An exploration of the solution of direct methanol fuel cell cost effectiveness.

The work in this paper incorporated printed circuit board (PCB) technology into micro-direct methanol fuel cells (µDMFCs) and conjectured and verified the performance degradation factors of PCB current collectors in µDMFCs by testing different designed configuration µDMFCs. The experiment results showed that all kinds of PCB coating can benefit from the porous stainless-steel plates covering to a great extent. At the end of 48 h discharging, µDMFCs with porous stainless-steel plates between MEA and PCB coating achieved higher performance than those of the direct contacting series. It can be inferred from various types of experimental data that because of stainless-steel porous plate isolating, the impact of corrosion on the surface of the PCB electrode plate was reduced to a certain extent. The corrosion of the electrode plate was slowed down in the µDMFC discharging as a result of the passivation behavior on the iron surface and a decrease in corrosion current. Consequently, the attenuation of the PCB performance was delayed. The conclusion of this work explores a practical direction to enhance the cost-effectiveness of fuel cells, promoting the large-scale application of DMFCs in the future.

A Geomagnetic/Odometry Integrated Localization Method for Differential Robot Using Real-Time Sequential Particle Filter.

Geomagnetic matching navigation is extensively utilized for localization and navigation of autonomous robots and vehicles owing to its advantages such as low cost, wide-area coverage, and no cumulative errors. However, due to the influence of magnetometer measurement noise, geomagnetic localization algorithms based on single-point particle filters may encounter mismatches during continuous operation, consequently limiting their long-range localization performance. To address this issue, this paper proposes a real-time sequential particle filter-based geomagnetic localization method. Firstly, this method mitigates the impact of noise during continuous operation while ensuring real-time performance by performing real-time sequential particle filtering. Then, it enhances the long-range positioning accuracy of the method by rectifying the trajectory shape of the odometry through odometry calibration parameters. Finally, by performing secondary matching on the preliminary matching results via the MAGCOM algorithm, the positioning error of the method is further minimized. Experimental results show that the proposed method has higher positioning accuracy compared to related algorithms, resulting in reductions of over 28.58%, 37.11%, and 0.77% in RMSE, max error, and error at the end, respectively.