Systems and methods for predicting loose connection in battery energy storage system for electric vehicle

A method for battery health monitoring in a battery energy storage system, the method comprising:

• receiving, by one or more processors, operational data from a plurality of batteries of the battery energy storage system;
• applying, by the one or more processors, a machine learning model to the operational data from the plurality of batteries to generate a determination that the operational data meets one or more threshold values associated with a battery health assessment, wherein applying the machine learning model to the operational data includes:
• detecting, by the one or more processors, one or more abnormal current patterns in the operational data,
• detecting, by the one or more processors, one or more connection failure progressions based on the one or more abnormal current patterns and historical degradation data in the operational data, and
• generating, by the one or more processors, the battery health assessment based on the one or more abnormal current patterns and the one or more connection failure progressions;
• and
• initiating, by the one or more processors, a maintenance action when the battery health assessment meets the one or more threshold values.

A method for training a machine learning model to predict a health of a battery in a battery energy storage system, the method comprising:

• receiving, by one or more processors, first data associated with each of a plurality of batteries of the battery energy storage system, wherein the first data includes a plurality of operational parameters;
• extracting, by the one or more processors, one or more features from the first data based on analyzing processing algorithms identifying one or more battery operational parameters;
• training, by the one or more processors, the machine learning model to determine correlations between the plurality of operational parameters and one or more health assessment values based on the one or more features;
• and
• validating, by the one or more processors, the machine learning model using validation data.