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<p>Predictive cruise control (PCC) is an intelligence-assisted control technology that can significantly improve the overall performance of a vehicle by using road and traffic information in advance. With the continuous development of cloud control platforms (CCPs) and telematics boxes (T-boxes), cloud-based predictive cruise control (CPCC) systems are considered an effective solution to the problems of map update difficulties and insufficient computing power on the vehicle side. In this study, a vehicle-cloud hierarchical control architecture for PCC is designed based on a CCP and T-box. This architecture utilizes waypoint structures for hierarchical and dynamic cooperative inter-triggering, enabling rolling optimization of the system and commending parsing at the vehicle end. This approach significantly improves the anti-interference capability and resolution efficiency of the system. On the CCP side, a predictive fuel-saving speed-planning (PFSP) algorithm that considers the throttle input, speed variations, and time efficiency based on the waypoint structure is proposed. It features a forward optimization search without requiring weight adjustments, demonstrating robust applicability to various road conditions and vehicles &#xa0;equiped with constant cruise (CC) system. On the vehicle-side T-box, based on the reference control sequence with the global navigation satellite system position, the recommended speed is analyzed and controlled using the acute angle principle. Through analyzing the differences of the PFSP algorithm compared to dynamic programming (DP) and Model predictive control (MPC) algorithms under uphill and downhill conditions, the results show that the PFSP&#xa0;achieves good energy-saving performance compared to CC without exhibiting significant speed fluctuations, demonstrating strong adaptability to the CC system. Finally,&#xa0;by building an experimental platform and running field tests over a total of 2000 km, we verified the effectiveness and stability of the CPCC system and proved the fuel-saving performance of the proposed PFSP algorithm. The results showed that the CPCC system equipped with the PFSP algorithm achieved an average fuel-saving rate of 2.05%–4.39% compared to CC.</p>

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Method Design and Field Experiment Validation of Predictive Fuel-saving Cruise Control Based on Cloud Control Platform

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月16日

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  • Keke Wan,
  • Shuyan Li,
  • Bolin Gao,
  • Fachao Jiang,
  • Yanbin Liu

摘要

Predictive cruise control (PCC) is an intelligence-assisted control technology that can significantly improve the overall performance of a vehicle by using road and traffic information in advance. With the continuous development of cloud control platforms (CCPs) and telematics boxes (T-boxes), cloud-based predictive cruise control (CPCC) systems are considered an effective solution to the problems of map update difficulties and insufficient computing power on the vehicle side. In this study, a vehicle-cloud hierarchical control architecture for PCC is designed based on a CCP and T-box. This architecture utilizes waypoint structures for hierarchical and dynamic cooperative inter-triggering, enabling rolling optimization of the system and commending parsing at the vehicle end. This approach significantly improves the anti-interference capability and resolution efficiency of the system. On the CCP side, a predictive fuel-saving speed-planning (PFSP) algorithm that considers the throttle input, speed variations, and time efficiency based on the waypoint structure is proposed. It features a forward optimization search without requiring weight adjustments, demonstrating robust applicability to various road conditions and vehicles  equiped with constant cruise (CC) system. On the vehicle-side T-box, based on the reference control sequence with the global navigation satellite system position, the recommended speed is analyzed and controlled using the acute angle principle. Through analyzing the differences of the PFSP algorithm compared to dynamic programming (DP) and Model predictive control (MPC) algorithms under uphill and downhill conditions, the results show that the PFSP achieves good energy-saving performance compared to CC without exhibiting significant speed fluctuations, demonstrating strong adaptability to the CC system. Finally, by building an experimental platform and running field tests over a total of 2000 km, we verified the effectiveness and stability of the CPCC system and proved the fuel-saving performance of the proposed PFSP algorithm. The results showed that the CPCC system equipped with the PFSP algorithm achieved an average fuel-saving rate of 2.05%–4.39% compared to CC.