Multi-functional anodes boost the transient power and durability of proton exchange membrane fuel cells.

Proton exchange membrane fuel cells have been regarded as the most promising candidate for fuel cell vehicles and tools. Their broader adaption, however, has been impeded by cost and lifetime. By integrating a thin layer of tungsten oxide within the anode, which serves as a rapid-response hydrogen reservoir, oxygen scavenger, sensor for power demand, and regulator for hydrogen-disassociation reaction, we herein report proton exchange membrane fuel cells with significantly enhanced power performance for transient operation and low humidified conditions, as well as improved durability against adverse operating conditions. Meanwhile, the enhanced power performance minimizes the use of auxiliary energy-storage systems and reduces costs. Scale fabrication of such devices can be readily achieved based on the current fabrication techniques with negligible extra expense. This work provides proton exchange membrane fuel cells with enhanced power performance, improved durability, prolonged lifetime, and reduced cost for automotive and other applications

Identification of Partial Discharge Through Cable-Specific Adaption and Neural Network Ensemble

[EN] This paper proposes to administer a multi-step artificial intelligence approach with an ensemble of adaptive neural networks (NNs) trained on 50000 samples to identify partial discharge (PD) diagnostic measurements for in-service medium voltage (MV) power cables. To evaluate the performance of the algorithm, a case study was performed on cables deliberately selected to contain both uncomplicated measurements and disruptive irregularities representative of conditions during field testing. The experimental test results prove that the proposed cable-specific adaptation improves PD identification accuracy, with further increment through the NN ensembles. The main contribution of the approach is in both the cable-specific adaption and the NN ensemble being applied to MV cable field measurements.Yeo, J.; Jin, H.; Rodrigo Mor, A.; Yuen, C.; Tushar, W.; Saha, TK.; Seng Ng, C. (2021). Identification of Partial Discharge Through Cable-Specific Adaption and Neural Network Ensemble. IEEE Transactions on Power Delivery. 1-10. https://doi.org/10.1109/TPWRD.2021.3093670S11

Short-term Performance Limits of MIMO Systems with Side Information at the Transmitter

The fundamental performance limits of space-time block code (STBC) designs when perfect channel information is available at the transmitter (CSIT) are studied in this report. With CSIT, the transmitter can perform various techniques such as rate adaption, power allocation, or beamforming. Previously, the exploration of these fundamental results assumed long-term constraints, for example, channel codes can have infinite decoding delay, and power or rate is normalized over infinite channel-uses. With long-term constraints, the transmitter can operate at the rate lower than the instantaneous mutual information and error-free transmission can be supported. In this report, we focus on the performance limits of short-term behavior for STBC systems. We assume that the system has block power constraint, block rate constraint, and finite decoding delay. With these constraints, although the transmitter can perform rate adaption, power control, or beamforming, we show that decoding-error is unavoidable. In the high SNR regime, the diversity gain is upperbounded by the product of the number of transmit antennas, receive antennas, and independent fading block channels that messages spread over. In other words, fading cannot be completely combatted with short-term constraints. The proof is based on a sphere-packing argument

Optimal sizing and placement of Electrical Vehicle charging stations to serve Battery Electric Trucks

For Norway to reach the emission limits in the Paris Agreement, a substantial amount of CO2 must be reduced. Road traffic alone accounts for a high percentage of the total emissions during 2021. This thesis will focus on electrifying the transport sector and analyzing charging infrastructure for heavy-duty electric vehicles. New charging infrastructure for heavy-duty Electric Vehicles (EVs) provides issues regarding profitability due to the currently low adaption rates. However, heavy-duty EVs use the same charging sockets as EVs. As a result, EVs may finance the charging infrastructure needed to increase the adaption of heavy-duty EVs. Projections from Norwegian grid operators suggest that the total electricity surplus is diminishing during the next years and will be negative by 2027. This highlights the importance of modeling the power system in combination with finding optimal locations for charging stations. This study uses prescriptive analytics to suggest optimal locations for charging infrastructure to maximize returned profits to motivate station builders to implement more charging stations. A soft-linking will be done with PyPSA-eur to model the power system, where the new infrastructure is added as an additional load. Analyzing the results, it is possible to see that charging infrastructure has the potential to become profitable as the adaption rate for heavy-duty EVs rise. The collaboration between the models offers an open-source tool for scholars, researchers, and planners to study how new charging infrastructure affects key components in the Norwegian power system and could be useful in modeling state-of-the-art technologies