Week 7 State of charge estimation

AIM:

To define coulomb counting and how the BMS implements in coulomb counting for soc estimation

Procedure:

# The definition of coulomb counting to be explained briefly.

# To mention the steps carried out for BMS implements in coulomb counting for soc estimation.

Coulomb counting:

# Coulomb counting is a technique used to track the State of Charge of a battery pack. It works by integrating the active flowing current over time to derive the total sum of energy entering or leaving the battery pack. This produces a capacity that is typically measured in Amp-hours.

# The Coulomb counting technique 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, discharging current, and previously estimated SOC values. Integrating the current into or out of a battery gives the relative value of its charge.

# Coulomb counting needs a starting point. If the initial charge in the battery is known, from then on coulomb counting can be used to calculate the charge.

For example, a 2-A current into a battery, for 3 hours, will add 2 * 3 = 6 Ah charge to the battery.

# The battery’s DOD will have decreased by 6 Ah. Without knowing the initial DOD, though, we cannot know the final DOD. The charging process is 100% efficient. The cell voltage during discharge is lower than during charge, so the energy discharged is lower than the energy during charging, even though the charge in and out is the same.

Coulomb Counting can be a very accurate technique, with two limitations:

# Leakage current within a cell does not go through the current sensor and is therefore not taken into account.

# The offset in the measurement of the battery current will result in the SOC drifting up (or down) over time.

Coulomb counting works well with Li-Ion cells because they have low leakage. Drift remains a major limitation due to the offset in the current sensor, especially Hall effect sensors.

Drift can become significant in applications that, for long periods, use very little battery current or shuttle current back and forth. In particular:

# Standby Batteries: Even if the battery is full, a small offset in the current sensor in the discharging direction will result in the reported SOC drifting all the way to 0% SOC over time.

# HEV pack Uses energy from the battery when it needs it, and replenishes it when it can, trying to maintain a 50% SOC. While the reported SOC may very well stay around 50%, over time the actual SOC will drift due to the small offset in the current sensor. Eventually, the actual battery charge will approach either the full or the empty state.

`SOC(t)=SOC(t−1)−∫n⋅I(t).dtQn`

Where,

$\mathrm{*\; I}\left(t\right)$ = discharging current

$\mathrm{*\; S}OC\left(t\right)$= previously estimated SOC

${\mathrm{*\; Q}}_n$ = battery capacity

$\mathrm{*\; n}$  = discharge efficiency

Coulomb counting method has a simple structure and ease of implementation, however, it does not consider battery temperature, cycle life, and battery history during SOC calculation. Battery efficiency is influenced by battery temperature which causes inaccurate SOC calculation. It is very necessary to calibrate the initial SOC value for this method.

BMS implements coulomb counting for SOC estimation:

# The estimation of the SOC of a Li-ion battery by the developed method is based on monitoring the voltage and the current. The operation mode of the battery is recognized by the direction of the current through the battery system.

# When the battery is in an open circuit mode, the battery current is zero, and compensation of self-discharge losses will be considered. The information needed to perform the monitoring are the measurement of the battery voltage and the current flowing through it.

# Coulomb counter is used to track the SOC when the battery is charging, discharging, and self-discharging.

# Coulomb counting method can be implanted in BMS with a suitable algorithm. The following chart is giving a general idea, how SOC estimation is going to work. This algorithm can be implemented with a state machine in Simulink, which can be further uploaded in flesh memory of BMS hardware using the code generation option.

Now, look at the inside of a Simulink model of the battery management system. In this model, SOC is estimated with different methods – Kalman filter and coulomb counting. Coulomb counting method is taking pack current values and divided by capacity values, which is given by a look up table to show the variation of capacity with temperature. Further values are integrated to estimate the charge.

# The adopted Coulomb-counting algorithm for SOC estimation was implemented on a hardware platform. The monitoring functions include the measurement of battery voltage and the current flowing through it by the 10 bits Analog to Digital Converter MCU PIC18F.

# The battery parameters detection is one of the most important issues in the control and management of a BMS. It includes cell voltage measurement and current detection. Estimation of SOC and other battery states imposes high requirements on cell voltage precision.

# In order to measure the voltage and the current with which the battery is charged or discharged, a sensing resistor Rsens is placed in series with the battery. The battery voltage Vbat is obtained by measuring the voltage across it. The difference between the voltages at the two terminals of the resistor will derive the current value bat I through the sensing resistance.

# This resistance, which allows the current sensing in the battery gauge, should not be too high in order to avoid power dissipation during the charging at a constant current of 1C-rate, but it should still be enough so that the analog/digital converter in the microcontroller can detect a voltage variation across it, which corresponds eventually to a 10 mA current, in order to detect the end of charging.

# In our case, a 10-bit converter is used to detect a voltage variation of 1mV. A 0.1 Ohms resistance can detect a current of 10 mA (for U = 1mV) and does not dissipate too much power while charging the maximum current value (P=100mW).

# The transmission of the battery's electrical parameters is ensured by a USB connection between the multimedia application and the MCU PIC18F. The ambient temperature is measured using a negative temperature coefficient resistor RNTC.

# This thermistor is biased by the IO of the MCU and connected close to the battery.