Markov velocity predictor based on state space optimization and its applications in PHEV energy management

Wang, Rong; He, Yanze; Song, Tinglun

doi:10.1177/09544070231152000

Proceedings of the Institution of Mechanical Engineers, Part D:

2023

DOI: 10.1177/09544070231152000

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Markov velocity predictor based on state space optimization and its applications in PHEV energy management

Rong Wang

Yanze He

Tinglun Song

Abstract: Predictive energy management (PEM) strategy has shown great advantages in improving fuel economy for plug-in hybrid electric vehicles (PHEV). A Markov velocity predictor optimization method and its applications in PHEV energy management is studied in this paper. The initial Markov velocity predictor is constructed using complete driving cycle information and the state space of the Markov velocity predictor is then optimized for specified driving conditions using simulated annealing algorithm (SAA). The practic… Show more

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2024

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Cited by 3 publications

References 34 publications

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Vehicle speed prediction using a convolutional neural network combined with a gated recurrent unit with attention

Zhang,

Wang,

Jiao

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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Vehicle speed prediction can facilitate many applications, such as optimizing vehicle propulsion systems and designing advanced driver assistance control systems. In a complex and variable traffic environment, many dynamic factors affect vehicle speed and make it difficult to predict accurately. The development of intelligent transportation systems and machine learning methods makes it possible to predict short-term vehicle speed accurately. A novel vehicle speed prediction model is proposed in this paper to improve prediction accuracy based on a deep learning method. A practical temporal and channel attention module (TCAM) is designed for convolutional neural networks (CNNs) to strengthen meaningful information and reduce the amount of unnecessary information. A gated recurrent unit (GRU) network with an attention mechanism is constructed to explore significant hidden relationships among time-series data with its memory function. These two subprediction models are fused to enhance the performance of vehicle speed prediction. Simulation experiments using IPG Carmaker software validate that the proposed model provides better predictive accuracy than traditional and existing vehicle speed prediction methods based on deep learning.

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Vehicle speed prediction using a convolutional neural network combined with a gated recurrent unit with attention

Zhang,

Wang,

Jiao

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

A dynamic competitive velocity prediction method based on Markov state space reconstruction

Wang,

Ti,

Shi

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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Predictive energy management (PEM) strategy has shown great advantages in improving fuel economy for plug-in hybrid electric vehicles (PHEVs). A key technology in PEM is velocity prediction and its accuracy greatly affects the effectiveness of a PEM strategy. This paper proposes a novel dynamic competitive velocity prediction method based on Markov state space (SS) reconstruction. The basic Markov model is introduced and its performance is fully evaluated. The Markov SS is designed by the K-Means++ clustering method to support online reconstruction. The transition probability matrix (TPM) is updated to adapt to the actual driving scenario. The dynamic competitive prediction method combines the basic and the reconstructed Markov models to achieve better performance. The velocity prediction performance is validated through repetitive complex driving conditions. Simulation result shows that the proposed method has superior performance in both prediction accuracy and computing time. For the complex driving condition scenario, the proposed method can reduce prediction error by 5.7%–9.1% comparing to the basic Markov model and its computing time is about 1% of that of LSTM when the prediction horizon is 5 s.

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A predictive energy management strategy for plug-in hybrid electric vehicles using real-time traffic based reference SOC planning

Wang,

Shi,

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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The fuel economy of plug-in hybrid electric vehicles (PHEVs) is strongly affected by the battery state of charge (SOC) depletion pattern. This paper proposes and studies a real-time traffic-based SOC reference planning method. The method uses a dataset to collect and capture real traffic information and then enriches the dataset using a data augmentation method developed in this paper. The augmented dataset is optimized by dynamic programing (DP) algorithm to obtain the optimal reference SOC for model training. The traffic information and optimal reference SOC are processed and used to train a long-short term memory (LSTM) neural network, which is used for online reference SOC planning. Finally, a predictive energy management (PEM) strategy is adopted to follow the SOC reference by optimizing instantaneous power allocation with the predicted velocities. Simulation results show that the proposed method outperforms the linear reference SOC planning method in both smooth and congested traffic scenarios.

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Markov velocity predictor based on state space optimization and its applications in PHEV energy management

Cited by 3 publications

References 34 publications

Vehicle speed prediction using a convolutional neural network combined with a gated recurrent unit with attention

Vehicle speed prediction using a convolutional neural network combined with a gated recurrent unit with attention

A dynamic competitive velocity prediction method based on Markov state space reconstruction

A predictive energy management strategy for plug-in hybrid electric vehicles using real-time traffic based reference SOC planning

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