The research described in this dissertation focuses on the development of computationally efficient design methodology to optimize the hydraulic hybrid power-split transmission for fuel efficiency, acceleration performance and robustness against powertrain uncertainties. This research also involve experimental implementation of a three-level hierarchical control approach on two test beds, requiring powertrain control design and fine-tuning. Hybrid powertrains have the potential to benefit the fuel efficiency of highway and off-highway vehicles. Hydraulic hybrid has high power density. Hydraulic power-split architecture is chosen in this study for its flexibility in operation and combined advantage of series and parallel architecture. An approach for optimizing the configuration and sizing of a hydraulic hybrid power-split transmission is proposed. Instead of considering each mechanical configuration consisting of combinations of gear ratios, a generalized kinematic relation is used to avoid redundant computation. The Lagrange multiplier method for computing the optimal energy management control is shown to be 450 times more computationally efficient for use in transmission design iterations. To exploit the benefit of high power density of hydraulics, a classical multi-objective solver is utilized to incorporate the acceleration performance criteria into the transmission design optimization. By considering worst-case uncertainty, the transmission design is optimized to be robust against powertrain uncertainties and insensitive to operating condition variations, and yet fuel efficient. The Generation I and II vehicles are experimental platforms built to implement controls and to validate the fuel efficiency gain for power-split transmission. The powertrain for the platforms are modeled to predict the potential fuel efficiency improvement by different energy management strategies. Results show maximum of 74\% fuel efficiency gain by optimizing engine management from CVT to full optimal hybrid operation. The three-level control strategy is implemented on the Generation I vehicle. This control strategy segregates the tasks of the drive-train into three layers that respectively 1) manages the accumulator energy storage (high level); 2) performs vehicle level optimization (mid-level); and 3) attains the desired vehicle operating condition (low level). Results validated the modularity and effectiveness of this control structure.