Transportation: Difference between revisions
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=== Alternative fuels and electrification ===
* [[Electric vehicles driving cycle optimization|'''Electric vehicles driving cycle optimization''']]: Optimizing the driving efficiency of electric vehicle has specific challenges related to batteries. ML can help predict various relevant metrics such as battery state based on driving cycles.
*[[Electric vehicle charging infrastructure|'''Electric vehicle charging infrastructure''']]: Deploying electric vehicles at scale requires an adequate charging infrastructure, with various planning, scheduling and management issues. ML can help for example with predicting usage of the infrastructure and load prediction.
*'''[[Accelerated Science|Accelerated science]] for alternative fuels:''' Alternative fuels have the potential to provide low-carbon solutions while retaining the properties of fossils, but most of them remain at an early stage of development. Machine learning can help accelerate this development by learning patterns in experimental or operational data in order to guide future experiments/operations.
* '''Electric vehicle [[demand response]]:''' Battery electric vehicles are typically not used for more than a fraction of the day, allowing them to act as energy storage for the grid at other times, where charging and discharging is controlled for example by price signals. ML can help enable demand response application by forecasting or controlling signals.
=== Modal shift ===
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