This master thesis project analyses how an increased number of electric vehicles would affect the electric network in Stockholm. The project was started in order to help Ellevio, the DSO of Stockholm, understand how to manage the increased amount of requests of network-capacity connected to electric vehicle charging.

This thesis models the charging of electric vehicles using a Markov Decision Process and uses Reinforcement Learning solution models to derive an intelligent charging algorithm. This algorithm can take concepts such as electricity price, battery degradation and electrical losses into account in order to minimise the overall operational costs, and add more value to the use of electric vehicles.

The main objective of this research is to develop robust cell balancing technique to reduce cell balancing time, extending driving range and protection of the battery pack.

This thesis outlines a comprehensive approach to designing an efficient EV powertrain by optimizing the BMS, with a focus on energy management, state estimation, and cell balancing.

The main aim of the thesis is to investigate the issues related to static charging for storage system of Hybrid Electric Vehicle (HEV). Here main focus has been given to make comprehensive literature review related to the state of art technologies available across the globe.