Stochastic Self-Optimizing Power Management for Hybrid Power Sources of Electric Vehicles
Date Issued
2015
Date
2015
Author(s)
Lee, Chao-Ming
Abstract
This dissertation concerns about the power management strategy of electric vehicles supplied by hybrid power sources (HPS). The first type of the HPS is composed of a Lithium-ion battery (LIB) pack and an ultracapacitor (UC) bank. The LIB/UC HPS can provide load power and receive regenerative power by controlling the current through the bi-directional buck/boost DC/DC converter. The second type of the HPS consists of the fuel-cell system (FCS) and the passive hybrid energy storage system (HESS). For these HPSs, we proposed the stochastic power management strategy (SPMS) and the stochastic self-optimizing power management strategy (SSOPMS), respectively. Both these two strategies are composed of radial basis function neural networks (RBFNN), which are trained and optimized by the reinforcement learning scheme and the minimum principle. The main objective of these PMSs is to determine the optimal output power of the major power source through the stochastic driving cycles. In this dissertation, stochastic driving cycles are implemented by Markov chain that can take more complex and real-world driving conditions into account. Therefore, the PMSs optimized through the stochastic driving cycles can deal with driving conditions. For the LIB/UC HPS, the SPMS controls the output power of the LIB and utilizes the UC to provide transient peak load power and to receive most of the regenerative power. Meanwhile, enough residue energy of the UC bank is maintained. For the FCS and passive HESS HPS, the SSOPMS controls the output power and maximizes the operating efficiency of the FCS. Moreover, the SSOPMS utilizes the passive HESS to furnish transient peak load power and receive most of the regenerative power. Meanwhile, it can maintain proper residue charge of the LIB and keep the LIB from damage. The simulation results show that both the SPMS and the SSOPMS can achieve excellent power management on standard and stochastic driving cycles.
Subjects
Hybrid power system
stochastic driving cycle
electric vehicle
self-optimizing power management strategy
SDGs
Type
thesis
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