ENERGY & INDUSTRIAL SYSTEMS

Battery charging & BMS (PyBaMM)

Battery charging & BMS (PyBaMM) is an OpenEnv-compatible reinforcement learning environment in our Energy & Industrial Systems catalog. An agent acts through the tools below, and a deterministic verifier scores every episode, so the reward can be used for GRPO or other RLVR post-training.

Task and verification

Synthesize optimal fast-charging current profiles for Li-ion cells without triggering lithium plating. Electrochemical Doyle-Fuller-Newman (DFN) ODEs, temperature, and degradation limits gate reward.

Tools and runtime

PyBaMM + DFN battery physics + C-rate optimization

OPENENV / PYBAMM

Related Energy & Industrial Systems gyms

• Building HVAC energy optimization
• Continuous chemical process control
• Industrial automation & PLC ladder logic
• Robust state-space controller synthesis
• Chemical process flowsheet engineering
• Power systems grid contingency analysis
• Protection relay coordination
• Electricity market clearing & dispatch
• Battery storage system (BESS) balancing
• Building envelope energy modeling
• Water distribution & stormwater networks
• Smart home & IoT automation

See how these environments are run privately on the custom RL environments page, and how their rewards are made hard to game in our verifier design guide.

Plan a training run

Check the environment’s availability, compatible models and compute in Studio. A catalog entry does not guarantee that hosted training is enabled for your workspace.