RL-based EMS control

  • Control and optimization
  • Automobile


To increase an electric vehicle's power consumption efficiency and thus to achieve a longer range, the air conditioning system (HVAC) needs to be controlled by adjusting various parameters. The current HVAC system engineering incorporates a fluid dynamics simulator. This, however, is both slow and does not describe the actual system sufficiently. As a result, the control performance obtained from the simulation cannot be realized in the actual system, along with slowed model development speeds.


To create a dynamic model that simulates the internal condition of the HVAC system, measurement data from an actual electric car's air conditioning system is used. In place of an HVAC simulator, the trained dynamics model is used to predict changes in the HVAC's internal state, and it serves as the basis for development of an efficient air conditioning control model.


As a result, the target temperature can be reached in less time and with less energy than the conventional control method, resulting in improved battery performance for electric vehicles.

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