AI-Driven Vehicle Efficiency and Performance Evaluation System

Abstract

Vehicleefficiencyandperformanceevaluationarecriticalfactorsinmoderntransportationsys- tems,directlyinfluencing fuel consumption,emissions,safety,andsustainability. Traditionalevaluationtech- niques rely heavily on manual testing and fixed-rule analysis, which are time-consuming and limited in their abilitytoadapttodynamicdrivingconditions. ThispaperproposesanAI-DrivenVehicleEfficiencyandPer- formance Evaluation System that leverages machine learning techniques to analyze real-time and historical vehicledata. Thesystemintegratessensordata,enginecontrolunit(ECU)parameters,anddrivingbehavior metrics to predict efficiency trends and overall vehicle performance.By employing intelligent analytics, the proposed framework enables accurate performance prediction, efficiency optimization, and data-driven de- cision support for drivers and automotive engineers.Experimental results demonstrate improved prediction accuracyandactionableinsights compared totraditiona levaluationapproaches,contributingtosustainable and intelligent transportation systems.

Introduction

The text presents a comprehensive AI-driven framework for vehicle efficiency and performance evaluation aimed at overcoming the limitations of traditional laboratory-based and rule-based assessment methods. Conventional techniques such as chassis dynamometer testing and fixed-threshold diagnostics, while standardized, are costly, time-consuming, and unable to capture real-world driving variability related to driver behavior, traffic, road conditions, and environment.

With the growth of onboard sensors, ECUs, GPS, and CAN bus systems, modern vehicles generate large volumes of operational data. The effective use of this data requires intelligent analytics. Artificial Intelligence (AI) and Machine Learning (ML) enable adaptive, data-driven evaluation by identifying complex patterns, predicting performance trends, estimating fuel efficiency, and analyzing emission behavior more accurately than traditional methods.

The proposed system integrates real-time multi-source data acquisition, preprocessing, feature extraction, and supervised ML models such as Random Forests and Artificial Neural Networks. These models generate efficiency scores, performance indicators, anomaly detection, and predictive alerts. A modular system architecture supports continuous learning through feedback loops, enabling scalability for individual vehicles and large fleets.

Experimental results demonstrate that the AI-driven approach outperforms traditional methods, achieving higher prediction accuracy (88% vs. 72%), improved fuel efficiency estimation across driving conditions, reduced emission trends, and strong real-time scalability. Overall, the framework provides an intelligent decision-support platform that enhances vehicle performance, reduces fuel consumption and emissions, and contributes to smarter, cleaner, and more sustainable transportation systems.

Conclusion

ThispaperpresentedanAIDrivenVehicleEfficiencyandPerformanceEvaluationSystemthatleveragesmachinelearningtechniquestodeliveraccurate,scalable,andintelligentvehicleanalytics.ByintegratingmultisourcevehiculardatawithadvancedAImodels,theproposedsystemeffectivelyovercomesthelimitationsof traditional performance evaluation approaches that rely on static testing and manual analysis. Theproposedframeworkenablescomprehensiveassessmentofvehicleefficiencyandoperationalperfor- mancebyanalyzingreal-timesensordata,extractingmeaningfulfeatures,andgeneratingpredictiveinsights. ExperimentalresultsdemonstratethattheAI-driven approach significantly improvesperformanceprediction accuracy and provides more reliable efficiency evaluation compared to conventional methods.The ability to capture non-linear relationships and adapt to diverse driving conditions makes the system suitable for real- world automotive applications. Beyond accuracy improvements, the system contributes to sustainable transportation by supporting fuel optimization, emission reduction, and proactive maintenance.The decision-support outputs in the form of dashboardsandanalyticalreportsassistdrivers,fleetoperators,andautomotiveengineersinmakinginformed decisions that enhance vehicle longevity and operational efficiency.The modular architecture also ensures scalability, allowing the framework to be deployed across individual vehicles as well as large fleet environ- ments. Future work will focus on extending the system toward real-time edge deployment to reduce latency and improveresponsivenessindynamicdrivingscenarios. Additionalresearchdirectionsincludeintegrationwith electric vehicle (EV) analytics, incorporation of advanced deep learning and reinforcement learning models, and support for connected and autonomous vehicle ecosystems.These enhancements will further strengthen the role of AI-driven evaluation systems in intelligent transportation and sustainable mobility.

References

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Copyright

Copyright © 2026 A. Ravi Kumar, A. Jitendra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.