The adoption rate of electric mobility, renewable energy systems, and smart transportation infrastructures has exacerbated the demand for real-time, high-performance and energy-efficient systems. While high latency, low bandwidth, and low responsiveness are commonly encountered drawbacks in existing cloud-based energy optimization techniques used in these mobility-driven systems. The deployment of Edge AI and IoT technologies will pave the path to efficient, low-latency and real-time distributed renewable energy optimization within the smart sustainable transportation system. This paper presents a detailed survey on the latest trends in AI based renewable energy integration, smart grid, EV, battery management systems, and real-time transportation data analytics. The application of deep learning, reinforcement learning, federated learning, and predictive analytics toward improved renewable energy generation forecasting, smart charging, load balancing, and Vehicle-to-Grid (V2G) co-ordination is also elaborated. Edge computing platforms and IoT devices can support a high-performance real-time monitoring, predictive maintenance and a self-sufficient energy distribution network for smart transportation. The experimental validation on contemporary research works reveal an average 70% reduction in transmission latency, more than 50% increase in the renewable energy consumption, approximately 30% increase in battery lifetime, and nearly 80% reduction in charging infrastructure offline duration for edge systems with AI assistance. Important concerns regarding the edge system’s security, scale-ability, connectivity, privacy, and the direction of future research on enabling smart transportation were also discussed.
ChapmanL. Transport and climate change: a review. J Transp Geogr2007; 15(5): 354–367.
2.
CollazosJSGArdilaLMCCardonaCJF. Energy transition in sustainable transport: concepts, policies, and methodologies. Environ Sci Pollut Res2024; 31(49): 58669–58686. https://doi.org/10.1007/s11356-024-34862-x
3.
BabuTSParamasivanMStephenV, et al. Enhancing electric vehicle charging station placement in practical power distribution networks. In: 2025 8th international conference on circuit, power & computing technologies (ICCPCT), 2025, pp.59–63.
4.
ShiWCaoJZhangQ, et al. Edge computing: vision and challenges. IEEE Internet Things J2016; 3(5): 637–646.
5.
ZhouZChenXLiE, et al. Edge Intelligence: paving the last mile of artificial intelligence with edge computing. Proc IEEE2019; 107(8): 1738–1762.
6.
Sai RameshAVigneshwarSVickramS, et al. Artificial intelligence driven hydrogen and battery technologies – a review. Fuel2023; 337: 126862.
7.
VellaiyanSAljohaniKAljohaniBS, et al. A comprehensive review of AI-enabled predictive maintenance for hydrogen-powered transport: advancing process safety and sustainability. J Loss Prev Process Ind2026; 101: 105953.
8.
GhasemiAKeshavarziAAbdelmoniemM, et al. Edge intelligence for intelligent transport systems: approaches, challenges, and future directions. Expert Syst Appl2025; 280: 127273.
9.
TanKMRamachandaramurthyVKYongJY. Integration of electric vehicles in smart grid: a review on vehicle to grid technologies and optimization techniques. Renew Sustain Energ Rev2016; 53: 720–732.
10.
GongTZhuLYuFR, et al. Edge intelligence in intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst2023; 24(9): 8919–8944.
11.
ArandaJASdos Santos CostaRVWde Vargas, et al. Context-aware edge computing and Internet of things in smart grids: a systematic mapping study. Comput Electr Eng2022; 99: 107826.
12.
RaghupathiMDhanrajSPoyyamozhiN, et al. Optimized multi-objective energy management strategy for solar-fuel cell hybrid electric vehicles using RSM and SFOA. Results Eng2025; 28: 107106. https://doi.org/10.1016/j.rineng.2025.107106
13.
PawarPTarunKumarMVittal KP. An IoT based intelligent smart energy management system with accurate forecasting and load strategy for renewable generation. Measurement2020; 152: 107187.
14.
SamirAElKoranyAElKadyM. Prediction of transportation systems energy demand using intelligent techniques. Egypt Inform J2026; 33: 100912.
15.
SriabishaRKumarJAJayaprabakarJ, et al. A comprehensive exploration of electric vehicles: classification, charging methods, obstacles, and approaches to optimization. Energy Sustain Dev2025; 85: 101671.
16.
AlgburiSAbed Al Kareem SSabeehSapaevIB, et al. The role of artificial intelligence in accelerating renewable energy adoption for global energy transformation. Unconv Resour2025; 8: 100229.
17.
GomathiSKannanECarmel Mary BelindaMJ, et al. Solar energy prediction with synergistic adversarial energy forecasting system (Solar-SAFS): harnessing advanced hybrid techniques. Case Stud Therm Eng2024; 63: 105197.
18.
DengSZhaoHFangW, et al. Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence. IEEE Int Things J2020; 7(8): 7457–7469. https://doi.org/10.1109/JIOT.2020.2984887
19.
BanadYMSharifSSRezaeiZ. Artificial intelligence and machine learning for smart grids: from foundational paradigms to emerging technologies with digital twin and large language model-driven intelligence. Energy Convers Manag2025; 28: 101329.
20.
RajaramGCarmel Mary BelindaMJAlex DavidS, et al. Q-DASTNet: integrating quantum optimization and deep learning for robust solar energy forecasting in smart grid systems. Results Eng2025; 28: 107306.
21.
AhmadiMAlyHGuJ. A comprehensive review of AI-driven approaches for smart grid stability and reliability. Renew Sustain Energ Rev2026; 226: 116424.
22.
KrishnamurthySAdewuyiOBSalimonSA. Recent advances in artificial intelligence-based optimization for power system applications: a review of techniques, challenges, and future directions. Renew Sustain Energ Rev2026; 226: 116340.
23.
AhmadiMAlyH. A comprehensive review of reinforcement learning-based voltage control in smart grids. Renew Sustain Energ Rev2026; 227: 116526.
24.
SinghARSeshu KumarRBajajM, et al. A blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response in renewable energy grids. Energy Strategy Rev2025; 62: 101905.
25.
TusharWSahaTKYuenC, et al. Peer-to-peer trading in electricity networks: an overview. IEEE Trans Smart Grid2020; 11: 3185–3200.
26.
TavaresTMGiesbrechtM. Deep learning-based fault diagnosis in wind turbine bearings and gearboxes using vibration signals: survey, challenges, and recommendations. IEEE Access2025; 13: 207013–207032.
27.
GunduzMZDasR. Smart grid security: an effective hybrid CNN-based approach for detecting energy theft using consumption patterns. Sensors2024; 24: 1148.
28.
Ortega-VazquezMA. Optimal scheduling of electric vehicle charging and vehicle-to-grid services at household level including battery degradation and price uncertainty. IET Gener Transm Distrib2014; 8: 1007–1016.
29.
GrataloupAJonasSMeyerA. A review of federated learning in renewable energy applications: potential, challenges, and future directions. Energy AI2024; 17: 100375.
30.
ArcasGICioaraTAnghelI, et al. Edge offloading in smart grid. Smart Cities2024; 7: 680–711.
31.
ElassyMAl-HattabMTakruriM, et al. Intelligent transportation systems for sustainable smart cities. Transp Eng2024; 16: 100252.
32.
PrasadMBGaneshPVinay KumarK, et al. Renewable Energy Integration in modern power systems: challenges and opportunities. E3S Web Conf2024; 591: 03002.
33.
Al-FuqahaAGuizaniMMohammadiM, et al. Internet of things: a survey on enabling technologies, protocols and applications. IEEE Commun Surv Tutor2015; 17: 2347–2376. https://doi.org/10.1109/comst.2015.2444095
34.
VedulaRT. Interoperability of the electric vehicle charging ecosystem. Prem J Sci2024; 4.
35.
AlwarafyAAl-ThelayaKAbdallahM, et al. A survey on security and privacy issues in edge computing-assisted internet of things. IEEE Internet of Things J2021; 8(6): 4004–4022. https://doi.org/10.1109/JIOT.2020.3015432
36.
VelagaKSGuoYYuW. Edge AI for smart cities: foundations, challenges, and opportunities. Smart Cities2025; 8: 211.
37.
BibraEConnellyEDhirS, et al. Global EV outlook 2022: securing supplies for an electric future. 2022.
OpreaSVBâraA. An edge-fog-cloud computing architecture for IoT and smart metering data. Peer-to-Peer Netw Appl2023; 16(2): 818–845. https://doi.org/10.1007/s12083-022-01436-y
40.
WangXHanYLeungVCM, et al. Convergence of edge computing and deep learning: a comprehensive survey. IEEE Commun Surv Tutor2020; 22(2): 869–904.
41.
MinhQNNguyenVHQuyVK, et al. Edge computing for IoT-enabled smart grid: the future of energy. Energies2022; 15(17): 6140.
42.
PathareAASethiD. Development of iot-enabled solutions for renewable energy generation and net-metering control for efficient smart home. Discov Internet Things2024; 4(1): 11. https://doi.org/10.1007/s43926-024-00065-6
43.
FengCWangYChenQ, et al. Smart grid encounters edge computing: opportunities and applications. Adv Appl Energy2021; 1: 100006.
44.
MubarakaliASamsudeenSAlkhayyatA, et al. Optimized flexible network architecture creation against 5G communication-based IoT using information-centric wireless computing. Wirel Netw2023; 30(2): 883–907. https://doi.org/10.1007/s11276-023-03531-1
45.
MastoiMSWangDMaN, et al. AI-driven control and optimization for renewable energy integration in smart grids: challenges, applications, and future research directions. Energy Strategy Rev2026; 64: 102049.
46.
XuLDHeWLiS. Internet of things in industries: a survey. IEEE Trans Ind Inform2014; 10: 2233–2243.
47.
HuJMoraisHSousaT, et al. Electric vehicle fleet management in smart grids: a review of services, optimization and control aspects. Renew Sustain Energ Rev2016; 56: 1207–1226.
48.
OrugantiKSPVaithilingamCAKumareshSS, et al. An integrative review of standalone solar powered EV charging stations: Standards, policies, design aspects, and future directions. Energy Rep2025; 14: 2893–2913.
49.
EikelandOFHovemFDOlsenTE, et al. Probabilistic forecasts of wind power generation in regions with complex topography using deep learning methods: an Arctic case. Energy Convers Manag2022; 15: 100239.
50.
CecatiCCitroCPiccoloA, et al. Smart grids operation with distributed generation and demand side management. In: WangL (ed.) Modeling and control of sustainable power systems: towards smarter and greener electric grids. Springer, 2012, pp.27–46.
51.
AlamgirSHassanSJUMehdiA, et al. A comprehensive review of vehicle–to–grid (V2G) technology as an ancillary services provider. Results Eng2025; 27: 106813.
52.
UrsuaAGandiaLMSanchisP. Hydrogen production from water electrolysis: current status and future trends. Proc IEEE2012; 100: 410–426.
53.
HeMWangSZhugeC. A data-driven large-scale micro-simulation approach to deploying and operating wireless charging lanes. Transp Res D Transp Environ2023; 121: 103835.
54.
MahmoodAJavaidNRazzaqS. A review of wireless communications for smart grid. Renew Sustain Energ Rev2015; 41: 248–260.
55.
YangYBremnerSMenictasC, et al. Battery energy storage system size determination in renewable energy systems: a review. Renew Sustain Energ Rev2018; 91: 109–125.
56.
AmamraSA. A hybrid blockchain solution for electric vehicle energy trading: balancing proof of work and proof of stake. Energies2025; 18(7): 1840.
LiuSLinZDongY, et al. Editorial: power system operation and optimization considering high penetration of renewable energy. Front Energy Res2024; 12: 1483215. https://doi.org/10.3389/fenrg.2024.1483215
59.
DebSTammiKKalitaK, et al. Impact of electric vehicle charging station load on distribution network. Energies2018; 11(1): 178.
60.
ParkinsonPMechamEGrovesF, et al. Designing electric vehicle charging infrastructure to benefit residents in a near-port community. Transp Res Interdiscip Perspect2025; 34: 101696.
61.
KumarNSoodSKSainiM. Internet of Vehicles (IoV) based framework for electricity demand forecasting in V2G. Energy2024; 297: 131199.
62.
Narayana GowdaSNathKZhangC, et al. Short-term aggregate electric vehicle charging load forecasting in diverse conditions with minimal data using transfer and meta-learning. Energy Syst2026; 17: 347–366. https://doi.org/10.1007/s12667-024-00682-8
63.
KhalidMRAlamMSSarwarA, et al. A comprehensive review on electric vehicles charging infrastructures and their impacts on power-quality of the utility grid. eTransportation2019; 1: 100006.
64.
RamkumarGKannanSMohanavelV, et al. The future of green mobility: a review exploring renewable energy systems integration in electric vehicles. Results Eng2025; 27: 105647.
65.
SbordoneDBertiniIDi PietraB, et al. EV fast charging stations and energy storage technologies: a real implementation in the smart micro grid paradigm. Elect Power Syst Res2015; 120: 96–108.
66.
YanH. Integration of electric vehicles in smart grids: challenges and opportunities in achieving carbon neutrality goals. Appl Comput Eng2024; 93: 89–97.
67.
AbdullahiUAdnanA. Integration of renewable energy into electric vehicle (EV) charging networks. World J Adv Eng Technol Sci2024; 13: 156–165. https://doi.org/10.30574/wjaets.2024.13.2.0554
68.
TusharWYuenCMohsenian-RadH, et al. Transforming energy networks via peer-to-peer energy trading: the potential of game-theoretic approaches. IEEE Signal Process Mag2018; 35: 90–111.
69.
liuZWuQShahidehpourM, et al. Transactive real-time electric vehicle charging management for commercial buildings with PV on-site generation. IEEE Trans Smart Grid2019; 10(5): 4939–4950.
70.
SongSZhangCQiC, et al. Adaptive energy management strategy for hybrid electric vehicles in dynamic environments based on reinforcement learning. Design2024; 8(5): 102.
71.
R. SinghAKumarRSBajajM, et al. Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources. Sci Rep2024; 14(1): 19207. https://doi.org/10.1038/s41598-024-70336-3
72.
LiYQinDPoorHV, et al. Introducing edge intelligence to smart meters via federated split learning. Nat Commun2024; 15(1): 9044. https://doi.org/10.1038/s41467-024-53352-9
73.
HeHMengXWangY, et al. Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives. Renew Sustain Energy Rev2024; 192: 114248. https://doi.org/10.1016/j.rser.2023.114248
74.
Cajas OrdóñezSASamantaJSuárez-CetruloAL, et al. Intelligent edge computing and machine learning: a survey of optimization and applications. Future Internet2025; 17(9): 417.
75.
Ramirez-VergaraJBosmanLBWollegaE, et al. Review of forecasting methods to support photovoltaic predictive maintenance. Clean Eng Technol2022; 8: 100460.
76.
WeiTWangYZhuQ. Deep reinforcement learning for building HVAC control. In: Proceedings of the 54th annual design automation conference 2017 (DAC ’17), 2017. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3061639.3062224
77.
ZhangKYangZBaşarT. Multi-agent reinforcement learning: a selective overview of theories and algorithms. In: VamvoudakisKGWanYLewisFL, et al. (eds) Handbook of Reinforcement Learning and Control. Studies in Systems, Decision and Control, Springer, Cham, 2021; 325. https://doi.org/10.1007/978-3-030-60990-0_12
78.
AggarwalMKhullarVRaniS, et al. Federated learning on internet of things: extensive and systematic review. Comput Mater Contin2024; 79(2): 1795–1834.
79.
KongXLuLXiongK. Privacy-preserving estimation of electric vehicle charging behavior: a federated learning approach based on differential privacy. Internet of Things2024; 28: 101344.
80.
RawatDBBajracharyaC. Cyber security for smart grid systems: Status, challenges and perspectives. In: Conference proceedings–IEEE SOUTHEASTCON, 2015.
81.
Al-SaifNAhmadRWSalahK, et al. Blockchain for Electric Vehicles Energy Trading: Requirements, Opportunities, and Challenges. IEEE Access2021; 9: 156947–15696.
82.
NorouziFHoppeTElizondoLR, et al. A review of socio-technical barriers to Smart Microgrid development. Renew Sustain Energ Rev2022; 167: 112674.
83.
ZhangYXiongRHeH, et al. Lithium-ion battery remaining useful life prediction with box–cox transformation and Monte Carlo Simulation. IEEE Trans Ind Electron2019; 66: 1585–1597.
84.
RavikumarNVARamadeviKPoojithaD, et al. AI-driven energy management system for renewable powered electric vehicle charging stations. Int J Sci Res Eng Manag2025; 09: 1–9.
XuHZhaoFGuoY. State of charge estimation for lithium-ion batteries: an online method combining deep neural network and adaptive Kalman filter. Processes2025; 13(11): 3559. https://doi.org/10.3390/pr13113559
87.
WangTTsengKJZhaoJ, et al. Thermal investigation of lithium-ion battery module with different cell arrangement structures and forced air-cooling strategies. Appl Energy2014; 134: 229–238.
88.
BerecibarMGandiagaIVillarrealI, et al. Critical review of state of health estimation methods of Li-ion batteries for real applications. Renew Sustain Energ Rev2016; 56: 572–587.
89.
MengelkampEGärttnerJRockK, et al. Designing microgrid energy markets: a case study: the Brooklyn microgrid. Appl Energy2018; 210: 870–880.
90.
YilmazMKreinPT. Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles. IEEE Trans Power Electron2013; 28: 2151–2169.
DasPHewageKKotagodahettiR, et al. Lithium-ion battery recycling: a critical review of techno-economical and socio-environmental impacts. Sep Purif Technol2026; 382: 135847.
93.
AndoniMRobuVFlynnD, et al. Blockchain technology in the energy sector: a systematic review of challenges and opportunities. Renew Sustain Energ Rev2019; 100: 143–174.
94.
MansourHSESaadLAMAbdel-AzizM, et al. Artificial intelligence applications in battery Management systems for electric vehicles. Proc., 2025 Twelfth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt, 2025, pp. 267–274. https://doi.org/10.1109/ICICIS66182.2025.11313201
95.
MühendisAAkkayaR. Review of artificial intelligence based integration techniques of battery management system for electric vehicles (641-657). 2023. https://doi.org/10.13140/RG.2.2.22037.50403
96.
SardaJPatelHPopatY, et al. Review of management system and state-of-charge estimation methods for electric vehicles. World Electr Veh J2023; 14(12): 325.
97.
HannanMAHoqueMMHussainA, et al. State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applications: issues and recommendations. IEEE Access2018; 6: 19362–19378.
98.
ChemaliEKollmeyerPJPreindlM, et al. Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries. IEEE Trans Ind Electron2018; 65: 6730–6739.
99.
LiHBin KaleemMChiuIJ, et al. A digital twin model for the battery management systems of electric vehicles. Proc., 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), Haikou, Hainan, China, 2021, pp. 1100–1107. https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00171
100.
MbassoWFHarrisonADagalI, et al. Digital twins in renewable energy systems: a comprehensive review of concepts, applications, and future directions. Energy Strategy Rev2025; 61: 101814.
101.
XueQShenSLiG, et al. Remaining useful life prediction for lithium-ion batteries based on capacity estimation and box-cox transformation. IEEE Trans Vehicular Technol2020; 69: 14765–14779. https://doi.org/10.1109/tvt.2020.3039553
102.
JardineAKSLinDBanjevicD. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process2006; 20(7): 1483–1510.
103.
LiuYWenJWangG. A comprehensive overview of remaining useful life prediction: from traditional literature review to scientometric analysis. Mach Learn Appl2025; 21: 100704.
104.
Mohsenian-RadAHLeon-GarciaA. Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid2010; 1: 120–133.
105.
PalenskyPDietrichD. Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inform2011; 7: 381–388.
106.
LundHØstergaardPAConnollyD, et al. Smart energy and smart energy systems. Energy2017; 137: 556–565.
107.
LeongWY. Integration of renewable energy sources in the planning of EV charging infrastructure in Malaysia. IET Conf Proc2026; 2025: 986–993. https://doi.org/10.1049/icp.2025.3953
108.
HussainAEswarakrishnanVAslamA, et al. Charging stations demand forecasting using LSTM based hybrid transformer model. Sci Rep2025; 15: 36639.
109.
MohammadiANápolesGSalgueiroY. Deep learning for time-series forecasting with exogenous variables in energy consumption: a performance and interpretability analysis. IEEE Access2025; 13: 86746–86767.
110.
BaoLWangQJiangY. Review of digital twin for intelligent transportation system. Proc., 2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT), Lanzhou, China, 2021, pp. 309–315. https://doi.org/10.1109/ICEERT53919.2021.00064
111.
HouYZhengXHanC, et al. Deep learning methods in short-term traffic prediction: a survey. Inf Technol Control2022; 51: 139–157.
112.
SadeghiARWachsmannCWaidnerM. Security and privacy challenges in industrial Internet of Things. Proc., 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), San Francisco, CA, 2015, pp. 1–6. https://doi.org/10.1145/2744769.2747942
113.
RomanRZhouJLopezJ. On the features and challenges of security and privacy in distributed internet of things. Comput Netw2013; 57(10): 2266–2279.
114.
SahaSHotaAChattopadhyayAK, et al. A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities. Artif Intell Rev2024; 57: 1–67. https://doi.org/10.1007/s10462-024-10766-7
115.
EkwuemeCPAdamIHDwivediA. Lightweight cryptography for internet of things: a review. EAI Endorsed Trans Internet Things2024; 10: 1–9.
116.
AbdulsalamYSHedabouM. Security and privacy in cloud computing: technical review. Future Internet2021; 14: 11.
117.
MerkleRC. A digital signature based on a conventional encryption function. In: PomeranceC (ed.) Advances in cryptology — CRYPTO ’87. Springer, 1988, pp.369–378.
GoodfellowIShlensJSzegedyC. Explaining and harnessing adversarial examples. arXiv 14126572. 2014.
124.
LiHGeLTianL. Survey: federated learning data security and privacy-preserving in edge-internet of things. Artif Intell Rev2024; 57: 130. https://doi.org/10.1007/s10462-024-10774-7
125.
MianjiEFardadMMunteanGM, et al. A survey on multi-agent reinforcement learning applications in the internet of vehicles. 2024.
126.
KairouzPMcMahanHB. Advances and open problems in federated learning. Found Trends Mach Learn2021; 14(1–2): 1–210. https://doi.org/10.1561/2200000083
127.
GeyerRKleinTNabiM. Differentially private federated learning: a client level perspective. 2017.
128.
FerragMADerdourMMukherjeeM, et al. Blockchain technologies for the internet of things: research issues and challenges. IEEE Internet Things J2019; 6: 2188–2204. https://doi.org/10.1109/jiot.2018.2882794
129.
NingZXieL. A survey on multi-agent reinforcement learning and its application. J Autom Intell2024; 3: 73–91.
