Week 7 State of charge estimation

AIM: State of charge estimation

OBJECTIVE:

1. Simulate the 3 test cases from the 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?

INTRODUCTION:

BATTERY MANAGEMENT :

Due to growing environmental concerns and depletion of conventional fuels, the automobile industry is currently focusing on the development of Electric (EVs) and Hybrid Electric vehicles (HEVs). A rechargeable high voltage battery is the driving force for EVs/HEVs. A Battery Management System (BMS) is necessary to ensure the safe operation of the battery. BMS consists of sensors, electronic units housed on or away from the battery, and a display unit.

• Functions of BMS:
  1. Monitoring of battery current, voltage (individual cell voltages as well as pack voltage)
  2. Temperature monitoring (pack temp)
  3. State Of Charge(SOC) and State Of Health(SOH estimation)
  4. Balancing of cells
  5. Generating Safety-Critical alarm and derating the output if required.
  6. Emergency Shutdown in Critical state.

In this paper, we are going to explore various SOC estimation techniques. Before, proceeding further, let us understand what do you mean by SOC? SOC of a cell or a battery at a given instant is the ratio of the charge available at that point to the charge available when it is fully charged. It is expressed in percent, from 100% when full to 0% when empty. Analogous to how much fuel is available in a car's tank.

• Importance of SOC

2. SOC indicates whether the available charge is sufficient enough to carry out a particular function or not.
3. Knowledge of SOC helps in operating the battery within the limits of charge and discharge.
4. Very high or low SOC tends to permanent damage to the battery.

SOC estimation is difficult as it represents the internal state of the battery. There is no sensor /instrument to measure SOC

directly. It needs to be inferred from battery voltage or battery current. Hence, the word SOC Estimation and not SOC measurement.

1. SOC ESTIMATION TECHNIQUES
   1. OCV Method

OCV refers to the Open Circuit Voltage of the battery (battery no-load condition). It is the simplest method for SOC estimation. With some battery chemistries, OCV voltage is a linear function of SOC. Thus, by measuring the OCV we can predict the SOC of the battery easily. While with other Li-ion chemistries, OCV vs SOC is a flat curve [18]. In such conditions, usage of this method is limited. Other limitations of using this method. It is an offline method. The battery should not be connected to any load for at least 2 hours to accurately measure the OCV. Also, OCV not only varies with SOC, but with a cell's internal resistance.

1. Coulomb Counting

Coulomb Counting is the most readily used algorithm for SOC estimation. It is based on the fundamental relation between charge and current. Integrating the current in and out of the battery to give the relative value of the charge.

= 0 ()

0

Where, = Residual Capacity at the nth time

0 = Initial battery capacity I (t) = Battery current.

To start with initial SOC of the battery should be known from then onwards CC algorithm is implemented. Limitations of this algorithm are cell leakage current is not considered. Current sensor offset can result in a drift in SOC estimation. As Li-ion cells have low self-discharge, their leakage current is not a problem here but offset in the current sensor drifts the SOC graph [18].

2. Electro-Chemical Spectroscopy

EIS is an AC technique where a small magnitude AC signal typically mV is injected into battery terminals and the resultant voltage response is measured. The measured DC Battery impedance is dependent on the magnitude of the applied signal, the magnitude of the response signal, and its phase shift.

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

Test case 1:

We can observe from this flow chart that driving is done for 3000 seconds and then balancing is done for 4000 seconds, during which time the vehicle is in standby mode. The next stage after balancing is charging, which takes 9000 seconds. After that, post-charge balancing is carried out till the simulation is completed.

RESULTS

• The first plot shows us the individual cell voltages for 6 cells. The fluctuating voltage between 3.2 and 3.7 is provided during the driving state time (between 0 and 3000 seconds), indicating that the nominal voltage was 3.7 and it decreased to 3.2 per cell. During the charging phase, it rises to 4.2V. (Between 4000 to 9000 secs). The fully charged voltage of the battery is 4.2. Because the BMS was in standby mode, it did not diminish.
• The entire battery pack current is shown in the second plot. Because the battery loses current when driving, it is displayed in the negative direction (till -60A). The current rises to 30A during charging and then drops to zero during standby.
• The third figure depicts the battery pack's cell temperatures, which range from 290K to 315K. The temperature of the cells rises during battery depletion and gradually drops during charging and standby mode. We can observe from the graph that the sixth cell has the highest temperature, indicating that it is discharged the most.
• The BMS states are depicted in the fourth plot. As previously stated, the driving time ranged from 0 to 3000 seconds, the charging time ranged from 4000 to 9000 seconds, and the standby time ranged from 9000 to 20000 seconds.
• The fifth graph depicts the SOC in three separate graphs, with the SOC rapidly decreasing to 0.4 during driving and fully charging back to 100% during driving. In standby mode, the SOC will not change.
• The cell balancing signals are shown in the sixth block. From the sixth cell of the battery pack to the first cell, the balancing of cells is presented in descending sequence. The temperature block in the cell confirms this.

TEST CONDITION (2):

The driving state is maintained until 10000 seconds have passed, following which charging begins.

RESULTS

• The BMS enters a driving mode in this second situation until the battery pack's SOC is depleted. The battery pack discharges current for 8692 seconds, as seen in the graph. We may deduce from this that the battery pack lasts 8692 seconds on a single charge. Here, there is no charging or standby mode. The electric vehicle continues to run until the battery pack is entirely depleted.
• The voltages range from 3.7 to 3 volts. This 3V is the individual cell's cutoff voltage. The battery pack can discharge up to 60A of electricity, which is its maximum capacity. The temperature of the cells continues to rise as a result of the constant discharge of current. The SOC continues to fall until it hits 0%. Because the battery pack has not been charged or stood by, cell balancing is not performed while charging. The cells have been discharged evenly until they are shut off, hence there is no cell balancing signal.
• Using this sort of case scenario will allow us to better grasp the battery's endurance and life duration

TEST CONDITION(3)

There is just one BMS state shown in this flow chart, which is charging. So, from 0 to 20000 seconds, only charging takes place.

RESULTS

• The charging duration is fully in this third circumstance. There is no such thing as a driving or standby state. The SOC was set to 80 percent at first, and then the pack was fully charged. After being fully charged, the pack current increased to 30A and then decreased to zero. The in-rush current is limited by the contactor circuits. After being fully charged, the battery can no longer take current.
• The fully charged voltage range for the cell is between 4 and 4.2 volts. The cell temperatures were also somewhat higher at first due to the initial charging current, but they quickly stabilized thanks to the contactors. According to the plot, the balancing orders were followed from the sixth cell to the first cell.
• As a result, the BMS system was thoroughly examined, and the three separate test case situations were thoroughly discussed.

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

Coulomb Counting Method:

The Coulomb counting method estimates SOC by measuring the discharge current of a battery and integrating it over time. The SOC(t), which is determined using the discharging current, I(t), and previously estimated SOC values, SOC, is calculated using the Coulomb counting technique (t-1). The following equation is used to determine SOC:

SOC(t)=SOC(t−1)+Ic(t)QnΔtSOC(t)=SOC(t-1)+Ic(t)QnΔt

Flow Chart for Coulomb Counting:

The SOC of a Li-Ion battery is estimated using this approach by monitoring both the voltage VbatVbat and the current IbatIbat. The direction of current via the battery system identifies the battery's operation mode.

When the battery is in open circuit mode, Ibat=0Ibat=0 and compensation of self-discharge losses will be taken into account as explained in sub-section C.

The battery voltage, the current flowing through it, and operational temperature are all required for monitoring. Coulomb counter ΔQΔQ is used to track the SOC when the battery is charged or discharged.

ΔQ=∫t+τtIbatdtΔQ=∫tt+τIbatdt (2)

This variance ΔQΔQ that will be used in the following equations is negative if the battery is in discharge, positive if in charge.

1. Charge Mode
   In this mode, the coulomb counter is presented by ܳQga∈edQga∈edas expressed in (3), which represents the number of charges accumulated during ττ

Qga∈ed(t+τ)=Qga∈ed(t)+ΔQQga∈ed(t+τ)=Qga∈ed(t)+ΔQ (3)

So, the variation of the state of charge gained in this same operating period is obtained by the relation (4)

ΔSOC(t+τ)=Qga∈ed(t+τ)Qrated⋅100%ΔSOC(t+τ)=Qga∈ed(t+τ)Qrated⋅100% (4)

We may get the instantaneous value of SOC by adding the prior state of charge indicator and the acquired one.

SOC(t+τ)=SOC(t)+ΔSOC(t+τ)SOC(t+τ)=SOC(t)+ΔSOC(t+τ) (5)

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.

SOC(t)+DOD(t)=100%SOC(t)+DOD(t)=100% (6)

1. Discharge Mode
   In the discharging mode the coulomb counter is presented by QlostQlost, which represents the amount of charges losses in the operating period ττby equation (7):

Qlost(t+τ)=Qlost(t)+ΔQQlost(t+τ)=Qlost(t)+ΔQ (7)

The depth of discharge variation is derived using (8) and utilized in (9) to determine the DOD value.

ΔDOD(t+τ)=Qloss(t+τ)Qrated⋅100%ΔDOD(t+τ)=Qloss(t+τ)Qrated⋅100% (8)

DOD(t+τ)=DOD(t)+ΔDOD(t+τ)DOD(t+τ)=DOD(t)+ΔDOD(t+τ) (9)

After that, the equation (6) is used to determine the SOC

1. 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 ܳQocQocis calculated by equation (10) represents the accumulative losses during the store hours.

Qoc(h+1)=Qoc(h)+qperhourQoc(h+1)=Qoc(h)+qperhour (10)

ℎ: Hour of storage
This value will be added to the amount of charge lost ܳQlostQlost during discharge mode and subtracted from the amount of charge accumulated ܳQga∈edQga∈ed 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.

Qlost=Qlost+QocQlost=Qlost+Qoc (11)

Q→t=Q→t−QocQ→t=Q→t-Qoc (12)

1. 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 interval's variations (13)

⎧⎪⎨⎪⎩0.5T<−20o1−20o≤T≤40o0.8T>40o{0.5T<-20o1-20o≤T≤40o0.8T>40o (13)

1. Recalibration
   When the loaded voltage ܸVbatVbatdrops under a limit voltage VlimVlim(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 QmaxQmax which represents the maximum capacity that can be released by the battery. This recalibration allows rectifying the
   indication of SOC by replacing ܳQratedQrated by QmaxQmaxin relation (4). Also, we recalibrate the State-of-Health (SOH) of the battery SOH defined is a relation (14) as:

SOH(nc)=Qmax(nc)Qrated⋅100%SOH(nc)=Qmax(nc)Qrated⋅100% (14)

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

To compute the estimated soc of the battery management system, we must insert the plant block into this model, which incorporates BMS ECU and PLANT.

We require an accurate estimate of battery SOC before we charge our car. In the battery system, we don't assess the state of charge; instead, we measure things like battery pack current, cell voltage, and cell voltage using signals from the plant.

after entering the plant block, we can see the state machine, SOC estimation, and balancing logic block.

In this section, we'll look into the SOC estimates.

Three distinct state of charge estimating strategies are shown in this SOC estimate block. Each strategy has its own set of benefits and drawbacks. Columb counting is the first approach, while unscented(ukf) and extended Kalman filter are the other two (ekf). We're talking about the Columb counting approach here

In the Coulomb counting method block

It accepts two input signals: current and temperature, with the current signal going to the gain block and the temperature signal going to the battery time and current lookup tables. Both blocks are linked to the split block, and these signals are sent to the discrete-time integrator to track the charge, which then aids in the calculation of the state of charge.

The positives of this are its simplicity and cheap capital cost, while the disadvantages include a buildup of current sensor error and its inability to recover from the erroneous start condition. due to a lack of voltage measurement feedback.

CONCLUSION: The estimation of Soc is done, and the results of Different parameters are shown with the help of the cooulomb technique it is one of the way to find out the SOC estimation.