Battery geometric and flow parameters optmization using optmization toolbox?

Hi,

I am in mid way of designing an optmized battery pack that have maximum heat dissipiation. I have chosen some key parameters that have an impact on the performance on the battery pack and i want to optimize those to get best value and hence develop the battery pack for further analysis. Does optmization toolbox in MATLAB serves the purpose in this regard. i have gone through various related examples and videos available in documentations but none are anywhere related to my concern area.

Any suggestion/insight in this regard will be of great help.

Thank you.

Answer 1

[Jaynik]

Hi Sudeep,

The Optimization Toolbox can indeed serve your purpose for optimizing battery geometric and flow parameters to maximize heat dissipation. There were no direct examples related to battery pack design but the principles of optimization are still applicable. Here is a way to approach the problem:

• Define the objective function: Since you are interested in maximizing the heat dissipation, the objective function can be the negative of the heat dissipation rate, assuming you have the formula to calculate this based on your battery pack's parameters. The model will take the parameters to be optimized as input and output the heat dissipation rate.
• You will need to define the bounds and constrains for your parameters and need to specify the allowable ranges in the form of lower bounds and upper bounds. Linear and nonlinear constraints can also be defined if any.
• There are several solvers available. fmincon might be a suitable solver to start with as it allows for constrained nonlinear optimization.

Here is a sample code that can be used based on your parameters:

function obj = objectiveFunction(params)
    % Calculate the heat dissipation rate based on these parameters.
    heatDissipationRate = calculateHeatDissipation(params);
    obj = -heatDissipationRate;
end

% Fill these as per the requirements
lb = ;
ub = ;
initialGuess = ; 
options = optimoptions('fmincon', 'Display', 'iter', 'Algorithm', 'sqp');
[optimalParams, optimalValue] = fmincon(@objectiveFunction, initialGuess, [], [], [], [], lb, ub, [], options);

The output optimalParams will give you the set of parameters that maximizes heat dissipation under the constraints defined. optimalValue variable will be the negative of the maximum heat dissipation rate, so its negative will be the actual value.

Optimization is often an iterative process. Initial results can provide insights that may lead to refining the model or exploring other aspects of the design that weren't initially considered. Also, depending on the complexity and nature of the optimization problem, other solvers available might offer better results or efficiency.

You can refer the following documentation to know more about these functions:

• optimoptions: https://www.mathworks.com/help/optim/ug/optim.problemdef.optimizationproblem.optimoptions.html
• fmincon: https://www.mathworks.com/help/optim/ug/fmincon.html

Hope this helps!

[SUDEEP SINGH RAWAT]

Hi,

Thank you for your valueable insight. is there any examples availabe related to the heat transfer enhancement in design optmization? The same concept is valid for heat exchangers, fins or any heat dissipiating surface. Kindly share if anything related to it is in your knowledge.

Thank you once again :)