Series hydraulic hybrid technology has the potential to significantly improve fuel economy and reduce emission. The series hydraulic hybrid is very different from electric and parallel hydraulic configuration and requires a unique power management control strategy to realize its optimal potential. In this dissertation, three approaches to achieve optimality are proposed and analyzed. These are rule-based, intelligent, and mixed power management control strategy.;For evaluating the performance of control strategies, a forward-facing closed-loop simulation model based on physical features is first established in the MATLAB/SIMULINK environment. We then introduce a simple, valid and easily implementable rule-based power management control strategy. To derive the control signals, a PID-based multi-stage controller is presented. A thorough analysis on a class VI medium truck is elucidated. The simulation results demonstrate that a series hydraulic hybrid medium truck with the proposed rule-based power management control strategy results in fuel economy increases of 117% and 44% over the conventional baseline respectively over Federal Urban Driving Schedule (FUDS) and Federal Highway Driving Schedule (FHDS).;Then, an intelligent power management control strategy incorporating artificial neural networks (ANNs) and dynamic programming (DP) algorithm applied to series hydraulic hybrid propulsion systems is presented. ANNs are used to forecast vehicle speed and DP is utilized to find the optimal control actions for gear shifting and dual power source splitting. A thorough analysis of effect on fuel economy with different prediction window size on the class VI medium truck over FUDS and FHDS is presented. Compared with conventional baseline, the simulation results demonstrate that series hydraulic hybrid medium truck with 20 seconds short-term prediction window enables fuel economy increase of 135% and 48% respectively over FUDS and FHDS.;Although the intelligent power management control strategy has obvious advantages over rule-based control strategy in improving fuel economy, this approach is somewhat limited in a realistic application due to prediction error. Finally, we proposed a mixed power management control strategy incorporating intelligent and rule-based approach to obtain a practicable near-optimal control strategy. Validations of these three power management control strategies are performed by Vehicle Propulsion Systems Evaluation Tool (VPSET) developed at Southwest Research Institute.
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