Analysis of Instantaneous Energy Consumption and Recuperation in Electric Buses During SORT Tests Using Linear and Neural Network Models
Edward Kozłowski (),
Magdalena Zimakowska-Laskowska,
Piotr Wiśniowski,
Boris Šnauko,
Piotr Laskowski,
Jan Laskowski,
Jonas Matijošius,
Andrzej Świderski and
Adam Torok
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Edward Kozłowski: Faculty of Management, Lublin University of Technology, 38D Nadbystrzycka Str., 20-618 Lublin, Poland
Magdalena Zimakowska-Laskowska: Motor Transport Institute, 80 Jagiellońska Str., 03-301 Warsaw, Poland
Piotr Wiśniowski: Motor Transport Institute, 80 Jagiellońska Str., 03-301 Warsaw, Poland
Boris Šnauko: Center of European Projects, 9 Halicka Str., 31-036 Kraków, Poland
Piotr Laskowski: Faculty of Automotive and Construction Machinery Engineering, Warsaw University of Technology, 84 Narbutta Str., 02-524 Warsaw, Poland
Jan Laskowski: Faculty of Management, Lublin University of Technology, 38D Nadbystrzycka Str., 20-618 Lublin, Poland
Jonas Matijošius: Mechanical Science Institute, Vilnius Gediminas Technical University-VILNIUS TECH, 25 Plytinės Str., LT-10105 Vilnius, Lithuania
Andrzej Świderski: Motor Transport Institute, 80 Jagiellońska Str., 03-301 Warsaw, Poland
Adam Torok: Department of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3, H-1111 Budapest, Hungary
Energies, 2025, vol. 18, issue 19, 1-28
Abstract:
With the growing deployment of electric buses (e-buses), accurate energy use modelling has become essential for fleet optimisation and operational planning. Using the SORT methodology, this study analyses instantaneous energy consumption and recuperation (IECR). Three vehicle configurations were tested (one battery with pantograph, four batteries, and eight batteries), each with ten repeatable runs. Four approaches were compared: a baseline linear regression, an extended linear model (ELM) due to the state, a feed-forward neural network, and a recurrent neural network (RNN). The extended linear model achieved a determination coefficient of R 2 = 0.9124 (residual standard deviation 4.26) compared with R 2 = 0.7859 for the baseline, while the determination coefficient for the RNN is 0.9343, and the RNN provided the highest accuracy on the test set (the correlation coefficient between real and predicted values is 0.9666). The results confirm the dominant influence of speed and acceleration on IECR and show that battery configuration mainly affects consumption during acceleration. Literature-consistent findings indicate that regenerative systems can recover 25–51% of braking energy, with advanced control methods further improving recovery. Despite non-normality and temporal dependence of residuals, the state-aware linear model remains interpretable and competitive, whereas recurrent networks offer superior fidelity. These results support real-time energy management, charging optimisation, and reliable range prediction for electric buses in urban public transport.
Keywords: electric buses; energy consumption; recuperation; predictive modelling; recurrent neural network (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: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:19:p:5107-:d:1758284
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