ARIMA-Based Road Gradient and Vehicle Velocity Prediction for Hybrid Electric Vehicle Energy Management

Guo, Jinquan; He, Haibo; Sun, Chao

doi:10.1109/tvt.2019.2912893

Cited by 116 publications

(39 citation statements)

References 30 publications

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“…records the error. A comprehensive testing error is the average value of testing errors with different driving cycles, which could be calculated as Equation (6)…”

Section: Analysis Of Training Resultsmentioning

confidence: 99%

“…The predicted velocity is of great significance to vehicle intelligence, safety assisted driving and vehicle dynamics control [1,2]. Especially in the field of hybrid electric vehicles, a suitable velocity prediction model is very helpful for performing energy-saving optimization in the framework of model predictive energy management [3][4][5][6]. Specifically, the concept of velocity prediction in this paper mainly refers to predicting short-term future velocity during a city bus's driving mission.…”

Section: Introductionmentioning

confidence: 99%

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City buses’ future velocity prediction for multiple driving cycle: A meta supervised learning solution

Cao

Cui³

2021

IET Intelligent Trans Sys

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Vehicular velocity prediction is of great significance to intelligent transportation system, as it provides a possible future velocity sequence for vehicle's decision-making system. A velocity prediction method via meta learning is proposed, which provides an adaptive and generative framework for multiple-driving cycles. The prediction model is devised using a deep neural network structure. The model's training is performed by the recently proposed meta-supervised learning, which ensures that one trained model could meet the adaptability to multiple driving cycles. The complete framework consists of three parts: Pre-training, fine-tune-training and real-time prediction, which is tested to predict the hybrid electric city buses' future velocity in a variable traffic scenario. The average prediction accuracy of 3, 5 and 10 s horizons is 0.51, 0.63 and 0.88 m s −1 , which is 25.9%, 16.78% and 7.47% higher than that trained by the conventional supervised learning method. As suggested, the proposed prediction method is effective and could meet the requirement of energysaving control for hybrid electric city buses. With further study, potential application of this method may also exist in the field of driving behaviour prediction and transportation mode recognition. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…records the error. A comprehensive testing error is the average value of testing errors with different driving cycles, which could be calculated as Equation (6)…”

Section: Analysis Of Training Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

City buses’ future velocity prediction for multiple driving cycle: A meta supervised learning solution

Cao

Cui³

2021

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

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“…The study of prediction task is an important part of the field of time series. Traditional time series prediction models can be divided into two categories: mathematical statistics based prediction methods such as ARIMA [35], and nonlinear prediction methods such as chaotic model [36].…”

Section: Air Traffic Flow Prediction Methodsmentioning

confidence: 99%

Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Gui

Zhou

Wang

et al. 2020

IEEE Trans. Veh. Technol.

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Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft aggravate the burden of the ATFM. Thanks to the advanced automatic dependent surveillance-broadcast (ADS-B) technique, the aerial vehicles can be tracked and monitored in a real-time and accurate manner, providing possibility for establishing a more intelligent ATFM architecture. In this paper, we first form an aviation big data platform by using the distributed ADS-B ground stations and the obtained ADS-B messages. By exploring the constructed dataset and mapping the extracted information to the routes, the air traffic flow between different cities can be counted and predicted, where the prediction task is implemented on the basis of two machine learning methods, respectively. The experimental results based on real-world data demonstrate that the proposed traffic flow prediction model adopting long shortterm memory (LSTM) can achieve better performance, especially when abnormal factors in traffic control are considered.

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“…Consequently, real-time optimization adaptive to dynamic driving conditions is challenging [6] (2) Battery durability. Compared with the constant load conditions, the durability of fuel batteries tends to be significantly reduced under a dynamic loading condition [7]. Moreover, frequent charging and discharging process, switching voltages, and the flow of energy tend to accelerate battery aging…”

Section: Introductionmentioning

confidence: 99%

Advanced Power Management and Control for Hybrid Electric Vehicles: A Survey

Jiang

Chen

et al. 2021

Wireless Communications and Mobile Computing

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With the trend of low emissions and sustainable development, the demand for hybrid electric vehicles (HEVs) has increased rapidly. By combining a conventional internal combustion engine with one or more electric motors powered by a battery, HEVs have the advantages over traditional vehicles in better fuel economy and lower tailpipe emissions. Nevertheless, the power management strategies (PMSs) for conventional vehicles which mainly focus on the efficiency of internal combustion engine are no longer applicable due to the complex internal structure of HEVs. Hence, a large number of novel strategies appropriate for HEVs have been surveyed, but most of the researches concentrate on discussing the classifications of PMSs and comparing their cons and pros. This paper presents a comprehensive review of power management strategies adopted in HEVs aiming at specific challenges for the first time. The categories of the existing PMSs are presented based on the different algorithms, followed by a brief study of each type including the analysis of its pros and cons. Afterwards, the implementation and optimization of power management strategies aiming at proposed challenges are introduced in detail with the description of their optimization objectives and optimized results. Finally, future directions and open issues of PMSs in HEVs are discussed.

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ARIMA-Based Road Gradient and Vehicle Velocity Prediction for Hybrid Electric Vehicle Energy Management

Cited by 116 publications

References 30 publications

City buses’ future velocity prediction for multiple driving cycle: A meta supervised learning solution

City buses’ future velocity prediction for multiple driving cycle: A meta supervised learning solution

Machine Learning Aided Air Traffic Flow Analysis Based on Aviation Big Data

Advanced Power Management and Control for Hybrid Electric Vehicles: A Survey

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