A battery overcharge preventing device according to an embodiment of the present invention includes: a voltage distribution unit connected to both ends of at least one battery cell in a battery module including multiple battery cells, the voltage distribution unit being configured to distribute a voltage of the at least one battery cell according to a preset ratio; a voltage sensing unit operating so as to allow a control current to flow when the voltage distributed by the voltage distribution unit is greater than a preset reference voltage; and a second relay configured to block, by operation of the voltage sensing unit, operation of a first relay that establishes an electrical connection between the battery module and a charging module.

A battery overcharge preventing device according to an embodiment of the present invention includes: a voltage distribution unit connected to both ends of at least one battery cell in a battery module including multiple battery cells, the voltage distribution unit being configured to distribute a voltage of the at least one battery cell according to a preset ratio; a voltage sensing unit operating so as to allow a control current to flow when the voltage distributed by the voltage distribution unit is greater than a preset reference voltage; and a second relay configured to block, by operation of the voltage sensing unit, operation of a first relay that establishes an electrical connection between the battery module and a charging module.

A battery management system (BMS) for a vehicle includes a module for estimating the state of a rechargeable battery, such as its state of charge, in real time. The module includes a learning model for predicting the state of a battery based on the vehicle's usage and related factors unique to the vehicle, in addition to a sensed voltage, current and temperature of a battery.

In an aspect of the present disclosure is a computing device for predicting battery temperature in an electric aircraft, the computing device including a controller communicatively connected to the electric aircraft, the controller comprising: a battery model, the battery model configured to: receive a flight plan; instantiate a machine-learning model, wherein the machine-learning model is trained as a function of a training set correlating flight plan data to battery temperature labels.

In an aspect of the present disclosure is a computing device for predicting battery temperature in an electric aircraft, the computing device including a controller communicatively connected to the electric aircraft, the controller comprising: a battery model, the battery model configured to: receive a flight plan; instantiate a machine-learning model, wherein the machine-learning model is trained as a function of a training set correlating flight plan data to battery temperature labels.

A method of coordinating a fleet of vehicles includes receiving a first preconditioning characteristic of a first battery from a first vehicle of the fleet of vehicles. The method further includes receiving a second preconditioning characteristic of a second battery from a second vehicle of the fleet of vehicles. The method further includes comparing the first preconditioning characteristic and the second preconditioning characteristic to determine a preconditioning ranking for the first vehicle and the second vehicle. The method further includes determining a queue of the first and second vehicles for a charging station using the preconditioning ranking of the first and second vehicles.

A method of coordinating a fleet of vehicles includes receiving a first preconditioning characteristic of a first battery from a first vehicle of the fleet of vehicles. The method further includes receiving a second preconditioning characteristic of a second battery from a second vehicle of the fleet of vehicles. The method further includes comparing the first preconditioning characteristic and the second preconditioning characteristic to determine a preconditioning ranking for the first vehicle and the second vehicle. The method further includes determining a queue of the first and second vehicles for a charging station using the preconditioning ranking of the first and second vehicles.

An example apparatus includes: a gate driver with a control output terminal, a power transistor with a gate terminal and a first current terminal, the gate terminal coupled to the control output terminal, and drain-derived supply circuitry with an output coupled to the first current terminal.

An example apparatus includes: a gate driver with a control output terminal, a power transistor with a gate terminal and a first current terminal, the gate terminal coupled to the control output terminal, and drain-derived supply circuitry with an output coupled to the first current terminal.

A vehicle includes a battery, an electric machine, and a controller. The battery has a state of charge. The electric machine is configured to draw electrical power from the battery to propel the vehicle in response to an acceleration request and to deliver electrical power to the battery to recharge the battery. The controller is programmed to adjust an estimation of battery state of charge based on a feed forward control that includes a coulomb counting algorithm, a first feedback control that includes a first battery model, and a second feedback control that includes a second battery model. The controller is further programmed to control the electrical power flow between the battery and the electric machine based on the estimation of the state of charge of the battery.