"Khalatbarisoltani, A."

Khalatbarisoltani, A., Zhou, H., Tang, X., Kandidayeni, M., Boulon, L. et Hu, X. (2023). Energy management strategies for fuel cell vehicles: A comprehensive review of the latest progress in modeling, strategies, and future prospects. IEEE Transactions on Intelligent Transportation Systems . ISSN 1524-9050 1558-0016 DOI 10.1109/TITS.2023.3309052

Khalatbarisoltani, A., Boulon, L. et Hu, X. (2023). Integrating model predictive control with federated reinforcement learning for decentralized energy management of fuel cell vehicles. IEEE Transactions on Intelligent Transportation Systems . ISSN 1558-0016 DOI 10.1109/TITS.2023.3303991

Khalatbarisoltani, A., Kandidayeni, M., Boulon, L. et Hu, X. (2022). A decentralized multi-agent energy management strategy based on a look-ahead reinforcement learning approach. SAE International Journal of Electrified Vehicles, 11 (2). ISSN 2691-3747 DOI 10.4271/14-11-02-0012

Khalatbarisoltani, A., Kandidayeni, M., Boulon, L. et Hu, X. (2021). Comparison of decentralized ADMM optimization algorithms for power allocation in modular fuel cell vehicles. IEEE/ASME Transactions on Mechatronics . pp. 1-12. ISSN 1941-014X DOI 10.1109/TMECH.2021.3105950

Kandidayeni, M., Macias, A., Khalatbarisoltani, A., Boulon, L., Kelouwani, S. et Chaoui, H. (2019). An online energy management strategy for a fuel cell/battery vehicle considering the driving pattern and performance drift impacts. IEEE Transactions on Vehicular Technology, 68 (12). pp. 11427-11438. ISSN 0018-9545 DOI 10.1109/TVT.2019.2936713

Kandidayeni, M., Macias, A., Khalatbarisoltani, A., Boulon, L. et Kelouwani, S. (2019). Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms. Energy, 183 . pp. 912-925. ISSN 0360-5442 1873-6785 DOI 10.1016/j.energy.2019.06.152

Kandidayeni, M., Macias, A., Khalatbarisoltani, A., Boulon, L. et Kelouwani, S. (2019). Corrigendum to “Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms” [Energy 183 (2019) 912–925]. Energy, 189 . p. 116454. ISSN 0360-5442 DOI 10.1016/j.energy.2019.116454