Abstract: A method and a system for detecting vehicle battery cell imbalance are provided. The method includes: acquiring a plurality of raw vehicle data of a target vehicle type in a preset time period; grouping the plurality of raw vehicle data into a predefined number of bins based on the data type and the value range of raw vehicle data; determining an observed bin-level cell voltage imbalance of the bin based on the raw vehicle data in each bin; building machine learning model to predict bin-level cell voltage imbalance of each bin, based on bin parameters defined; determining a difference between observed bin-level cell voltage imbalance and predicted bin-level cell voltage imbalance of each bin, to obtain an unaccounted bin-level cell voltage imbalance; and determining whether there is a high risk of cell imbalance in a target vehicle based on a weighted average of unaccounted bin-level cell voltage imbalances.

Abstract: A method and a system for predicting battery health based on distance driven on a full battery load with machine learning model are provided. The method includes: obtaining historical vehicle telematics of vehicles, wherein the historical vehicle telematics comprise at least one of the following: odometer readings, battery SOC, vehicle speed, battery-module temperatures, and battery-cell voltages; creating a distance driven model according to a relationship between a distance driven on a full battery load of vehicles and the historical vehicle telematics; obtaining a distance driven on a full battery load of a vehicle based on the distance driven model by using real-time vehicle telematics of the vehicle as model input; predicting battery health of the vehicle by comparing the obtained distance with a reference distance value.