Abstract
The accelerated migration towards electric vehicles (EV) presents several problems to solve. The main aspect is the management and prediction of the state of charge (SOC) in real long-range routes of different variations in altitude for a more efficient energy consumption and vehicle recharge plan. This paper presents the implementation of a new algorithm for SOC estimation based on continuous learning and meta-experience replay (MER) with reservoir sample. It combines the reptile meta-learning algorithm with the experience replay technique for stabilizing the reinforcement learning. The proposed algorithm considers several important factors for the prediction of the SOC in EV such as: speed, travel time, route altimetry, consumed battery capacity, regenerated battery capacity. A modified principal components analysis is used to reduce the dimensionality of the route altimetry data. The experimental results show an efficient estimation of the SOC values and a convergent increase in knowledge while the vehicle travels the routes.
Original language | English |
---|---|
Pages (from-to) | 624-633 |
Number of pages | 10 |
Journal | Ieee Latin America Transactions |
Volume | 20 |
Issue number | 4 |
DOIs | |
State | Published - 1 Apr 2022 |
Bibliographical note
Publisher Copyright:© 2003-2012 IEEE.
Keywords
- Electric vehicles (EV)
- Meta-experience replay (MER
- Neural networks (NN)
- Principal component analysis (PCA)
- Reinforcementlearning (RL)
- State of charge (SOC)