Dynamic Programming - HEV : ChatGPT

I was just trying my hand with chatgpt. And I just asked it for writing DP algorithm in MATLAB for HEV energy management. The results as with all the results out of chatgpt were surprising and quite good. So, just to keep continuance with the previous blog post on the same topic, I am posting the chatgpt results here.

Here is an example of a dynamic programming algorithm for an Hybrid Electric Vehicle (HEV) energy management system written in MATLAB:

function [optimal_policy, optimal_value] = dp_energy_management(current_state, prediction_horizon, vehicle_parameters)
    % Extract current state variables
    current_soc = current_state(1);
    current_speed = current_state(2);
    current_acceleration = current_state(3);

    % Initialize value and policy arrays
    value = zeros(prediction_horizon+1, vehicle_parameters.num_soc);
    policy = zeros(prediction_horizon+1, vehicle_parameters.num_soc);

    % Set initial value and policy at time t=0
    value(1,:) = 0;
    policy(1,:) = 0;

    % Loop through prediction horizon
    for t = 1:prediction_horizon
        % Loop through state of charge (SOC) values
        for soc = 1:vehicle_parameters.num_soc
            % Initialize minimum value and corresponding policy
            min_value = Inf;
            min_policy = 0;
            % Loop through power values
            for power = vehicle_parameters.min_power:vehicle_parameters.max_power
                % Calculate fuel consumption
                fuel = power*vehicle_parameters.fuel_efficiency;
                % Calculate next SOC
                next_soc = min(vehicle_parameters.num_soc, max(1, soc + power*vehicle_parameters.timestep));
                % Calculate value
                temp_value = value(t, soc) + fuel + value(t+1, next_soc);
                % Update minimum value and policy if necessary
                if temp_value < min_value
                    min_value = temp_value;
                    min_policy = power;
                end
            end
            % Update value and policy for current SOC and time
            value(t+1, soc) = min_value;
            policy(t+1, soc) = min_policy;
        end
    end

    % Set output variables
    optimal_policy = policy(:, current_soc);
    optimal_value = value(:, current_soc);
end

This dynamic programming algorithm takes as input the current state of the vehicle (soc, speed, acceleration), a prediction horizon, and a structure of vehicle parameters (timestep, min_power, max_power, fuel_efficiency, num_soc). It then initializes an array to store the optimal value and policy at each time step and SOC value. The algorithm then loops through the prediction horizon and calculates the minimum value and corresponding policy for each time step and SOC value. The optimal policy and value for the current SOC are returned as output.

I haven't tested the algorithm. But the first impressions are quite good. The best part is, it has also tried to give a short explanation of the algorithm at the end.

Note : The above algorithm is copied from chatgpt. You can try your hand with chatgpt here.