Electronic – Measuring average current with uC and current shunt

adccurrent measurementmeasurementmicrocontrollershunt

This is my first post. I'm a software guy trying to do hardware so be gentle 🙂

Circuit

I'm designing a small circuit (see pic, and sorry for messy schematic) that plain and simply is a bunch of MOSFETS and gate drivers designed to switch resistive loads (heating pads in this case) from a microcontroller. The heating elements often has a very low resistance, and to keep power at the desired level, the MOSFETS are switched using PWM.

Measurement

Besides the purely functional aspect, there's an educational focus as well. I want to be able to get some feedback on the current consumption. And my naive approach was to simply throw in some current shunt sensor IC's.
When using a multimeter to measure the output voltage from the sensor, I actually get something that looks like the average current (with PWM switching) due to the "slowness" of the ammeter. But when connecting same output to i.e. an atmega328p ADC, I get some bad readings – the speed here puts a reading anywhere on the PWM square wave.

So, my question is how do I go about measuring the (average) current when switching with PWM?

It seems the design is OK, but I might have missed something in both the design and how the uC ADC should be used in this context.

schematic

Best Answer

Sometimes what looks simple is not that simple. You have a quite complex measurement to do, but you want a simple result. What you want to measure is not constant, it's varying in time. Depending on your level of requirement, you could compute one or many properties of the current consumption. These properties will help you to better monitor the system. I propose you 3 different solutions, in ascending complexity.

Solution 1: Average

You want to get a one-value result -> get the average in time. As already proposed by @akellyirl, use a low-pass filter. Compute float y = alpha*input + (1-alpha)*y for each sample, where alpha is the smoothing factor. See Wikipedia for the details.

Solution 2: Max + Average

You are interesting in getting the average, and the max value. Monitoring the max value could be interesting for component dimensioning for example.

if (y > max)
  max = y;

Solution 3: Standard deviation + Max + Average

Why?

See below charts. There are 3 signals of different shapes. A triangle, a sine, and a spike signal. They are all periodic with same period, same amplitude, same average, and same min and max. But, they have different shapes, and indeed they have a completely different story...

Signals and their histogram

One of the difference is the standard deviation. That's why I suggest you to extend your measurements, and include the standard deviation. The problem is that the standard way to compute it is CPU consuming. Hopefully, there is one solution.

How?

Use the histogram method. Build an histogram of all the measurements, and extract efficiently the statistics (min, max, avg, standard deviation) of the dataset. The histogram groups values together that have the same value, or same range of value. The advantage is to avoid storing all the samples (increasing count in time), and to have a fast computation on a limited number of data.

Before starting acquiring measurements, create an array to store the histogram. It is a 1 dimension integer array, of size 32 for example:

int histo[32];

Depending on the range of the ammeter, adapt below function. For example, if the range is 256mA it means that bin 0 of the histogram will be incremented by value between 0 and 8 mA, bin 1 by value between 8 and 16 mA etc...So, you'll need an integer to represent the histogram bin number:

short int index;

Each time you get a sample, find the corresponding bin index:

index = (short int) floor(yi);

And increment this bin:

histo[index] += 1;

To compute the mean, run this loop:

float mean = 0;
int N = 0;
for (i=0; i < 32 ; i++) {
  mean = i * histo[i]; // sum along the histogram
  N += i; // count of samples
}
mean /= N; // divide the sum by the count of samples.
mean *= 8; // multiply by the bin width, in mA: Range of 256 mA / 32 bins = 8 mA per bin.

To compute the standard deviation, run this loop:

float std_dev = 0;

for (i=0; i < 32 ; i++) {
  std_dev = (i - mean) * (i - mean) * histo[i]; // sum along the histogram
}
std_dev /= N; // divide the sum by the count of samples.
std_dev = sqrt(std_dev); // get the root mean square to finally convert the variance to standard deviation.

The strategy of the histogram method is to make the slow operations on a few number of bins, instead of all the acquired signal samples. The longer the sample size, the better. If you want more details, read this interesting page The Histogram, Pmf and Pdf.

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