Steady state resistance estimation for overheating protection of a nicotine e-vaping device
Various example embodiments relate to a nicotine electronic vaping device, system, method, and/or non-transitory computer readable medium for protecting a nicotine electronic vaping device from overheating based on a steady state resistance prediction. The nicotine electronic vaping device includes a reservoir containing a nicotine pre-vapor formulation, a heating element configured to heat nicotine pre-vapor formulation drawn from the reservoir, and control circuitry configured to monitor a resistance value of the heating element over a first time period after a first application of negative pressure to the nicotine electronic vaping device, determine an estimated steady state resistance value of the heating element based on the monitored resistance value using a trained neural network, and control power to the heating element based on the estimated steady state resistance value.
1 . A nicotine electronic vaping device (EVD) comprising:
a heating element configured to heat nicotine pre-vapor formulation; and
control circuitry configured to,
monitor a resistance value of the heating element over a first time period after a first application of negative pressure to the nicotine EVD,
determine an estimated steady state resistance value of the heating element based on the monitored resistance value, and
control power to the heating element based on the estimated steady state resistance value.
2 . The nicotine EVD of claim 1 , wherein the control circuitry is further configured to:
determine the estimated steady state resistance value of the heating element based on the monitored resistance value using a trained neural network, the trained neural network trained using a dataset including a plurality of puff events and decay events.
3 . The nicotine EVD of claim 2 , wherein the control circuitry is further configured to monitor the resistance value of the heating element by:
measuring a plurality of resistance values of the heating element during the first time period; and
input the plurality of resistance values into the trained neural network to determine the estimated steady state resistance value.
4 . The nicotine EVD of claim 1 , wherein the control circuitry is further configured to monitor the resistance value of the heating element by:
determining a peak resistance value of the heating element during the first time period; and
determining at least one additional resistance value of the heating element at a time after the determined peak resistance value during the first time period.
5 . The nicotine EVD of claim 4 , wherein
the peak resistance value is determined at a time when the power being applied to the heating element is stopped after the first application of negative pressure to the nicotine EVD.
6 . The nicotine EVD of claim 5 , wherein the control circuitry is further configured to determine the estimated steady state resistance value of the heating element by:
estimating the estimated steady state resistance value of the heating element using a trained neural network based on the peak resistance value and the at least one additional resistance value.
7 . The nicotine EVD of claim 6 , wherein the trained neural network is a function-fitting network configured to:
receive the peak resistance value and the at least one additional resistance value as input values;
determine a decay in the input values over the first time period; and
output the estimated steady state resistance value of the heating element based on results of the determined decay in the resistance value of the heating element over the first time period.
8 . The nicotine EVD of claim 1 , wherein
the heating element is connected to a Wheatstone bridge circuit.
9 . The nicotine EVD of claim 8 , wherein the control circuitry is further configured to:
detect a resistance value corresponding to the Wheatstone bridge circuit over the first time period; and
estimate the estimated steady state resistance value of the heating element based on the detected resistance value corresponding to the Wheatstone bridge circuit.
10 . The nicotine EVD of claim 1 , wherein the control circuitry is further configured to:
detect a variable resistance value corresponding to the heating element over the first time period; and
estimate the estimated steady state resistance value of the heating element based on the detected variable resistance value corresponding to the heating element.
11 . The nicotine EVD of claim 1 , wherein the control circuitry is further configured to:
detect dry puff conditions at the nicotine EVD based on the estimated steady state resistance value of the heating element; and
disable power to the heating element in response to the detected dry puff conditions.
12 . The nicotine EVD of claim 1 , wherein the control circuitry is further configured to:
detect dry puff conditions at the nicotine EVD based on the estimated steady state resistance value of the heating element; and
prevent power from being applied to the heating element in response to detection of a second application of negative pressure to the nicotine EVD.
13 . A nicotine electronic vaping device (EVD) comprising:
a heating element configured heat nicotine pre-vapor formulation; and
control circuitry configured to,
detect dry puff conditions at the nicotine EVD based on an estimated steady state resistance value of the heating element, and
prevent power from being applied to the heating element in response to detection of a current application of negative pressure to the nicotine EVD and the detected dry puff conditions.
14 . The nicotine EVD of claim 13 , wherein the control circuitry is further configured to:
monitor a resistance value of the heating element over a first time period after a previous application of negative pressure to the nicotine EVD; and
determine the estimated steady state resistance value of the heating element based on the monitored resistance value.
15 . The nicotine EVD of claim 14 , wherein the control circuitry is further configured to:
input the monitored resistance value of the heating element into a trained neural network to determine the estimated steady state resistance value of the heating element.
16 . The nicotine EVD of claim 15 , wherein the monitored resistance value of the heating element includes:
a peak resistance value of the heating element during the first time period; and
at least one additional resistance value of the heating element during the first time period.
17 . The nicotine EVD of claim 16 , wherein the trained neural network is configured to:
estimate the steady state resistance value of the heating element based on the peak resistance value and the at least one additional resistance value.
18 . The nicotine EVD of claim 16 , wherein the trained neural network is a function-fitting network configured to:
receive the peak resistance value and the at least one additional resistance value as input values;
determine a decay in the input values over the first time period; and
output the estimated steady state resistance value of the heating element based on results of the determined decay in the resistance value of the heating element over the first time period.
19 . The nicotine EVD of claim 16 , wherein
the peak resistance value is determined at a time when the power being applied to the heating element is stopped after the previous application of negative pressure to the nicotine EVD.
20 . The nicotine EVD of claim 16 , wherein
the at least one additional resistance value is determined at a time following the time when the peak resistance value is determined.