Week 7 State of charge estimation

bms::

aim::

1.Simulate the 3 test cases from harness dashboard

2.coulomb counting. Refer to the above model and BMS implements coulomb counting for SOC estimation.

software used::

matlab R2020a.

download link of bms :

https://in.mathworks.com/matlabcentral/fileexchange/72865-design-and-test-lithium-ion-battery-management-algorithms

1 Simulate the 3 test cases from harness dashboard::

procedure::

first open the matlab and then download the file from the folder.

the folder is in zip file. now double click on the zip file . it create a file.m

then go to simulink, open the new file. and 'open' the folder project and file.

open the closed loop harness dashboard.

The Closed Loop Harness Dashboard model by Mathworks is as shown below.

test case 1:

to select the test condition 1. click on test varient it shows the below window on that chose 1 for test case 1::

for test sequence::

click on bms test sequence and then click on test varient 1 and then this window opens:

As we look at the test variant as shown below, it states that at the start of the simulation the model will be in the driving condition, thereafter, after 3000 seconds it will be in balancing mode, after 1000 seconds it will be in charging and balancing mode and after 5000 seconds it will be in post charge balancing mode.

after simulation run of 20000sec the dashboard is seen like this:

result:

It can be observed from the plot that the vehicle is initially in the driving state and charging was done till 90%. After the charging mode it reaches to the standby mode which can be seen from the BMS state plot. From the Cell temperatures plot it is observed that the battery temperature rises in driving and charging. The battery voltage was increased to 4.2 V while charging. The current plot shows the current value supplied to the vehicle while driving. The SOC was observed to be decreased around 50% in driving condition, which increased while charging.

test case2:

to select the test condition 2. click on test varient it shows the below window on that chose 2 for test case 2::

for test sequence::

click on bms test sequence and then click on test varient 2 and then this window opens:

As we look at the test variant as shown below, it states that at the start of the simulation the model will be in the driving condition, thereafter, after 10000 seconds it will be in balancing mode

result::

run the simulation for 10000 second.

this is the test 2 result, here it has the driving only for 10000 sec.It was observed from the plot that the battery discharges completely in 8692 seconds and the vehicle does not go forward and the charging state was not achieved at 10000 seconds. Hence the vehicle comes to standby condition and simulation stops midway.

test case :3::

to select the test condition 3. click on test varient it shows the below window on that chose 3 for test case 3::

for the test sequence::

click on bms test sequence and then click on test varient 3 and then this window opens:

Looking at the condition below for third variant of the simulation. It is just the continuous charging condition from the start till the end of 20000 seconds of simulation.

result::

Looking at the plot it can be seen that the battery starts charging and the SOC starts increasing from beginning to around 2000 seconds and stays stable at 100% continuously from 2000 seconds until 20000 seconds. The peak current plot drops from 0 to 2000 seconds and stays constant as the charge is not used anywhere as the driving is not present.

report::::

From the above simulated 3 variants of the simulation carried out it was observed that,

• In the first simulation all three conditions were simulated such as driving charging and the stable state hence the charging and discharging were observed and in the end the constant 100% SOC was observed because of the standby state of the vehicle.
• In the second simulation it was just the discharging as the SOC dropped to 0% in the driving state within 8600 seconds and the charging state was not achieved at 10000 seconds.
• In the third simulation, only charging was observed for the first 2000 seconds and the SOC remained at 100% constant for the next 18000 seconds.

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2. coulomb counting.BMS implements coulomb counting for SOC estimation .

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 SOCThe SOC is one of the most important parameters for batteries, but its definition presents many different issues . In general, the SOC of a battery is defined as the ratio of its current capacity to the nominal capacity . The nominal capacity is given by the manufacturer and represents the maximum amount of charge that can be stored in the battery. The SOC can be defined as follows:

The various mathematical methods of estimation are classified according to methodology. The classification of these SOC estimation methods is different in the various literatures. However, some literatures allow a division into the following four categories.(i)Direct measurement: this method uses physical battery properties, such as the voltage and impedance of the battery.(ii)Book-keeping estimation: this method uses discharging current as the input and integrates the discharging current over time to calculate the SOC. (iii)Adaptive systems: the adaptive systems are self-designing and can automatically adjust the SOC for different discharging conditions. Various new adaptive systems for SOC estimation have been developed.(iv)Hybrid methods: the hybrid models benefit from the advantages of each SOC estimation method and allow a globally optimal estimation performance. The literature shows that the hybrid methods generally produce good estimation of SOC, compared to individual methods.

SOC Estimating Mathematical Methods::

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:

The experimental results show that the accuracy of the modified Coulomb counting method is superior to the conventional Coulomb counting method [1].

From the given model by Mathworks as shown below of BMS Closed Loop model, when seen inside the BMS ECU,

battery coulomb::

The Coulomb efficiency is usually used to describe the released battery capacity. It refers to the ratio of the discharge capacity after the full charge and the charging capacity of the same cycle. It is usually a fraction of less than 1.

go to SOC estimation model-ECU block.

procedure::

first,click on bmsclosed loop block.

then it shos the below.

from the above click on bms ecu block.

then this subsystem open:

And then inside the SOC estimation block:

then click on the above block we see this:

The Coulomb counting block can be observed on the top of the model, where the Coulomb counting was carried out. The model for the same can be seen below.

this is coloumb counting formula block which is ceating according to the formula::

This system carries out the estimation by coulomb counting equation mentioned earlier. The main advantage of coulomb counting is that of its simplicity and low computational cost. Temperature and current are taken as input from the battery. Current signal is given to the gain block and the temperature is given to capacity as it varies according to the temperature. These inputs are given to integration where current will be integrated wrt time. This output is given to another block where it is divided by capacity and henceforth the SOC is estimated.

Conclusion:

• Simulation of the 3 test cases from harness dashboard and reported the results.
• coulomb counting is explained.Refer to the above model and explained how BMS implements coulomb counting for SOC estimation.

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References:

1.https://www.hindawi.com/journals/isrn/2013/953792/

2.https://in.mathworks.com/matlabcentral/fileexchange/72865-design-and-test-lithium-ion-battery-management-algorithms

3.https://www.sciencedirect.com/topics/engineering/coulomb-efficiency

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