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The Energy Storage System (ESS) in hybrid and electric vehicles, primarily composed of batteries, is the most critical component, as it powers all the vehicle's components to ensure proper operation. The management of energy stored in batteries is handled by the Battery Management System (BMS), which monitors, controls, and optimizes energy, as well as assesses the state of charge (SOC) and health (SOH) of the batteries, thereby ensuring safe use. Accurate prediction of SOC and SOH requires mathematical models that accurately describe the electrical behavior of the batteries. This work proposes a low-cost system for monitoring voltage and current during battery discharge tests. Additionally, the Least Squares Orthogonal Distances (LSOD) method is applied for parameter identification of mathematical models based on equivalent electrical circuits, including the internal resistance, Thevenin, and modified Thevenin models. Discharge tests were conducted on LiPo batteries (4000 mAh) and Li-ion batteries (1200 mAh and 4300 mAh) to determine the model parameters. Finally, the approximation errors of the models obtained were evaluated and analyzed.

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Offline Identification of RC Battery Models by the LSOD Algorithm: Experimental Cases on Li-Ion and LiPo Cells Under Discharge Conditions

  • 章节

  • 发表日期：

    2026年

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  • Thomas Shaí Marmolejo-Salas,
  • Aldo Barbosa-Palacios,
  • Cristian Rojas-Méndez,
  • Luis Alberto Cantera-Cantera,
  • Ilse Cervantes

摘要

The Energy Storage System (ESS) in hybrid and electric vehicles, primarily composed of batteries, is the most critical component, as it powers all the vehicle's components to ensure proper operation. The management of energy stored in batteries is handled by the Battery Management System (BMS), which monitors, controls, and optimizes energy, as well as assesses the state of charge (SOC) and health (SOH) of the batteries, thereby ensuring safe use. Accurate prediction of SOC and SOH requires mathematical models that accurately describe the electrical behavior of the batteries. This work proposes a low-cost system for monitoring voltage and current during battery discharge tests. Additionally, the Least Squares Orthogonal Distances (LSOD) method is applied for parameter identification of mathematical models based on equivalent electrical circuits, including the internal resistance, Thevenin, and modified Thevenin models. Discharge tests were conducted on LiPo batteries (4000 mAh) and Li-ion batteries (1200 mAh and 4300 mAh) to determine the model parameters. Finally, the approximation errors of the models obtained were evaluated and analyzed.