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Alpha Feedback - Nissan Leaf


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On 2/20/2019 at 12:31 AM, Jason (ABRP) said:

That's actually really good feedback, we often get feedback from very involved power users who learn all the little features and bugs and how to really use the website.  Better is feedback from those who don't go to that level, so if you wouldn't mind I'd love to hear more about your father's feedback.  What specifically does he find difficult? Anything we could do to make it easier for him?

@JohnT

This one brings up an important point.  There are two pieces at play to calculate a route, one is the consumption model which spits out a power (in kW) for an input speed, and the other is the capacity of the battery.  At the moment, I've set the battery capacity, and tuned the consumption model based on real-world inputs to where the achievable range matches the real-world experiences.  My guess is that I've set the battery capacity too low, but since I adjusted the consumption curve to match you're getting accurate %'s but the energy usage doesn't match.

What I suppose I need to do is go back upthread to where the Gids were listed out and do 


(Gids_max - Gids_min)*80Wh

To set the available battery capacity for each model, then re-adjust the consumption model to match.  This will be important when I analyze all the LeafSpy data you've all contributed, as there's no raw power information in the LeafSpy stream, so I have to figure out the power consumption from the battery % reported.

Sure I will check more with him. However I think the main point is this: They every week drive trips so long that they have to charge during the day. Or actually they are often on the limit of if they need to charge or not. But they do not dare to not charge as there is no reliable range estimation in the Leaf.

So for this it would be good for them to use ABRP to see if they really need to charge, and if they charge how much that is needed. However as the Dash% vs ABRP% have not corresponded they have not really seen how this will make them more confident. Which I can understand.

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Hi, ist Workshop! 🙂 👍 And because of the need I figured out how to use it on one device. So I split the screen so I could use it on one device 💡 And it worked. 

Great to see the new Leaf on ABRP, thank you! As the Leaf does not like to charge too often (#rapidgate) I would love to see an option to set up a minimum charge level when leaving a charger. I know t

Nice! Thanks! What I can see it looks quite good. As the Leaf is my parent’s car I have limited possibilities to test, other people might have better feedback. However we used the car during the weeke

Posted Images

Have now checked a couple of points for the Leaf 40kWh now to see how the new equation for Dash% works out. There have been a difference of a couple of percent. So i plotted the data points with the equation active in LeafSpy. Unfortunalty it looks like it is not really linear. 

image.png.589b22efb70b4370c1b5b46befdd8f0e.png

image.png.34cd37cf753ffa1dbe9ce4c50bb77db4.png

 

Also plotted Gids and LeafSpy SoC against Dash% and it is quite clear that it is not linear.

image.png.001400df849a55a3d81bfac4ecc8855e.png

 

Have asked my father to make notes of more data points to get more data. There is not more people here that have this kind of data for the Leaf 40kWh that we could compare with?

 

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25 minutes ago, Jason (ABRP) said:

Am I allowed to complain about Nissan's bizarre SoC scheme now?

Maybe it'd be better to just go back to using the LeafSpy "raw" SoC value?

Feel free to complain Jason, you wont be alone 🙂  I can't speak for 40 kWh Leaf but for 24 KWh model the Leafspy SOC will be even more complex.  I can show you data that indicates the relationship between SOC and dash%/gids is quite non linear below around 30% and temperature dependant.

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5 hours ago, Jilldris said:

Have now checked a couple of points for the Leaf 40kWh now to see how the new equation for Dash% works out. There have been a difference of a couple of percent. So i plotted the data points with the equation active in LeafSpy. Unfortunalty it looks like it is not really linear. 

image.png.589b22efb70b4370c1b5b46befdd8f0e.png

image.png.34cd37cf753ffa1dbe9ce4c50bb77db4.png

 

Also plotted Gids and LeafSpy SoC against Dash% and it is quite clear that it is not linear.

image.png.001400df849a55a3d81bfac4ecc8855e.png

 

Have asked my father to make notes of more data points to get more data. There is not more people here that have this kind of data for the Leaf 40kWh that we could compare with?

 

I don't have an expectation that ABRB and my car will agree to the nth degree of accuracy, but I agree that this looks like a bigger margin of error than one would like. I'll put a post on the NZ Leaf Owners FB page to see if I can encourage anyone else to contribute.  There are only a small number of 40KWh models here - Nissan don't sell Leafs here and they come in on the second hand car market ex UK and Japan.

 

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8 hours ago, Stefan_K said:

I can do this to. But my daily trips are to short. I drive the whole week and have 60% left in the battery pack.

 The next longer trips I will use ABRP and Leaf Spy and post the results.

That is no problem. If you want you can make one reading per day, that would be very useful. And you don't need to run ABRP. You only need to note the % the Leaf show in the dash (Dash% we have called it above) and the data from LeafSpy (Gids, SOC, SOH, Battery temp). 

I can make a public google sheet were people could contribute data if they want.

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5 hours ago, JohnT said:

Feel free to complain Jason, you wont be alone 🙂  I can't speak for 40 kWh Leaf but for 24 KWh model the Leafspy SOC will be even more complex.  I can show you data that indicates the relationship between SOC and dash%/gids is quite non linear below around 30% and temperature dependant.

Yes it is very strange why they do like this. Someone that understands the reason for implementing these non-linear behaviours?

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5 hours ago, JohnT said:

I don't have an expectation that ABRB and my car will agree to the nth degree of accuracy, but I agree that this looks like a bigger margin of error than one would like. I'll put a post on the NZ Leaf Owners FB page to see if I can encourage anyone else to contribute.  There are only a small number of 40KWh models here - Nissan don't sell Leafs here and they come in on the second hand car market ex UK and Japan.

 

Nice! I can put up a public google sheet were people can contribute. Can post the link in a swedish facebook group also.

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20 hours ago, Jilldris said:

Found more data on saved picture in my phone, some of it from when the car was driven down to 0%. Also got some from a couple of youtube videos.

image.png.5b03314ac5fcef70f9af956888e1a623.png

An other point to note is that SOH also comes into play.  While your data is with consistent SOH, more generally the linear relationship is postulated to be dash% vs (Gid-Gmin)/(Gmax-Gmin)/SOH

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@JohnT @Jason (ABRP)

Yes. My data is with 99% SOH. I plotted that with the data I got from TeslaBjörns videos with the Leaf that seems to have 95% SOH. An then added the linear equations from ABRP for 99 and 95% SOH.
I guess it is difficult to say much with so little data. But generally it looks better for higher SOC. But I guess it is quite ok even though the difference is a little higher in the lower end.

image.thumb.png.1dac86bb15e3afa3abf930102b720390.png

I have some thoughts though:

-Maybe both Gids_min and Gids_max changes with SOH? It would be interesting with more data at low SOC for more cars with different SOH.

-As long as we have a good value for Gids_min I guess it is better to have this linear calculation instead of mimicking the somewhat non-linear behavior of the Dash%. This as the Gids give the best measurement of actual available energy, right? Even if a slightly non-linear approximation would correspond better to the dash% it would probably be worse for the route planning as the available energy would be less true?

-What is the reason for the large spread of the measurement values at high SOC? What is the advantage with the manipulation they do instead of just making a straight conversion from Gids? One thing I have noticed is that after charging full the Gids have sometimes decreased overnight. I guess this is due to balancing so the energy in the battery really decreases. They might also correct the estimated Gids value at high state of charge where the energy vs cell voltage curve is steeper.
However I have noted that the Dash% still shows 100% even though the Gids values have decrease to what normally correspond to 98%. But if I restart the charging then the Dash% jumps to 98% and it starts to charge. So it might be that they don’t want customers wondering why 2% energy have decreases overnight so they “sheet” and freeze the Dash% during balancing. Then when you start to drive they need to smooth out these lacking 2% and that is one reason for the variation at high Gids?

What do you think of the points above?

 

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1 hour ago, Jilldris said:

@JohnT @Jason (ABRP)

Yes. My data is with 99% SOH. I plotted that with the data I got from TeslaBjörns videos with the Leaf that seems to have 95% SOH. An then added the linear equations from ABRP for 99 and 95% SOH.
I guess it is difficult to say much with so little data. But generally it looks better for higher SOC. But I guess it is quite ok even though the difference is a little higher in the lower end.

image.thumb.png.1dac86bb15e3afa3abf930102b720390.png

I have some thoughts though:

-Maybe both Gids_min and Gids_max changes with SOH? It would be interesting with more data at low SOC for more cars with different SOH.

-As long as we have a good value for Gids_min I guess it is better to have this linear calculation instead of mimicking the somewhat non-linear behavior of the Dash%. This as the Gids give the best measurement of actual available energy, right? Even if a slightly non-linear approximation would correspond better to the dash% it would probably be worse for the route planning as the available energy would be less true?

-What is the reason for the large spread of the measurement values at high SOC? What is the advantage with the manipulation they do instead of just making a straight conversion from Gids? One thing I have noticed is that after charging full the Gids have sometimes decreased overnight. I guess this is due to balancing so the energy in the battery really decreases. They might also correct the estimated Gids value at high state of charge where the energy vs cell voltage curve is steeper.
However I have noted that the Dash% still shows 100% even though the Gids values have decrease to what normally correspond to 98%. But if I restart the charging then the Dash% jumps to 98% and it starts to charge. So it might be that they don’t want customers wondering why 2% energy have decreases overnight so they “sheet” and freeze the Dash% during balancing. Then when you start to drive they need to smooth out these lacking 2% and that is one reason for the variation at high Gids?

What do you think of the points above?

 

The equation that I have suggested would plot as a straight line with Y axis of dash%*SOH and X axis of Gids.  The X axis value at y=0 would be Gmin and and y=100 would be Gmax.  The assumption that Gids is available remaining energy is gospel in the 24 kWh battery world so the figures from the 40 kWh model are still a surprise to me 🙂  It makes me wonder if Nissan now put some other data out on the bus that Leafspy isn't looking for.    The non linearity in your data looks much like the SOC behaviour in the data for my car.  Pure speculation on my part though.

Your  hypothesis on what is happening with charge/recharge might be correct, an alternative is that SOH gets recalculated again when you restart charging and that causes the dash% to change.  In the end we are probably saying much the same thing.

 

 

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  • 3 weeks later...
49 minutes ago, moestaverne said:

Hello,

I wanted to test the beautiful function. Unfortunately I get a URL error


 03/13/2019 17:15:55 06  29  88 true URL = https://abetterrouteplanner.com:4441/leaf##, Error = unsupported URL

is this URL correct?

https://abetterrouteplanner.com:4441/leaf40
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You are absolutely welcome to mess with the settings! Weight is a relatively low-impact setting, unless you're driving in a lot of mountains.  Adding wind accounts for the extra drag that's incurred by driving into the wind (or the lower drag by driving with the wind).

My experience is that the base settings are very good for a "nice" day.  If you're driving and you have live data, the planner should calibrate to the actual conditions you're driving in, and offer a replan after it has a chance to calibrate (if it becomes needed).

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  • 1 month later...

I haven't hooked this up to the car yet (2012 Leaf 24 SoH 70%), but I notice when using the planner with all the assumptions loaded, it gives a very optimistic charge time of around 9 minutes to get from say 15% to 61% and 10 minutes from 23% to 72%. I know from 3 years of driving this car, that it will never achieve those charge times. Is there a bug in the way these are calculated, or is there a lack of real world data from the older Leafs, or some other reason I've missed?

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13 hours ago, geeknz said:

I haven't hooked this up to the car yet (2012 Leaf 24 SoH 70%), but I notice when using the planner with all the assumptions loaded, it gives a very optimistic charge time of around 9 minutes to get from say 15% to 61% and 10 minutes from 23% to 72%. I know from 3 years of driving this car, that it will never achieve those charge times. Is there a bug in the way these are calculated, or is there a lack of real world data from the older Leafs, or some other reason I've missed?

Interesting, to investigate could use some more information.  In my experience with my wife's 2015 Leaf the charge times have been pretty close.  Perhaps the Leaf throttles charge rates at high degradation?  I do know the Leaf throttles when the battery is too hot, after multiple fast charges and we definitely don't account for that.  What power levels do you observe when fast charging at a >50kW charger?

Ideally, if we got a charge curve from you something like this one from FastNed we could incorporate that and improve our estimates:

https://support.fastned.nl/hc/en-gb/articles/204784998-Charging-with-a-Nissan-Leaf-e-or-e-NV200

When I have some time I need to work through the analysis of the Leaf Data that's been sent in to see if I can build a real-world model from what I have access to.  Since the LeafSpy data is somewhat limited, it'll be interesting to see what the results are.

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For 24kwh Leaf the battery does charge slower if the degradation is more than 20%. So it is better to stop more frequently but drive faster.
From my recent experience in 700km trip I realized that the fastest way to go is to charge for 10-20 minutes from 15 to 70% and stop to charge each 50-60 km while going at maximum speed. Note that this will only be possible when the weather is below 10c. Otherwise the battery will get hot within 300 km and will not accept QCs anymore.

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