Week 7 State of charge estimation
AIM
To Simulate the 3 test cases from harness dashboard and write a detailed report on the results
To Explain coulomb counting by refering the given model and to describe how BMS implements coulomb counting for SOC estimation.
PROCEDURE
The battery management system files are downloaded from the given link and extracted.
The files are opened in MATLAB
THE MODEL
This model contains two versions of the battery pack: a small one with just 6 cell series, and a larger 16-module pack, each module containing a 6-cell series string. The architecture is 6s-1p and 96s-1p packs, respectively.
The Model can be used to mimic diverse usage cycles and environmental conditions to evaluate a systems response to a potentially unsafe condition. For instance, voltage, current, or temperature outside the recommended limits. Lets say this BMS is a part of an EV powertrain and the initial SOC is 75% with the ambient temperature being 15 degrees C. The vehicle is run for a while and then stops to charge. Once the vehicle and battery are at rest and charging, the cell balancing cycle kicks in. How do we know that during the charging stage the battery pack will remain within recommended electrical and thermal limits? What if the temperature of the pack is 40 degrees C instead of close to 15? Will an aggressive drive cycle cause an under-voltage situation when the SOC is running low? A model such as this one allows the vehicle designer to test these types of situations.
BMS ECU
This describes the software part which defines the state of charge, balancing logic, state machine and Bus Management.
The elctronic control unit controls nall the electronic and electrical sections
State Machine defines the main operating state of the BMS. The state machine has four parallel states (parallel meaning that they can be active at the same time):
The first one defines the variable BMS_State which indicates standby, driving, charging, and fault. Charging comprises constant current and constant voltage stages.
The second state switches the fault state ON in case a current, voltage, or temperature value reaches an unsafe level.
The third and fourth states define the contactor ON and OFF switching sequence for the charger and inverter. This is needed to avoid an excessively large current inrush at the beginning of the charging stage.
PLANT
Plant represents the hardware part of the model, with the battery pack, precharge circuit, and charger.
The charger load contains a motor and charging load.
Battery Management system closed loop harness dashborad
The harness dashboard is shown below. It consists of the test sequence variant subsystem which hold three different test cases. This dashboard also helps in determining the fault states and other parameters as shown in the diagram below. The green colour indicates that the system is working without any errors while the red signal will indicate that there is a fault in the system.
to check firstrly one is selected in Test sequent Variant
OUTPUT
1 Cell voltage is the first graph of all the cells involved in the battery pack it can be observed that cell voltages changing as the current flowing in and out. at the beginning of the simulation, the voltages are slightly different because we initialize the model with a slight associate of imbalance the voltages are deflecting from the range of 3.1 v to 3.9 volts for the time period of 0.3*10^4 sec, and then there is balances state of all voltage of their respective cells and then there is a sudden hike in cell voltage till they reach 4.2 volts and at the end of the simulation, the values converge towards one another and trying to balance the voltages till the end still there is a small difference in their voltages and the voltage line flows between 4 to 4.2 volt. because the system goes on standby mode.
2 Pack current in charging state during the constant current phase current(current is deflecting in the range of 19 A TO -70 for 0.3*10^4 sec) is debated because the maximum cell voltage is high, enough compared to prescribe 4.4-volt limit that an excessively high current control the voltage beyond the threshold significantly limiting the life of the battery
3 The temperature(it's starting from the range of 289-degree Celcius and at the end, all the cells have a different temperature) traces show the significant discrepancy between the hottest and coldest cell the reason is mainly because of symmetry in the merger layer in terms of thermal behavior. the cell no 6 is significantly hotter the cell no 1 because it is thermally insulated on one side. even if the maximum temperature reached during the simulation is not concerned in terms of safety. the temperature difference at cell no 6 at its higher temperature will eventually cost much faster degradation of cell 6 compared to cell 1 leading to undesirable and uneveness in cell condition. therefore need an active thermal management system to keep thermal differences within a few degree Celcius.
4 the graph no 3 show the soc of three different battery cell 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 is wrong 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. with the ekf slightly outperforming then ukf.
5 the other scopes indicate the bms state at each of the balance command signals respectively
Second case
Test sequent variant is changed to 2
1 In 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 0.85*10^4 sec. (throughout the simulation BMS has put on driving mode).
2 The current graph has exactly flowed in the same manner but in the opposite direction when voltage is getting drop current required is high and current flows in the range of -70 A to 20 A.
3 The temperature of all the cells initiating from 288-degree Celcius and they get scattered all the cells have a different temperatures traces show the significant discrepancy between the hottest and coldest cell the reason is mainly because of symmetry in the merger layer in terms of thermal behavior. cell no 6 is significantly hotter the cell no 1 because it is thermally insulated on one side. even if the maximum temperature reached during the simulation is not concerned in terms of safety.
4. The cells are initially at 80% of soc and then they're falling at different time periods. to set their capability to recover.it 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 within the first hour of simulated time. with the ekf slightly outperforming then ukf then 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 initial error within the first hour of simulated time. with the elf slightly outperforming the ukf.
5.the other scopes indicate the bms state at each of the balance command signals respectively. where it's on driving mode and command signals on zero lines.
changing test sequent variant to 3.
Output
1 All the cells are initiated with at different voltages the where the 6 the one having the higher voltage of 4.2 volts and the 1st one having near about 4.1 v and remaining falling under this range BMS on charging state throughout the simulation. so the voltage graph move along in a straight line because the voltage supply continues .there have some deflection of voltage lines of different cells but that is a very less amount of change in voltage .at the end of the simulation BMS trying to balance the voltage very much
2.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
3.all the cells initiate with 287-degree Celcius 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 cell the reason is mainly because of symmetry in the merger layer in terms of thermal behavior. cell no 6 is significantly hotter the cell no 1 because it is thermally insulated on one side. even if the maximum temperature reached during the simulation is not concerned in terms of safety. the temperature difference at cell no 6 at its higher temperature 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 degree Celcius.
4.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.
5. 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.
COLOUMB COUNTING
state of charge of a battery(SOC) represents the amount of energy left inside the battery with respect to the total amount of energy in a battery at the Fully charged condition. SOC can help a user to determine when to charge the battery or stop charging and how much energy he can extract from a battery at that time. There is no direct method to calculate SOC for a Li-ion battery but there are several ways in which we can calculate SOC with the help of some additional parameters.
The easiest method to calculate the SOC of a battery is by using a battery voltage translation method. This method is not accurate but helps to approximately and quickly calculate SOC and can be used in devices like power banks where SOC is shown in range and not the exact percentage. For example, a typical power bank shows SOC in the range from 0-25%, 25-50%, 50-75%, 75-100%.
There are several limitations to this method and hence more accurate methods are needed like the Coulomb counting method, the Fuzzy logic method, the Kalman filter, the RBF Neural network, the Fuzzy neural network, the Hybrid methods, etc.
Each method has its limitations, accuracy, and complexity and hence proper decision should be made while selecting methods for SOC estimation.
Coulomb Counting Method :
The coulomb counting method is also known as the battery current integration method. In this method, a discharging/charging current I(t) is measured and integrated over time to calculate the amount of energy remaining inside the battery. In this method, SOC is taken at the previous time step SOC(t-1) and the amount of energy removed/added at the current time step is subtracted/added from the SOC of the previous time step SOC(t-1) to obtain the amount of energy present in the battery at the present instant.
The SOC estimation in the coulomb counting method is done using the below equation,
`SOC(t) = SOC(t-1)+(I(t))/(Qn) * t`
SOC(t-1) = SOC at previous time step
t = current time step
t-1 = previous time step
Qn = Charge capacity of the battery
This method is accurate and reliable than the voltage translational method but does not take into account the discharge rate, temperature, hysteresis, age of the battery, etc
Modified coloumb 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:
`Ic(t) = k2I(t)^2 + k1(t)+k0`
where , and 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)+(Ic(t))/(Qn)*t`
The experimental results show that the accuracy of the modified Coulomb counting method is superior to the conventional Coulomb counting method.
How BMS implements coulomb counting for SOC estimation.
Methods for SOC estimation.
Go for BMS ECU Subsystem to have a deep study
Click the SOC Estimation block
select coloumb counting
by clicking on the coulomb counting block, we get the below block diagram
in this coulomb counting method, the entering and leaving currents of the cell are integrated to track the safe chargeable time.
here the two input signals current and temperature are taken
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 of the coulomb counting method is its simplicity and low computational cost.
the drawback of the coulomb counting method is an accumulation of current sensor error and the inability to recover from the wrong initial condition because of lack of feedback from voltage measurement.