The paper provides a review of modern diagnostic methods for the technical condition of gasoline internal combustion engines based on the analysis of exhaust gas composition. The paper is devoted to solving the problem of increasing the efficiency and accuracy of determining the parameters of the engine operation using non-invasive monitoring methods. The paper uses the method of a systematic review of literary sources for the period of 2000-2025. The classification of diagnostic approaches by type of measuring technology is carried out: traditional gas analyzers, spectroscopic methods (FTIR, NDIR), portable PEMS complexes, on-board sensor systems and intelligent algorithms based on machine learning. The scientific novelty consists in summarizing modern trends in the development of gas analytical diagnostics of gasoline internal combustion engines and identifying trends in the transition from periodic monitoring to continuous monitoring of technical condition. The results of the analysis show that the use of spectroscopic and intelligent methods improves the accuracy of determining the concentrations of exhaust components and allows detecting malfunctions at an early stage. It is concluded that it is necessary to integrate such systems with electronic control units and develop domestic portable diagnostic systems.
diagnostics, analysis, engine, FTIR spectroscopy, emissions, technologies, on-board OBD-II systems, training, environmental standards
1. Riegel J, Neumann H, Wiedenmann H-M. Exhaust Gas Sensors for Automotive Emission Control. Solid State Ionics. 2002;152–153:783–800. DOI:https://doi.org/10.1016/S0167-2738(02)00329-6.
2. Moos R. Catalysts as Sensors-A Promising Novel Approach in Automotive Exhaust Gas Aftertreatment. Sensors. 2010;10(7):6773–6787. DOI:https://doi.org/10.3390/s100706773.
3. Moos R. A Brief Overview on Automotive Exhaust Gas Sensors Based on Electroceramics. International Journal of Applied Ceramic Technology. 2005;2:401–413. DOI:https://doi.org/10.1111/j.1744-7402.2005.02041.x.
4. Giechaskiel B, Clairotte M. Fourier Transform Infrared (FTIR) Spectroscopy for Measurements of Vehicle Exhaust Emissions: Review. Applied Sciences. 2021;11(16):7416. DOI:https://doi.org/10.3390/app11167416.
5. Fuśnik Ł, Szafraniak B, Paleczek A, Grochala D, Rydosz A. A Review of Gas Measurement Set-Ups. Sensors. 2022;22(7):2557. DOI:https://doi.org/10.3390/s22072557.
6. Bante S, Karale S, Awari G. A Systematic Review on Real Time Exhaust Gas Sensing System for On Board Sensing of Harmful Gases in IC Engine. IOP Conference Series, 2021: Materials Science and Engineering. 2021;1170:012012. DOI:https://doi.org/10.1088/1757-899X/1170/1/012012.
7. Awad OI, Ma X, Kamil M, Ali OM, Zhang Z, Shuai S. Particulate Emissions from Gasoline Direct Injection Engines: Review. Science of the Total Environment. 2020;718:137302. DOI:https://doi.org/10.1016/j.scitotenv.2020.137302.
8. Giechaskiel B. Assessment of On-Board and Laboratory Gas Measurement Systems for Future Heavy-Duty Emissions Regulations. International Journal of Environmental Research and Public Health. 2022;19(10):6199. DOI:https://doi.org/10.3390/ijerph19106199.
9. Kousoulidou M, Fontaras G, Ntziachristos L, Bonnel P, Samaras Z. Use of Portable Emissions Measurement System (PEMS) for Regulatory Purposes: The EU Perspective. Atmospheric Environment. 2013;64:329–338. DOI:https://doi.org/10.1016/j.atmosenv.2012.09.062.
10. Lee S. On-Road Portable Emission Measurement Systems Test Data Analysis and Light-Duty Vehicle In-Use Emissions Development. SAE International Journal of Electric Vehicles. 2020;9(2):111–131. DOI:https://doi.org/10.4271/14-09-02-0007.
11. Kommersant. 2025 [Internet]. [cited 2025 Nov 30]. Available from: https://www.kommersant.ru/doc/6427198
12. Kochanek A. The Analysis of Exhaust Composition Serves as the Foundation for the Development of a Diagnosing Methodology of Internal Combustion Engines. Sustainability. 2025;17(8):3420. DOI:https://doi.org/10.3390/su17083420.
13. Dzieniszewski G, Kubon M, Zieliński D. Assessment of the Possibility of Using Exhaust Gas Composition in Predicting the Technical Condition of the Engine. Journal of Research and Applications in Agricultural Engineering. 2025. DOI:https://doi.org/10.53502/jraae-199729.
14. Steiner C. Catalyst State Diagnosis of Three-Way Catalytic Converters Using Different Resonance Parameters with Special Focus on Amplitude and Phase. Sensors (Basel). 2019;19(16):3559. DOI:https://doi.org/10.3390/s19163559.
15. Salehi R, Alasty A, Shahbakhti M. Detection and Isolation of Faults in the Exhaust Path of Turbocharged Automotive Engines. International Journal of Automotive Technology. 2015;16:127–138. DOI:https://doi.org/10.1007/s12239-015-0014-5.
16. Tamura M, Saito H, Murata Y, Kokubu K, Morimoto S. Misfire Detection on Internal Combustion Engines Using Exhaust Gas Temperature in Combination with Crankshaft Angle Variation. Applied Thermal Engineering. 2011;31:4125–4131. DOI:https://doi.org/10.1016/j.applthermaleng.2011.08.026.
17. Torres NNS. Fault Diagnosis in Internal Combustion Engines Using Artificial Intelligence Predictive Models. Applied System Innovation. 2025;8(5):147. DOI:https://doi.org/10.3390/asi8050147.
18. Michailidis ET, Panagiotopoulou A, Papadakis A. A Review of OBD-II-Based Machine Learning Applications for Sustainable, Efficient, Secure, and Safe Vehicle Driving. Sensors (Basel). 2025;25(13):4057. DOI:https://doi.org/10.3390/s25134057.
19. Miranda MHR. Novel Prediction Approach for Exhaust Gases Using Elman Neural Network Combined with Particle Swarm Optimization. Energy. 2025;331(C).
20. Baghani A, Chitsaz I, Teymoori MM. A Novel Method for Real Driving Emission Prediction Utilizing an Artificial Neural Network. Engineering Applications of Artificial Intelligence. 2024;137:109267.
21. Pugazhendi P, Albuquerque C, Mendes A, Vanaja S, Pichandi C, Raja M. Investigations on the Applicability of Machine Learning Algorithms for Predicting Emissions and Performance of Ethanol–Water-Fueled Spark-Ignition Engines. Combustion Theory and Modelling. 2025:1–28. DOI:https://doi.org/10.1080/13647830.2025.2552850.
22. Workman J. A Review of the Latest Research Applications Using FT-IR Spectroscopy. Spectroscopy. 2024:22–28. DOI:https://doi.org/10.56530/spectroscopy.ak9689m8.
23. Ménil F, Coillard V, Lucat C. Critical Review of Nitrogen Monoxide Sensors for Exhaust Gases of Lean Burn Engines. Sensors and Actuators B: Chemical. 2000;67:1–23. DOI:https://doi.org/10.1016/S0925-4005(00)00401-9.
24. López Méndez Á. Characterization of NOx Sensor Performance for On-Board Diagnosis [dissertation]; 2023.
25. Arulogun OA, Fakolujo A, Waheed MA, Omidiora E, Ogunbona P. Characterization of Gasoline Engine Exhaust Fumes Using Electronic Nose. Global Journal of Research Engineering. 2011;11.
26. Zeng W. Transfer Learning for Transient NOx, PN and THC Emission Prediction of Non-Road Diesel Engines Based on NRTC Experiments. Environmental Science: Processes and Impacts. 2025;27(10):3272–3285. DOI:https://doi.org/10.1039/d5em00321k.
27. Best Exhaust 5 Gas Analyzer for Automotive [Internet]. [cited 2025 Nov 12]. Available from: https://www.forensicsdetectors.com/blogs/articles/small-engine-exhaust-gas-analyzer
28. Konchakov AA. Analysis of exhaust gases from internal combustion engines of automobiles: guideline. 2020.
29. BEA Exhaust Gas Analysis [Internet]. [cited 2025 Nov 12]. Available from: https://simplepage.narod.ru/eva/catalog/bosch/bea.pdf
30. Harach T. Novel Method for Determining Internal Combustion Engine Dysfunctions on Platform as a Service. Sensors (Basel). 2023;23(1):477. DOI:https://doi.org/10.3390/s23010477.
31. Brandt E, Wang Y, Grizzle J. A Simplified Three-Way Catalyst Model for Use in On-Board SI Engine Control and Diagnostics. Proc. ASME Dynamic System and Control Division. 2000;61.
32. Fremerey P. Determination of the NOx Loading of an Automotive Lean NOx Trap by a Microwave-Based Method. Sensors (Basel). 2011;11(9):8261–8280. DOI:https://doi.org/10.3390/s110908261.
33. Lyu P, Wang P, Liu Y, Wang Y. Review of the Studies on Emission Evaluation Approaches for Operating Vehicles. Journal of Traffic and Transportation Engineering. 2021;8(4):493–509. DOI:https://doi.org/10.1016/j.jtte.2021.07.004.
34. He P, Li Y, Wang J. Study on the Exhaust System Parameters of a Small Gasoline Engine. SAE Technical Paper. 2008. DOI:https://doi.org/10.4271/2008-01-1766.
35. Rodrigues NF. Misfire Detection in Automotive Engines Using a Smartphone Through Wavelet and Chaos Analysis. Sensors (Basel). 2022;22(14):5077. DOI:https://doi.org/10.3390/s22145077.
36. Lee B, Guezennec Y, Rizzoni G. Model-Based Fault Diagnosis of Spark-Ignition Direct-Injection Engine Using Nonlinear Estimations. SAE Transactions. 2005;114:190–200.
37. Romahadi D. Bayesian Networks Approach on Intelligent System Design for the Diagnosis of Heat Exchanger. SINERGI. 2022;26:127–136. DOI:https://doi.org/10.22441/sinergi.2022.2.001.
38. Ognev II, Ognev IG, Pyataev MV. Analysis of exhaust gas toxicity standards for diesel engine vehicles. Collection of Scientific Papers, 2021: Innovative Development of Land Transport Equipment and Technologies; Ural University. Yekaterinburg: Publishing House of Ural University; 2021.
39. Grigoriev MV, Zenchenko VA. Computer diagnostics of EJ253 engine on a Subaru Out-back car: educational guideline. Moscow: Moscow Automobile and Road Construction State Technical University (MADI); 2022.
40. Zaskin DV, Popov DM. Improvement of repair process of internal combustion engines and aggregates with the development of test rig for disassembly and assembly of internal combustion engines. In: Catalog of Final Papers of Kuzbass State Agrarian University – 2023. Kemerovo: Kuzbass State Agrarian University; 2023.
41. Which Emissions Does a 5 Gas Analyzer Read Automotives [Internet]. [cited 2024 April 27]. Available from: https://www.globalmrv.com/which-emissions-does-a-5-gas-analyzer-read-automotives/?utm
42. HC, CO, CO₂, O₂, NO Emission Analyzer Gasoline and Diesel Exhaust Gas Analyzer [Internet]. [cited 2024 April 27]. Available from: https://www.hkrok.com/products/hc-co-co2-o2-no-emission-analyzer-gasoline-and-diesel-exhaust-gas-analyzer/?utm.
