Continual Refoircement Learning Using Real-World Data for Intelligent Prediction of SOC Consumption in Electric Vehicles



Meta-experience replay (MER, reinforcementlearning (RL), state of charge (SOC), electric vehicles (EV), neural networks (NN), principal component analysis (PCA)


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.


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Author Biographies


Juan P. Ortiz obtained his B.Sc. in Electronic Engineering and his M.Sc. in Industrial Automation and Control from Universidad Politecnica Salesiana, in 2010 and 2014 respectively. He is professor of Automotive Engineering at the Universidad Politecnica Salesiana. He is a member of the Grupo de Investigacion en Ingenieria del Transporte (GIIT). His research interests are nonlinear control theory, intelligent vehicles, electric vehicles and artificial intelligence. 


German Ayabaca was born in Cuenca, Ecuador. He is a student of Automotive Mechanical Engineering from Universidad Politecnica Salesiana. He is currently a member of the Transportation Engineering Research Group (GIIT). His research interests are electrically powered vehicles and automotive technologies.


Angel Cardenas was born in Cuenca, Ecuador. He is a student of Automotive Mechanical Engineering From Universidad Politecnica Salesiana. He is currently a member of the Transportation Engineering Research Group (GITT). His research interests are artificial neural networks and software engineering.


Diego Cabrera received his B.Sc. in Electronic Engineering from the Universidad Politecnica Salesiana, Ecuador, in 2012. He received his M.Sc. degree in Logic, Computation and Artificial Intelligence, and his Ph.D. degree in Computer Science from the Seville University, Spain, in 2014 and 2018 respectively. Currently, He is a Postdoctoral Fellow at the Dongguan University of Technology, China, and a Professor at the Universidad Politecnica Salesiana, Ecuador, from 2014. His research interests include intelligent systems, data-driven modelling and fault diagnosis.

Juan D. Valladolid, Universidad Politecnica Salesiana

Juan D. Valladolid was born in Cuenca, Ecuador. He received the B.Sc. degree in electronic engineering from Universidad Politécnica Salesiana, Cuenca, Ecuador, the M.Sc. degree in Industrial Automation and Control from Universidad Politécnica Salesiana. He is currently pursuing the Ph.D degree, in engineering with the Pontificia Universidad Javeriana, Bogotá,  Colombia. He is currently a Full-Time Professor with Universidad Politécnica Salesiana and member of the Grupo de Investigación en Ingeniería del Transporte (GIIT). He is the author of several articles. His current research interests include hybrid dynamical systems, power converter, systems optimization, electric vehicle, and nonlinear control theory


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How to Cite

ORTIZ , J. P., AYABACA, G. P., CARDENAS, ÁNGEL R., CABRERA, D., & Valladolid, J. D. (2021). Continual Refoircement Learning Using Real-World Data for Intelligent Prediction of SOC Consumption in Electric Vehicles. IEEE Latin America Transactions, 20(4), 624–633. Retrieved from