Battery Thermal Management (BTM) for Electrical Vehicles (EVs) Environment

So I just ended a Bachelor’s in chemical engineering, and for my thesis I created an environment for test control strategies, one of them was reinforcement learning, specifically I used SAC, I ended up using stable baselines since the system model was already using a lot of files, and I lowkey dislike to not have organization on a project.

However. The point is that this environment works using a driving cycle dataset (i.e., UDDS) as the velocity for an EV, this is accomplished by coupling high fidelity models as next: an epsilon-NTU model for the internal refrigeration cycle, an ECM for the ion-lithium battery and entropy data retrieved from an open-source article.

Also, I tried to use SAC by using some kind of receding horizon (giving it future perturbations) which is also something I tried to understand from l-step lookahead in the lectures of god Bertsekas, (this was a bit bad implemented I think).

The complete system is configurable so that one can changes the initial state (i.e., SOC, Tbatt), weight of the vehicle, the brake regeneration efficiency and so on. For my work the benchmark is to use a simple thermostat and compare its reliability & performance with RL and Model Predictive Control (deterministic & stochastic) and how these strategies complement each other. The reinforcement learning part is written in JAX and the MPC in CaSADi. I had a lot of fun comparing strategies and also is great to see how an agent learns this kind of slow dynamics. Hope somebody tries it and criticizes the architecture or something like that because is currently under “revision” there may be some errors.

Repo:

https://github.com/BalorLC3/MPC-and-RL-for-a-Battery-Thermal-System-Management

Any comment or also if someone could share me his/her usage of RL in another area would be amazing.