Methods and systems for managing chargers for electrical vertical takeoff and landing aircrafts

1. A system for managing chargers for electric vertical takeoff and landing aircrafts, the system comprising:

a first battery charger;

at least a sensor incorporated with the first charger, wherein the at least a sensor comprises at least a gas sensor, the at least a sensor configured to:

detect at least a battery metric, wherein the at least a battery metric includes charger status; and

detect a battery health parameter, wherein detecting the battery health parameter comprises detecting at least a cell failure as a function of the at least a gas sensor;

a computing device communicatively connected to the first battery charger and a mesh network including at least a second battery charger, the computing device configured to:

receive the at least a battery metric and the at least a battery health parameter from the at least a sensor;

transmit data packets to the mesh network;

receive communication feedback from at least a node in the mesh network;

select an initial node in the mesh network as a function of the communication feedback; and

transmit the at least a battery metric and the at least a battery health parameter to the initial node in the mesh network.

2. The system of claim 1 , wherein a battery charger is configured to support bidirectional charging.

3. The system of claim 1 , wherein the at least a sensor is configured to detect an electrical parameter.

4. The system of claim 1 , wherein the at least a sensor is configured to detect non-gaseous byproducts of cell failure.

5. The system of claim 1 , wherein a battery charger is a trickle charger.

6. The system of claim 1 , wherein the at least a sensor comprises a sensor suite, the sensor suite configured to capture a plurality of data.

7. The system of claim 1 , wherein the computing device is configured to select a datum of a plurality of data and utilize the datum to determine a charger status.

8. The system of claim 7 , wherein the charger status comprises a charge time remaining estimate.

9. The system of claim 1 , wherein, the computing device is configured to determine a charge cycle as a function of a machine-learning model.

10. The system of claim 1 , wherein the network includes one or more aircrafts.

11. A method for managing chargers for electric vertical takeoff and landing aircrafts, the method comprising:

detecting, by at least a sensor, at least a battery metric;

detecting, by at least a gas sensor, at least a battery health parameter;

transmitting, by at least a sensor, at least a battery metric to a computing device;

transmitting, by at least a gas sensor, at least a battery health metric to the computing device, wherein the battery health parameter comprises of at least cell failure detection;

receiving, by the computing device, the at least a battery metric and the at least a battery health parameter from the at least a sensor;

transmitting, by the computing device, data packets to the mesh network;

receiving, by the computing device, communication feedback from at least a node in the mesh network;

selecting, by the computing device, an initial node in the mesh network as a function of the communication feedback; and

transmitting, by a computing device, at least a battery metric and at least a battery health parameter to the initial node in the mesh network of communicatively connected chargers.

12. The system of claim 11 , wherein a battery charger is configured to support bidirectional charging.

13. The system of claim 11 , wherein the at least a sensor is configured to detect an electrical parameter.

14. The system of claim 11 , wherein the at least a sensor is configured to detect non-gaseous byproducts of cell failure.

15. The system of claim 11 , wherein a battery charger is a trickle charger.

16. The system of claim 11 , wherein the at least a sensor comprises a sensor suite, the sensor suite configured to capture a plurality of data.

17. The system of claim 11 , wherein the computing device is configured to select a datum of a plurality of data and utilize the datum to determine a charger status.

18. The system of claim 17 , wherein the charger status comprises a charge time remaining estimate.

19. The system of claim 11 , further comprising determining, a rate of charge as a function of a machine-learning model.

20. The system of claim 11 , wherein the network includes one or more aircrafts.