Model Predictive Control of a Hybrid Li-Ion Energy Storage System with Integrated Converter Loss Modeling

Paula Arias (), Marc Farrés, Alejandro Clemente and Lluís Trilla
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Paula Arias: Power Systems Group, Catalonia Institute for Energy Research (IREC), 08930 Barcelona, Spain
Marc Farrés: Power Systems Group, Catalonia Institute for Energy Research (IREC), 08930 Barcelona, Spain
Alejandro Clemente: Power Systems Group, Catalonia Institute for Energy Research (IREC), 08930 Barcelona, Spain
Lluís Trilla: Power Systems Group, Catalonia Institute for Energy Research (IREC), 08930 Barcelona, Spain

Energies, 2025, vol. 18, issue 20, 1-37

Abstract: The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions.

Keywords: hybrid electrochemical energy storage system; power electronics converters; Model Predictive Control; lithium-ion batteries (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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