The goal in this project is to implement a battery fuel gauge that continuously estimates the state of charge (SOC) and time to shut down (TTS) of the battery.
Coulomb Counting Approach
You will use the Coulomb counting approach to estimate the SOC. The Coulomb counting is achieved recursively through the following equation
s(k) = s(k − 1) + ∆i(k)(1)
Qwhere s(k) is the SOC at the present time instant k, Q is the battery capacity in Ampere hours (Ah), ∆ is the sampling time in hours, and, i(k) is the current at the present time instant k in Amperes.
TTS Estimation Approach
TTS is the time it would take until the terminal voltage reaches OCVmin. Follow the steps below for TTS estimation:
1. Let us denote the target terminal voltage as vt = OCVmin.
2. Compute the voltage drop vd = iLR0, where iL is the load current. For this project, it is given that iL = −200mA.
3. Compute target OCV
V◦t = vt − vd = OCVmin − vd > OCVmin (2)
4. Compute target SOC using SOC lookup method
st = fo−1(V◦t) (3)
5. Compute SOC difference. Here, s(k) is the SOC computed using the CC approach (1)
sd(k) = s(k) − st (4)
6. Compute TTS at time k as
TTS(k) = sd(k)Q |iL| (5)
The following two datasets are provided. Both data belong to the same battery.
TIVdata1 You will use the time, voltage and current data (shown in Figure 1) from the matlab data file named ‘TIVdata1.mat’ to accurately estimate the capacity (Q) and the internal resistance(R0) of the battery. Table 1 shows the first seven data points from ‘TIVdata1.mat’.
TIVdata2 You will use the time and current data (shown in Figure 2) from the matlab data file named ‘TIVdata2.mat’ to implement a battery fuel gauge (BFG).
Table 2 shows the first seven data points from ‘TIVdata2.mat’.
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