US 12,755,366 B1Granted

Evaluation of an electric vehicle battery

AssigneeUVeye Ltd.

InventorsAmir Hever, Itai Orr

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Abstract

There is provided a system for non-destructive contact-less evaluation of an electric vehicle (EV) battery of an electric vehicle, comprising: an electromagnetic (EM) transducer(s), an acoustic transducer(s), a thermal camera(s), and a processor(s) configured for: operating the EM transducer(s) and the acoustic transducer(s) for generating EM radiation and acoustic energy towards the EV battery, operating the EM transducer(s), and the acoustic transducer(s) for intercepting EM radiation and acoustic energy reflected off the EV battery and/or passing through the EV battery, operating the thermal camera(s) for capturing a thermal image(s) of the vehicle, extracting features from the intercepted EM radiation, the acoustic energy, and the thermal image(s), generating a multi-dimensional dataset by aggregating the features, feeding the multi-dimensional dataset into a machine learning model, and obtaining from the machine learning model, an indication of an anomaly in the EV battery.

Claims21

Dependent claims

A system for non-destructive contact-less evaluation of an electric vehicle (EV) battery of an electric vehicle, comprising:

at least one electromagnetic (EM) transducer configured for generating EM radiation and for intercepting the EM radiation;
at least one acoustic transducer configured for generating acoustic energy towards the EV battery and for intercepting the acoustic energy reflected off the EV battery or passing through the EV battery;
at least one thermal camera;
a data interface configured to access and/or receive the intercepted EM radiation and the intercepted acoustic radiation;
and
at least one processor configured for:
operating the at least one EM transducer and the at least one acoustic transducer for generating EM radiation and acoustic energy towards the EV battery;
operating the at least one EM transducer, and the at least one acoustic transducer for intercepting EM radiation and acoustic energy reflected off the EV battery and/or passing through the EV battery;
operating the at least one thermal camera for capturing at least one thermal image of the vehicle;
extracting a plurality of features from the intercepted EM radiation, the acoustic energy, and the at least one thermal image;
generating a multi-dimensional dataset by aggregating the plurality of features;
feeding the multi-dimensional dataset into a machine learning model;
obtaining from the machine learning model, an indication of an anomaly in the EV battery;
operating the
at least one EM transducer over a time interval during which the electric vehicle is in motion relative to the at least one EM transducer;
computing a plurality of datasets of the intercepted EM radiation during the time interval by performing synthetic aperture radar (SAR) processing;
and
computing at least one field variation map by processing the plurality of datasets,
wherein the plurality of features for generating the multi-dimensional dataset include the at least one field variation map.

16 more elements

A system for non-destructive contact-less evaluation of an electric vehicle (EV) battery of an electric vehicle, comprising:

at least one electromagnetic (EM) transducer configured for generating EM radiation and for intercepting the EM radiation;
at least one acoustic transducer configured for generating acoustic energy towards the EV battery and for intercepting the acoustic energy reflected off the EV battery or passing through the EV battery;
at least one thermal camera;
a data interface configured to access and/or receive the intercepted EM radiation and the intercepted acoustic radiation;
and
at least one processor configured for:
operating the at least one EM transducer and the at least one acoustic transducer for generating EM radiation and acoustic energy towards the EV battery;
operating the at least one EM transducer, and the at least one acoustic transducer for intercepting EM radiation and acoustic energy reflected off the EV battery and/or passing through the EV battery;
operating the at least one thermal camera for capturing at least one thermal image of the vehicle;
extracting a plurality of features from the intercepted EM radiation, the acoustic energy, and the at least one thermal image;
generating a multi-dimensional dataset by aggregating the plurality of features;
feeding the multi-dimensional dataset into a machine learning model;
obtaining from the machine learning model, an indication of an anomaly in the EV battery;
and operating the
at least one acoustic transducer over a time interval during which the electric vehicle is in motion relative to the at least one acoustic transducer, for generating vibrations by the motion of the electric vehicle for exciting latent defects in the EV battery,
wherein the at least one acoustic transducer intercepts the acoustic energy while the electric vehicle is in motion,
wherein the plurality of features extracted from the acoustic energy intercepted while the electric vehicle is in motion include abnormal acoustic signatures.

14 more elements

A system for training a machine learning model, comprising:

at least one electromagnetic (EM) transducer configured for generating EM radiation and for intercepting the EM radiation;
at least one acoustic transducer configured for generating acoustic energy towards an EV battery installed within a vehicle and for intercepting the acoustic energy reflected off the EV battery or passing through the EV battery;
at least one thermal camera;
a data interface configured to access and/or receive the intercepted EM radiation and the intercepted acoustic radiation;
and
at least one processor configured for:
generating training dataset comprising a plurality of records for a plurality of sample vehicles each with a sample EV battery installed therein, each record generated by:
operating the at least one EM transducer and the at least one acoustic transducer for generating EM radiation and acoustic energy towards the sample EV battery;
operating the at least one EM transducer, and the at least one acoustic transducer for intercepting EM radiation and acoustic energy reflected off the sample EV battery or passing through the sample EV battery;
operating the at least one thermal camera for capturing at least one thermal image of the sample vehicle;
extracting a plurality of features from the intercepted EM radiation, the acoustic energy, and the at least one thermal image;
generating a multi-dimensional dataset by aggregating the plurality of features;
and
defining a ground truth indicating whether the sample EV battery of the sample electric vehicle has an anomaly or not;
and
training a machine learning model on the training dataset, wherein the:
sample vehicle is in motion with respect to the at least one EM transducer, the at least one acoustic transducer, and the at least one thermal camera.

14 more elements

Dependencies are inferred from the claim text.