Abstract: A system for monitoring a battery assembly includes a processing device configured to receive measurement data from a plurality of battery components, and input the measurement data to a battery model configured to determine parametric data. Based on the battery model, the processing device is configured to acquire the parametric data, extract statistical information based on at least one parameter of each battery component, and input the statistical information to a failure identification module that includes a first classifier configured to determine whether the battery assembly is in a failure condition based on the statistical information. The processing device is configured to output a health indicator having a first value indicating that the battery assembly is healthy based on first classifier determining that the battery assembly is in the healthy condition, and a faulty value based on the first classifier determining that the battery assembly is in a failure condition.

Abstract: A system for monitoring a battery assembly includes a processing device configured to receive measurement data from a plurality of battery components, and input the measurement data to a battery model configured to determine parametric data. Based on the battery model, the processing device is configured to acquire the parametric data, extract statistical information based on at least one parameter of each battery component, and input the statistical information to a failure identification module that includes a first classifier configured to determine whether the battery assembly is in a failure condition based on the statistical information. The processing device is configured to output a health indicator having a first value indicating that the battery assembly is healthy based on first classifier determining that the battery assembly is in the healthy condition, and a faulty value based on the first classifier determining that the battery assembly is in a failure condition.

Abstract: A cloud-based platform server includes a memory, a receiving module, and a reporting module. The memory stores a DTC configuration table including DTC rules and a DTC data table. The receiving module: receives DTC configuration information transmitted from a configuration server to the cloud-based platform server and DTC data uploaded from a data server to the cloud-based platform server, where at least some of the DTC data was originally generated by multiple vehicles; based on one or more selected vehicle platforms, parse the DTC configuration information and the DTC data; convert the parsed DTC configuration information to a standardized format and the parsed DTC data having different formats to a standardized format; generate or update the DTC configuration table based on the parsed and converted configuration information; and generate or update the DTC data table based on the parsed and converted DTC data and the DTC rules.

Abstract: A system for validating diagnostic trouble codes (DTCs) in vehicles includes a processor and a memory storing instructions which when executed by the processor configure the processor to receive DTC data from the vehicles, filter the DTC data using predefined rules, generate one or more metrics based on the filtered DTC data, and determining based on the metrics whether the DTCs meet specifications for the DTCs.

Abstract: A method of using an adaptive fault diagnostic system for motor vehicles is provided. A diagnostic tool collects unlabeled data associated with a motor vehicle, and the unlabeled data is transmitted to a central computer. An initial diagnostic model and labeled training data associated with previously identified failure modes and known health conditions are transmitted to the central computer. The central computer executes a novelty detection technique to determine whether the unlabeled data is novelty data corresponding with a new failure mode or normal data corresponding with one of the previously identified failure modes or known health conditions. The central computer selects an informative sample from the novelty data. A repair technician inputs a label for the informative sample, and the central computer propagates the label from the informative sample to the associated novelty data. The central computer updates the labeled training data to include the labeled novelty data.

Abstract: A method of using an adaptive fault diagnostic system for motor vehicles is provided. A diagnostic tool collects unlabeled data associated with a motor vehicle, and the unlabeled data is transmitted to a central computer. An initial diagnostic model and labeled training data associated with previously identified failure modes and known health conditions are transmitted to the central computer. The central computer executes a novelty detection technique to determine whether the unlabeled data is novelty data corresponding with a new failure mode or normal data corresponding with one of the previously identified failure modes or known health conditions. The central computer selects an informative sample from the novelty data. A repair technician inputs a label for the informative sample, and the central computer propagates the label from the informative sample to the associated novelty data. The central computer updates the labeled training data to include the labeled novelty data.