Dynamic Energy Management Strategy of Hybrid Electric Vehicles based on Velocity Prediction

Li, Xiangcheng; Lin, Weipeng; Jin, Yuanyang; Chen, Daxin; chen, Tao

doi:10.1016/j.ifacol.2021.10.189

Cited by 3 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The velocity prediction results under different prediction horizons were shown in Figure 5a, and we found that the proposed velocity prediction method was able to improve prediction accuracy effectively compared with the previous method without DPR. The other is DCP based on positioning, sensing, interaction-aware, and other traffic information service technologies, such as V2X, V2V, and ITS [81][82][83]. The development of communication technologies has promoted the timely acquisition of long-term and shortterm traffic information.…”

Section: Driving Characteristic Predictionmentioning

confidence: 99%

“…For instance, Adelberger et al [84] proposed a long-term velocity prediction method utilizing real-world V2X data. Hyeon et al [85] proposed a simple and The other is DCP based on positioning, sensing, interaction-aware, and other traffic information service technologies, such as V2X, V2V, and ITS [81][82][83]. The development of communication technologies has promoted the timely acquisition of long-term and shortterm traffic information.…”

Section: Driving Characteristic Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

View full text Add to dashboard Cite

Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

show abstract

Section: Driving Characteristic Predictionmentioning

confidence: 99%