State of Charge Estimation
Aim:- State of charge estimation
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.
After downloading the zip file we first have to extract the zip file to a folder and then I choose again Battery_System_R2020a to extract to a a folder.
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.
After opening this file Matlab will ask the following question:-
I have selected to Open Project and Model.
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:-
Test Sequence-1:
Test Sequence-1 Report:
The above graph indicates 6 different graphs that are Cell Voltages, Pack Current, Cell Temperature, BMS Status, SOC using 3 different methodologies and Cell Balance Command Signals.
The fourth graph gives us an idea about the BMS state which is the vehicle undergoing that are Driving, Charging and Standby mode or Balance mode.
In the first graph, Cell voltages changes in value as the current is going in and out of the cell by charging and discharging it respectively. At first, we can observe that the voltage values of the cells differ in values slightly; it is because we initialized the model with a slight imbalance between the given cells. For 0 to 3000 seconds the cell voltages range between 3.1 to 3.9 volts(Driving part) approximately and then balances for a very short amount of time to the nominal value of 3.7 volts. Later, cell voltages touch the 4.2-volt mark by sudden rise(Charging state) behaviour and ultimately settles down to a lower value, at the end of the simulation. At that time the cell voltage tries to converge to each other values with the balancing methodology and ends up between 4-4.2 volt range.
The second graph shows the current rating of the battery pack which basically defines the current characteristics of all the cells as a whole. At driving mode, the pack current has the ranges from -70 Amps to 20 Amps. The pack current increases to 30 Amps at the time of charging(the charging current is taken as positive here) and then stays zero at the time of standby balanced mode for the rest of the simulation, giving zero value due to zero power demands.
The third graph is showing us the cell temperatures for all the cell voltages. When the BMS is in the driving mode, the cell temperature rises due to the different power demands of the drive cycles. The balanced mode happens for a very short duration; it tries to converge, but the window is of very little time they can not converge. After that when the charging state comes, all the cell temperature rises and after a point(5550 seconds) it again starts to decrease due to the balancing effect but a decrease in pack current. The clear difference between the hottest cell(No.6) and the coldest cell(No.1) because of the physical positions that they are placed in or maybe because of the underperforming cell. Cell No. 6 is significantly hotter the cell no 1 because it is thermally insulated on one side. The higher temperature of cell no.6 will eventually cost much faster degradation of cell 6 compared to cell 1 leading to undesirable and unevenness in cell condition. Therefore need an active thermal management system to keep thermal differences within a few degrees Celcius so that the performance of the battery pack is not compromised.
In the fifth graph, the SOC of three different battery cells each performed with different matter yellow corresponds to column counting, blue corresponds to ukf and orange to ekf, the initial soc is 75%, however, the simulation so estimates need to initialize it to 80% to set their capability to recover. It is apparent that columb counting never does because it has no way to realize it due to the absence of voltage information. both common filter algorithms on the other hand recover from the initial error within the first hour of simulated time. Here, EKF slightly outperforms UKF.
The sixth graph gives us the balance commands of the cells. We can see here that from the falling edge of the BMS driving mode, the balance command is initiated for all the batteries except cell no.1 which is the coldest. It is because, from this point in time, all the cell's temperature significantly increases and crosses a certain difference with the temperature of cell no.1.
Test Sequence-2:
Test Sequence-2 Report:
The fourth graph shows us that the simulation is run throughout the BMS driving mode.
In graph 1, the initial state the voltage of all the cells are deflecting in the same manner and dropping slightly in the range of 3 to 3.9 volt it has 4 fluctuation zones and in between each of these fluctuating zone, they are stable for while and this phase lasts for 8500 sec.
Graph two also shows us the same behaviour as the voltages, but it has negative values most of the time whenever the higher voltage drop is required and have a -70A to 20A value like before.
The temperature graph here is showing is all the cells temperatures were the same at the start of the simulation which is 288 degree-kelvin. Here, temperature discrepancies are also observed between each cell. Cell no.6 is the hottest and cell no.1 is the coldest like before and the temperature difference here is 20 degrees approx. This can easily degrade the overall performance of the battery pack.
The SOC of three different battery cells each performed with different matter yellow corresponds to column counting, blue corresponds to ukf and orange to ekf, the initial soc is 75%, however, the simulation so estimates need to initialize it to 80% to set their capability to recover. It is apparent that columb counting never does because it has no way to realize it due to the absence of voltage information. both common filter algorithms on the other hand recover from the initial error within the first hour of simulated time.
The SOC for all three values is decreasing from 80% of the value and continuously decreases because the entire simulation is in driving mode. Columb counting soc value decreases at a lesser rate than the other two, where ukf shows even lesser than ekf.
The balance command is not initiated for any of the cells for this specific simulation. That may be since for continuous driving mode this much difference or disbalance can be allowed because no overcharging will be taking place.
Test Sequence-3:
Test Sequence-3 Report:
The fourth graph clearly shows that the entire simulation is based on the charging mode.
From the first graph, all the cells are initiated at different voltages where cell no. 2 has the highest voltage and cell no.1 has the lowest voltage. After some time, cell number 6 quickly comes near the cell number 1 voltage value whereas there is a significant difference in the cell number 2 voltage.
The pack current is showing a higher charging current at the initiation of the simulation but as time goes the pack current decreases and becomes zero at the end. The battery pack current is initiated with 20 A and then it falls to zero current till its complete simulation time of 2000 sec and then it maintains the constant current because it is put on charging state and it remains constant throughout the simulation.
The cells initiate with 287 degree-kelvin and then all the cells maintaining the straight line but with different temperatures range. the end all the cells have a different temperature) traces show the significant discrepancy between the hottest and coldest cells the reason is mainly because of symmetry in the merger layer in terms of thermal behaviour. cell no 6 is significantly hotter the cell no 1 because it is thermally insulated on one side.
The soc is initial soc of three different cells is at 80%. As the system is put on charging state, the soc rises to 100% for the yellow cell,ukf and ekf initially having different charging rates but after 4000 sec they having an equal state of charge of 91% and it maintains throughout the simulation. It is apparent that columb counting never does because it has no way to realize it, making it erroneous 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.
The balance commands of the cells, we can see from the sixth graph. The command for cell no.1 is never initiated because it is coldest but for the other cells, it is initiated from the beginning. When after 16000 seconds the voltage temperature is come close enough to each other, the balance command for all the other cells been reset to 0 value at 18000 seconds. Between this time, the command status changes rapidly from 1 to 0 by constantly monitoring the temperatures and ultimately falls to zero when the temperature difference becomes very less.
Task - 2
Coulomb Counting Method:-
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 SOC(t) , which is estimated from the discharging current,I(t) , and previously estimated SOC values,SOC(t-1) .
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 , 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 estimation in Battery model:-
To observe the coulomb counting method we need to open the BMS_ClosedLoop>BMS ECU and then on SOC_ Estimation block
Flowchart for Columb Counting Method
The flowchart given in Fig above and 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. When the battery is in open circuit mode, Ibat = 0 and compensation of self-discharge losses will be taken into account as explained in sub-section C. The information needed to carry monitoring is the measurement of the battery voltage, the current flowing through it, and the operating temperature. Coulomb counter ∆ܳQ is used to track the SOC when the battery is charged or discharged.
The amount of charges ∆ܳQ in an operating period 'τ' is obtained by temporal integration of a measured charging/discharging current Ibat like expressed in (2)
This variance ∆ܳQ that will be used in the following equations is negative if the battery is in discharge, positive if in charge.
(I). Charge Mode
In this mode, the coulomb counter is presented by ܳQgained as expressed in (3), which represents the number of charges accumulated during 'τ'
So the variation of the state of charge gained in this same operating period is obtained by the relation (4)
By cumulating the previous state of charge indication and the obtained one we can have the instantaneous value of ܱܵSOC.
Knowing the relationship indicated about (6) the value of depth of discharge DOD is updated in every charging operation to get it back every switching to discharging mode.
(II). Discharge Mode
In the discharging mode the coulomb counter is presented by Qlost, which represents the amount of charges losses in the operating period 'τ' by equation (7):
The depth of discharge variation is calculated according to (8) and used in (9) to calculate the value of DOD
The SOC indication then is done by applying the equation (6).
(III). Self-Discharge Mode
At the battery storage periods, the percentage of monthly self-discharge is converted to the amount discharged per hour; this amount is designed by the constant q(per/hour). Considering a 5% rate of self-discharge per month q(per/hour) is approximated to 0.0016 Ah. So the quantity of charges dissipated in the open circuit period ܳQoc is calculated by equation (10) representing the accumulative losses during the store hours.
ℎ: Hour of storage
This value will be added to the amount of charge lost ܳQlost during discharge mode and subtracted from the amount of charge accumulated ܳQgained in charge mode as expressed in relations (11) and (12). This compensation of self-discharging loss is made at each open circuit period and before switching to
another operating battery’s mode.
(IV). Temperature effect
The temperature variation affects considerably the state of charge of the Li-Ion battery [16]. To improve the accuracy of the battery state of charge, a temperature coefficient ߙ is introduced and multiplied by the value of the calculated SOC [17]. This coefficient varies depending on the operating temperature according to the intervals variations (13)
(V). Recalibration
When the loaded voltage ܸVbat drops under a limit voltage Vlim (3V), during the discharging mode the battery is considered totally discharged. When it’s empty, the battery can no longer be used and should be recharged. So after a cycle of charge/discharge, a recalibration should be made of Qmax which represents the maximum capacity that can be released by the battery. This recalibration allows rectifying the
indication of SOC by replacing ܳQrated by Qmax in relation (4). Also, we recalibrate the State-of-Health (SOH) of the battery SOH defined is a relation (14) as:
Moreover, the battery is fully charged. Then ܱܵSOH should be updated by assigning the number of charges releasable from the battery to Qmax. The SOH indeed represents the ageing degree of the battery; in fact, it’s at 100% maximum value when the battery is new. When the battery is getting old and ܳQmax became more and more inferior than ܳQrated, SOH value drops to indicate the health degradation of the battery.