{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:20:29Z","timestamp":1780392029516,"version":"3.54.1"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The widespread adoption of electric vehicles (EVs) necessitates advanced energy management strategies to alleviate range anxiety and improve overall energy efficiency. This study presents a novel framework for optimizing energy consumption in EVs by integrating driver behavior patterns, road conditions, and environmental factors. Utilizing a comprehensive dataset of 3395 high-resolution charging sessions from 85 EV drivers across 25 workplace locations, we developed a multi-modal prediction model that captures the complex interactions between driving behavior and environmental conditions. The proposed methodology employs a combination of driving scenario recognition and reinforcement learning techniques to optimize energy usage. Specifically, we utilize contrastive learning to extract meaningful representations of driving states by leveraging the symmetric relationships between positive pairs and the asymmetric nature of negative pairs and implement graph attention networks to model the intricate relationships between road environments and driving behaviors. Our experimental results demonstrate that the proposed framework achieves a significant reduction in energy consumption compared to baseline methods, with an average improvement of 17.3% in energy efficiency under various driving conditions. Furthermore, we introduce an adaptive real-time optimization strategy that dynamically adjusts vehicle parameters based on instantaneous driving patterns and environmental contexts. This research contributes to the advancement of intelligent energy management systems for EVs and provides insights into the development of more efficient and environmentally sustainable transportation solutions.<\/jats:p>","DOI":"10.3390\/sym17060930","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T03:59:17Z","timestamp":1749700757000},"page":"930","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Symmetry-Guided Electric Vehicles Energy Consumption Optimization Based on Driver Behavior and Environmental Factors: A Reinforcement Learning Approach"],"prefix":"10.3390","volume":"17","author":[{"given":"Jiyuan","family":"Wang","sequence":"first","affiliation":[{"name":"The Fuqua School of Business, Duke University, Durham, NC 27708, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haijian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4503-8259","authenticated-orcid":false,"given":"Bi","family":"Wu","sequence":"additional","affiliation":[{"name":"Anderson School of Management, University of California Los Angeles, Los Angeles, CA 90095, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhe","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1109\/TTE.2023.3290069","article-title":"Deep reinforcement learning-based energy-efficient decision-making for autonomous electric vehicle in dynamic traffic environments","volume":"10","author":"Wu","year":"2023","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.procir.2016.03.014","article-title":"Determining the main factors influencing the energy consumption of electric vehicles in the usage phase","volume":"48","author":"Li","year":"2016","journal-title":"Procedia Cirp"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jpowsour.2014.01.075","article-title":"The impact of range anxiety and home, workplace, and public charging infrastructure on simulated battery electric vehicle lifetime utility","volume":"257","author":"Neubauer","year":"2014","journal-title":"J. Power Sources"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.trd.2015.10.010","article-title":"Electric vehicles\u2019 energy consumption estimation with real driving condition data","volume":"41","author":"Zhang","year":"2015","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6077","DOI":"10.20964\/2019.07.06","article-title":"A review of lithium-ion battery thermal management system strategies and the evaluate criteria","volume":"14","author":"Yang","year":"2019","journal-title":"Int. J. Electrochem. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8573","DOI":"10.3390\/en8088573","article-title":"Energy consumption prediction for electric vehicles based on real-world data","volume":"8","author":"Coosemans","year":"2015","journal-title":"Energies"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7837","DOI":"10.1109\/TIE.2015.2475419","article-title":"Reinforcement learning of adaptive energy management with transition probability for a hybrid electric tracked vehicle","volume":"62","author":"Liu","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fu, Q., Zhang, L., Xu, Y., and You, F. (2025). The Review of Human\u2014Machine Collaborative Intelligent Interaction with Driver Cognition in the Loop. Syst. Res. Behav. Sci.","DOI":"10.1002\/sres.3141"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.energy.2015.12.041","article-title":"Real-world performance of battery electric buses and their life-cycle benefits with respect to energy consumption and carbon dioxide emissions","volume":"96","author":"Zhou","year":"2016","journal-title":"Energy"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hayes, J.G., De Oliveira, R.P.R., Vaughan, S., and Egan, M.G. (2011, January 6\u20139). Simplified electric vehicle power train models and range estimation. Proceedings of the 2011 IEEE Vehicle Power and Propulsion Conference, Chicago, IL, USA.","DOI":"10.1109\/VPPC.2011.6043163"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1049\/iet-its.2018.5169","article-title":"Prediction of energy consumption for new electric vehicle models by machine learning","volume":"12","author":"Fukushima","year":"2018","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"131780","DOI":"10.1016\/j.energy.2024.131780","article-title":"Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework","volume":"302","author":"Feng","year":"2024","journal-title":"Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-025-94946-7","article-title":"Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study","volume":"15","author":"Hussain","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"117096","DOI":"10.1016\/j.apenergy.2021.117096","article-title":"Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving","volume":"297","author":"Serrano","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.jclepro.2018.09.184","article-title":"A data-driven two-level clustering model for driving pattern analysis of electric vehicles and a case study","volume":"206","author":"Li","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"758","DOI":"10.3390\/en4050758","article-title":"Large-scale battery system development and user-specific driving behavior analysis for emerging electric-drive vehicles","volume":"4","author":"Wu","year":"2011","journal-title":"Energies"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.1109\/TIV.2024.3372625","article-title":"Augmenting reinforcement learning with transformer-based scene representation learning for decision-making of autonomous driving","volume":"9","author":"Liu","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2582","DOI":"10.1109\/LRA.2024.3354552","article-title":"Self-supervised representation learning from temporal ordering of automated driving sequences","volume":"9","author":"Lang","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1049\/iet-its.2010.0137","article-title":"Impact of driving characteristics on electric vehicle energy consumption and range","volume":"6","author":"Bingham","year":"2012","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"125320","DOI":"10.1016\/j.apenergy.2025.125320","article-title":"An adaptive spatio-temporal graph recurrent network for short-term electric vehicle charging demand prediction","volume":"383","author":"Wang","year":"2025","journal-title":"Appl. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"121703","DOI":"10.1016\/j.energy.2021.121703","article-title":"Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle","volume":"238","author":"Qi","year":"2022","journal-title":"Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1049\/itr2.12451","article-title":"Contrastive learning for traffic flow forecasting based on multi graph convolution network","volume":"18","author":"Guo","year":"2024","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5388","DOI":"10.1109\/TKDE.2023.3333824","article-title":"Spatio-temporal graph neural networks for predictive learning in urban computing: A survey","volume":"36","author":"Jin","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14284","DOI":"10.1109\/TITS.2024.3401850","article-title":"A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction","volume":"25","author":"Qu","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sutton, R.S., and Barto, A.G. (1998). Reinforcement Learning: An Introduction, MIT Press.","DOI":"10.1109\/TNN.1998.712192"},{"key":"ref_26","unstructured":"Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S. (2018, January 10\u201315). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. Proceedings of the 2018 International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_27","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 12\u201318). A simple framework for contrastive learning of visual representations. Proceedings of the 2020 International Conference on Machine Learning, Online."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum contrast for unsupervised visual representation learning. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_29","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (May, January 30). Graph Attention Networks. Proceedings of the 2018 International Conference on Learning Representations, Vancouver, BC, USA."},{"key":"ref_30","unstructured":"Fujimoto, S., Hoof, H., and Meger, D. (2018, January 10\u201315). Addressing function approximation error in actor-critic methods. Proceedings of the 2018 International Conference on Machine Learning, Stockholm, Sweden."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/6\/930\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:50:13Z","timestamp":1760032213000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/6\/930"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,11]]},"references-count":30,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["sym17060930"],"URL":"https:\/\/doi.org\/10.3390\/sym17060930","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,11]]}}}