The starter/alternator technology is considered an easily realizable hybrid electric vehicle (HEV) configuration to achieve significant fuel economy without compromising consumer acceptability. Several examples can be found in production or near-production vehicles, with implementation based on a spark ignition (SI) engine coupled with either a belted starter/alternator (BSA) or an integrated starter/alternator (ISA). One of the many challenges in successfully developing a starter/alternator HEV is to achieve engine start and stop operations with minimum passenger discomfort. This requires control of the electric motor to start and stop the engine quickly and smoothly, without compromising the vehicle noise, vibration, and harshness signature. The issue becomes more critical in the case of diesel hybrids, as the peak compression torque is much larger than in SI engines. This paper documents the results of a research activity focused on the control of the start and stop dynamics of a HEV with a belted starter/alternator. The work was conducted on a production 1.9 l common-rail diesel engine coupled to a 10.6 kW permanent magnet motor. The system is part of a series/parallel HEV powertrain, designed to fit a midsize prototype sport utility vehicle. A preliminary experimental investigation was done to assess the feasibility of the concept and to partially characterize the system. This facilitated the design of a control-oriented nonlinear model of the system dynamics and its validation on the complete HEV hardware. Model-based control techniques were then applied to design a controller for the belted starter/alternator, ensuring quick and smooth engine start operations. The final control design has been implemented on the vehicle. The research outcomes demonstrated that the BSA is effective in starting the diesel engine quickly and with very limited vibration and noise.
The energy management strategy in a hybrid electric vehicle is viewed as an optimal control problem and is solved using Model Predictve Control (MPC). The method is applied to a series hybrid electric vehicle, using a linearized model in state space formulation and a linear MPC algorithm, based on quadratic programming, to find a feasible suboptimal solution. The significance of the results lies in obtaining a real-time implementable control law. The MPC algorithm is applied using a quasi-static simulator developed in the MATLAB environment. The MPC solution is compared with the dynamic programming solution (offline optimization). The dynamic programming algorithm, which requires the entire driving cycle to be known a-priori, guarantees the optimality and is used here as the benchmark solution. The effect of the parameters of the MPC (length of prediction horizon, type of prediction) is also investigated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.