Abstract
A novel adaptive energy management strategy is proposed for real-time power split between fuel cells (FCs) and supercapacitors (SCs) in a hybrid electric vehicle in view of the fact that driving patterns greatly affect fuel economy. The driving pattern recognition (DPR) is achieved based on the features extracted from the historical velocity window with a multilayer perceptron neural network. After the DPR has been obtained, an adaptive fuzzy energy management controller is utilized for power split according to the required power for vehicle running. In order to prolong the FC lifetime while decreasing the hydrogen consumption, a genetic algorithm is applied to optimize critical factors such as adaptive gains and fuzzy membership function parameters for several standard driving cycles. In the proposed method, the future driving cycles are not required and the current driving pattern can be successfully recognized, demonstrating that less current fluctuations and fuel consumption can be achieved under various driving conditions. Compared with conventional energy management systems, the proposed framework can ensure the state of charge of SCs within the desired limit.
Highlights
ENERGY crisis, environmental pollution and global warming cause fuel cells (FCs) powered vehicles to draw a lot of attention due to their high reliability and low pollutant emission [1]
Online power prediction in hybrid electric vehicle (HEV) should be considered because the demand power is unknown in practice, but here the study is focusing on the optimal fuzzy management control design and the required power is obtained by an advanced vehicle simulator (ADVISOR) [35] from the congested urban roads, flowing urban roads, subway and high way conditions, which is utilized to show the effectiveness of the proposed fuzzy energy management strategy
The energy management controller in the HEV is to split the instantaneous power between the fuel cell and the supercapacitor, where its output gain can be changed adaptively based on real time driving patterns
Summary
ENERGY crisis, environmental pollution and global warming cause fuel cells (FCs) powered vehicles to draw a lot of attention due to their high reliability and low pollutant emission [1]. Multi-objective optimization considering fuel cell lifetime and driving performance for energy management are studied continuously and has obtained promising simulation results [12][13][20], how to determine a fuzzy EMS including multiple objectives is still a challenge. A neural network classifier based adaptive fuzzy logic energy management controller is proposed without using future driving patterns, which can be implemented in real time. Online power prediction in HEV should be considered because the demand power is unknown in practice, but here the study is focusing on the optimal fuzzy management control design and the required power is obtained by an advanced vehicle simulator (ADVISOR) [35] from the congested urban roads, flowing urban roads, subway and high way conditions, which is utilized to show the effectiveness of the proposed fuzzy energy management strategy.
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