Our Technology
A look at the innovation behind Qnovo
Heading
Lorem ipsum
Our adaptive charging algorithms sit at the intersection of battery chemistry, data and software
When it comes to accelerating battery degradation, charging is a top factor. Adaptive algorithms utilize charging to diagnose the internal properties of every battery in real-time.
Our data provides continuous diagnostics of a battery's age while our algorithms optimize the rate and degree of charging, reducing battery wear and maximizing battery life.
The adaptive charging algorithms repeat this process every time the battery is plugged into the charger.
Our data provides continuous diagnostics of a battery's age while our algorithms optimize the rate and degree of charging, reducing battery wear and maximizing battery life.
The adaptive charging algorithms repeat this process every time the battery is plugged into the charger.
When it comes to accelerating battery degradation, charging is a top factor. Adaptive algorithms utilize charging to diagnose the internal properties of every battery in real-time.
Our data provides continuous diagnostics of a battery's age while our algorithms optimize the rate and degree of charging, reducing battery wear and maximizing battery life.
The adaptive charging algorithms repeat this process every time the battery is plugged into the charger.
Our data provides continuous diagnostics of a battery's age while our algorithms optimize the rate and degree of charging, reducing battery wear and maximizing battery life.
The adaptive charging algorithms repeat this process every time the battery is plugged into the charger.
Though statistically infrequent, batteries can explode or cause deadly thermal events. Our predictive algorithms anticipate such hazardous failures before they occur.
Our algorithms rely on detailed chemical models of the battery and machine-learning from a vast amount of field data, detecting the presence of latent defects or excessive internal degradation.
Defects may originate during the manufacturing of the battery, or during the assembly process of the battery pack or the smartphone. Defects may include the presence of lithium metal plating inside the battery, external mechanical damage or even the use of counterfeit batteries.
Our algorithms rely on detailed chemical models of the battery and machine-learning from a vast amount of field data, detecting the presence of latent defects or excessive internal degradation.
Defects may originate during the manufacturing of the battery, or during the assembly process of the battery pack or the smartphone. Defects may include the presence of lithium metal plating inside the battery, external mechanical damage or even the use of counterfeit batteries.
Lorem ipsum
..while field data, chemical models and learning algorithms provide accurate predictions of battery health
Though statistically infrequent, batteries can explode or cause deadly fires. Our predictive algorithms anticipate such hazardous failures before they occur.
Our algorithms rely on detailed chemical models of the battery and machine-learning from a vast amount of field data, detecting the presence of latent defects or excessive internal degradation.
Defects may originate during the manufacturing of the battery, or during the assembly process of the battery pack or the smartphone. Defects may include the presence of lithium metal plating inside the battery, external mechanical damage or even the use of counterfeit batteries.
Our algorithms rely on detailed chemical models of the battery and machine-learning from a vast amount of field data, detecting the presence of latent defects or excessive internal degradation.
Defects may originate during the manufacturing of the battery, or during the assembly process of the battery pack or the smartphone. Defects may include the presence of lithium metal plating inside the battery, external mechanical damage or even the use of counterfeit batteries.
Our Innovation
Real-time diagnostics
of the battery’s electrochemistry and intrinsic material properties using Electrochemical Impedance Spectroscopy (EIS) on the device itself. Diagnostic data collected over the life of the battery provide a unique insight into aging and other degradation mechanisms.
Chemical battery models
use real-time diagnostic data as input to determine optimal charging. These models are constantly learning from field data and are core to making accurate predictions of battery health and safety, as well as determining the optimal rate and depth of discharging (DOD) in order to maximize battery life and lifespan.
Closed-loop feedback
acts in response to the chemical battery models and controls the charging process in real-time. It changes the rate and depth of discharging (DOD) for each individual battery, maximizing battery life. Closed-loop controls adapt to the uniqueness of each battery and address inherent variability in battery manufacturing.
Predictive health and safety
algorithms combine field data with chemical battery models to anticipate excessive degradation or failure. When they detect a safety fault, they shut down the battery before thermal event occurs. Our algorithms provide accurate present and future chemical state-of-health of the battery.
Our adaptive charging algorithms sit at the intersection of battery chemistry, data and software
Heading
When it comes to accelerating battery degradation, charging is a top factor. Adaptive algorithms utilize charging to diagnose the internal properties of every battery in real-time.
Heading
Our data provides continuous diagnostics of a battery's age while our algorithms optimize the rate and degree of charging, reducing battery wear and maximizing battery life.
Heading
The adaptive charging algorithms repeat this process every time the battery is plugged into the charger.
while field data, chemical models and learning algorithms provide accurate predictions of battery health.
Heading
Though statistically infrequent, batteries can explode or cause deadly fires. Our predictive algorithms anticipate such hazardous failures before they occur.
Heading
Our algorithms rely on detailed chemical models of the battery and machine-learning from a vast amount of field data, detecting the presence of latent defects or excessive internal degradation.
Heading
Defects may originate during the manufacturing of the battery, or during the assembly process of the battery pack or the smartphone. Defects may include the presence of lithium metal plating inside the battery, external mechanical damage or even the use of counterfeit batteries.
Our Innovation
Real-time diagnostics
of the battery’s electrochemistry and intrinsic material properties using Electrochemical Impedance Spectroscopy (EIS) on the device itself. Diagnostic data collected over the life of the battery provide a unique insight into aging and other degradation mechanisms.
Our Innovation
Chemical battery models
use real-time diagnostic data as input to determine optimal charging. These models are constantly learning from field data and are core to making accurate predictions of battery health and safety, as well as determining the optimal rate and degree of charging in order to maximize battery life and lifespan.
Our Innovation
Closed-loop feedback
acts in response to the chemical battery models and controls the charging process in real-time. It changes the rate and degree of charging for each individual battery, maximizing battery life. Closed-loop controls adapt to the uniqueness of each battery and address inherent variability in battery manufacturing.
Our Innovation
Predictive health and safety
algorithms combine field data with chemical battery models to anticipate excessive degradation or failure. When they detect a safety fault, they shut down the battery before an explosion or fire occurs. Our algorithms provide accurate present and future chemical state-of-health of the battery.