Automated multi-dimensional dynamic planning algorithm for solving energy management problems in fuel cell electric vehicles
Description
1. The AuDP algorithm does not require any hyperparameter adjustment and can meet various constraints. You can compute the optimal control results under different gas purge time constraints by adjusting the variable [OFF2ON] below. 2. The battery model integrates the SOP algorithm recommended by the battery supplier and includes a simple battery thermal model. However, we currently do not have permission to disclose the SOP data, so parts of the code involving battery data have been encapsulated. The open-source code is sufficient to demonstrate most of the details of AuDP. In the future, we will seek publicly available SOP data and open-source all the code. /*You can find the SOP algorithm details in [+Pseudocode/Cal_SOC].*/ 3. It is recommended to read the AuDP files in the following order: +AuDP_Function/AuDP.m +AuDP_Function/AuDP_Single_Step.m +AuDP_Function/Mode_Masking.m +AuDP_Function/Start_Mode.m +AuDP_Function/Work_Mode.m +AuDP_Function/Close_Mode.m +AuDP_Function/EV_Mode.m +AuDP_Function/SOC_Masking.m +AuDP_Function/Data_Merge.m +AuDP_Function/Result_Display.m 4. Running [main.m] to get the result of the calculation. You can also directly read the data in [+Data_Storage] to view the calculation results, and use [+AuDP_Function/Result_Display] to visualize the data. 5. This code was developed using Matlab 2022b, so it is recommended to run it in Matlab 2022b to avoid potential errors. On a laptop configured with a 12th Gen Intel(R) Core(TM) i9-12900HX 2.30 GHz CPU, 32.0 GB RAM, and RTX3080Ti, the runtime of this code is approximately 185–195 seconds. Note that different Matlab versions and laptop configurations may result in varying computation times, which is normal.