Fusion AI: Consensus Driven Multimodal Models and Autonomous Agents for Fault Tolerant Energy Storage Management

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Bapu Dada Kokare, Sanjay A. Deokar, Mangesh Kale, Ravikant Nanwatkar

10.14445/23488379/IJEEE-V12I7P102

How to Cite?

Bapu Dada Kokare, Sanjay A. Deokar, Mangesh Kale, Ravikant Nanwatkar, "Fusion AI: Consensus Driven Multimodal Models and Autonomous Agents for Fault Tolerant Energy Storage Management," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 7, pp. 6-17, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I7P102

Abstract:

Rapid Energy Storage Systems (ESS) penetration in Electric Vehicles (EVs), smart grid, and renewable energy applications demands robust, intelligent, and fault-tolerant control algorithms. This paper proposes a new energy storage management framework with Fusion AI that combines the consensus-driven multimodal models and decentralized Multiagent Systems (MAS). The goal is to monitor both the integrity of the system and its operation, in order to guarantee system reliability, safety and performance, making use of real-time information coming from thermal, electrical, structural and vision sensors. Fusion AI refers to the combination of AI models, which include Feedforward Neural Networks (FNN), Random Forests (RF), and Long Short-Term Memory Networks (LSTM), trained from multiple modalities. These models cooperate through consensus mechanisms in order to provide reliable and accurate predictions, overcoming challenges such as sensor faults, sensitivity to noise, and anomalous data. The multi-modal fusion approach enables end-to-end monitoring of ESS metrics, including SOC, SOH, thermal performance, etc. The incorporation of autonomous agents provides more intelligence so that ESS can be distributed and adaptively controlled. These agents learn, consult, and act on their own, providing real-time checking of errors, reconfiguration and optimization. The system increases fault-tolerance and accuracy by comparing predictions, resolving discrepancies and tuning optimal model mixtures. Experimental validation with lithium ion battery aging data on urban driving cycles shows that the prediction accuracy is 96.30%, F1 score 0.958, and Fault prediction Success Rate 96.1% which is 6.69% greater than that from standalone models, with different levels of reduction in RMSE and false positives by 18%. The gain over the best single model (XGBoost) was about 1.2% accuracy and 1.3% F1-score. This work opens a way for a smart, green, and low-cost energy storage administration of the advanced EV systems at a large scale.

Keywords:

Energy storage systems, Fault-tolerant control algorithms, Fusion AI, Feedforward Neural Networks, Random forests, Long Short-Term Memory networks.

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