Applications and Challenges of Electromechanical Integrated Transmission Systems in Intelligent Vehicles
Research Article
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Applications and Challenges of Electromechanical Integrated Transmission Systems in Intelligent Vehicles

Wenbo Wang 1*
1 College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, 29 Nanjian Road, Nankou Town, Changping District, Beijing 102202, China
*Corresponding author: ddzdzdj@163.com
Published on 27 June 2025
Volume Cover
ACE Vol.169
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-209-6
ISBN (Online): 978-1-80590-210-2
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Abstract

With the rapid development of intelligent vehicles and the global push for carbon neutrality, Electromechanical Transmission (EMT) systems have attracted wide attention for their unique benefits. This paper provides an in-depth analysis of the three primary EMT architectures—series, parallel, and series-parallel hybrid configurations—highlighting their structural characteristics and operational principles. The study identifies key research directions focusing on enhancing energy utilization efficiency, improving drivability, and reducing system costs. Furthermore, it examines the current applications of EMT systems in intelligent vehicles, delineates the challenges encountered, and explores critical technologies such as lightweight design, hybrid power cooperative drive, and intelligent control strategies. The paper also evaluates various methodologies and algorithms employed within these technologies, assessing their respective advantages and limitations. Looking ahead, EMT systems are poised to play an increasingly pivotal role across multiple domains. However, to fully realize their potential, it is imperative to address prevailing issues related to system reliability, cost-effectiveness, and the integration of advanced intelligent control mechanisms, thereby contributing to the sustainable development of the automotive industry.

Keywords:

Electromechanical Transmission Systems, Hybrid Electric Vehicles, Energy Management Strategies, Control Strategies, Lightweight Design

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Wang,W. (2025). Applications and Challenges of Electromechanical Integrated Transmission Systems in Intelligent Vehicles. Applied and Computational Engineering,169,35-44.

References

[1]. Liu, H., et al., Study on electromechanical coupling inherent vibration characteristics and parameter influencing law of EMT system used in HEV. Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering, 2025. 239(1): p. 351-372.

[2]. Sabri, M.F.M., K.A. Danapalasingam, and M.F. Rahmat, A review on hybrid electric vehicles architecture and energy management strategies. Renewable & Sustainable Energy Reviews, 2016. 53: p. 1433-1442.

[3]. Xiao, B., et al., A review of pivotal energy management strategies for extended range electric vehicles. Renewable & Sustainable Energy Reviews, 2021. 149.

[4]. Ko, J., et al., Development of Brake System and Regenerative Braking Cooperative Control Algorithm for Automatic-Transmission-Based Hybrid Electric Vehicles. Ieee Transactions on Vehicular Technology, 2015. 64(2): p. 431-440.

[5]. Enang, W. and C. Bannister, Modelling and control of hybrid electric vehicles (A comprehensive review). Renewable & Sustainable Energy Reviews, 2017. 74: p. 1210-1239.

[6]. Kim, S. and S.B. Choi, Cooperative Control of Drive Motor and Clutch for Gear Shift of Hybrid Electric Vehicles With Dual-Clutch Transmission. Ieee-Asme Transactions on Mechatronics, 2020. 25(3): p. 1578-1588.

[7]. Larsson, V., L. Johannesson, and B. Egardt, Analytic Solutions to the Dynamic Programming Subproblem in Hybrid Vehicle Energy Management. Ieee Transactions on Vehicular Technology, 2015. 64(4): p. 1458-1467.

[8]. Tang, X.L., et al., Double Deep Reinforcement Learning-Based Energy Management for a Parallel Hybrid Electric Vehicle With Engine Start-Stop Strategy. Ieee Transactions on Transportation Electrification, 2022. 8(1): p. 1376 1388.

[9]. Yue, H., et al., Configurations and control strategies of hybrid powertrain systems. Energies, 2023. 16(2): p. 725.

[10]. Di Cairano, S., et al. Engine power smoothing energy management strategy for a series hybrid electric vehicle. in Proceedings of the 2011 American control conference. 2011. IEEE.

[11]. Di Cairano, S., et al., Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management. Ieee Transactions on Control Systems Technology, 2014. 22(3): p. 1018-1031.

[12]. Zeng, Y.P., et al., A Control Strategy for Driving Mode Switches of Plug-in Hybrid Electric Vehicles. Sustainability, 2018. 10(11).

[13]. Zhang, W. and J. Xu, Advanced lightweight materials for Automobiles: A review. Materials & Design, 2022. 221: p. 110994.

[14]. Mallick, P.K., Materials, design and manufacturing for lightweight vehicles. 2020: Woodhead publishing.

[15]. Lesch, C., N. Kwiaton, and F.B. Klose, Advanced High Strength Steels (AHSS) for Automotive Applications Tailored Properties by Smart Microstructural Adjustments. Steel Research International, 2017. 88(10).

[16]. Ahmad, H., et al. A review of carbon fiber materials in automotive industry. in IOP Conference Series: Materials Science and Engineering. 2020. IOP Publishing.

[17]. Schweighofer, B., K.M. Raab, and G. Brasseur, Modeling of high power automotive batteries by the use of an automated test system. Ieee Transactions on Instrumentation and Measurement, 2003. 52(4): p. 1087-1091.

[18]. Chaturvedi, N.A., et al., Algorithms for Advanced Battery-Management Systems MODELING, ESTIMATION, AND CONTROL CHALLENGES FOR LITHIUM-ION BATTERIES. Ieee Control Systems Magazine, 2010. 30(3): p. 49-68.

[19]. Wang, J.J., et al., Review on multi-power sources dynamic coordinated control of hybrid electric vehicle during driving mode transition process. International Journal of Energy Research, 2020. 44(8): p. 6128-6148.

[20]. Mohseni, N.A., N. Bayati, and T. Ebel, Energy management strategies of hybrid electric vehicles: A comparative review. Iet Smart Grid, 2024. 7(3): p. 191-220.

[21]. Qi, Y., et al., MODEL PREDICTIVE COORDINATED CONTROL FOR DUAL-MODE POWER-SPLIT HYBRID ELECTRIC VEHICLE. International Journal of Automotive Technology, 2018. 19(2): p. 345-358.

[22]. Shi, D.H., et al., Analysis and Optimization of Transient Mode Switching Behavior for Power Split Hybrid Electric Vehicle with Clutch Collaboration. Automotive Innovation, 2024. 7(1): p. 150-165.

[23]. Hong, J., et al., ENGINE SPEED REGULATION DURING GEAR SHIFT PROCESS OF TORQUE DECOUPLED HEV USING TRIPLE-STEP NONLINEAR METHOD. International Journal of Automotive Technology, 2021. 22(2): p. 415-428

[24]. Lin, Y.P., et al., Torque coordination control strategy in engine starting process for a single motor hybrid electric vehicle. International Journal of Electric and Hybrid Vehicles, 2018. 10(2): p. 177-196.

[25]. Peng, J.K., H.W. He, and R. Xiong, Rule based energy management strategy for a series-parallel plug-in hybrid electric bus optimized by dynamic programming. Applied Energy, 2017. 185: p. 1633-1643.

[26]. Ramadan, H.S., M. Becherif, and F. Claude, Energy Management Improvement of Hybrid Electric Vehicles via Combined GPS/Rule-Based Methodology. Ieee Transactions on Automation Science and Engineering, 2017. 14(2): p. 586-597.

[27]. Zeng, Y.P., et al., Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS. Sustainability, 2018. 10(6)

[28]. Maino, C., et al., Optimal mesh discretization of the dynamic programming for hybrid electric vehicles. Applied Energy, 2021. 292.

[29]. Zhang, J.Y. and T.L. Shen, Real-Time Fuel Economy Optimization With Nonlinear MPC for PHEVs. Ieee Transactions on Control Systems Technology, 2016. 24(6): p. 2167-2175.

[30]. Guo, L.X., et al., Predictive energy management strategy of dual-mode hybrid electric vehicles combining dynamic coordination control and simultaneous power distribution. Energy, 2023. 263.

[31]. Hu, Y., et al., Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning. Applied Sciences-Basel, 2018. 8(2).

[32]. Liu, T., et al., Reinforcement Learning Optimized Look-Ahead Energy Management of a Parallel Hybrid Electric Vehicle. Ieee-Asme Transactions on Mechatronics, 2017. 22(4): p. 1497-1507.

[33]. Zou, Y., et al., Reinforcement learning-based real-time energy management for a hybrid tracked vehicle. Applied energy, 2016. 171: p. 372-382.

[34]. Yang, N.K., et al., Reinforcement learning-based real-time intelligent energy management for hybrid electric vehicles in a model predictive control framework*. Energy, 2023. 270.

[35]. Zhao, P., et al. A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles. in 2018 23rd Asia and South Pacific design automation conference (ASP-DAC). 2018. IEEE.

[36]. Han, X., et al., Energy management based on reinforcement learning with double deep Q-learning for a hybrid electric tracked vehicle. Applied Energy, 2019. 254: p. 113708.

References

[1]. Liu, H., et al., Study on electromechanical coupling inherent vibration characteristics and parameter influencing law of EMT system used in HEV. Proceedings of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering, 2025. 239(1): p. 351-372.

[2]. Sabri, M.F.M., K.A. Danapalasingam, and M.F. Rahmat, A review on hybrid electric vehicles architecture and energy management strategies. Renewable & Sustainable Energy Reviews, 2016. 53: p. 1433-1442.

[3]. Xiao, B., et al., A review of pivotal energy management strategies for extended range electric vehicles. Renewable & Sustainable Energy Reviews, 2021. 149.

[4]. Ko, J., et al., Development of Brake System and Regenerative Braking Cooperative Control Algorithm for Automatic-Transmission-Based Hybrid Electric Vehicles. Ieee Transactions on Vehicular Technology, 2015. 64(2): p. 431-440.

[5]. Enang, W. and C. Bannister, Modelling and control of hybrid electric vehicles (A comprehensive review). Renewable & Sustainable Energy Reviews, 2017. 74: p. 1210-1239.

[6]. Kim, S. and S.B. Choi, Cooperative Control of Drive Motor and Clutch for Gear Shift of Hybrid Electric Vehicles With Dual-Clutch Transmission. Ieee-Asme Transactions on Mechatronics, 2020. 25(3): p. 1578-1588.

[7]. Larsson, V., L. Johannesson, and B. Egardt, Analytic Solutions to the Dynamic Programming Subproblem in Hybrid Vehicle Energy Management. Ieee Transactions on Vehicular Technology, 2015. 64(4): p. 1458-1467.

[8]. Tang, X.L., et al., Double Deep Reinforcement Learning-Based Energy Management for a Parallel Hybrid Electric Vehicle With Engine Start-Stop Strategy. Ieee Transactions on Transportation Electrification, 2022. 8(1): p. 1376 1388.

[9]. Yue, H., et al., Configurations and control strategies of hybrid powertrain systems. Energies, 2023. 16(2): p. 725.

[10]. Di Cairano, S., et al. Engine power smoothing energy management strategy for a series hybrid electric vehicle. in Proceedings of the 2011 American control conference. 2011. IEEE.

[11]. Di Cairano, S., et al., Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management. Ieee Transactions on Control Systems Technology, 2014. 22(3): p. 1018-1031.

[12]. Zeng, Y.P., et al., A Control Strategy for Driving Mode Switches of Plug-in Hybrid Electric Vehicles. Sustainability, 2018. 10(11).

[13]. Zhang, W. and J. Xu, Advanced lightweight materials for Automobiles: A review. Materials & Design, 2022. 221: p. 110994.

[14]. Mallick, P.K., Materials, design and manufacturing for lightweight vehicles. 2020: Woodhead publishing.

[15]. Lesch, C., N. Kwiaton, and F.B. Klose, Advanced High Strength Steels (AHSS) for Automotive Applications Tailored Properties by Smart Microstructural Adjustments. Steel Research International, 2017. 88(10).

[16]. Ahmad, H., et al. A review of carbon fiber materials in automotive industry. in IOP Conference Series: Materials Science and Engineering. 2020. IOP Publishing.

[17]. Schweighofer, B., K.M. Raab, and G. Brasseur, Modeling of high power automotive batteries by the use of an automated test system. Ieee Transactions on Instrumentation and Measurement, 2003. 52(4): p. 1087-1091.

[18]. Chaturvedi, N.A., et al., Algorithms for Advanced Battery-Management Systems MODELING, ESTIMATION, AND CONTROL CHALLENGES FOR LITHIUM-ION BATTERIES. Ieee Control Systems Magazine, 2010. 30(3): p. 49-68.

[19]. Wang, J.J., et al., Review on multi-power sources dynamic coordinated control of hybrid electric vehicle during driving mode transition process. International Journal of Energy Research, 2020. 44(8): p. 6128-6148.

[20]. Mohseni, N.A., N. Bayati, and T. Ebel, Energy management strategies of hybrid electric vehicles: A comparative review. Iet Smart Grid, 2024. 7(3): p. 191-220.

[21]. Qi, Y., et al., MODEL PREDICTIVE COORDINATED CONTROL FOR DUAL-MODE POWER-SPLIT HYBRID ELECTRIC VEHICLE. International Journal of Automotive Technology, 2018. 19(2): p. 345-358.

[22]. Shi, D.H., et al., Analysis and Optimization of Transient Mode Switching Behavior for Power Split Hybrid Electric Vehicle with Clutch Collaboration. Automotive Innovation, 2024. 7(1): p. 150-165.

[23]. Hong, J., et al., ENGINE SPEED REGULATION DURING GEAR SHIFT PROCESS OF TORQUE DECOUPLED HEV USING TRIPLE-STEP NONLINEAR METHOD. International Journal of Automotive Technology, 2021. 22(2): p. 415-428

[24]. Lin, Y.P., et al., Torque coordination control strategy in engine starting process for a single motor hybrid electric vehicle. International Journal of Electric and Hybrid Vehicles, 2018. 10(2): p. 177-196.

[25]. Peng, J.K., H.W. He, and R. Xiong, Rule based energy management strategy for a series-parallel plug-in hybrid electric bus optimized by dynamic programming. Applied Energy, 2017. 185: p. 1633-1643.

[26]. Ramadan, H.S., M. Becherif, and F. Claude, Energy Management Improvement of Hybrid Electric Vehicles via Combined GPS/Rule-Based Methodology. Ieee Transactions on Automation Science and Engineering, 2017. 14(2): p. 586-597.

[27]. Zeng, Y.P., et al., Energy Management for Plug-In Hybrid Electric Vehicle Based on Adaptive Simplified-ECMS. Sustainability, 2018. 10(6)

[28]. Maino, C., et al., Optimal mesh discretization of the dynamic programming for hybrid electric vehicles. Applied Energy, 2021. 292.

[29]. Zhang, J.Y. and T.L. Shen, Real-Time Fuel Economy Optimization With Nonlinear MPC for PHEVs. Ieee Transactions on Control Systems Technology, 2016. 24(6): p. 2167-2175.

[30]. Guo, L.X., et al., Predictive energy management strategy of dual-mode hybrid electric vehicles combining dynamic coordination control and simultaneous power distribution. Energy, 2023. 263.

[31]. Hu, Y., et al., Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning. Applied Sciences-Basel, 2018. 8(2).

[32]. Liu, T., et al., Reinforcement Learning Optimized Look-Ahead Energy Management of a Parallel Hybrid Electric Vehicle. Ieee-Asme Transactions on Mechatronics, 2017. 22(4): p. 1497-1507.

[33]. Zou, Y., et al., Reinforcement learning-based real-time energy management for a hybrid tracked vehicle. Applied energy, 2016. 171: p. 372-382.

[34]. Yang, N.K., et al., Reinforcement learning-based real-time intelligent energy management for hybrid electric vehicles in a model predictive control framework*. Energy, 2023. 270.

[35]. Zhao, P., et al. A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles. in 2018 23rd Asia and South Pacific design automation conference (ASP-DAC). 2018. IEEE.

[36]. Han, X., et al., Energy management based on reinforcement learning with double deep Q-learning for a hybrid electric tracked vehicle. Applied Energy, 2019. 254: p. 113708.

Cite this article

Wang,W. (2025). Applications and Challenges of Electromechanical Integrated Transmission Systems in Intelligent Vehicles. Applied and Computational Engineering,169,35-44.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MSS 2025 Symposium: Machine Vision System

ISBN: 978-1-80590-209-6(Print) / 978-1-80590-210-2(Online)
Editor: Cheng Wang, Marwan Omar
Conference website: https://www.confmss.org/chicago.html
Conference date: 5 June 2025
Series: Applied and Computational Engineering
Volume number: Vol.169
ISSN: 2755-2721(Print) / 2755-273X(Online)

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