2018-01-1109 Published 0 3 Apr 2018 © 2018 SAE International. All Rights Reserved.Simultaneous Design and Control Optimization of a Series Hybrid Military Truck Zifan Liu, Abdullah-al Mamun, and Simona Onori Clemson-ICAR Citation: Liu, Z., Mamun, A.-al, and Onori, S., “Simultaneous Design and Control Optimization of a Series Hybrid Military Truck,” SAE Technical Paper 2018-01-1109, 2018, doi:10.4271/2018-01-1109. Abstract This paper investigates the fuel saving potential of a series hybrid military truck using a simultaneous battery pack design and powertrain supervisory control optimization algorithm. The design optimization refers to the sizing of the Lithium-ion battery pack in the hybridized configuration. On the other hand, the powertrain supervisory control optimization finds the most efficient way to split power demands between the battery pack and the engine. Most of the previous literatures implement them sepa - rately. In contrast, combining the sizing and energy management problem into a single optimization problem produces the global optimal solution. This study proposes a novel unified framework to couple Genetic Algorithm (GA) with Pontryagin’s Minimum Principle (PMP) to determine the battery pack sizing and the power split control sequence simultaneously. As GA and PMP are global optimization methodologies under suitable conditions, the results can be regarded as benchmark results for the application under study. Military drive cycles were further applied under the simulta - neous optimization framework to evaluate the impact of different driving conditions. Introduction The challenges for military vehicles include the increasing power and energy needs for superior dynamic performance, reliable power exportability and durable silent watch capability. The fuel efficiency of military vehicles has always been the focus since the fuel cost can be as high as $100/L in the battlefield [ 1]. Powertrain hybridiza - tion is a common technology for passenger and commercial vehicles to achieve significant fuel efficiency improvement. The hybrid electric vehicle (HEV) combines multiple power sources onboard and enables several fuel-saving functions, such as regenerative braking, engine idling elimination and engine operating region shift. However, the deployment of military HEVs is still under active research due to challenges such as reliability in complex operating and environmental conditions [ 2]. A systematic research on the optimal design and control of military HEV considering various military driving conditions and unique military operating require - ments must be carried out for a path forward. The HEVs, comprised of series, parallel and power-split topologies, introduce alternative energy storage devices and power electronics. A high-level supervisory energy manage - ment strategy (EMS) manages the power flows among different power sources at any instance for certain objectives (maxi - mized fuel efficiency or minimum tailpipe emission) under appropriate constraints (satisfactory drivability and component specifications) [ 3]. Two main research questions for HEVs are the optimal sizing of the added components and the optimal control of power flows, which have been often discussed but in a separate manner.There are a variety of optimization algorithms available for the design of HEV with fixed EMS. As the design space of an HEV involves many local minima, gradient-based algo - rithms such as sequential quadratic programming (SQP) is disadvantageous in finding the global minima. In contrast, gradient-free algorithms have drawn much attention for the HEV design optimization because they tend to search the entire design space for the global solutions. Popular candidates consist of DIRECT, Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). They were evaluated in [ 4] to find the optimal HEV design variables, including the battery pack sizing, engine power rating, motor po