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Efficient model predictive control for real‐time energy optimization of battery‐supercapacitors in electric vehicles
Author(s) -
Yu Shiming,
Lin Di,
Sun Zhe,
He Defeng
Publication year - 2020
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.5473
Subject(s) - model predictive control , battery (electricity) , supercapacitor , electric vehicle , optimization problem , energy (signal processing) , optimal control , energy storage , engineering , computer science , control theory (sociology) , mathematical optimization , control (management) , algorithm , mathematics , power (physics) , capacitance , artificial intelligence , chemistry , physics , statistics , electrode , quantum mechanics
Summary Integration of batteries and supercapacitors (B‐SCs) is widely used to improve performance of electric vehicles (EVs). In this article, we consider the energy optimization problem of B‐SCs in EVs and propose an efficient model predictive control (MPC) algorithm for real‐time energy optimization of the hybrid energy storage system of EVs. Back propagation neural network is firstly adopted to learn the velocity prediction ability over a finite horizon by standard driving cycles. Then real‐time energy optimization of B‐SCs in EVs is formulated as the finite horizon optimal control problem by taking into account the constraints, the cost function on battery current, and the predicted velocity of the EV. Moreover, to lessen the computational burden of online solving the problem, the Pontryagin's Minimum Principle is used in a fashion of receding horizon. Compared with traditional nonlinear MPC, simulation results verify the effectiveness of the proposed MPC algorithm for real‐time energy optimization of B‐SCs in EVs.