc is initial soc of three different cells is at 80% as the. as the system is put on charging state the soc hikes up to 100% for the yellow cell,ukf and ekf initially having different charging rates but after 0.4*10^4 sec they having equal state of charge of 91% and it maintains throughout the simulation .t is apparent that columb counting never does because it has no way to realize it is wrong due to the absence of voltage information. both common filter algorithms on the other hand recover from the initial error. ekf and urf slightly outperforming then coulomb counting.
Peak current :-
The battery pack current is initiated with 20 A and then it falls to zero current till its complete simulation time of 0.2*10^4 sec and then it maintains the constant current because it is put on charging state and it remains constant throughout the simulation
BMS :-
The other scopes indicate the bms state at each of the balance command signals respectively. bms on charging state and battery command passing signal between 0 to 1.
Cell balancing :-
Here from 0sec the cell 6 and 2 starts to balance with respect to cell 1 and all cells are try to balancing at end of simulation.
2.What is coulomb counting. Refer to the above model and explain how BMS implements coulomb counting for SOC estimation
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 [20]. Coulomb counting method is done to estimate the SOC(t), which is estimated from the discharging current I(t) , and previously estimated SOC values SOC(t-1) .But there are several factors that affect the accuracy of Coulomb counting method including temperature, battery history, discharge current, and cycle life
SOC is calculated by the following equation:
SOC(t)=SOC(t−1)+(I(t)/Qn)*Δt
MODIFIED COULOMB COUNTING MODEL :-
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.
Ic(t)=k2I(t)^2+k1I(t)+k0
where k2,k1 and k0 are constant values obtained from the practice experimental data.
In modified Coulomb counting method, SOC is calculated by the following equation:
SOC(t)=SOC(t−1)+(I(t)/Qn)*Δt
The experimental results show that the accuracy of the modified Coulomb counting method is superior to the conventional Coulomb counting method.
FLOW CHART FOR COULOMB COUNTING :-
The flowchart explains the algorithm used to manage the battery monitoring by switching from a battery operating mode to another by the conditions of the transition. The estimation of the SOC of a Li-Ion battery in this method is based on monitoring both the voltage ܸVbat and the current Ibat. The operation mode of the battery is recognized by the direction of current through the battery system.
It can be shown that this method is similar to integrating the standard deviation of current over time. The use of the integrating plot provides a form of low-pass filtering that may make it easier to see trends, but this can also be accomplished by digital filtering. The use of only the current noise restricts the value of the technique, as it does not allow for the estimation of the noise resistance (and hence the corrosion rate), and it is difficult to see what real advantages the method has over the conventional three-electrode method, although the use of integrating plots may be of value.
Refer to the above model and explain how BMS implements coulomb counting for SOC estimation
To see the SOC estimation model, we need open the different model block. The directions to open the SOC estimation modle is, BMS_ClosedLoop >> BMS ECU >>SOC_Estimation >> Coulomb Counting. With the help of above mentioned directions, we can open the SOC estimation model and coulomb counting model.
To see the model of coulomb counting technique in BMS model click the subsystem of SOC_Estimation and click Coulomb counting.
in this SOC estimation block we can see three different techniques for the state of charge estimation. all three techniques have their own merits and limitations. the first method is columb counting and the other two are unscented and extended Kalman filter.
Here we are concern with the columb counting method.
In coulomb counting method block, it takes two input signals such as current and temp and the current signal goes to gain block and the temp signal goes to lookup tables of battery time and current. and both the blocks are connected to the divided block and these signals are connected to the discrete-time integrator to track the charge and which finally helps to calculate the state of charge.
The Amph hour pack current and temperature get divided in the devide block and the output from the devide block is connceted to the Discrete- time integrator block to integrate the devided current and temperature signals. From the output of discrete-time integrator block we can get the desired final SOC The block parameters of coulomb counting method are gioven blow.
Here the input signal blocks are :-
Gain:-
Here the discharge current from battery fed to the gain block with value of 1/3600 to convert current to 'Ahr'. The value is divided by capacity due to temperature change, then integrated with a 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 percentage of SOC.
Lookup table:-
Perform n-dimensional interpolated table lookup including index searches. The table is a sampled representation of a function in N variables. Breakpoint sets relate the input values to positions in the table. The first dimension corresponds to the top (or left) input port.
Product:-
The divide block get the inputs from gain block pack current to multiply port and temperature from the 1-D lookup table to the divide port. the output from the divide block is fed to the discrete- time integrator block to integrate the divioded pack current and temperature signals to get the SOC.
Discrete integration:-
The integrator block get the inputs from the divide block. The devided pack current and temperature values are integrated in the discrete -time integrator to get the desired final SOC ( state of charge). The output SOC from the integrator block is fed to the output block to visualize the SOC in the results graph.
Inport:-
Provide an input port for a subsystem or model. For Triggered Subsystems, 'Latch input by delaying outside signal' produces the value of the subsystem input at the previous time step. For Function-Call Subsystems, turning 'On' the 'Latch input for feedback signals of function-call subsystem outputs' prevents the input value to this subsystem from changing during its execution. The other parameters can be used to explicitly specify the input signal attributes.
CONCLUTION
One of the main problems in coulomb counting is that it is necessary to calibrate the initial battery SOC. In several applications, the initialization involves rest and the initial SOC is estimated based on the voltage
Kalman filtering is an algorithm that provides estimates of some unknown variables given the measurements observed over time.
REFERENCE :-