Machine learning-based approach for reduction of energy consumption in hybrid energy storage electric vehicle

This research introduces a novel machine learning-based strategy for generating supercapacitor (SC) reference current to optimize energy distribution in Battery Electric Vehicles (BEV) and Hybrid Battery Electric Vehicles (HBEV). A Long Short-Term Memory (LSTM) neural network is trained using real-world drive cycle data and exported in Open Neural Network Exchange (ONNX) format for real-time deployment within a Simulink-based control environment. This enables adaptive SC current prediction to dynamically offload high transient loads from the battery. The system is modeled using the Nissan Sakura EV and evaluated under EUDC and IM240 drive cycles. With SC support, the EUDC cycle exhibits a 21.3% reduction in battery peak current, 18.1% reduction in peak power demand, and 5.75% lower battery energy consumption. In the IM240 cycle, peak battery current is reduced by 33.5%, peak power by 31.6%, and energy consumption by 12.36%. These improvements validate the proposed LSTM-ONNX framework in reducing battery stress, improving thermal performance, and enhancing energy efficiency. Additionally, SC assistance leads to smoother traction motor torque, reduced current ripple, and optimized power delivery. A comprehensive model is developed and tested in Simscape to confirm the real-time applicability of this data-driven control strategy for electric vehicles.