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High-Resolution Data-Driven Energy Consumption Prediction for Battery-Electric Buses Using Boosting Algorithms.

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Title: High-Resolution Data-Driven Energy Consumption Prediction for Battery-Electric Buses Using Boosting Algorithms.
Authors: Wu, Yong1 (AUTHOR), Xin, Zhichao1,2 (AUTHOR), Li, Jiachang1 (AUTHOR), Ma, Zhenliang2 (AUTHOR), Xing, Jianping1 (AUTHOR) xingjp@sdu.edu.cn
Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2058. 25p.
Subject Terms: *Boosting algorithms, *Energy consumption forecasting, *Real-time computing, *Electric motor buses, *Metadata, *Machine learning, *Public transit
Geographic Terms: Jinan (Shandong Sheng, China)
Abstract: Accurate prediction of energy consumption is essential for the operation and charging management of battery-electric buses. Existing prediction studies are often constrained by incomplete or low-resolution input data, limiting their robustness under real-world operating conditions. This paper presents a high-resolution, sensor-rich energy consumption modeling framework using second-by-second operational data and tests on an electric bus fleet operating on Route 49 in Jinan, China. The dataset integrates synchronized measurements of vehicle kinematics, powertrain variables, and thermal conditions, providing a substantially more complete description of bus operation against previous studies. Boosting-based machine learning models are developed to predict the instantaneous power demand, and their performance is evaluated in comparison with a physics-based energy model and other variants of machine learning models. Results show that the data-driven boosting models demonstrate excellent explanatory power ( R 2 values of up to 0.99 (training) and 0.95 (test)) and remain reliable under nonlinear operating conditions. Feature and SHAP analyses identify physically consistent energy drivers, supporting the applicability of the approach to real-world public transport operations. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 193715954
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: High-Resolution Data-Driven Energy Consumption Prediction for Battery-Electric Buses Using Boosting Algorithms.
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  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Yong%22">Wu, Yong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xin%2C+Zhichao%22">Xin, Zhichao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jiachang%22">Li, Jiachang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Zhenliang%22">Ma, Zhenliang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xing%2C+Jianping%22">Xing, Jianping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xingjp@sdu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2058. 25p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+consumption+forecasting%22">Energy consumption forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+motor+buses%22">Electric motor buses</searchLink><br />*<searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Public+transit%22">Public transit</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Jinan+%28Shandong+Sheng%2C+China%29%22">Jinan (Shandong Sheng, China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate prediction of energy consumption is essential for the operation and charging management of battery-electric buses. Existing prediction studies are often constrained by incomplete or low-resolution input data, limiting their robustness under real-world operating conditions. This paper presents a high-resolution, sensor-rich energy consumption modeling framework using second-by-second operational data and tests on an electric bus fleet operating on Route 49 in Jinan, China. The dataset integrates synchronized measurements of vehicle kinematics, powertrain variables, and thermal conditions, providing a substantially more complete description of bus operation against previous studies. Boosting-based machine learning models are developed to predict the instantaneous power demand, and their performance is evaluated in comparison with a physics-based energy model and other variants of machine learning models. Results show that the data-driven boosting models demonstrate excellent explanatory power ( R 2 values of up to 0.99 (training) and 0.95 (test)) and remain reliable under nonlinear operating conditions. Feature and SHAP analyses identify physically consistent energy drivers, supporting the applicability of the approach to real-world public transport operations. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193715954
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19092058
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 2058
    Subjects:
      – SubjectFull: Boosting algorithms
        Type: general
      – SubjectFull: Energy consumption forecasting
        Type: general
      – SubjectFull: Real-time computing
        Type: general
      – SubjectFull: Electric motor buses
        Type: general
      – SubjectFull: Metadata
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Public transit
        Type: general
      – SubjectFull: Jinan (Shandong Sheng, China)
        Type: general
    Titles:
      – TitleFull: High-Resolution Data-Driven Energy Consumption Prediction for Battery-Electric Buses Using Boosting Algorithms.
        Type: main
  BibRelationships:
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      – PersonEntity:
          Name:
            NameFull: Wu, Yong
      – PersonEntity:
          Name:
            NameFull: Xin, Zhichao
      – PersonEntity:
          Name:
            NameFull: Li, Jiachang
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            NameFull: Ma, Zhenliang
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            NameFull: Xing, Jianping
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
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              Value: 19
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Energies (19961073)
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