Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines

Li, Ji; Zhou, Quan; Williams, Howard; Xu, Ping; Xu, Hongming; Lu, Guoxiang

doi:10.1016/j.apenergy.2022.118534

Cited by 7 publications

(2 citation statements)

References 42 publications

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“…Air mass flow rate was measured by means of a Sensycon hot wire anemometer, with a measurement error of 1%. Considering the diversity of experiment dataset (e.g., system noise and operators), engine experiment is carried out on the two testing benches for the same type dedicated hybrid engine [27] developed by BYD Auto Ltd. 7829 samples were collected at different steady engine load conditions of the operating range of the studied engine. The range of the operating points was as follows: 1000-6000 rpm for engine speed, 1.5 -135 N m for engine torque, and 0-100% for LP EGR positions.…”

Section: Methodsmentioning

confidence: 99%

Statistics-Guided Accelerated Swarm Feature Selection in Data-Driven Soft Sensors for Hybrid Engine Performance Prediction

Zhou

Williams

et al. 2023

IEEE Trans. Ind. Inf.

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The accurate prediction of soft sensors is essential for development of modern combustion engines to achieve better performance, lower emissions, and reduced fuel consumption. To precisely predict engine performance i.e., indicated thermal efficiency, volumetric efficiency, and fuel consumption rate of a hybrid engine, this paper proposes a novel data-driven approach of statistics-guided accelerated swarm feature selection to find the most effective features for engine soft sensors. Differing from the existing filter or wrapper feature selection approaches, this approach uses external measure information to direct velocity updates in the accelerated swarm feature selection. Several filter and wrapper methods were developed and comprehensively compared. The experimental dataset was collected from a BYD 1.5L gasoline engine. Validated by bench test, the results demonstrate that the proposed approach finds the most effective features and optimal network structure for datadriven performance prediction of the hybrid engine that was studied.

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Section: Methodsmentioning

confidence: 99%

Statistics-Guided Accelerated Swarm Feature Selection in Data-Driven Soft Sensors for Hybrid Engine Performance Prediction

Zhou

Williams

et al. 2023

IEEE Trans. Ind. Inf.

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“…Based on the key milestones of AI applications in automotive systems, we can categorize the development of AI into four levels as shown in Figure 1. For Level I, AI models are used to model the nonlinearity of the vehicle systems, e.g., engine performances [15]. The AI models are expected to assist in some R&D tasks, e.g., component sizing [16] and control calibration [17] if they have the capability of offline optima searching (Level II).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Its Roles in the R&D of Vehicle Powertrain Products

Zhou

2022

IJAMM

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Review Artificial Intelligence and Its Roles in the R&D of Vehicle Powertrain Products Quan Zhou , * , Ji Li , and Hongming Xu , * Birmingham CASE Automotive Research and Education Centre, University of Birmingham, Birmingham, B15 2TT UK * Correspondence: q.zhou@bham.ac.uk (Q.Z.); h.m.xu@bham.ac.uk (H.X.) Received: 5 October 2022 Accepted: 20 November 2022 Published: 25 December 2022 Abstract: Decarbonization requires global actions, and the transport sector is the main battlefield since it contributes more than 20% of CO 2 emissions. Vehicle electrification is an effective routine to reduce vehicle carbon emissions, but it increases the complexity of the vehicle systems, especially the powertrain systems. The rapid development of artificial intelligence (AI) is promoting the development of new automation technologies that can benefit the automotive industry. This paper reviews the key milestones of AI technology development for vehicle research and development (R&D) and highlights the advantage of AI-based methods in powertrain design and control. An outlook of future research directions will be discussed, and conclusions will be summarized.

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Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles

Zhang

Liu²,

Lei³

et al. 2022

Applied Energy

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Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines

Cited by 7 publications

References 42 publications

Statistics-Guided Accelerated Swarm Feature Selection in Data-Driven Soft Sensors for Hybrid Engine Performance Prediction

Statistics-Guided Accelerated Swarm Feature Selection in Data-Driven Soft Sensors for Hybrid Engine Performance Prediction

Artificial Intelligence and Its Roles in the R&D of Vehicle Powertrain Products

Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles

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