Environmental challenges and reduction of global crude oil reserves gained the attention of researchers and automobile manufacturers for exploration of novel vehicle technologies. Hybrid Electric Vehicles (HEVs) established a thought for minimizing the fuel consumption and greenhouse gases (GHG) emissions. Transportation sector consumes about 66% of total oil consumption in the world and 50% of that is utilized by small passenger cars and trucks. The main challenge for the designing of Hybrid Electric Vehicles is the coordination of onboard energy sources and optimal power flow control for both the electrical and the mechanical paths. This requires the utilization of an appropriate control strategy or energy management strategy. Energy management technique is employed, ensuring optimal power sharing between two energy sources (engine and motor) while keeping the battery state of charge in the charge-sustaining mode. On the basis of research, conducted by the industry and the academia, different energy management strategies have been proposed. These strategies can be categorized into non-implementable and implementable energy management strategies, relying on the data required for real time implementation. Normally, the non-implementable strategies formulate the energy management problem as an optimal control problem of minimizing a performance index over a finite time interval under components operational constraints. These strategies are considered as bench mark strategies providing global optimal solution. The implementable strategies have been developed for implementation in real vehicles and provide near optimal solution. The main emphasis of this research is to develop the energy management strategy of HEV (Three Wheeler Auto Rickshaw), as the energy management strategy has a key role in fuel economy and reduction of emissions. By introducing the Dynamic Programming for the evaluation of fuel economy for a particular vehicle provides a bench-mark fuel economy for other energy management strategies. The main contribution of the dissertation is to evaluate the bench-mark fuel economy for parallel hybrid electric rickshaw through dynamic programming. DP is used as a feasible technique for powertrain benchmark analysis. A parallel hybrid electric three-wheeler vehicle is modeled in Matlab/Simulink through forward facing simulator. The DP technique is employed through the backward facing simulator, x ensuring optimal power-sharing between two energy sources (engine and motor) while keeping the battery state of charge in the charge-sustaining mode. The extracted rules from DP forming near-optimal control strategies is playing a vital role in deciding overall fuel consumption. Unlike the DP control actions, these extracted rules are implementable through the forward facing simulator. From the simulation results, it can be concluded that a substantial improvement of fuel economy up to 27% through DP is achieved for HEV (33 Km/liter) in comparison with conventional vehicle (24 Km/liter) and is taken as reference value for other strategies. Equivalent Consumption Minimization Strategy is also implemented, which shows fuel economy of 31.35 Km/liter showing 5% more fuel consumption than DP. Results also indicate that there is an improvement of about 9% in fuel economy, in comparison with the heuristics based strategy (not conforming to DP rules). The rule-based strategy (rules extracted from DP) is then compared with non-optimal rules based heuristics controller. It is shown that non-optimal rule based controller has 18% more fuel consumption than DP results. The dissertation also narrates a comprehensive comparison of the different proposed energy management strategies. Additionally, an attempt is made to devise and demonstrate Energy Management Strategy (EMS) by giving full consideration to the powertrain using Atkinson cycle engine. A novel energy management strategy based on the vehicle speed for Atkinson cycle engine for HEV is proposed. The proposed EMS with Atkinson cycle engine control framework exhibits the significant improvement in the fuel economy around 12.30% for standard Manhattan driving cycle at part load conditions and 7.22% for the modified Federal Urban Driving Schedule (FUDS) driving cycle in comparison with the Otto cycle engine.