Systems, methods, and storage media for generating a predicted discharge profile of a vehicle battery pack are disclosed. A method includes receiving, by a processing device, data pertaining to cells within a battery pack installed in each vehicle of a fleet of vehicles operating under a plurality of conditions, the data received from at least one of each vehicle in the fleet of vehicles, providing, by the processing device, the data to a machine learning server, directing, by the processing device, the machine learning server to generate a predictive model, the predictive model based on machine learning of the data, generating, by the processing device, the predicted discharge profile of the vehicle battery pack from the predictive model, and providing the discharge profile to an external device.

The present disclosure discloses a system for vehicle battery life diagnosis, the system comprised of a measuring unit which is installed inside a vehicle and measures a state of charge change and a temperature of a vehicle battery, a calculating unit which calculates battery relaxation voltage when a battery power line is cut off, and a diagnosis unit which uses the state of charge change and temperature measured at the measuring unit and the battery relaxation voltage calculated at the calculating unit to diagnose battery life and status.

The present disclosure discloses a system for vehicle battery life diagnosis, the system comprised of a measuring unit which is installed inside a vehicle and measures a state of charge change and a temperature of a vehicle battery, a calculating unit which calculates battery relaxation voltage when a battery power line is cut off, and a diagnosis unit which uses the state of charge change and temperature measured at the measuring unit and the battery relaxation voltage calculated at the calculating unit to diagnose battery life and status.

A self-regulating battery may generate a basis state for one or more modules using one or more system inputs. The system inputs comprise current, voltage, and battery degradation. The battery generates a family of weighted forecasts for future bus demands using usage and performance based weights. The battery generates one or more plans to support demand from the battery using the weighted forecasts. The battery scores the one or more plans based on efficiency of power extraction from the battery, and combines the scored one or more plans with an updated SoH value based on induced degradation from a usage plan. The battery generates the combined scored for each of the one or more plans, and transmits one of the combined scored to the battery management unit for execution.

A vehicle battery diagnosis method and system are provided. A controller completes charging a battery by stopping supply of current from a charger to the battery. The controller measures a relaxation voltage corresponding to a decrement of a voltage of the battery during a predetermined time period directly after the charging of the battery is completed. The controller estimates the state of health (SoH) of the battery in accordance with the relaxation voltage.

A vehicle battery diagnosis method and system are provided. A controller completes charging a battery by stopping supply of current from a charger to the battery. The controller measures a relaxation voltage corresponding to a decrement of a voltage of the battery during a predetermined time period directly after the charging of the battery is completed. The controller estimates the state of health (SoH) of the battery in accordance with the relaxation voltage.

A system or method for determining a battery state can include generating a set of models based on a measured response of a plurality of batteries to an applied load, measuring battery properties of a battery, and using a state estimator to determine a battery state associated with a battery.

A system or method for determining a battery state can include generating a set of models based on a measured response of a plurality of batteries to an applied load, measuring battery properties of a battery, and using a state estimator to determine a battery state associated with a battery.

A deterioration estimation apparatus includes a storage processing unit and a calculation unit. The storage processing unit acquires a plurality of models from a model generation apparatus, and stores the models in a model storage unit. A plurality of models are generated by performing machine-learning on training data, the training data using, as input values, measurement data for training indicating a result of measuring a state of a storage battery when the number of charge and discharge times is α.sub.i to α.sub.j (where j≥i), and using, as a target value, SOH indicating a deterioration state of the storage battery when the number of charge and discharge times is β (where β>α.sub.j). The calculation unit uses a plurality of models stored in a model storage unit to calculate an estimation result of transition of SOH of a storage battery managed by the deterioration estimation apparatus.

A deterioration estimation apparatus includes a storage processing unit and a calculation unit. The storage processing unit acquires a plurality of models from a model generation apparatus, and stores the models in a model storage unit. A plurality of models are generated by performing machine-learning on training data, the training data using, as input values, measurement data for training indicating a result of measuring a state of a storage battery when the number of charge and discharge times is α.sub.i to α.sub.j (where j≥i), and using, as a target value, SOH indicating a deterioration state of the storage battery when the number of charge and discharge times is β (where β>α.sub.j). The calculation unit uses a plurality of models stored in a model storage unit to calculate an estimation result of transition of SOH of a storage battery managed by the deterioration estimation apparatus.