Combined passivity based control and optimal control for energy management of fuel cell/battery hybrid system

Abstract

The energy management of hybrid electric vehicles is becoming an interesting topic for many researchers. Furthermore, the wise choice of the energy management strategy allows not only the best distribution of the power between the used sources, but also it allows reduction of consumption, increase in the lifetime of the sources, and improves the autonomy of the hybrid electric vehicle.The autonomy is guaranteed by the optimization of the embedded sources. In this study, the hybrid system consists of combining the fuel cell as the main source with the battery as the auxiliary source. The novelty of the proposed energy management strategy for the studied hybrid system is the combination between interconnection and damping assignment-passivity based control and the Hamiltonian Jacobi Bellman method. The stability proof is given and the efficiency of the proposed strategy is proved by the experimental work, where the obtained results show the good and adequate results to the proposed scenario.

Optimal control of aero‐engine systems based on a switched LPV model

“…It can be observed that (8-12) along with their differentiations (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27) form a two-point boundary value DAE of Δx( , x n + 1 , x n + 2 ), ( , x n + 1 , x n + 2 ), Δu( ,…”

“…In recent years, with the continuous improvement of the performance requirements in modern aircraft, people gradually realize the importance of optimal control for the aero-engine system. Many results, which report progress regarding theoretical or practical issues for continuous-time or discrete-time versions of optimal control problems, have appeared in the literature (see, e.g., [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]). For example, a maximum principle approach was designed to solve a general optimal switched control problem [21,22].…”

A novel optimal control design for unknown nonlinear systems based on adaptive dynamic programming and nonlinear model predictive control

“…The trajectories of the system states FIGURE 3 The optimal control calculated from the proposed algorithm…”

“…The optimal control of nonlinear systems has been studied for many years and the research findings have been applied to the actual systems [1][2][3][4]. The target of optimal control is to design a controller to maximize or minimize the performance index.…”

Model Predictive Direct Torque Control and Fuzzy Logic Energy Management for Multi Power Source Electric Vehicles

“…Nevertheless, the performance of the two EMSs depends strongly on the design of the filter which is a complicated task to be performed. Controller methods such as Backstepping [ 12 ], Passivity Control [ 13 ], Proportional-Integral Control [ 14 ], Sliding Control [ 15 ], etc, can obtain an exact calculation of the reference while taking into account the losses of the system. Rule-based strategies are mainly Fuzzy Logic, Artificial Neural Network [ 16 ], and State Machine [ 17 ].…”