Assessing the Limits of Equivalent Circuit Models and Kalman Filters for Estimating the State of Charge: Case of Agricultural Robots

German Monsalve, Alben Cardenas (), Diego Acevedo-Bueno and Wilmar Martinez
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German Monsalve: Electrical and Computer Engineering Department, University of Quebec at Trois-Rivieres, 3351, Boulevard des Forges, Trois-Rivieres, QC G8Z 4M3, Canada
Alben Cardenas: Electrical and Computer Engineering Department, University of Quebec at Trois-Rivieres, 3351, Boulevard des Forges, Trois-Rivieres, QC G8Z 4M3, Canada
Diego Acevedo-Bueno: Electrical and Computer Engineering Department, University of Quebec at Trois-Rivieres, 3351, Boulevard des Forges, Trois-Rivieres, QC G8Z 4M3, Canada
Wilmar Martinez: Department of Electrical Engineering (ESAT), KU Leuven—EnergyVille, Thor Park 8310-bus 12135, 3600 Genk, Belgium

Energies, 2023, vol. 16, issue 7, 1-15

Abstract: The battery State of Charge (SoC) is critical information to overcome agricultural robots’ limitations related to battery and energy management. Although several SoC estimation methods have been proposed in the literature, the performance of these methods has not been validated for different battery chemistries in agricultural mobile robot applications. Compared to previous work, this paper evaluates the limits of the SoC estimation using the RC model and the Thevenin model for a Lithium Iron Phosphate (LFP) battery and a Sealed Lead Acid (SLA) battery. This evaluation used a custom agricultural robot in a controlled indoor environment. Consequently, this work assessed the limitations of two ECM-based SoC estimation methods using battery packs, low-cost sensors and discharge cycles typically used in agricultural robot applications. Finally, the results indicate that the RC model is not suitable for SoC estimation for LFP battery; however, it achieved a mean absolute error (MAE) of 2.2% for the SLA battery. On the other hand, the Thevenin model performed properly for both chemistries, achieving MAE lower than 1%.

Keywords: state of charge estimation; lithium iron phosphate; sealed lead acid; RC model; Thevenin model; agricultural robots (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: 2023
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