Question:
I have measured data for the discharging state of charge (SOC) and inverter power of a Redox Flow Battery (RFB). From this data, I need to determine the auxiliary power loss of the battery. I’m trying to find an equation for this non-linear relationship using curve fitting or other methods.
I want to create a model where the equation is in the form of f(X)+f(Y) where X represents SOC and Y represents inverter power. The key requirement is that X and Y should be treated as independent variables, meaning they should not be combined into one term in the equation.
I have tried some equations where X and Y are separate in the equation, but I am getting a low R-squared value (around 0.6). However, when I combine Xand Y together in one equation, the R-squared value improves to 0.9, which is better.
Now, I need to apply piecewise linearization to the model with X and Ytogether. How can I proceed with this approach and still maintain the independent nature of X and Y in the equation for the fitting process? Any advice or guidance would be much appreciated!Question:
I have measured data for the discharging state of charge (SOC) and inverter power of a Redox Flow Battery (RFB). From this data, I need to determine the auxiliary power loss of the battery. I’m trying to find an equation for this non-linear relationship using curve fitting or other methods.
I want to create a model where the equation is in the form of f(X)+f(Y) where X represents SOC and Y represents inverter power. The key requirement is that X and Y should be treated as independent variables, meaning they should not be combined into one term in the equation.
I have tried some equations where X and Y are separate in the equation, but I am getting a low R-squared value (around 0.6). However, when I combine Xand Y together in one equation, the R-squared value improves to 0.9, which is better.
Now, I need to apply piecewise linearization to the model with X and Ytogether. How can I proceed with this approach and still maintain the independent nature of X and Y in the equation for the fitting process? Any advice or guidance would be much appreciated! Question:
I have measured data for the discharging state of charge (SOC) and inverter power of a Redox Flow Battery (RFB). From this data, I need to determine the auxiliary power loss of the battery. I’m trying to find an equation for this non-linear relationship using curve fitting or other methods.
I want to create a model where the equation is in the form of f(X)+f(Y) where X represents SOC and Y represents inverter power. The key requirement is that X and Y should be treated as independent variables, meaning they should not be combined into one term in the equation.
I have tried some equations where X and Y are separate in the equation, but I am getting a low R-squared value (around 0.6). However, when I combine Xand Y together in one equation, the R-squared value improves to 0.9, which is better.
Now, I need to apply piecewise linearization to the model with X and Ytogether. How can I proceed with this approach and still maintain the independent nature of X and Y in the equation for the fitting process? Any advice or guidance would be much appreciated! redox flow battery, soc, inverter power, auxiliary, curve fitting, nonlinear MATLAB Answers — New Questions

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