Electric vehicles (EVs) are finally making their way onto the highways worldwide but the challenges with respect to their long charging time and its impact on the utility distribution grid are still waiting to be resolved. Uncontrolled charging is likely to cause power network congestion that will result in voltage violations, power losses and imbalances, overloading of grid infrastructure and even their failures. For this reason, many control algorithms considering centralized and decentralized solutions have been proposed. However, none has been standardized as a practical solution that can easily be implemented at end-nodes. The today's Internet also had a similar problem on its early days. Its core control protocol has led to its ever-growing nature that still stably operates today allowing for the integration of millions of new devices almost every day. Behind this success story of the Internet lies its congestion avoidance algorithm: Additive Increase and Multiplicative Decrease (AIMD). This algorithm has proven itself to provide a stable and fair operation, and therefore can be considered as an important candidate for the EV charging problem as well. This thesis work adapts the AIMD concept from the Internet and proposes a counterpart EV charging algorithm. It investigates the core similarities and differences between the Internet and power distribution network and presents a detailed analysis on distribution network modeling to show how AIMD can be adapted and implemented. Finally, it proposes some statistical methods for a fully decentralized and practical implementation of AIMD on real distribution networks.