We consider the problem of managing Firmware Over-The-Air (FOTA) updates for cars at a massive scale over a cellular network. This problem is constrained by factors like large number of cars, a need to perform updates quickly and securely, ability to do the update anywhere and any time, and delivering the often large update without harming the network. We present a scheduling approach for managing large FOTA downloads in a cellular network that combines historical network load with car usage and location analytics. The proposed approach uses a combination of heuristics and an optimized scheduler and applies them to schedule the admission of cars to download FOTA. Using real network data from a large cellular provider that includes a million cars and nearly a billion radio-level network connections, we show that our scheduling approach is feasible and practical. We also show that it is possible to manage FOTA even when device and network models use high levels of aggregation over time. Simulation results show that our method improves over random uncontrolled approaches by (i) reducing median download startup delay by 48%, (ii) reducing the number of cars that don’t complete the update by 10% and most importantly, (iii) reducing the load in busy cells by up to 37%.