Journal
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
Volume 44, Issue 9, Pages 7495-7506Publisher
WILEY
DOI: 10.1002/er.5473
Keywords
electric vehicles; energy optimization; model predictive control; neural network; Pontryagin's minimum principle
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Funding
- National Natural Science Foundation of China [61773345]
- Natural Science Foundation of Zhejiang Province [LR17F030004]
- State Key Laboratory of Automotive Simulation and Control [20171103]
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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.
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