Electric Vehicle Energy Management Under Unknown Disturbances from Undefined Power Demand: Online Co-State Estimation via Reinforcement Learning

C. Treesatayapun (), A. J. Munoz-Vazquez, S. K. Korkua, B. Srikarun and C. Pochaiya
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C. Treesatayapun: Robotics and Advanced Manufacturing, Center for Research and Advanced Studies (CINVESTAV), 1062 Industria Metalurgica Av., Ramos Arizpe 25903, Mexico
A. J. Munoz-Vazquez: Higher Education Center at McAllen, Texas A&M University (TAMU), College Station, TX 78504, USA
S. K. Korkua: School of Engineering and Technology, Walailak University, Nakhonsrithammarat 80161, Thailand
B. Srikarun: School of Engineering and Technology, Walailak University, Nakhonsrithammarat 80161, Thailand
C. Pochaiya: School of Engineering and Technology, Walailak University, Nakhonsrithammarat 80161, Thailand

Energies, 2025, vol. 18, issue 15, 1-20

Abstract: This paper presents a data-driven energy management scheme for fuel cell and battery electric vehicles, formulated as a constrained optimal control problem. The proposed method employs a co-state network trained using real-time measurements to estimate the control law without requiring prior knowledge of the system model or a complete dataset across the full operating domain. In contrast to conventional reinforcement learning approaches, this method avoids the issue of high dimensionality and does not depend on extensive offline training. Robustness is demonstrated by treating uncertain and time-varying elements, including power consumption from air conditioning systems, variations in road slope, and passenger-related demands, as unknown disturbances. The desired state of charge is defined as a reference trajectory, and the control input is computed while ensuring compliance with all operational constraints. Validation results based on a combined driving profile confirm the effectiveness of the proposed controller in maintaining the battery charge, reducing fluctuations in fuel cell power output, and ensuring reliable performance under practical conditions. Comparative evaluations are conducted against two benchmark controllers: one designed to maintain a constant state of charge and another based on a soft actor–critic learning algorithm.

Keywords: energy management system; electric vehicle; co-state optimal control; online data-driven; unknown disturbances (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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