A hybrid CNN–LSTM-attention network for adaptive energy management of battery–supercapacitor systems in electric vehicles

Abstract

Integrating renewable energy and advanced storage and management systems are vital solutions to overcome variability, reliability, and efficiency issues in modern power systems. This article introduces the intelligent energy management system (IEMS) which integrates photovoltaic (PV) generation, lithium-ion batteries (Li-ion) and supercapacitors (SCs) are controlled by a CNN–LSTM-attention mechanism. The hybrid configuration leverages complimentary qualities: batteries have a high density of energy for sustained supply, while SCs deliver high-power density with millisecond charge–discharge response, ideal for handling sudden load variations. The CNN–LSTM-attention mechanism is trained with historical and real-time data of solar power, temperature, load demand, and state of charge (SoC). It predicts power generation and utilization to optimize allocation: PV energy is prioritized during peak availability, SCs manage transient surges up to 300% of rated current, and batteries stabilize long-term operation. This strategy reduces rapid battery cycling, extending lifespan, while enhancing system efficiency and minimizing losses. The system is tested with MATLAB/Simulink simulation of PV array, Li-ion, and SC with their performance. The results have shown that the proposed system is more efficient than the traditional rule-based control system, it has reduced the peak current stress on battery, and the voltage regulation is achieved during dynamic loads. The results highlight the resilience and adaptability of the CNN–LSTM-attention-based IEMS to the application in solar powered electric vehicles. An experimental prototype of 100 W also developed to check the feasibility of the proposed system.