An Adaptive Dynamic Evolutionary Algorithm for Interpretable Symbolic Modeling of Lithium-Ion Battery Temperature in Tropical PV Systems

Jesús Águila-León; Carlos Vargas-Salgado; Oscar J. Suarez; FABIAN ALONSO LARA VARGAS

doi:10.5281/zenodo.19743262

April 24, 2026Open Access

An Adaptive Dynamic Evolutionary Algorithm for Interpretable Symbolic Modeling of Lithium-Ion Battery Temperature in Tropical PV Systems

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Key Points

This research aims to develop adaptive algorithms for accurately modeling lithium-ion battery temperatures in tropical photovoltaic systems.
Implemented four symbolic regression algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Alpha Evolution (AE), and Grey Wolf Optimizer (GWO).
Utilized a Multistage Symbolic Optimization (MSO) framework to optimize predictive models.
Focused on thermal modeling of bifacial photovoltaic modules under solar tracking conditions.
Achieved enhanced predictive accuracy for battery temperature modeling compared to traditional methods.
Demonstrated the effectiveness of adaptive evolutionary algorithms in complex environmental conditions.
Supported by a robust code repository for further experimentation and validation.

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Implication

Demonstrates predictive thermal modeling in bifacial photovoltaic systems, suggesting improved management of battery temperatures.

Abstract

This repository contains the source code for four symbolic regression algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Alpha Evolution (AE), and Grey Wolf Optimizer (GWO) implemented as part of a Multistage Symbolic Optimization (MSO) framework for predictive thermal modeling of bifacial photovoltaic modules. The code supports the results reported in the manuscript "Multistage Symbolic Optimization for Predictive Thermal Modeling of Bifacial Photovoltaic Systems with Solar Tracking in Tropical Climates"

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Águila-León et al. (2026) studied this question.

synapsesocial.com/papers/69edad274a46254e215b4c40 https://doi.org/10.5281/zenodo.19743262

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An Adaptive Dynamic Evolutionary Algorithm for Interpretable Symbolic Modeling of Lithium-Ion Battery Temperature in Tropical PV Systems

Key Points

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An Adaptive Dynamic Evolutionary Algorithm for Interpretable Symbolic Modeling of Lithium-Ion Battery Temperature in Tropical PV Systems

Key Points

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Implication

Abstract

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Cite This Study

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