Operational Strategy of Hybrid Heavy-Duty Trucks by Utilizing a Genetic Algorithm to Optimize the Fuel Economy Multiobjective Criteria

Abstract

An optimum operating strategy is needed for early amortization of expensive hybrid electric vehicle powertrain parts. Especially in the operation of long-haul trucks, fuel costs have a large impact on the total cost of ownership, which is the key entrepreneurial figure in the transportation business. Combined with route information, a predictive powertrain control (PPC) system increases the fuel-saving. In a MATLAB/Simulink model-based generic approach, the operating strategy and the PPC are optimized using a genetic algorithm. The tradeoff between minimizing the fuel consumption and simultaneously maximizing the vehicle speed and gradeability to decrease time-related fixed costs has to be solved. This leads to a multi-optimization problem. The operating strategy is developed for a parallel hybrid topology that includes the fuel-saving functions of regenerative braking, boosting, shifting the load point, and electric drive only operation. The methodology developed answers the search for an optimum control parameter setup combining the operational strategy and the PPC system in long-haul operations. This paper describes the model building, simulation, and optimization of a rule-based control strategy. The route profile and fuel consumption of an internal combustion engine long haul truck were measured in a real-life test run. The recorded data are used for model building and to validate the simulation tool. With an optimized parameter setup, fuel-saving of up to 11% were achieved in theory with acceptable vehicle speed and gradeability.