Review of Artificial Intelligent Algorithms for Engine Performance, Control, and Diagnosis

Landry Frank Ineza Havugimana, Bolan Liu (), Fanshuo Liu, Junwei Zhang, Ben Li and Peng Wan
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Landry Frank Ineza Havugimana: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Bolan Liu: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Fanshuo Liu: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Junwei Zhang: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Ben Li: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Peng Wan: School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Energies, 2023, vol. 16, issue 3, 1-25

Abstract: This paper reviews the artificial intelligent algorithms in engine management. This study provides a clear image of the current state of affairs for the past 15 years and provides fresh insights and improvements for future directions in the field of engine management. The scope of this paper comprises three main aspects to be discussed, namely, engine performance, engine control, and engine diagnosis. The first is associated with the need to control the basic characteristics that prove that the engine is working properly, namely, emission control and fuel economy. Engine control refers to the ability to identify and fulfill the requirements derived from performance, emissions, and durability. In this part, hybrid electric vehicle (HEV) application and transient operations are discussed. Lastly, engine diagnosis entails assessment techniques that can be used to identify problems in the engine and solve them accordingly. In this part, misfire detection, knock detection, and intake system leakage will be evaluated. In engine performance, neural network algorithms provide efficient results in terms of emission control and fuel economy as the requirements are easily achievable. However, when it comes to engine control and diagnosis, the fuzzy logic rule with its strong robustness and neural networks algorithms are limited in efficiency due to the complex nature of the processes and the presence of big data, for instance, in HEVs in engine control. That has brought forward the usage of reinforcement learning and novel machine learning algorithms in recent years to maximize efficiency in engine control and engine diagnosis, as highlighted in the following part. The PRISMA methodology was used to justify the reference selection in this review.

Keywords: internal combustion engine; artificial intelligence algorithm; engine performance; engine control; engine diagnosis (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: 2023
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