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Switched optimization control of power allocation of hybrid energy storage systems
Author(s) -
Song Xiulan,
Meng Limin,
Wang Lei
Publication year - 2018
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2336
Subject(s) - control theory (sociology) , controller (irrigation) , lyapunov function , energy storage , linear quadratic regulator , optimization problem , optimal control , power (physics) , lyapunov optimization , computer science , mathematical optimization , state (computer science) , process (computing) , control (management) , lyapunov redesign , mathematics , nonlinear system , physics , algorithm , quantum mechanics , artificial intelligence , agronomy , biology , operating system
Summary In this paper, we present a switched optimization control method for power allocation of hybrid energy storage systems (HESSs) subject to constraints on the state of charge and power split. By the energy conservation principle, a continuous‐time switching model is established to describe changes of charge quantities of the HESS during its charging‐or‐discharging process. Then an analytic switched state feedback law with some free parameters is constructed by the concept of common control Lyapunov functions, which is used to allocate the power of storage units during the charging‐or‐discharging process. To cope with the constraints and performance functions formulating the power allocation requirements of storage units, the receding horizon control principle is used to compute the parameters of the analytic switched control law by online solving a constrained optimization problem. The results on asymptotical stability and common section region (0.5, ∞) of the switched optimization controller are established in the presence of constraints by using the properties of common control Lyapunov functions. By comparing to linear‐quadratic regulator control of the HESS, an example is used to illustrate the effectiveness and performance of the switched optimization controller presented here.