# Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle
###### Abstract  
Hybrid hydraulic technology has the advantages of high-power density and low price and shows good adaptability in construction machinery. A complex hybrid powertrain architecture requires optimization and management of power demand distribution and an accurate response to desired power distribution of the power source subsystems in order to achieve target performances in terms of fuel consumption, drivability, component lifetime, and exhaust emissions. For hybrid hydraulic vehicles (HHVs) that are used in construction machinery, the challenge is to design an appropriate control scheme to actually achieve fuel economy improvement taking into consideration the relatively low energy density of the hydraulic accumulator and frequent load changes, the randomness of the driving conditions, and the uncertainty of the engine dynamics. To improve fuel economy and adaptability of various driving conditions to online energy management and to enhance the response performance of an engine to a desired torque, a hierarchical model predictive control (MPC) scheme is presented in this paper using the example of a spray-painting construction vehicle. The upper layer is a stochastic MPC (SMPC) based energy management control strategy (EMS) and the lower layer is an MPC-based tracking controller with disturbance estimator of the diesel engine. In the SMPC-EMS of the upper-layer management, a Markov model is built using driving condition data of the actual construction vehicle to predict future torque demands over a finite receding horizon to deal with the randomness of the driving conditions. A multistage stochastic optimization problem is formulated, and a scenario-based enumeration approach is used to solve the stochastic optimization problem for online implementation. In the lower-layer tracking controller, a disturbance estimator is designed to handle the uncertainty of the engine, and the MPC is introduced to ensure the tracking performance of the output torque of the engine for the distributed torque from the upper-layer SMPC-EMS, and therefore really achieve high efficiency of the diesel engine. The proposed strategy is evaluated using both simulation MATLAB/Simulink and the experimental test platform through a comparison with several existing strategies in two real driving conditions. The results demonstrate that the proposed strategy (SMPC+MPC) improves miles per gallon an average by 7.3% and 5.9% as compared with the control strategy (RB+PID) consisting of a rule-based (RB) management strategy and proportional-integral-derivative (PID) controller of the engine in simulation and experiment, respectively. hybrid hydraulic vehicle (HHV); energy management strategy (EMS); setpoint tracking; model predictive control (MPC); Markov model; stochastic optimization +
Footnote †: journal: Applied  
hybrid electric power is the standard approach to improve powertrains for passenger cars, whereas hybrid hydraulic power is an interesting alternative for heavy vehicles, such as city buses, commercial trucks, and construction vehicles. This is due to the superior ability of the hydraulic accumulator to accept high rates and high frequencies of charging and discharging, and therefore cope with operating conditions that involve frequent load changes [2; 3]. Accordingly, hybrid hydraulic vehicles (HHVs) are usually characterized by lower travel speed, more obvious periodicity, higher amplitude and load change rate, as well as frequent start-stop operations [4; 5]. For hybrid power systems, the controlling distribution of the power demand between the engine and the electrical or hydraulic motors, namely the energy management control strategy (EMS), is crucial to the reduction of fuel consumption while satisfying performance constraints. Compared to hybrid electric vehicles (HEVs), to date, there have been relatively few related researches on HHVs. Although the proven techniques for controlling HEVs are used as reference, there is still a need to develop control strategies tailored specifically to HHVs.