Battery state of health estimator

A computer-implemented method for determining a state of health indication for a battery, comprising:

• receiving characteristics and process parameters for the battery;
• training a first supervised deep neural network (“DNN”), where the received characteristics are inputs to the first supervised DNN and the received process parameters are outputs of the first supervised DNN;
• training an unsupervised AI estimator using one or more clustering methods based on extracted features from the first supervised DNN, where the received characteristics are inputs to the unsupervised AI estimator;
• training a second supervised DNN using identified clusters from the unsupervised AI estimator, where the received characteristics are inputs to the second supervised DNN;
• validating the identified clusters with state of health indications;
• and
• determining the state of health indication for the battery using the second supervised DNN.

A non-transitory computer readable medium encoded with instructions that when executed by at least one processor causes the processor to carry out the following operations:

• receiving characteristics and process parameters for a battery;
• training a first supervised deep neural network (“DNN”), where the received characteristics are inputs to the first supervised DNN and the received process parameters are outputs of the first supervised DNN;
• training an unsupervised AI estimator using one or more clustering methods based on extracted features from the first supervised DNN, where the received characteristics are inputs to the unsupervised AI estimator;
• training a second supervised DNN using identified clusters from the unsupervised AI estimator, where the received characteristics are inputs to the second supervised DNN;
• validating the identified clusters with state of health indications;
• and
• determining a state of health indication for the battery using the second supervised DNN.