Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles

Hao Zhang, Nuo Lei, Shang Liu, Qinhao Fan and Zhi Wang

Energy, 2023, vol. 283, issue C

Abstract: Powertrain electrification incorporating advanced combustion-based dedicated hybrid engines (DHEs) is an effective and affordable approach to automotive energy saving. To explore the concealed fuel-saving potential of connected plug-in hybrid electric vehicles (CPHEVs) and manage engine dynamics, a data-driven predictive energy consumption minimization strategy (D-PECMS) is proposed in a hierarchical framework. The strategy relies on multi-source trip information provided by advanced driving assistance systems (ADAS) combined with maps and realizes power demand prediction by designing a multivariable long-term and short-term memory (M-LSTM) network. The upper level adopts dynamic programming (DP) to realize SOC planning, while the bottom layer utilizes D-PECMS to achieve computationally-efficient energy management with the engine combustion process being regulated in transient. This strategy is featured with predictive SOC tracking ability with less computational burden and look-ahead engine start-stop control. To ensure the credibility of validation, bench test data are used from a high-efficiency spark-induced compression ignition (SICI) engine to model the CPHEV, and the real-world driving scenarios are reconstructed based on real-time traffic data collected in China. The proposed D-PECMS strategy is tested through comprehensive experiments and compared against both the adaptive ECMS and offline DP. The results demonstrate that the proposed strategy effectively reduces fuel consumption by 3.1% and 13.2% in contrast to the adaptive ECMS and rule-based control respectively. Moreover, the D-PECMS strategy successfully avoids frequent engine operation mode switching as well as engine startup and shutdown.

Keywords: Connected plug-in hybrid electric vehicle; Predictive energy consumption minimization strategy; Power demand prediction; Dedicated hybrid engine; Predictive engine dynamic control (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:283:y:2023:i:c:s0360544223019084

DOI: 10.1016/j.energy.2023.128514

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