Real-World SoC Estimation for Lithium-Ion Batteries in Electric Buses: Sampling Resolution and Machine Learning Efficiencys

Johanna P. Orellana-Iñiguez; Waldyr L. R. Gallo; Madson C. de Almeida

doi:10.18618/REP.e202626

Authors

Johanna P. Orellana-Iñiguez University of Campinas https://orcid.org/0000-0003-0202-544X
Waldyr L. R. Gallo University of Campinas https://orcid.org/0000-0003-1284-1096
Madson C. de Almeida University of Campinas

DOI:

https://doi.org/10.18618/REP.e202626

Keywords:

Battery electric bus, Battery management systems, Embedded systems, Lithium-ion batteries, State of charge estimation.

Abstract

Accurate State of Charge (SoC) estimation is essential for safe and efficient lithium-ion battery operation in electric mobility. Although machine learning methods achieve high predictive capability, many studies rely on laboratory cycling data and overlook deployment constraints such as latency and embedded hardware. This paper presents a deployment-oriented evaluation of data-driven SoC estimation using large-scale operational data from a battery electric bus. The dataset includes more than 4.3 million field measurements of current, voltage, temperature, and SoC collected under realistic driving conditions. Five models are compared: Random Forest, LightGBM, XGBoost, Temporal Convolutional Networks (TCN), and Gated Recurrent Unit (GRU) networks. Models are evaluated across sampling intervals from 30 s to 210 s and multiple training-data fractions, considering accuracy, training time, and inference latency. Results show that temporal resolution strongly affects the accuracy-efficiency trade-off. The best configuration, LightGBM at 180 s using 75 % of training data, achieved a test MAE of 6.338, RMSE of 8.495, and median inference latency of 1.10 ms. Compared with the Random Forest baseline at 30 s, it reduced MAE by 34.4 %, RMSE by 35.6 %, and latency by 97.6 %, supporting efficient real-time battery management system deployment.

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Author Biographies

Johanna P. Orellana-Iñiguez, University of Campinas

was born in Cuenca, Ecuador (1993). She received the B.Sc. degree in Chemical Engineering from the Universidad de Cuenca and a professional Master's in Industrial Process Improvement from ESPOL (2023). In 2025, she obtained a Master's degree in Energy Systems Planning from UNICAMP. She is currently pursuing the Ph.D. degree in Electrical Engineering at the School of Electrical and Computer Engineering (FEEC), UNICAMP. Her research focuses on lithium-ion battery degradation, State-of-Charge estimation using machine learning, and data-driven diagnostics for electric mobility, with interests in battery energy storage systems, electrochemical data analysis, power electronics, and sustainable mobility.

Waldyr L. R. Gallo, University of Campinas

is a Full Professor in the Energy Department of the School of Mechanical Engineering at the University of Campinas. He holds both M.Sc. and Ph.D. degrees in Mechanical Engineering. His research focuses on energy systems planning, vehicle propulsion, biofuels, renewable energy integration, and advanced energy storage technologies. His work also includes thermodynamics, with emphasis on internal combustion engines, gas turbines, cogeneration, and energy efficiency in industrial and transportation systems. He collaborates on multidisciplinary projects aimed at developing cleaner and more sustainable energy solutions.

Madson C. de Almeida, University of Campinas

holds a Bachelor’s degree in Electrical Engineering from the Federal University of Minas Gerais (UFMG), a Ph.D. in Electrical Engineering from the University of Campinas (UNICAMP), and completed a postdoctoral fellowship at the University of Manchester, U.K. His research focuses on power and energy systems, including distribution system analysis, state estimation, distributed generation, renewable energy sources, fault location, and electric mobility. He served as Vice-Coordinator of the Technical Committee on Power Systems of the Brazilian Society of Automation (2015–2016). He is currently an Associate Professor in the Department of Systems and Energy (DSE) at the School of Electrical and Computer Engineering, University of Campinas (FEEC/UNICAMP), where he coordinates the Living Laboratory for Electric Mobility in Public Transportation.

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