ML and IoT Based Smart Vehicle Health Monitoring and Predictive Maintenance System

Abstract

Maintaining modern vehicles in today’s fast-paced and technology-driven environment is increasingly complex, especially as vehicle systems grow more sophisticated. Traditional maintenance practices often rely on fixed intervals, which may lead to either premature servicing or delayed interventions, both of which can compromise vehicle performance and safety. This research proposes a ML & IoT Based smart car maintenance framework that leverages Machine Learning (ML) and Internet of Things (IoT) technologies to deliver a predictive and automated solution for vehicle health management. The primary focus is on monitoring and forecasting engine oil degradation, a critical factor in engine longevity and performance. IoT-enabled sensors embedded within the vehicle continuously collect real-time data such as mileage, engine temperature, oil viscosity, and driving patterns. This data is processed by an ML-based predictive model trained to accurately estimate the remaining useful life of the engine oil. Once predefined thresholds are reached, the system autonomously schedules a service appointment through a connected mobile application, ensuring timely and efficient maintenance without user intervention. By enabling condition-based maintenance, the system reduces unnecessary servicing, lowers operational costs, prevents mechanical failures, and enhances the overall driving experience. The proposed solution represents a significant step toward intelligent, connected, and sustainable automotive maintenance systems.

Introduction

1. Background & Motivation

The automotive industry has evolved rapidly with the rise of electronics, software, and telematics, yet vehicle maintenance remains largely reactive, relying on scheduled checks or post-failure repairs. These methods are inefficient, causing high costs and breakdowns—about 30% of failures are due to delayed diagnostics.

To address this, the project proposes an IoT and ML-based system for real-time health monitoring and predictive maintenance of vehicles.

2. Key Innovations

• Modular and scalable architecture supports integration with EVs, autonomous systems, and V2X.

• Edge computing (Arduino/Raspberry Pi) enables on-device diagnostics with low latency.

• Multi-sensor fusion for comprehensive health analysis (e.g., oil quality, fuel, engine temp).

• ML algorithms (e.g., Isolation Forest, LSTM) provide predictive maintenance.

• Cloud backend (Firebase) stores data and predictions securely and in real time.

• Flutter-based Android app delivers alerts, dashboards, and automatic service scheduling.

3. System Architecture

A. Sensor Layer
Real-time data collection using sensors for:

• Engine temperature

• Battery voltage

• Fuel level

• Radiator water level/quality

• Oil quality and level

• Vehicle speed

B. Microcontroller (Arduino/Raspberry Pi)

• Aggregates and formats sensor data.

• Sends data to the cloud via REST API.

C. Cloud Backend (Firebase)

• Stores raw and processed data.

• Handles user authentication and device communication.

D. Machine Learning Scripts

• Analyze sensor data to predict:

  • Engine oil life

  • Drivable kilometers based on fuel

  • Optimal time for service booking

E. Mobile Applications

• User App: Displays vehicle health, allows service booking, and tracks service status.

• Service Center App: Receives alerts, manages diagnostics, uploads progress, and generates bills.

4. Key ML Models

A. Oil Life Prediction Model

• Uses regression algorithms to forecast when engine oil should be changed.

• Input: oil temp, viscosity, mileage, engine speed/load.

B. Fuel Range Estimation Model

• Predicts remaining kilometers based on fuel level and driving behavior.

C. Service Scheduling Model

• Classifies when service is due using trends from temperature, mileage, oil health, etc.

5. Literature Review Insights

• Previous systems focus on isolated issues (e.g., engine temp, oil level).

• Common gaps:

  • Lack of multi-sensor integration.

  • No smart service scheduling.

  • Limited mobile or user engagement.

  • No inventory sync with service centers.

6. Benefits of the System

• Predictive Maintenance reduces unexpected breakdowns.

• Real-Time Monitoring of vehicle vitals improves user safety.

• User Autonomy through mobile alerts and booking.

• Fleet Management potential for large operators.

• Eco-Sustainability by reducing unnecessary replacements.

• Data Privacy & Reduced Latency due to local processing and no third-party dependency.

7. Applications

• Private car owners

• Fleet operators

• Service stations

• Future use with EVs and autonomous vehicles

8. Future Scope

• Integration with smart part inventories

• EV-specific parameters

• Advanced driving behavior analytics

• Broader V2X communication features

Conclusion

This research presents the conceptual framework and design of a ML & IoT Based Smart Car Maintenance System that integrates Internet of Things (IoT) sensors with Machine Learning (ML) models to provide intelligent, real-time monitoring and predictive maintenance for vehicles. The system is designed to collect and analyze key vehicle parameters such as engine oil quality, fuel level, temperature and battery voltage. These inputs are used to generate meaningful insights, such as predicting the remaining life of engine oil, estimating fuel-based travel range, and automating service appointment bookings. The final outcome of this project is a smart, interconnected system that empowers vehicle owners with advanced diagnostics, timely alerts, and maintenance automation. By utilizing a user-friendly mobile application and cloud-based services like Firebase or AWS, the system offers a seamless experience for monitoring vehicle health and scheduling services without manual intervention.

References

[1] R. Patel and J. Joshi, \"Vehicle Maintenance Using IoT,\" International Journal of Engineering Research & Technology (IJERT), vol. 11, no. 2, pp. 45–48, Feb. 2022. [2] A. Mohammad, R. Kumar, and M. F. Ansari, \"Cloud-Based Automotive Diagnostic System for Preventive Maintenance Using IoT,\" International Journal of Scientific Research in Engineering and Management (IJSREM), vol. 5, no. 8, pp. 1–7, Aug. 2021. [3] S. Joshi and R. Mehta, \"An IoT-Based Predictive Connected Car Maintenance,\" International Journal of Computer Applications Technology and Research (IJCATR), vol. 10, no. 1, pp. 1–5, Jan. 2021. [4] M. Prajapati and M. Pathan, \"Intelligent Vehicular Maintenance System using IoT,\" International Journal of Computer Applications (IJCA), vol. 175, no. 18, pp. 20–24, Oct. 2020. [5] K. Singh and P. Kumar, \"Predictive Maintenance using IoT: A Case Study on Transportation,\" International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 9, no. 3, pp. 150–154, Jan. 2020.

Copyright

Copyright © 2025 Mrs. S. G. Bavachkar, Pratik M. Sarwade, Shreyash S. Limbikai, Tabish L. Narwade, Noris I. Shaikh. 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.