Integrated Multi Component Prognostics for EV Systems Using GRU and LSTM

Abstract

The Integrated Multi Component Prognostics is a key technology for electric vehicle (EV) systems, and can be applied to improve reliability, reduce maintenance expenses and prevent sudden failures. In the present study, the focus is placed on the prediction of Remaining Useful Life (RUL) of EV motors and other components by deep learning models like Deep Learning based LSTM, GRU networks. It analyzes sensor data such as current, voltage, temperature, brake pressure and pad wear to find patterns of component degradation. The methodology is based on the time-series preprocessing, normalization, sequence generation and training of a recurrent neural network. Implementation and evaluation is performed using Python, TensorFlow/Keras, NumPy, Pandas and Scikit-learn. The experimental results show that both models are able to predict the degradation trends effectively while the GRU is more computationally efficient. The system can realize real-time monitoring and early fault detection, which provides strong foundation for future intelligent EV maintenance system.

Introduction

Traditional maintenance methods (reactive and scheduled) are inefficient because they either respond too late or replace parts unnecessarily. Predictive maintenance solves this by continuously analyzing sensor data to detect early signs of failure. With advancements in AI, machine learning, and deep learning, especially models like LSTM and GRU, it has become possible to predict degradation patterns and estimate the Remaining Useful Life (RUL) of EV components using time-series data.

The research problem focuses on developing an integrated system that can monitor EV components in real time, predict degradation, estimate RUL, and detect faults before failures occur. The motivation includes improving passenger safety, reducing unexpected breakdowns, lowering maintenance costs, and supporting intelligent transportation systems.

The objectives include building a multi-component prognostic system using LSTM and GRU models, processing sensor data, performing real-time prediction, comparing deep learning models, and providing maintenance recommendations.

The literature review shows that while traditional statistical and machine learning methods (like SVM, Random Forest, and ANN) have been used for predictive maintenance, they struggle with complex time-dependent behavior. Deep learning models like LSTM are effective for sequential data but computationally heavy, while GRU offers similar accuracy with faster and lighter performance, making it more suitable for real-time applications.

The research gap highlights that most existing work focuses on industrial systems rather than EV subsystems, lacks real-time multi-component prediction, and often does not combine efficiency with scalability.

The dataset includes EV motor and battery parameters such as current, voltage, and temperature, which are used to model degradation and estimate lifespan.

Conclusion

Integrated Multi-Component Prognostics for EV system presents a deep learning framework for predictive maintenance of electric vehicle systems using LSTM and GRU recurrent neural networks. The system processes sequential sensor data to estimate remaining useful life and predict fault occurrence. Experimental results show that RNNs can be used to learn time-series degradation patterns from EV sensor data. Both LSTM and GRU models provide accurate predictions, GRU having lower computational complexity and better real-time deployment capabilities. The real-time inference, multi-step forecasting and visualization further boosts the framework\'s practicality for intelligent transportation systems. The experimental results indicated that LSTM and GRU algorithms both achieved good performance for real-time and multi-step ahead predictions of wear and tear of components. In terms of performance, the GRU model was able to match the level of accuracy as the LSTM algorithm but with less computation and faster performance. This system will enhance the reliability of the vehicle and prevent failures, as well as allow proper scheduling of maintenance. The increasing significance of AI driven maintenance systems in improving the reliability, safety, and efficiency of EVs highlights their role in the future of automotive technology.

References

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Copyright

Copyright © 2026 Munakala Vasant, Janaki Kandasamy. 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.