My winter Mustang Mach-E project

[Jiji]

Sometimes you just get lucky and this morning was a good day. First I woke up to an email that Rivian restored my cancelled R1T order so now I get better than Mach-E GTPE at a similar price (and a pickup bed too!). And I added a few lines of code to my iPhone app and now show the distance or energy used in the selected period.

I get distracted so easily, planned on working on coloring the Trip detail map with the colored lines but this earns me a coffee break.

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[Jiji]

CarScanner Elm Obd2 app has very similar GPS visualisation: "trip data over GPS map" (in Data Recording menu), but I like your summary of Charging sessions.

I am going to assume you haven't tried it as the MME GPS latitude/longitude is degraded to prevent any sort of accurate tracking. I wish I saved a trip I had where I failed to connect to my phone GPS and it used the MME GPS - it wasn't pretty.

I even scanned all 65K DID locations looking for alternatives and didn't find any that would work from the OBDII port. If you can create a street-level GPS map on CarScanner then share it here so I can ditch the need for a phone to provided accurate GPS data.

 

[Jiji]

I turned off detailed GPS for a trip to show what it looks like using the MME GPS latitude/longitude, no my MME does not float. Maybe it does but I didn't want to test the ice on a sunny March day.

I can now color the path with speed or elevation data, look for that in an upcoming post.

 

[MachTri]

Have you considered getting an inexpensive GPS USB puck? (around $20 on Amazon) and hooking it up to the raspberry pi? You can read the NEMA strings in using Python and sync it with the data based on the the time. (I assume the clock coming out of the MME's ODB is pretty accurate?) That way you don't have to have your iPhone running your app to collect data.

 

[Jiji]

Have you considered getting an inexpensive GPS USB puck? (around $20 on Amazon) and hooking it up to the raspberry pi? You can read the NEMA strings in using Python and sync it with the data based on the the time. (I assume the clock coming out of the MME's ODB is pretty accurate?) That way you don't have to have your iPhone running your app to collect data.

I have a 4G modem with GPS I could use but reading NEMA strings over a serial interface seems so backward compared to a REST API.

My goal is to learn new technologies and NEMA GPS and talking to OBDII dongles with AT commands didn’t inspire me to explore those solutions.

 

[SpaceEVDriver]

I just came across this project, which seems mostly EU and UK focused, but I wonder if it has anything of value to you. They don't seem to have the Ford Mustang Mach-E.

https://github.com/openvehicles/Open-Vehicle-Monitoring-System-3

 

[Jiji]

I just came across this project, which seems mostly EU and UK focused, but I wonder if it has anything of value to you. They don't seem to have the Ford Mustang Mach-E.

https://github.com/openvehicles/Open-Vehicle-Monitoring-System-3

I am trying to invent the wheel so not so interested in other projects.

That is an ESP32-based system, I use one in my home power monitoring system but I don’t think there is much debugging support. What drew me to the Raspberry Pi was a real 64-bit OS that I could connect to using SSH and debug using Visual Code. I’ll guess that they use Platform.io or the Arduino IDE. I'm sure it is good, just not my cup of tea.

 

[Jiji]

Here is the trip view with colorized paths showing speed and elevation:

The speed map on the left shows slower speeds as red and the fastest speeds as green, on this trip the long green path is a 35 mph climb as you leave town and the two circled areas are traffic control devices. The scale is not fixed but relative so 10 mph will be green if that is the highest speed seen on the trip. The displayed speed is the average of the speed at the segment start and end points.

For the elevation map on the right I chose to show the change in elevation between segment endpoint, red line segments are uphill stretches and green are the downhill stretches. So where you see red the energy use should be higher and where green the use is lower.

 

[Jiji]

And another small adjustment to the Trips view, now you can see the energy used and efficiency for the selected period:

 

[hawkeye3point1]

And like the trip detailed view, we have available a graph of the input power measured at the charge plug, nominally 240 VAC and upwards of 48A:

Question about Energy Used, is that pulled from the EVSE, car, or computed on the fly?

 

[Jiji]

Question about Energy Used, is that pulled from the EVSE, car, or computed on the fly?

Energy used values in both the Trips and in the Charging views use different methods.

The MME has a kWh meter that goes down when you use energy and up when you use regenerative braking and return energy to the battery. So you simply read the meter value at the start of the trip and again at the end of a trip, subtract the two values and you have the energy used for that trip.

it is a little more complicated on the charging side. The MME doesn’t provide any other power or energy values for us, the inputs we have available are the charging current and charging voltage. These can be multiplied to calculate the charging power which is then integrated to arrive at the charging energy.

There are a number of conversions from an AC wall charger to the DC voltage/current delivered to the battery, each having a conversion loss. I use the voltage/current measured at the plug since it will most closely reflect what you pulled from the grid to charge the MME and multiplied by your cost of electricity you can convert to your cost in your local currency.

 

[hawkeye3point1]

I use the voltage/current measured at the plug since it will most closely reflect what you pulled from the grid to charge the MME and multiplied by your cost of electricity you can convert to your cost in your local currency.

Thanks for the explanation. That method would be applicable to DC charging as well, I assume.

 

[Jiji]

Thanks for the explanation. That method would be applicable to DC charging as well, I assume.

Just a different set of MME data to read. I haven’t been to a DC charger so I don’t have anything to show yet.

 

[Jiji]

I decided to add the charging efficiency to the Charges view, it now looks like this:

The Energy Used field is the kWh measurement at the plug, Energy Added is the change in the energy added to the batteries, in this case I measured 70 kWh at the plug and the batteries received 64 kWh. Dividing the used value by the added value and multiplying by 100 we see how efficient the MME was converting the level 2 AC charger output into battery energy storage.

 

[hawkeye3point1]

Like it, 92% is what I have been seeing for Level 2 by doing the math.

Level 3 is quite a bit less efficient because of battery heating. Small sample size and temp. dependent, but I have calculated less than 80%.

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