Changjun Xie

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Sensors are critical for the monitoring and real-time control of fuel cell system, according to the reliability requirements of multi-sensor of 60kW automotive fuel cell system designed by our group, a two-level neural networks based fault diagnosis method is put forward in this paper. The two-level neural networks include a main net and five sub nets which(More)
This paper presents a neural network predictive control strategy to optimize oxygen supply for a proton exchange membrane fuel cell system. We propose using a time varying and local linearization auto-regressive moving average with exogenous (ARMAX) to model the nonlinear system, and employing recurrent neural network to estimate coefficients of the ARMAX(More)
Considering only about one third of the world's energy consumption is effectively utilized for functional uses, and the remaining is dissipated as waste heat, thermoelectric (TE) materials, which offer a direct and clean thermal-to-electric conversion pathway, have generated a tremendous worldwide interest. The last two decades have witnessed a remarkable(More)
In order to achieve the intelligent and automation AC charging of EV-Charging Station plug-in electric vehicles, a solution based on ZigBee technology is proposed that Communication between electric vehicle on-board charger and AC charging spot. The hardware system use TI's CC2480 RF chips as core components. This paper focuses on the studying for the(More)
To overcome inferior rate capability and cycle stability of MnO-based materials as a lithium-ion battery anode associated with the pulverization and gradual aggregation during the conversion process, we constructed robust mesoporous N-doped carbon (N-C) protected MnO nanoparticles on reduced graphene oxide (rGO) (MnO@N-C/rGO) by a simple top-down(More)
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