A Review of Intelligent Road Preview Methods for Energy Management of Hybrid Vehicles

Gu, Wenjin; Zhao, Dezong; Mason, Byron

doi:10.1016/j.ifacol.2019.09.104

Cited by 9 publications

(7 citation statements)

References 44 publications

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“…The simulation results indicate that high prediction accuracy of the vehicle velocity can improve the overall controlling performance of HEVs. In [29], a NN based trip model is proposed to help manage the energy distribution of PHEV, and simulation results validates that the built model can increase the prediction accuracy and contribute to the fuel economy improvement. Nonetheless, forecasting performance of NN models can be easily influenced by their fixed network structures, learning rates and inputs; and moreover, the evaluation metrics and the amount of collected training data can also affect the model precision.…”

Section: Introductionmentioning

confidence: 88%

Prediction of vehicle driving conditions with incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles

Liu

Gao

et al. 2021

Mechanical Systems and Signal Processing

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Prediction of short-term future driving conditions can contribute to energy management of plug-in hybrid electric vehicles and subsequent improvement of their fuel economy. In this study, a fused short-term forecasting model for driving conditions is established by incorporating the stochastic forecasting and machine learning. The Markov chain is applied to calculate the transition probability of historical driving data, by which the stochastic prediction is conducted based on the Monte Carlo algorithm. Then, a neural network is employed to learn the current driving information and main knowledge after the simplified correlation of characteristic parameters, and meanwhile the genetic algorithm is adopted to optimize the initial weight and thresholds of networks. Finally, the short-term velocity prediction is achieved by combining them, and the overall performance is evaluated by four typical criteria. Simulation results indicate that the proposed fusion algorithm outperforms the single Markov model, the radial basis function neural network and the back propagation neural network with respect to the prediction precision and the difference distribution between expectation and prediction values. In addition, a case study is conducted by applying the built prediction algorithm in energy management of a plug-in hybrid electric vehicle, and simulation results highlight that the proposed algorithm can supply preferable velocity prediction, thereby facilitating improvement of the operating economy of the vehicle.

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Section: Introductionmentioning

confidence: 88%

Prediction of vehicle driving conditions with incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles

Liu

Gao

et al. 2021

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

show abstract

“…Roads include urban roads, highways, rural roads and some functional roads such as ramps ( Li et al., 2019b ), intersections, transitional sections, sharp turns ( Liu et al., 2018b ; Qian et al., 2020 ; Yeon et al., 2019 ). The road properties (i.e., slope ( Gu et al., 2019a ), super elevation, curvature and roughness), environmental attributes (e.g., cluster, schools, hospitals) and traffic rules (e.g., speed hump, speed limits, lane properties) have an impact on speed ( Shin and Sunwoo, 2019 ).…”

Section: Definitions and Preliminariesmentioning

confidence: 99%

A comprehensive study of speed prediction in transportation system: From vehicle to traffic

et al. 2022

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“…In the research regarding EMSs, the energy management issues can be transformed into a cost function problem with different optimization goals (Gu et al, 2019). Generally, the energy management issues can be transformed into the energy management issue of the SOC, the energy management issue of equivalent fuel consumption, the energy manage-ment issue of the instantaneous condition, and the energy management issue of emissions.…”

Section: Research On Energy Management Issuesmentioning

confidence: 99%

“…With the continuous development of the ITS and IoV technology, the application of intelligent traffic data information to the optimization control of HEV energy management has become a hot topic for scholars (Gu et al, 2019). Generally speaking, ITS-based EMSs use two methods: the first method is based on using static data (e.g., navigation information and historical traffic data) to predict driving conditions in the predicted time domain to realize energy management, whereas the second method is to monitor and predict the behavior of nearby vehicles, traffic signals, and traffic participants according to the dynamic real-time traffic information provided by IoV in order to find the optimal speed to achieve energy management.…”

Section: The Ems Based On Intelligent Transportation Systemsmentioning

confidence: 99%

Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

Zhu

Liu

et al. 2022

Mech. Sci.

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Abstract. In order to prevent the aggravation of global environmental problems, all industries are facing the challenge of green development. In the automotive field, the development of “new-energy vehicles” (plug-in electric vehicles) is particularly necessary. Hybrid electric vehicles (HEVs) have been proven to be an efficient way of solving environmental and energy problems. As the core of HEVs, the energy management strategy (EMS) plays an important role in fuel economy, power performance, and drivability. However, considering the randomness of actual driving conditions, there are great challenges involved in the establishment of an EMS. Therefore, it is critical to develop an efficient and adaptable EMS. This paper presents a systematic review of EMSs for HEVs. First, different issues that can affect the performance of EMSs are summarized. Second, recent studies on EMSs for HEVs are reviewed. Third, the advantages and disadvantages of different categories of EMSs are compared in detail. Finally, promising EMS research topics for future study are put forward.

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A Review of Intelligent Road Preview Methods for Energy Management of Hybrid Vehicles

Cited by 9 publications

References 44 publications

Prediction of vehicle driving conditions with incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles

Prediction of vehicle driving conditions with incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles

A comprehensive study of speed prediction in transportation system: From vehicle to traffic

Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

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