Week 7 State of charge estimation

Objective:

     To perform the 3 test cases from harness dashboard and also coulomb counting, we need to perform the BMS implements coulomb counting for SOC estimation by using MATLAB

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

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

Step1: 

         Open a file in the directory 

Click Battery_System.prj

Open the BMS_closedloop_Harness_Dashboard.slx

Step2:

        It will open the file Test harness.

Step3: 

       Run the Simulation

 

A)Now Select Test Case-1:

In BMS_Closedloop_Harness_Dashboard 

We need to Choose Test case as 1

If we go into BMS_StateRequest subsytem, the following system opens up. Now we have to select "BMS_TestSequence".

 

Selecting the first test Sequence in Test Sequence Variant now we can check whether the condition is TestSequence1.

In test case 1 there is  Driving, Balancing, Charging and Balancing and also Post charge Balancing phase.

From the above figure we can observed that,

Driving is for 3000 seconds then Balancing for 1000 seconds followed by charging and balancing for 5000 seconds then there is the Post charge balancing.

By running the simulation,

 

1)The First plot shows the individual cell voltages,the Voltage fluctuates between 3.2 and 3.7 volts between 0 to 3000 seconds.

The Nominal voltage of each cell is 3.7 volts and it decreases to 3.2 volts. Fluctuations in cell voltages from 0 to 3000 seconds shows that the vehicle is in driving mode.

from 3000 to 4000 seconds, we have balancing mode, thus the cell voltages gets balanced to 4 volts till 4000 seconds, from 4000 to 8000 seconds, there is charging and balancing state thus cell voltage rises to 4.2 volts.

 2) Entire battery pack current is shown in pack current graph.

As the current decreases while charging thus the current is -60A from 0 to 3000 seconds, now from 3000 to 4000 seconds, there is cell balancing, thus pack current reduce to 0A and from  4000 to 8000 seconds, there is charging state, thus current increases to 30A, then occur postt charge balancing, thus pack current comes to 0A.

3)The third graph is cell temperature graph.

The cell temperature increases in driving stage as vehicle is in driving mode then during balancing stage, cell temperature reduces and then at start of charging the temperature increases suddenly then later at charging and post charge balancing, the cell temperature shows the reducing behaviour.

4) The BMS state graph shows that during 0 to 3000 seconds shows that the vehicle is in driving mode from 3000 to 4000 seconds, we have standby mode from 4000 to 8000 seconds, there is charging state.

5)The Soc graph tells that, during driving state, the SOC reduces from 80% to 45%  during balancing state, SOC remains constant at 45% during charging state, SOC increases to 100% and then remains constant.

6) The cell balancing signal is shown in the balance command graph.

B)Now Select Test Case-2:

In BMS_Closedloop_Harness_Dashboard 

We need to Choose Test case as 2

If we go into BMS_StateRequest subsytem, the following system opens up. Now we have to select "BMS_TestSequence".

 

Selecting the Second test Sequence in Test Sequence Variant now we can check whether the condition is TestSequence2.

In test case 2 there is  Charging and Driving phase.

If we Run the Simulation

The driving stage is from 0 to 10000 seconds then the charging stage occur.

1)The cell Voltage Fluctuates from 3.9 volts to 3.2 volts repeatedly as seen in graph of cell voltages.

2)The battery pack current fluctuates from 0 to -60 A and then rises to 10A repeatedly as shown.

3)The cell temperature increases as there is driving stage from 0 to 10000 seconds.

4)As the Driving Stage is there from 0 to 10000 seconds, thus BMS graph shows the BMS driving.

5)The SOC continue to reduce from 80%  to 0%. The SOC do not increses because there is no charging and balancing stage.

Though the driving stage is till 10000 seconds but as battery completly discharge at 9000 seconds( as SOC is 0% at 9000 seconds) thus Vehicles runs till 9000 seconds only.

 

C) NOW SELECT TEST CASE-3:

In BMS_Closedloop_Harness_Dashboard 

We need to Choose Test case as 3

If we go into BMS_StateRequest subsytem, the following system opens up. Now we have to select "BMS_TestSequence".

 

Selecting the third test Sequence in Test Sequence Variant now we can check whether the condition is TestSequence 3.

In test case 3 there is only Charging phase.

If we Run the Simulation, the output is shown below

As there is only one stage that is Chaging stage thus from 0 to 20000 seconds, only charging will takes place.

1) The cell Voltage varies till 20000  seconds and then remains constant.

2) As Start the pack current increases to 30A and then reduces to 0A at 20000 seconds and then remains constant 0A.

3)Cell temperature Varies as shown in the graph.

4)As there is only charging stage, thus BMS graph shows BMS charging,

5)As only Charging takes place.

Thus SOC was 80% at first, then SOC increases to 100% in 2000 seconds then SOC remains constant at 100%.

 

 

2)What is coulomb counting? Refer to the above model and explain how BMS implements coulomb counting for SOC estimation ? 

         The “coulomb counting” or CoulCount method was developed by Schmitt it is a heuristic method, although the underlying theory is reasonably accessible . It depends on the recording of the current noise only. The signal is filtered with a high-pass filter with a cut-off frequency of 0.01 Hz. The filter may be either analogue (although the implementation of a good-quality 0.01 Hz analogue filter is difficult) or digital. The absolute value of the measured current samples is then summed over time. A steep slope is taken as an indication of rapid corrosion, although it is not possible to calibrate this in terms of an estimated corrosion rate.

 

State of charge (SOC):

It is a relative measure of the amount of energy stored in a battery, defined as the ratio between the amount of charge extractable from the cell at a specific point in time and the total capacity. Accurate state-of charge estimation is important because battery management systems (BMSs) use the SOC estimate to inform the user of the expected usage until the next recharge, keep the battery within the safe operating window, implement control strategies, and ultimately improve battery life. Traditional approaches to state-of-charge estimation, such as open-circuit voltage (OCV) measurement and current integration (coulomb counting), can be reasonably accurate for cell chemistries with a significant OCV variation throughout the SOC range, as long as the current measurement is accurate. However, estimating the state of charge for battery chemistries that exhibit a flat OCV-SOC discharge signature, such as lithium iron phosphate (LFP), is challenging.

Kalman filtering is a promising alternative approach that circumvents these challenges with a slightly higher computational effort. Such observers typically include a nonlinear battery model, which uses the current and voltage measured from the cell as inputs, as well as a recursive algorithm that calculates the internal states of the system, including state of charge. However, estimating the SOC for modern battery chemistries that have flat OCVSOC discharge signatures requires a different approach. Extended Kalman filtering (EKF) is one such approach that has been shown to provide accurate results for a reasonable computational effort.

Coulomb Counting Method.

The Coulomb counting method is associated with monitoring the input and the output current continuously. Since capacity is the integral of current with respect to time, by measuring the input and the output current, change in capacity or capacity degradation of a battery can be measured easily. In this method, So His calculated by dividing measured capacity (after discharging the battery to 0% SoC value) to its rated capacity. It is an extensively used method by researchers for its simplicity .But  , the accuracy of this method is not very high. Therefore, to improve its accuracy, for example, Ng et al. proposed a smart coulomb counting method to estimate both SoC and SoH accurately. Similarly, an adaptive neuro fuzzy inference system (ANFIS) was modelled in the paper. It considered the cell’s nonlinear characteristics to get the relationship between SoC and open circuit voltage (OCV) at different temperatures. During the estimation of SoC, at some random OCV and temperature modelling of cell characteristics was done by ANFIS. Done on the cell level instead of the pack level for better precision. The coulomb counting method, also known as ampere hour counting and current integration, is the most common technique for calculating the SOC. This method employs battery current readings mathematically integrated over the usage period to calculate SOC values given by

where SOC(t0) is the initial SOC, Crated is the rated capacity, Ib is the battery current, and I loss is the current consumed by the loss reactions. The coulomb counting method then calculates the remaining capacity simply by accumulating the charge transferred in or out of the battery.

The accuracy of this method resorts primarily to a precise measurement of the battery current and accurate estimation of the initial SOC. With a pre known capacity, which might be memorized or initially estimated by the operating conditions, the SOC of a battery can be calculated by integrating the charging and discharging currents over the operating periods. However, the releasable charge is always less than the stored charge in the charging and discharging cycle. In other words, there are losses during charging and discharging. These losses, in addition with the self discharging, cause accumulating errors. For more precise SOC estimation, these factors should be taken into account. In addition, the SOC should be recalibrated on a regular basis and the declination of the releasable capacity should be considered for more precise estimation.

Enhanced Coulomb Counting Algorithm:

In order to overcome the shortcomings of the coulomb counting method and to improve its estimation accuracy, an enhanced coulomb counting algorithm has been proposed for estimating the SOC and SOH parameters of Li-ion batteries. The initial SOC is obtained from the loaded voltages (charging and discharging) or the open circuit voltages. The losses are compensated by considering the charging and discharging efficiencies. With dynamic recalibration on the maximum releasable capacity of an operating battery, the SOH of the battery is evaluated at the same time. This in turn leads to a more precise SOC estimation.

Technical Principle:

The releasable capacity (C-releasable), of an operating battery is the released capacity when it is completely discharged. Accordingly, the SOC is defined as the percentage of the releasable capacity relative to the battery rated capacity (Crated), given by the manufacturer.

                                         

A fully charged battery has the maximal releasable capacity (Cmax), which can be different from the rated capacity. In general, Cmax is to some extent different from Crated for a newly used battery and will decline with the used time. It can be used for evaluating the SOH of a battery.

                                         

When a battery is discharging, the depth of discharge (DOD) can be expressed as the percentage of the capacity that has been discharged relative to Crated,

                                       

where Creleased is the capacity discharged by any amount of current. With a measured charging and discharging current (Ib), the difference of the DOD in an operating period (Ʈ) can be calculated by

                                   

where Ib is positive for charging and negative for discharging. As time elapsed, the DOD is accumulated.

                                      DOD(t) = DOD(t0) + ∆DOD

To improve the accuracy of estimation, the operating efficiency denoted as ŋ is considered and the DOD expression becomes, with ŋ equal to ŋc during charging stage and equal to ŋd during discharging stage. Without considering the operating efficiency and the battery aging, the SOC can be expressed as

                                      SOC(t) = 100% – DOD(t)

Flfowchart of the  coulomb counting algorithm:

To compute the SOC of the battery, insert a plant block into the model.

 

BMS algorithm models protects and report the measurement from the battery Pack

When we click on the BMS ECU block get,

 

We get the below block diagram by clicking on the current power limit calculation block.

Here the cell voltage and cell temperature are used to decide maximum allowable charging and discharging current limits.

When the cell is at the lowest SOC, its Voltage is low, and thus it is important to prevent the cell from delivering a large amount of current because it will cause a large voltage drop that could be potential below the cut off voltage specified by the cell manufacture.

Click on discharge-current limit calc block, we get the following block diagram shown below, Hereby comparing the minimum cell voltage in the module against this lowest threshold voltage and then divided by maximum internal cell resistance value, we can compute the voltage based on the threshold.

It is also important to limit the current delivery and current intake when the temperature is too high or too low using the lockup table with rising and falling shape peofile, we can specify current threshold- based temperature and thus can modulate allowablw current delivery and it is recommended to avoid low and high temperature during discharging and low temperature during charging,

Then the above two threshold are then compared and a minimum of these two becomes the current limit.

Knowing how much longer wecan able to drive our car before we had to stop for recharge depends on the accurate estimation of battery SOC.In the battery system,we do not measure SOC directly, we actually measure something else and then relate it with SOC.

 

Methods are used for SOC estimation:

Coulomb Counting is Mostly used.

click on SOC estimation block, we get

by Clicking on the coulomb counting block, we get the below block diagram

This coulomb counting method,the entering and leaving current of the cell are integrated to track the safe chargeable time.Here the two input signals current and temperature are taken and the current signal goes to gain block and the temperature signal goes to the current lookup table.Then as seen, signals are multiplied and divided as shown and then output is given to discrete-time integrator to track the safe chargeable time and then SOC is estimated.

Advantage:

   Coulomb counting method is Simple and low Computational Cost.

Disadvantage:

   The drawback of the coulomb counting method ia an accumulation of current sensor error and the inability to recover from the wrong initial condition because of feedback from the voltage Measurement.

Conclusion:

                 Hence we perform both the test cases from harness dashboard and also coulomb counting in Battery Management System.