Electric vehicle battery charging with safe-RL
To become the standard power supply for electric vehicles(EVs), Li-ion batteries need balanced current profiles in order to avoidundesirable electrochemical reactions and excessive charging times. Inthis work, we propose a safe exploration deep reinforcement learning(SDRL) approach in order to deter...
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| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/165927 |
| Aporte de: |
| Sumario: | To become the standard power supply for electric vehicles(EVs), Li-ion batteries need balanced current profiles in order to avoidundesirable electrochemical reactions and excessive charging times. Inthis work, we propose a safe exploration deep reinforcement learning(SDRL) approach in order to determine optimal charging profiles undervariable operating conditions. One of the main advantages of reinforce-ment learning (RL) techniques is that they can learn from interactionwith the real or simulated system while incorporating the nonlinear-ity and uncertainty derived from fluctuating environmental conditions.However, since RL techniques have to explore undesirable states beforeobtaining an optimal policy, no safety guarantees are provided. The pro-posed approach aims at maintaining zero constraint violations through-out the learning process by incorporating a safety layer that corrects theaction if a constraint is likely to be violated. Tests performed on theequivalent circuit of a li-ion battery under variability conditions showearly results where SDRL is able to find safe policies while consideringa trade-off between the charging speed and the battery lifespan. |
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