Fca US LLC

Mcmaster University

Other

Pawel Malysz

Ali Emadi

Hong Yang

Jin Ye

Ran Gu

Jian Lin

Deqiang Wang

Shahin Sirouspour

More accurate and robust battery state estimation (BSE) techniques for a battery system of an electrified vehicle include estimating a current bias or offset generated by a current sensor and then adjusting the measured current to compensate for the estimated current bias. The techniques obtain nominal parameters for a battery model of the battery system based on a measured temperature and an estimated open circuit voltage (OCV). The techniques use these nominal parameters and the corrected measured current to estimate the OCV, a capacity, and an impedance of the battery system. The techniques utilize the OCV to estimate a state of charge (SOC) of the battery system. The techniques also estimate a state of health (SOH) of the battery system based on its estimated capacity and impedance. The techniques then control the electrified vehicle based on the SOC and/or the SOH.

Techniques for robust battery state estimation

A battery management system for an electrified powertrain of a hybrid vehicle includes one or more sensors configured to measure voltage, current, and temperature for a battery system of the hybrid vehicle and a controller. The controller is configured to obtain an equivalent circuit model for the battery system, determine a set of states for the battery system to be estimated, determine a set of parameters for the battery system to be estimated, receive, from the sensor(s), the measured voltage, current, and temperature for the battery system, using the equivalent circuit model and the measured voltage, current, and temperature of the battery system, estimate the sets of states and parameters for the battery system using a mixed sigma-point Kalman filtering (SPKF) and recursive least squares (RLS) technique, and using the sets of estimated states/parameters for the battery system, control an electric motor of the electrified vehicle.

Battery state and parameter estimation using a mixed sigma-point kalman filtering and recursive least squares technique

Systems, methods, and devices related to a microgrid system for providing power to a facility. A self-contained power system provides power to a facility using a combination of power storage elements and renewable energy sources. A connection to an external power grid may also be provided. The system optimizes power flow to the facility using power from the storage elements and the renewable energy sources and, if necessary, the external power grid. The optimization process predicts future power usage by the facility using power usage data from a predetermined time window. The optimization process can also take into account predicted energy generation amounts by the renewable energy sources. To optimize economic effects, the optimization process can also determine whether to buy and when to buy power from the external power grid.

Adaptive energy management system

An electrified vehicle and method for estimating peak power of a battery system of the electrified vehicle are presented. In one exemplary implementation, the method includes receiving, at a controller of the electrified vehicle, measured current, voltage, and temperature of the battery system and determining, at the controller, operating parameters for the battery system based on the measured current, voltage, and temperature. An initial peak current at a start of a current prediction period for the battery system is determined, at the controller, based on the operating parameters, and an instantaneous peak current of the battery system is determined based on its initial peak current by performing voltage-limited extrapolation of resistances and open-circuit voltage (VLERO) of a battery model for the battery system. The battery system and an electric motor of the electrified vehicle are controlled, by the controller, based on the instantaneous peak current.

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Hamilton, Canada

Pawel Malysz