The transition toward low-emission transport systems requires advanced tools for the modelling, planning, and management of large-scale electric mobility. This dissertation proposes an integrated methodological framework for analysing the energy consumption of Electric Vehicles (EVs), optimizing the siting of charging infrastructure, and assessing the interactions between e-mobility, electrochemical storage systems, and the power grid. The main scientific contribution lies in the development of a multi-layer modelling environment that combines high-fidelity vehicle-level dynamic models implemented in Simulink, agent-based simulations developed in AnyLogic, and data-driven analytical techniques applied to large-scale datasets. The dynamic model enables accurate estimation of energy consumption along real-world routes, accounting for altitude profiles, operating conditions, and the physical–mechanical characteristics of the vehicle. The agent-based simulations capture the collective behaviour of EVs along high-traffic highway corridors, providing insights into metrics such as charging-station saturation, waiting times, and the spatial distribution of charging demand. The integration of stationary and mobile Battery Energy Storage Systems (BESS/MESS) is assessed from both technical and operational perspectives, evaluating their role in peak-load mitigation, grid reliability enhancement, and overall system resilience. Finally, Big Data analytics and piecewise regression techniques are employed to characterize real charging curves and infrastructure-usage patterns, offering quantitative evidence to inform future planning strategies. The results demonstrate that the proposed framework constitutes a robust decision-support tool for engineering and policy applications aimed at developing an efficient, scalable, and resilient electric-mobility ecosystem.
La transizione verso sistemi di trasporto a basse emissioni richiede strumenti avanzati per la modellazione, pianificazione e gestione della mobilità elettrica su larga scala. Questa tesi propone un quadro metodologico integrato per l’analisi del consumo energetico dei veicoli elettrici (EV), l’ottimizzazione della collocazione delle infrastrutture di ricarica e la valutazione delle interazioni tra e-mobility, sistemi di accumulo elettrochimici e rete elettrica. Il principale contributo scientifico risiede nello sviluppo di un ambiente di modellazione multilivello che combina modelli dinamici ad alta fedeltà a livello veicolare implementati in Simulink, simulazioni basate su agenti sviluppate in AnyLogic e tecniche analitiche basate sui dati applicate a dataset di larga scala. Il modello dinamico consente una stima accurata del consumo energetico lungo percorsi reali, tenendo conto di profili altimetrici, condizioni operative e caratteristiche fisico-meccaniche del veicolo. Le simulazioni basate su agenti catturano il comportamento collettivo dei veicoli elettrici lungo corridoi autostradali ad alto traffico, fornendo informazioni su metriche quali la saturazione delle stazioni di ricarica, i tempi di attesa e la distribuzione spaziale della domanda di ricarica. L’integrazione di sistemi di accumulo di energia a batteria stazionari e mobili (BESS/MESS) viene valutata sia dal punto di vista tecnico sia operativo, analizzandone il ruolo nella mitigazione dei picchi di domanda, nel miglioramento dell’affidabilità della rete e nella resilienza complessiva del sistema. Infine, l’analisi di Big Data e le tecniche di regressione segmentata vengono impiegate per caratterizzare curve di ricarica reali e schemi di utilizzo delle infrastrutture, offrendo evidenze quantitative utili per guidare strategie di pianificazione future. I risultati dimostrano che il quadro metodologico proposto costituisce uno strumento solido di supporto alle decisioni per applicazioni ingegneristiche e politiche volte allo sviluppo di un ecosistema di mobilità elettrica efficiente, scalabile e resiliente.
Simulation and Big Data for electric mobility systems: a comprehensive modeling framework for energy consumption, charging infrastructure deployment, and grid interaction
SALDARINI, ALESSANDRO
2025/2026
Abstract
The transition toward low-emission transport systems requires advanced tools for the modelling, planning, and management of large-scale electric mobility. This dissertation proposes an integrated methodological framework for analysing the energy consumption of Electric Vehicles (EVs), optimizing the siting of charging infrastructure, and assessing the interactions between e-mobility, electrochemical storage systems, and the power grid. The main scientific contribution lies in the development of a multi-layer modelling environment that combines high-fidelity vehicle-level dynamic models implemented in Simulink, agent-based simulations developed in AnyLogic, and data-driven analytical techniques applied to large-scale datasets. The dynamic model enables accurate estimation of energy consumption along real-world routes, accounting for altitude profiles, operating conditions, and the physical–mechanical characteristics of the vehicle. The agent-based simulations capture the collective behaviour of EVs along high-traffic highway corridors, providing insights into metrics such as charging-station saturation, waiting times, and the spatial distribution of charging demand. The integration of stationary and mobile Battery Energy Storage Systems (BESS/MESS) is assessed from both technical and operational perspectives, evaluating their role in peak-load mitigation, grid reliability enhancement, and overall system resilience. Finally, Big Data analytics and piecewise regression techniques are employed to characterize real charging curves and infrastructure-usage patterns, offering quantitative evidence to inform future planning strategies. The results demonstrate that the proposed framework constitutes a robust decision-support tool for engineering and policy applications aimed at developing an efficient, scalable, and resilient electric-mobility ecosystem.| File | Dimensione | Formato | |
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2026_02_Saldarini Alessandro.pdf
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https://hdl.handle.net/10589/249077