Design and evaluation of a predictive powertrain control system for a plug-in hybrid electric vehicle to improve the fuel economy and the emissions
Proceedings of the Institution of mechanical engineers. Part D, journal of automobile engineering
estimation and control of complex dynamic mechanical and multi-domain physical systems, with special emphasis on advanced modelling and model reduction methods, sensitivity analysis techniques, non-linear and optimal control, with applications to advanced vehicle systems, such as modern automotive powertrains and vehicle dynamics control systems. Abstract In this article, a power management scheme for a plug-in power split hybrid electric vehicle (PHEV) is designed, based on the model
... control (MPC) concept for charge depletion/charge sustenance (CDCS) and the blended mode strategies. The commands of MPC are applied to the powertrain components through appropriate low-level controllers: standard PI controllers for electric machines and sliding mode control for the engine torque control. The engine emissions minimization is a key factor to design the engine low-level controller. Applying this control package to a validated highfidelity model of a PHEV, developed in the MapleSim environment with a chemistry-based Lithium-ion battery model, results in considerable fuel economy and emissions performance. Running head right side 3 controls design. Model-based control approach is time-saving and cost-effective, because the design procedure is conducted through a single model of the whole system. Therefore, it results in an optimized and validated system for all possible operating conditions. Based on the battery depletion profile, the power management scheme for PHEVs can be divided in two categories: charge depletion plus charge sustenance (CDCS) and the blended mode. In CDCS, the vehicle goes in pure electric mode first, so the battery is discharged from a high level, and when battery state of charge (SOC) drops to a reference value, the control strategy tries to keep it as close as possible to that level. This reference value is lower than what it is in an HEV. In fact, if demanded power in the first part of travel is more than what electric motor or battery can provide, the engine will compensate the remaining propulsion power. In the blended mode strategy, the engine tries to reduce the rate of battery discharge in order to delay the charge sustaining stage. So, in the blended mode strategy, the battery and the engine are used consistently during entire driving trip such that the battery SOC decreases continuously (9). For each of these mentioned categories, different control approaches can be considered. (10) derived an optimal power management scheme for a plug-in hybrid vehicle (power-split architecture) based on stochastic dynamic programming (11) . (12) demonstrated that the optimal scheme rations battery charge through blending the engine and battery power such that SOC reaches the minimum level exactly when the trip terminates, if the drive cycle is known a priori. Two algorithms -ECMS (Equivalent Consumption Minimization Strategy) and dynamic programming (DP) -are considered in (12) to optimize the power split between electrical and mechanical energy sources. The performance obtained using dynamic programming as a global optimal energy management scheme for a PHEV is used as the benchmark for evaluating an on-board implementable control scheme based on ECMS. Some studies have addressed battery health-conscious power management scheme for PHEVs. For instance, (13) suggested that to minimize battery degradation, a PHEV power management scheme should first, deplete battery charge quickly, then blend the engine and battery power to avoid charge sustenance. Some references (14) ,(15), (16), and (17) suggest that it is possible to improve the control scheme performance of PHEV if the trip information is determined a priori by means of recent advancements in intelligent transportation system (ITS) based on the use of global positioning system (GPS) and geographical information system (GIS). Model predictive control (MPC) seems a proper method to exploit the potentials of modern concepts and to fulfill the automotive requirements. The success of MPC in industrial applications is due to its ability to handle processes with many manipulated and controlled variables and constraints on them in a systematic way (18). Furthermore, MPC allows for the specification of an objective function which is optimized by the controller. Other advantageous MPC features are the capability of dealing with time delays (19) , of rejecting measured and unmeasured disturbances (20), and of taking advantage from future information (21). There is a philosophical attractiveness to MPC since it embodies both optimization and feedback adjustment. In fact, MPC has been developed to integrate the performance of optimal control with the robustness of feedback control (22). MPC determines the control input via receding horizon optimal control based on an open-loop model of the process, called prediction model. The prediction model is a compromise between simplicity and representativeness of the physics of the process. The prediction model used in MPC (as well as in any other model-based control design techniques) is usually very simple, yet representative enough to capture the main dynamical relations of the real plant. In other words, the prediction model should be control-oriented. Application of MPC to hybrid vehicles has been investigated before. (23) proposed a real-time control system for different hybrid architectures using the MPC concept. (24), applied MPC to a power-split hybrid electric vehicle, ignoring the dynamics of the powertrain against other faster dynamics for the model inside the controller. They proposed that the fuel economies achieved with MPC are better than those reported by the rule-based PSAT simulation software. (25) applied MPC to a power-split plug-in hybrid electric vehicle and used dynamic programming as a benchmark for evaluating the power management scheme performance.