I'm sure you were able to understand the intent of my post, and we don't need to delve into semantics to communicate
Your response suggests that you're still not quite understanding why indicating mAh consumed is more useful than calculating mWh consumed. Our industry (and most others that work with batteries) uses mAh because it consistently and somewhat quantitatively indicates remaining battery capacity, regardless of variable power draw, and without needing to characterise or log the battery.
Obviously, remaining battery capacity in mAh is disconnected from remaining endurance. As you stated in a previous reply, drones are essentially constant power systems (if you ignore takeoff and landing), as opposed to constant current systems. Given this, it's tempting to think we could just throw a few curve models together for different battery chemistries, gather some data points to calibrate for a specific battery, and have a much better system where we accurately indicate remaining flight time as a single percentage number.
I'm working towards a solution like this, but there are a lot of complexities that make it difficult. First and foremost is that knowing the state of a battery is critical for safe operations. While mAh consumed and combined voltage are cumbersome to work with, they provide actionable information about what's going on with a battery that isn't abstracted.
The second issue is that the sensors in current commercially available power modules are not very precise. You need to be able to count coulombs to accurately gauge remaining charge/energy/endurance, and through a combination of calibration errors, resolution at low currents, and low sample rates, they don't currently do this well (though it sounds like Olli's sensor has made a lot of progress). The error is maybe only 5%, but that small error leads into the next point.
You need to know the state of charge in order to know where the battery is along the discharge curve. This is absolutely critical, and even a small measurement error can result in a significant error in charge/energy/endurance estimation. To accurately know the state of charge, ideally you need to log every charge and discharge cycle, which has the additional bonus of allowing you to monitor battery health.
Again, there might be a temptation to think this isn't very critical. If you always use freshly charged batteries, can't you just assume they're full at power up? Not really. You might have mistakenly plugged in a half discharged battery, or maybe you needed to adjust somethings before takeoff that involved a bit of discharge and then a power cycle. The state of charge information is lost, and you're back to using imperfect sensors to guess what's going on. You feed slightly garbage information to your curve models, and they give you garbage back, but because it's been abstracted to a single percent figure, you have no way to tell until you're hit with a low voltage warning.
The best solution to this problem is something like a TI Fuel Gauge. 24-bit sensors that are factory calibrated, with high sample rates counting and logging every coulomb that goes in or out of the battery. Smart batteries are definitely the future, but they're a lot less flexible, and they add significant cost to each battery.
Like I said, this is something I'm actively working on, but there's still a lot of work to go.