Layout design and energetic analysis of a complex diesel parallel hybrid electric vehicle

The present paper is focused on the design, optimization and analysis of a complex parallel hybrid electric vehicle, equipped with two electric machines on both the front and rear axles, and on the evaluation of its potential to reduce fuel consumption and NOx emissions over several driving missions. The vehicle has been compared with two conventional parallel hybrid vehicles, equipped with a single electric machine on the front axle or on the rear axle, as well as with a conventional vehicle. All the vehicles have been equipped with compression ignition engines. The optimal layout of each vehicle was identified on the basis of the minimization of the overall powertrain costs during the whole vehicle life. These costs include the initial investment due to the production of the components as well as the operating costs related to fuel consumption and to battery depletion. Identification of the optimal powertrain control strategy, in terms of the management of the power flows of the engine and electric machines, and of gear selection, is necessary in order to be able to fully exploit the potential of the hybrid architecture. To this end, two global optimizers, one of a deterministic nature and another of a stochastic type, and two real-time optimizers have been developed, applied and compared. A new mathematical technique has been developed and applied to the vehicle simulation model in order to decrease the computational time of the optimizers. First, the vehicle model equations were written in order to allow a coarse time grid to be used, then, the control variables (i.e., power flow and gear number) were discretized, and the values of the main model variables were evaluated and stored in a matrix (referred to as configuration matrix), for all the possible combinations of control variables and for each time node, before the optimization process. In this way, the optimizers can read the actual values of the relevant variables from the pre-processed data, instead of calculating them iteratively during the optimization stage. The performance of the hybrid vehicles has been evaluated over several driving missions, including the NEDC, the FTP, the AUDC, the ARDR and the AMDC, and a detailed energetic analysis has been carried out in order to clearly identify the key operating modes that contribute most to the fuel consumption and NOx emission savings of the different hybrid architectures.