The MAF scaling that I have been doing has been Closed Loop - one always do close loop scaling first before scaling your open loop. (close loop has to be right before open loop scaling can be done).
I will not bore you the details of each iteration but will give the major milestones of my MAF Scaling. After using several rounds of williaty's method, I switched over to mickeyd2005's method (it was much much easier using his spreadsheet). Here's some of the results (from mickeyd2005's spreadsheet):
My correction vs MAFv:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhZ5fSP7mNoVwZBopMM3JcrLAmEyEHtYSkdms1AU-i5GFfqUCMvnzXxY52PRM22-QT0N-n2Y7TGdKE3AA6fQqKfXtOpDTfeyuUdJ3ZrH91hucV9MNbbidrFSN13qGix9HT0rhmYbfZ_62s/s320/CorrScaleMickey1.jpg)
As you can see, my scaling looks ok except for spots at 1.66 (-5.88), 2.38 (+3.07).
There are two minor spots at 1.44 (+2.75), 1.96 (+2.22) but these are close to 2% total correction so that are not too bad.
The other graphs for these data set is:
Maf Check2:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEidONcSVb0tw9NU7QvmcFCL7LEGqYvsji4agr4bywN-zUa699BK3MrdGXQGJRFaW4TZxVGk1xUMpeGyI-vCX-sCM6awipP3RCdDkuEmBXZSFx-EXcYwr3-Q7cju0LQ0twMhBfQAZai6j9o/s320/InjScaleMickey1.jpg)
Maf Check3 (Scatter Plot):
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEisviZczSTWtj5BK5EjcdJWlWAKBoQy_ZPiq2A4kHQuOJn66keb3gTgq0vuQr8gvqyg0M-csd713gsqRBn8b8Sz6r73BNCACC-uFW-IBSMSWJRT9bVzSkBWLp5r27nGaUjAIN5EBJ0tPhI/s320/CorrScatterMickey1.jpg)
Injector Scaling:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEijYUUDgx0XTj0BwX3P7-DeezjeMuLmk9VLhJLY7OB3xqrpjbSS5ncZ1vJM651bp5-9NAhB_ajjI7W7hyphenhyphenm3eizhKMzsq6Mb1ADCdzsKLbazWLY1g-UYrpCbydCb6WcjOV3KKZhH_2H-Myk/s320/1_InjScale.jpg)
Concentrating on these 4 spots, I went through another 3 rounds of iterations and here are the results:
My correction vs MAFv:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgUFH7jYkRRcGmUMNuG3tUnH9IdAEUCHNh8mt-lE2_WVdxS6l3BV_RcBlu6cR54yLVUjETcJWFZrkkKHx1wrt_uDA0Dy5xe7ii3bovY5Rp728LPOqVaMNhyphenhyphenALtfCvapjCv5iRLCeWrzsYk/s320/4_MafCheck1.jpg)
Compare that with the earlier graph (I reproduce here for easy reference):
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhZ5fSP7mNoVwZBopMM3JcrLAmEyEHtYSkdms1AU-i5GFfqUCMvnzXxY52PRM22-QT0N-n2Y7TGdKE3AA6fQqKfXtOpDTfeyuUdJ3ZrH91hucV9MNbbidrFSN13qGix9HT0rhmYbfZ_62s/s320/CorrScaleMickey1.jpg)
You can see that my curve has smoothed out quite alot in those areas I mentioned earlier, right? Heehee.. Finally I am getting somewhere (after 1 month of trial and error and wandering in the dark).
My Learning View also shows 0 correction for AF Learning D:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiz3rqqN5DCq-utqFXhlqBftbsxpRSLDer9PlwlbW43JWvIK2zWZ59g_PNSBrg5Jqlc8xCR0avJSMUxd-14MgrE_XLCLzXPWQJQ6U2Z4WyXl4MTj_W0hfy3F_Pc_C2z5TUyJDXDJ_xSKRs/s320/4_learningview_mafCL.jpg)
I'm quite happy with my progress :-)
Anyway, here are the other graphs from this dataset:
MAF Check2:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5ZzAdtzy0eDGw3uoo2CqBmAOPD_HDWVITmhw_SBWExDlFFXRcZf59fJnlaL-6jKPuYuDUkTwyrhJCvRA926QPBAacbQKf1VOcCrIQUQY7nf_57VTXrSAcH99V2D1-RWYCs-MZWlywLyA/s320/4_MafCheck2.jpg)
MAF Check3:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjSFFjVH7tpUFOsTDBCXzeChdQxYqEtvu4BfhPDs1rBW-AbqKRJcxbsn4dR1bZtpNAMwifxotcg1HBLwHBy-qDSoGK06al2mNVTJn1sOws0JzytDxYIYtUB3utIfWJT-6WOQmmpYhI4Szo/s320/4_MafCheck3.jpg)
Injector Scaling:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDnOzGsQKVuFgd8LwBo3L1QPKmPOxfchLnynjTKyJApGhK5hh2SwTmZHe4dhuxXukng6uNM7cPbT3gnjlXnxEjc_cFubtFWwmPAheSVqZF_xMyLz32PGo4-tX5r_24m94QYAWffn3WzzM/s320/4_InjScale.jpg)
Lessons learnt from the many many rounds of closed loop scaling:
1. Although williaty's method can be quite laborious, it helps to ensure that you are picking the right set of data for scaling.
2. mickeyd2005 spreadsheet is simply heaven but you can't just pick up the suggested corrections because you really have to study the graphs and number of data points to ensure that you are making the "right changes". I made the mistake of just copying the corrections directly and my results were worse (I made up to 11 iterations without getting anywhere!). In the end I started over again from where I left off using williaty's method (mickeyd2005's advice).
3. Once I became more picky on which changes to by looking at the graphs and the number of data points (the more the better), I began to have better scaling results.
4. When scaling for close loop, it's good to concentrate on a range when making changes rather than changing so many points at once. Patience is key here. I concentrated on those 2 major points and when they came close to 0, I made changes to the minor points.
5. For close loop, the higher MAFv ranges (>2.0) are harder to get data - you need to drive around 110km/h ~ 130km/h to get data. Here are some speed ranges and MAFv values
For MAFv around 1.6 ~ 1.8 drive at about 60km/h
For MAFv around 2.0 ~ 2.1 drive around 90km/h
For MAFv around 2.4 ~ 2.6 drive between 110km/h and 130km/h
6. Open the dashboard view when driving and just keep a quick watch on the MAFv and Close/Open Loop indicator to ensure you've got data for all the ranges you need.
7. I can only scale close loop up to MAFv 2.6 for my car. I guess I have to use Open Loop scaling for higher MAFv
8. Ignore high total corrections variations for MAFv < 1.3 as this range falls mainly within the idle range and it can be very erratic (maybe its my APS CAI that's causing this - what others have reported too).
9. Therefore when performing close loop scaling, concentrate on MAFv range 1.5 ~ 2.6. You can work on MAFv 1.3 ~ 1.5 when 1.5 ~ 2.6 have stabilised.
10. Keep your throttle constant and light when logging and always watch that open/closed loop indicator (8 is for closed loop). If you not, you could be just logging open loop which is useless for analysis.
Anyway, here are my MAF tables for two set of graphs:
First:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi8kc3FGC2N_YqtwD-nWctWd-BIcOtGdt9L-2a_yBD6yCyq38Nzq-CsR2vWkpMGTM8cw4YWAXWBFAuugxa8QfMKY5ahG5mOQbfTFp18MjmSx-voSzvHKrk3WycVvvGFDquf0KR0lqeX7kY/s320/1_MafTable.jpg)
Final:
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhbWeJD2VcULujTM6AD8prRsU2dYuz6DUobvHaz16-Fe-0FXOPCp8Gj8x0SLO7tcDCUph-kxpQpaiCNu5uYQ3tfNdhBJq8ZteXbFIhmWeDqGr-XUOHW4as59CiBBqRkSmeY4Hg_KHM_Gek/s320/4_MafTable.jpg)
Ok, that's all for now. Hope this post has been useful.