Jaime Gandarilla Ibarra
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jaime Gandarilla Ibarra has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Graciela Velasco Herrera
National Autonomous University of México
2 shared publications - Pablo Sánchez Álvarez
Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico
2 shared publications - Rodolfo Sosa Echeverría
Sección de Contaminación Ambiental, Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Mexico City, Mexico
2 shared publications - Victor Manuel Velasco Herrera
Instituto de Geofísica, Universidad Nacional Autónoma de México, Circuito Exterior, C.U., Coyoacán, CDMX, Mexico City 04510, Mexico
2 shared publications - Elías Granados Hernández
Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico
1 shared publication - Enrique Quiroga-González
Institute of Physics, Benemérita Universidad Autónoma de Puebla, Puebla 72570, México
1 shared publication
Latest Publications
- Open Access
- 10 January 2025
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 16 (1) , 33
Adoption of Electric Vehicles and Forecasting Air Emissions in the Metropolitan Area of Mexico City by 2050
Densely populated urban megacities, such as the Metropolitan Area of Mexico City, face the ongoing deterioration of air quality. Emissions from industrail factories and internal combustion vehicles are the main sources of pollutants. We have evaluated different transition trends from internal combustion engine vehicles as bus, truck and van, and motorcycle to electric vehicles through 2050. The total vehicle growth follows a second-degree polynomial trend. Bus growth exhibits a linear trend. Truck and van growth display a second-degree polynomial trend. Motorcycle growth also follows a second-degree polynomial trend. We found that the most significant reductions in transportation emissions are observed in , followed by , volatile organic compound (VOC), and particulate matter, with light and heavy vehicles being the primary contributors to total emissions. Mexico City serves as a pilot laboratory where both the challenges and potential solutions to an issue affecting millions of citizens can be observed. If proven effective and practical, these solutions could be applied to other megacities.- 10 January 2025
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 16 (1) , 33
Open AccessAdoption of Electric Vehicles and Forecasting Air Emissions in the Metropolitan Area of Mexico City by 2050
Densely populated urban megacities, such as the Metropolitan Area of Mexico City, face the ongoing deterioration of air quality. Emissions from industrail factories and internal combustion vehicles are the main sources of pollutants. We have evaluated different transition trends from internal combustion engine vehicles as bus, truck and van, and motorcycle to electric vehicles through 2050. The total vehicle growth follows a second-degree polynomial trend. Bus growth exhibits a linear trend. Truck and van growth display a second-degree polynomial trend. Motorcycle growth also follows a second-degree polynomial trend. We found that the most significant reductions in transportation emissions are observed in , followed by , volatile organic compound (VOC), and particulate matter, with light and heavy vehicles being the primary contributors to total emissions. Mexico City serves as a pilot laboratory where both the challenges and potential solutions to an issue affecting millions of citizens can be observed. If proven effective and practical, these solutions could be applied to other megacities. - Open Access
A New Methodology for Early Detection of Failures in Lithium-Ion Batteries
The early fault detection and reliable operation of lithium-ion batteries are two of the main challenges the technology faces. Here, we report a new methodology for early failure detection in lithium-ion batteries. This new methodology is based on wavelet spectral analysis to detect overcharge failure in batteries that is performed for voltage data obtained in cycling tests, subjected to a standard charge/discharge protocol. The main frequencies of the voltage temporal signal, the harmonic components in the regular cycling test, and a low frequency pattern were identified. For the first time, battery failure can be anticipated by wavelet spectral analysis. These results could be the key to the new early detection of battery failures in order to reduce out-of-control explosions and fire risks.
Open AccessA New Methodology for Early Detection of Failures in Lithium-Ion Batteries
The early fault detection and reliable operation of lithium-ion batteries are two of the main challenges the technology faces. Here, we report a new methodology for early failure detection in lithium-ion batteries. This new methodology is based on wavelet spectral analysis to detect overcharge failure in batteries that is performed for voltage data obtained in cycling tests, subjected to a standard charge/discharge protocol. The main frequencies of the voltage temporal signal, the harmonic components in the regular cycling test, and a low frequency pattern were identified. For the first time, battery failure can be anticipated by wavelet spectral analysis. These results could be the key to the new early detection of battery failures in order to reduce out-of-control explosions and fire risks.