A Bio-Inspired Hybrid Optimization Model for Enhancing Operating Performance and Efficiency of Electric Vehicles

Electric Vehicles (EVs) are critical for the advancement of sustainable transportation, however, there is still a need to optimize energy consumption, battery longevity, and system adaptability. In this study, we introduce Quantum-BioNet (QBN), a hybrid optimization framework that combines quantum computing principles and bio inspired algorithms to address these issues. For efficient exploration of a vast solution space, QBN utilizes parameterized quantum circuits; for real time responsiveness, it uses bio inspired adaptation. The model allows critical EV parameters to be dynamically optimized (i.e., energy management and regenerative braking) under varying operational conditions. Implemented in a MATLAB/Simulink simulation environment replicating real-world scenarios, QBN achieved significant performance improvements: 1) ${2 0 \%}$ decrease in energy consumption; 2) ${3 0 \%}$ increase in battery life; 3) fast adaptation within 5-6 seconds to changes in traffic density and road grade. QBN was found to have better computational efficiency (90 seconds to converge in a computer with ${6 0 \%}$ CPU and 950 MB memory) than state-of-the-art Quantum Optimization (QO) and Bio Inspired Optimization (BIO) algorithms, outperforming QO and BIO by $25 \%$ and $18 \%$, respectively. The results indicate QBN’s capability to revolutionize EV optimization with a scalable, energy efficient and cost-effective solution. QBN combines quantum parallelism and adaptive bio-inspired strategies to implement intelligent and green mobility systems that meet critical requirements of real-world EV operation and accommodate future progress.