Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network

Xuezhao Zhang, Zijie Chen (), Wenxiao Wang and Xiaofen Fang
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Xuezhao Zhang: Faculty of Mechanical and Electrical Engineering, Quzhou College of Technology, Quzhou 324000, China
Zijie Chen: School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China
Wenxiao Wang: Key Laboratory of River Basin Digital Twinning of Ministry of Water Resources, Macau University of Science and Technology, Macao 999078, China
Xiaofen Fang: Faculty of Mechanical and Electrical Engineering, Quzhou College of Technology, Quzhou 324000, China

Energies, 2024, vol. 17, issue 12, 1-21

Abstract: In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model’s prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R 2 value of 0.97 for unknown conditions.

Keywords: intelligent transportation; vehicle energy consumption modeling; optimized CNN-BiLSTM-Attention (OCBA); PHEV (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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