TY - JOUR
T1 - Continual Refoircement Learning Using Real-World Data for Intelligent Prediction of SOC Consumption in Electric Vehicles
AU - Ortiz, Juan P.
AU - Ayabaca, German P.
AU - Cardenas, Angel R.
AU - Cabrera, Diego
AU - Valladolid, Juan D.
N1 - Publisher Copyright:
© 2003-2012 IEEE.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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.
AB - 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.
KW - Electric vehicles (EV)
KW - Meta-experience replay (MER
KW - Neural networks (NN)
KW - Principal component analysis (PCA)
KW - Reinforcementlearning (RL)
KW - State of charge (SOC)
UR - http://www.scopus.com/inward/record.url?scp=85123685688&partnerID=8YFLogxK
U2 - 10.1109/TLA.2022.9675468
DO - 10.1109/TLA.2022.9675468
M3 - Article
AN - SCOPUS:85123685688
SN - 1548-0992
VL - 20
SP - 624
EP - 633
JO - Ieee Latin America Transactions
JF - Ieee Latin America Transactions
IS - 4
ER -