Quote of the Day
The difference between successful people and very successful people is that very successful people say 'no' to almost everything.
— Warren Buffet. Many people do not accomplish their goals because they spend the bulk of their time on items that really are just distractions. You need to look at your time usage, figure out which recurring items are taking time, yet providing little real value, and minimize them.
Many battery manufacturers do not specify the Ampere-Hour (AH) ratings for their automotive products because Cold Cranking Amperes (CCA) are more important in automotive applications than AH ratings. Car applications tend to focus on the ability of the battery to crank the engine when both the battery and car are cold. While reading a post on an automotive forum about batteries, I saw the following statement made about the relationship between a battery's CCA and AH ratings.
I have read on the box of that Inox battery conditioner that for a battery over 600 CCA you simply multiply the CCA by .07 to give you the Amp Hours for that battery …
I have seen this statement before and did not believe it because batteries intended for capacity-dependent applications (e.g. backup power) are designed differently than batteries intended to deliver surge current (e.g. car batteries). I decided that it was time that I demonstrate that this relationship does not hold for specific batteries, but does have some merit for batteries in general.
Because France requires battery manufacturers post the AH specifications for all car batteries, I was able to find both CCA and AH specifications for a number of car batteries on European web sites. Once I gathered the data, I generated a graph that shows that there is not a general relationship between the CCA and AH. All you can say is that on average, increasing CCA ratings means increasing AH ratings. There is no simple relationship between AH and CCA that holds for all lead-acid automotive batteries.
All the analysis was done in Rstudio.
My approach was simple:
- I randomly chose four car batteries from five different vendors.
- I generated plots of AH versus CCA for each manufacturer.
- I also generate a plot of AH versus CCA for all the batteries.
Figure 2 show the set of battery data that I gathered. The batteries were randomly chosen from among the hundreds of choices.
Figure 3 shows my graph of AH versus CCA for data of Figure 2. I also fitted lines to each of the vendors data. Note that there is a wide variation in how AH varies with CCA for each vendor. There is no formula that provides a good fit between AH and CCA for all automotive batteries. The fit is not even good for batteries from the same manufacturer. For a similar chart of batteries from a single manufacturer, see Appendix A.
Figure 4 shows my overall curve fit. This line has a slope of 0.0688 AH/CCA, which agrees with the 0.07 AH/CCA statement on the Inox conditioner box. However, you can see that specific batteries are scattered far from the line.
For those who like to look at curve fit statistics, I also include Figure 5. The statistics shown are for lines that are function of CCA alone (Figure 4), and CCA and Brand (Figure 3).
For AH versus CCA line, we see that CCA is a very significant factor (red underline) but our R-squared value (fraction of variability explained) is only 50%. For the AH versus CCA and Brand line, we see that CCA is very significant and some Brands are significant, but our R-squared value (fraction of variability explained) is only 85%.
While it might be tempting to estimate the AH rating of a battery from its CCA rating, there is not a simple relationship between these two battery parameters.
Appendix A: Yuasa Example
In Figure 6, Yuasa has published a graph similar to my Figure 3. Notice how the lines have roughly the same slopes, but different intercepts. As I always say, a battery is a nonlinear function of everything.