Technical Paper
Using Deep Reinforcement Learning for Hybrid Electric Vehicle Energy Management under Consideration of Dynamic Emission Models
2020-09-15
2020-01-2258
Hybrid electric vehicles (HEV) contribute to reduce emissions from transportation. The energy management controls the powertrain components in HEVs. In addition to minimizing fuel consumption, improving air quality is a major opportunity for hybrid vehicles. Pollutant emissions can only be mapped with sufficient accuracy using dynamic models. The introduced nitrogen oxide model is created using a supervised learning approach based on recorded measurement data. This dynamic model requires input data from previous time steps to ensure sufficient model quality. Classical algorithms such as Dynamic Programming are not able to find solutions for such high-dimensional problems in reasonable computing times. A promising approach to solve the resulting problem is Deep Reinforcement Learning (Deep RL), which has recently been introduced in the field of HEV energy management.
