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Because of G36's comments/questions below this answer, of late, I'dI'm editing this answer to include the development of the equation I provided at the outset. It's not complicated.

Because of G36's comments/questions below this answer, of late, I'd editing this answer to include the development of the equation I provided at the outset. It's not complicated.

Because of G36's comments/questions below this answer, of late, I'm editing this answer to include the development of the equation I provided at the outset. It's not complicated.

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jonk
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(See Appendix below.)

This equation approximates how much the LED current will change for some tiny percent change in the LED voltage. It's an interesting equation to examine.

##Appendix

Now, our goal is to compute sensitivity equations from the above. A sensitivity equation just quantifies the output uncertainties based upon the effects of input uncertainties. I didn't find anya really easy paper on the topic, but I did find a reasonably readable one here: Sensitivity Analysis for Uncertainty. So, feel free to read that if you have any doubts about the rest of what I write, below.

This equation approximates how much the LED current will change for some tiny percent change in the LED voltage. It's an interesting equation to examine.

Now, our goal is to compute sensitivity equations from the above. A sensitivity equation just quantifies the output uncertainties based upon the effects of input uncertainties. I didn't find any really easy paper on the topic, but I did find a reasonably readable one here: Sensitivity Analysis for Uncertainty. So, feel free to read that if you have any doubts about the rest of what I write, below.

(See Appendix below.)

This equation approximates how much the LED current will change for some tiny percent change in the LED voltage. It's an interesting equation to examine.

##Appendix

Now, our goal is to compute sensitivity equations from the above. A sensitivity equation just quantifies the output uncertainties based upon the effects of input uncertainties. I didn't find a really easy paper on the topic, but I did find a reasonably readable one here: Sensitivity Analysis for Uncertainty. So, feel free to read that if you have any doubts about the rest of what I write, below.

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jonk
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Because of G36's comments/questions below this answer, of late, I'd editing this answer to include the development of the equation I provided at the outset. It's not complicated.

We start out with the simple KVL equation:

$$V_\text{CC}-I_\text{LED}\cdot R_\text{LIMIT}-V_\text{LED}=0\:\text{V}$$

And solve it for \$I_\text{LED}\$:

$$I_\text{LED}=\frac{V_\text{CC}-V_\text{LED}}{R_\text{LIMIT}}$$

Now, our goal is to compute sensitivity equations from the above. A sensitivity equation just quantifies the output uncertainties based upon the effects of input uncertainties. I didn't find any really easy paper on the topic, but I did find a reasonably readable one here: Sensitivity Analysis for Uncertainty. So, feel free to read that if you have any doubts about the rest of what I write, below.

We want to find the % variation of something with respect to the % variation of something else. In calculus form, % variation looks like \$\%\,x = \frac{\text{d}\,x}{x}\$. This is the exact % variation, which is much better than the finite approximation variation that is \$\%\,x \approx \frac{\Delta\,x}{x}\$. It turns out that the calculus mindset is actually not so hard to do.

First, we apply the implicit product rule (or multivariate chain rule):

$$\text{d}\,I_\text{LED}=\frac{\text{d}\, V_\text{CC}}{R_\text{LIMIT}}-\frac{\text{d}\, V_\text{LED}}{R_\text{LIMIT}}$$

The we divide both sides by \$I_\text{LED}\$:

$$\begin{align*}\%\, I_\text{LED}=\frac{\text{d}\,I_\text{LED}}{I_\text{LED}}&=\frac{\text{d}\, V_\text{CC}}{R_\text{LIMIT}\,I_\text{LED}}-\frac{\text{d}\, V_\text{LED}}{R_\text{LIMIT}\,I_\text{LED}}\\\\&=\frac{\text{d}\, V_\text{CC}}{V_\text{CC}-V_\text{LED}}-\frac{\text{d}\, V_\text{LED}}{V_\text{CC}-V_\text{LED}}\end{align*}$$

Now, we need to convert the infinitesimals on the right side into % variations. This is simple to do:

$$\begin{align*}\%\, I_\text{LED}&=\frac{\frac1{V_\text{CC}}}{\frac1{V_\text{CC}}}\cdot\frac{\text{d}\, V_\text{CC}}{V_\text{CC}-V_\text{LED}}-\frac{\frac1{V_\text{LED}}}{\frac1{V_\text{LED}}}\cdot\frac{\text{d}\, V_\text{LED}}{V_\text{CC}-V_\text{LED}}\\\\&=\frac{\frac{\text{d}\, V_\text{CC}}{V_\text{CC}}}{1-\frac{V_\text{LED}}{V_\text{CC}}}-\frac{\frac{\text{d}\, V_\text{LED}}{V_\text{LED}}}{\frac{V_\text{CC}}{V_\text{LED}}-1}\\\\&=\frac{\%\, V_\text{CC}}{1-\frac{V_\text{LED}}{V_\text{CC}}}-\frac{\%\, V_\text{LED}}{\frac{V_\text{CC}}{V_\text{LED}}-1}\end{align*}$$

This allows us to focus on \$\%\,V_\text{LED}\$, by taking the last term and its sign on the right side, or to focus on \$\%\,V_\text{CC}\$, by taking the first term and its sign on the left side. (Or, of course, to take both into account at the same time.)


Because of G36's comments/questions below this answer, of late, I'd editing this answer to include the development of the equation I provided at the outset. It's not complicated.

We start out with the simple KVL equation:

$$V_\text{CC}-I_\text{LED}\cdot R_\text{LIMIT}-V_\text{LED}=0\:\text{V}$$

And solve it for \$I_\text{LED}\$:

$$I_\text{LED}=\frac{V_\text{CC}-V_\text{LED}}{R_\text{LIMIT}}$$

Now, our goal is to compute sensitivity equations from the above. A sensitivity equation just quantifies the output uncertainties based upon the effects of input uncertainties. I didn't find any really easy paper on the topic, but I did find a reasonably readable one here: Sensitivity Analysis for Uncertainty. So, feel free to read that if you have any doubts about the rest of what I write, below.

We want to find the % variation of something with respect to the % variation of something else. In calculus form, % variation looks like \$\%\,x = \frac{\text{d}\,x}{x}\$. This is the exact % variation, which is much better than the finite approximation variation that is \$\%\,x \approx \frac{\Delta\,x}{x}\$. It turns out that the calculus mindset is actually not so hard to do.

First, we apply the implicit product rule (or multivariate chain rule):

$$\text{d}\,I_\text{LED}=\frac{\text{d}\, V_\text{CC}}{R_\text{LIMIT}}-\frac{\text{d}\, V_\text{LED}}{R_\text{LIMIT}}$$

The we divide both sides by \$I_\text{LED}\$:

$$\begin{align*}\%\, I_\text{LED}=\frac{\text{d}\,I_\text{LED}}{I_\text{LED}}&=\frac{\text{d}\, V_\text{CC}}{R_\text{LIMIT}\,I_\text{LED}}-\frac{\text{d}\, V_\text{LED}}{R_\text{LIMIT}\,I_\text{LED}}\\\\&=\frac{\text{d}\, V_\text{CC}}{V_\text{CC}-V_\text{LED}}-\frac{\text{d}\, V_\text{LED}}{V_\text{CC}-V_\text{LED}}\end{align*}$$

Now, we need to convert the infinitesimals on the right side into % variations. This is simple to do:

$$\begin{align*}\%\, I_\text{LED}&=\frac{\frac1{V_\text{CC}}}{\frac1{V_\text{CC}}}\cdot\frac{\text{d}\, V_\text{CC}}{V_\text{CC}-V_\text{LED}}-\frac{\frac1{V_\text{LED}}}{\frac1{V_\text{LED}}}\cdot\frac{\text{d}\, V_\text{LED}}{V_\text{CC}-V_\text{LED}}\\\\&=\frac{\frac{\text{d}\, V_\text{CC}}{V_\text{CC}}}{1-\frac{V_\text{LED}}{V_\text{CC}}}-\frac{\frac{\text{d}\, V_\text{LED}}{V_\text{LED}}}{\frac{V_\text{CC}}{V_\text{LED}}-1}\\\\&=\frac{\%\, V_\text{CC}}{1-\frac{V_\text{LED}}{V_\text{CC}}}-\frac{\%\, V_\text{LED}}{\frac{V_\text{CC}}{V_\text{LED}}-1}\end{align*}$$

This allows us to focus on \$\%\,V_\text{LED}\$, by taking the last term and its sign on the right side, or to focus on \$\%\,V_\text{CC}\$, by taking the first term and its sign on the left side. (Or, of course, to take both into account at the same time.)

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