EPE Association: EPE 2025 - DS2a: Energy Storage and Management Systems

EPE 2025 - DS2a: Energy Storage and Management Systems
You are here: EPE Documents > 01 - EPE & EPE ECCE Conference Proceedings > EPE 2025 - Conference > EPE 2025 - Topic 03: Energy Storage Systems > EPE 2025 - DS2a: Energy Storage and Management Systems

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   A Comparative Study of Genetic Algorithm and Particle Swarm Optimization for Hybrid Renewable Systems with Battery and Hydrogen System
By Aqib KHAN, Mathieu BRESSEL, Dhaker ABBES, Arnaud DAVIGNY, Belkacem OULD BOUAMAMA [View]
[Download]

Abstract: This paper presents a comparative study ofGenetic Algorithm (GA) and Particle Swarm Optimization(PSO) for optimal sizing of two hybrid renewableenergy systems: Solar with Battery and Grid, and Solarwith H2 System and Grid. Real-time energy consumptiondata from a university is used to model these systems,aiming to minimize costs while meeting energy demandsand ensuring reliability. The performance of GA andPSO is compared based on solution quality, convergencespeed, and computational efficiency. Results show that GAprovides robust configurations, while PSO offers fasterconvergence. These findings support efficient and practicalhybrid system design.

   Development of Energy Management Strategies for a MG with Distributed Energy Generation and Storage
By Pierrick DAVAL, Bilal KABALAN, Margot GAETANI-LISEO, Hugo HELBLING, Emmanuel VINOT [View]
[Download]

Abstract: In this paper, energy management strategies are developed and compared for a microgrid connected to the grid, composed of solar panels, a storage system and a load profile. Three rule-based strategies and one strategy based on dynamic programming were implemented and compared in simulation. The first ruled-based strategy is based on self sufficiency principle and tries to use the grid as last resort. The second aims to use the battery in peak times. The third is predictive and uses weather and demand forecasts in order to limit the battery charging to be able to absorb all the surplus of solarenergy. The paper shows that none of these rule-based strategies can reach the optimal solution. This is why an optimal strategy was developed as a reference for results comparison, using dynamic programming. After explaining the different strategies, a comparison is done between them using results of a case simulation.

   Energy Management in Hybrid Energy Storage Systems for Electric Vehicles: A Reinforcement Learning Approach with Python-Simulink Integration
By Parisa RANJBARAN, Alireza BAKHSHAI, Praveen JAIN [View]
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Abstract: A Reinforcement Learning (RL) algorithm is employed for the energy management of a Hybrid Energy Storage System (HESS) in All Electric Vehicles (AEVs), focusing on enhancing battery life and vehicle mileage. Simulink\_gym is used as the interface between Python and Simulink environment to enable seamless communication for efficient simulation and training. The results indicate that the RL-based Energy Management Strategy (EMS) significantly outperforms conventional rule-based strategies by achieving superior power sharing in HESS, leading to enhanced energy efficiency and more balanced utilization of storage components.

   Improved Energy Management Strategy for Minimizing Capacitors Storage in Power Converters for Particle Accelerators
By Dimitris XYSTRAS, Ivan JOSIFOVIC, Olivier MICHELS, Gilles LE GODEC [View]
[Download]

Abstract: This study presents a new energy managementstrategy for electromagnetic loads supplied bypower electronic converters with integratedenergy recovery capability. It aims to minimizethe energy storage requirements of the powerconverters, thereby reducing their cost andvolume. The study covers a theoretical analysis,verified by simulation and experimental results.

   Modeling and Evaluating GaN HEMTs for Efficient Multicell Battery Balancing with Dual-Active Bridge Converters
By Robert Alfie PEÑA, Boud VERBRUGGE, Omar HEGAZY [View]
[Download]

Abstract: This paper evaluates the feasibility of gallium nitride (GaN) high-electron-mobility transistors (HEMTs) in battery active balancing circuits. A comparative analysis of GaN models, including loss and efficiency, is presented, which demonstrates the superior performance of GaN-based solutions and highlights its potential in efficient and reliable battery management systems (BMSs).

   Voltage-Controlled SoC Estimation in Lithium-Ion Batteries: A Comparative Analysis of Equivalent Circuit Models
By Hoda SOROURI, Ashkan SAFARI, Arman OSHNOEI, Remus TEODORESCU [View]
[Download]

Abstract: This paper introduces voltage-controlled methods to regulate battery voltage and extract state of charge (SoC) in lithium-ion batteries, comparing three equivalent circuit models (ECMs): a simple resistance model, an RC model, and an RC-Diffusion model. The models are tested under various conditions and compared against lab data, evaluating their accuracy.

EPE 2025 - DS2a: Energy Storage and Management Systems
You are here: EPE Documents > 01 - EPE & EPE ECCE Conference Proceedings > EPE 2025 - Conference > EPE 2025 - Topic 03: Energy Storage Systems > EPE 2025 - DS2a: Energy Storage and Management Systems
	[return to parent folder]

	A Comparative Study of Genetic Algorithm and Particle Swarm Optimization for Hybrid Renewable Systems with Battery and Hydrogen System By Aqib KHAN, Mathieu BRESSEL, Dhaker ABBES, Arnaud DAVIGNY, Belkacem OULD BOUAMAMA	[View] [Download]
Abstract: This paper presents a comparative study ofGenetic Algorithm (GA) and Particle Swarm Optimization(PSO) for optimal sizing of two hybrid renewableenergy systems: Solar with Battery and Grid, and Solarwith H2 System and Grid. Real-time energy consumptiondata from a university is used to model these systems,aiming to minimize costs while meeting energy demandsand ensuring reliability. The performance of GA andPSO is compared based on solution quality, convergencespeed, and computational efficiency. Results show that GAprovides robust configurations, while PSO offers fasterconvergence. These findings support efficient and practicalhybrid system design.

	Development of Energy Management Strategies for a MG with Distributed Energy Generation and Storage By Pierrick DAVAL, Bilal KABALAN, Margot GAETANI-LISEO, Hugo HELBLING, Emmanuel VINOT	[View] [Download]
Abstract: In this paper, energy management strategies are developed and compared for a microgrid connected to the grid, composed of solar panels, a storage system and a load profile. Three rule-based strategies and one strategy based on dynamic programming were implemented and compared in simulation. The first ruled-based strategy is based on self sufficiency principle and tries to use the grid as last resort. The second aims to use the battery in peak times. The third is predictive and uses weather and demand forecasts in order to limit the battery charging to be able to absorb all the surplus of solarenergy. The paper shows that none of these rule-based strategies can reach the optimal solution. This is why an optimal strategy was developed as a reference for results comparison, using dynamic programming. After explaining the different strategies, a comparison is done between them using results of a case simulation.

	Energy Management in Hybrid Energy Storage Systems for Electric Vehicles: A Reinforcement Learning Approach with Python-Simulink Integration By Parisa RANJBARAN, Alireza BAKHSHAI, Praveen JAIN	[View] [Download]
Abstract: A Reinforcement Learning (RL) algorithm is employed for the energy management of a Hybrid Energy Storage System (HESS) in All Electric Vehicles (AEVs), focusing on enhancing battery life and vehicle mileage. Simulink\_gym is used as the interface between Python and Simulink environment to enable seamless communication for efficient simulation and training. The results indicate that the RL-based Energy Management Strategy (EMS) significantly outperforms conventional rule-based strategies by achieving superior power sharing in HESS, leading to enhanced energy efficiency and more balanced utilization of storage components.

	Improved Energy Management Strategy for Minimizing Capacitors Storage in Power Converters for Particle Accelerators By Dimitris XYSTRAS, Ivan JOSIFOVIC, Olivier MICHELS, Gilles LE GODEC	[View] [Download]
Abstract: This study presents a new energy managementstrategy for electromagnetic loads supplied bypower electronic converters with integratedenergy recovery capability. It aims to minimizethe energy storage requirements of the powerconverters, thereby reducing their cost andvolume. The study covers a theoretical analysis,verified by simulation and experimental results.

	Modeling and Evaluating GaN HEMTs for Efficient Multicell Battery Balancing with Dual-Active Bridge Converters By Robert Alfie PEÑA, Boud VERBRUGGE, Omar HEGAZY	[View] [Download]
Abstract: This paper evaluates the feasibility of gallium nitride (GaN) high-electron-mobility transistors (HEMTs) in battery active balancing circuits. A comparative analysis of GaN models, including loss and efficiency, is presented, which demonstrates the superior performance of GaN-based solutions and highlights its potential in efficient and reliable battery management systems (BMSs).

	Voltage-Controlled SoC Estimation in Lithium-Ion Batteries: A Comparative Analysis of Equivalent Circuit Models By Hoda SOROURI, Ashkan SAFARI, Arman OSHNOEI, Remus TEODORESCU	[View] [Download]
Abstract: This paper introduces voltage-controlled methods to regulate battery voltage and extract state of charge (SoC) in lithium-ion batteries, comparing three equivalent circuit models (ECMs): a simple resistance model, an RC model, and an RC-Diffusion model. The models are tested under various conditions and compared against lab data, evaluating their accuracy.