Could EV charging be a remedy for grid congestion rather than a cause? The rapid adoption of EVs, heat pumps, and photovoltaic systems has increased electricity flows, often peaking at specific times and causing congestion. Smart EV charging, which entails shifting demand to periods of low grid usage, is widely seen as a potential solution. Yet, the most effective ways to harness this flexibility, especially from a system-wide perspective, remain unclear. This dissertation examines mechanisms grid operators can use to promote smart charging and explores two major barriers to its effective implementation. The first three chapters investigate how different mechanisms, such as capacity-limitation systems and alternative grid tariffs, can help grid operators mitigate congestion through smart EV charging. Chapter 2 focuses on a capacity-limitation product, where EVs optimize charging costs and/or emissions while keeping total load within transformer limits. It compares the costs and emissions of upgrading a transformer with the additional savings from smart charging under higher capacity. Across nearly all scenarios, the upgrade’s costs and emissions outweigh the savings, suggesting it is not optimal from a system value perspective. Chapter 3 examines how replacing traditional grid tariff structures can help mitigate congestion. It analyzes how EV charging patterns respond to various alternative tariffs and evaluates them using regulatory principles. Most alternatives reduce the need for grid investments and lower overall system costs, with only a small effect on charging costs. They also lead to a fairer distribution of grid costs between households and EV charging stations. Chapter 4 analyzes a case where a shared EV fleet operated by a car-sharing company helps mitigate grid congestion through a capacity-limitation system, without requiring private EVs to adjust their charging schedules. The results show that a small number of shared EVs can eliminate all congestion in the studied grid, with only a modest increase in their charging costs. Chapters 5 and 6 address two main barriers to implementing smart charging for grid congestion mitigation. Chapter 5 introduces a framework to reduce uncertainties in charging demand, timing, and session counts, which often lead to overly conservative strategies. By aggregating fleet data into three parameters for each 15-minute interval, the framework achieves high forecasting accuracy (R² up to 0.98). Chapter 6 examines technical barriers, drawing on large-scale tests that show many EV models cannot pause charging and require a constant minimum of 6 amperes, even during high grid load. Simulations reveal this limitation can halve the effectiveness of smart charging in reducing costs and congestion, and the chapter proposes several solutions. In conclusion, this thesis shows that the studied mechanisms for promoting smart charging can effectively reduce grid reinforcement needs and lower system costs, making smart charging a key solution to grid congestion. Grid operators and policymakers should therefore prioritize mechanisms that enable its widespread adoption. The thesis also identifies significant barriers to implementation and offers practical solutions to address them.