A battery charger and method is disclosed for detecting when a battery has a low state of health while simultaneously charging or maintaining the battery. A battery charger includes a processor; a non-transitory memory device; a power management device to receive an input power and to output a charging current; a pair of electrical conductors to electrically couple with a battery, and a display electrically coupled to the processor. The display being configured to indicate a bad battery indicator when the battery has a low state of health and whether the battery is good to start.

A power source control system for a vehicle is provided, which includes a traction motor, the system including a first battery that is a bipolar battery and is to be used as a power source for the traction motor; a second battery that is a bipolar battery different from the first battery; a mirror current generator circuit configured to generate a mirror current based on a current flowing through the first battery; a mirror current supply source circuit configured to cause the mirror current to flow through the second battery; and a diagnostic circuit configured to perform degradation diagnosis on the second battery on a basis of at least one of a voltage or a temperature of the second battery.

An apparatus or method for calibrating a battery emulator is proposed. The battery emulator emulates a plurality of cells connected in series, wherein each emulated cell has taps over which at least one emulated quantity is tapped, wherein the apparatus comprises a switching apparatus via which a calibration standard is switchably connectable with different taps.

An apparatus or method for calibrating a battery emulator is proposed. The battery emulator emulates a plurality of cells connected in series, wherein each emulated cell has taps over which at least one emulated quantity is tapped, wherein the apparatus comprises a switching apparatus via which a calibration standard is switchably connectable with different taps.

A protection circuit for a simulated battery cell having an output comprising an output voltage includes: at least one MOSFET connected to the output of the simulated battery cell for short-circuiting the output of the simulated battery cell; a capacitor connected to a gate electrode of the at least one MOSFET; an overvoltage detection device configured to charge the capacitor with the output voltage based on an overvoltage limit of the output voltage being exceeded; and a threshold voltage detection device configured to release the gate electrode based on a threshold value of a voltage at the capacitor not being reached.

A protection circuit for a simulated battery cell having an output comprising an output voltage includes: at least one MOSFET connected to the output of the simulated battery cell for short-circuiting the output of the simulated battery cell; a capacitor connected to a gate electrode of the at least one MOSFET; an overvoltage detection device configured to charge the capacitor with the output voltage based on an overvoltage limit of the output voltage being exceeded; and a threshold voltage detection device configured to release the gate electrode based on a threshold value of a voltage at the capacitor not being reached.

An artificial intelligence (AI)-based charging curve reconstruction and state estimation method for a lithium-ion battery is provided to estimate various states of a battery. In the method, a complete charging curve is reconstructed through deep learning with charging data segments as input. Then, a plurality of states of the battery can be extracted from the complete charging curve, including a maximum capacity, maximum energy, a state of charge (SOC), a state of energy (SOE), a state of power (SOP), and a capacity increment curve. The battery charging curve reconstruction and state estimation method is adaptively updated with a change in a working state of the battery.

An artificial intelligence (AI)-based charging curve reconstruction and state estimation method for a lithium-ion battery is provided to estimate various states of a battery. In the method, a complete charging curve is reconstructed through deep learning with charging data segments as input. Then, a plurality of states of the battery can be extracted from the complete charging curve, including a maximum capacity, maximum energy, a state of charge (SOC), a state of energy (SOE), a state of power (SOP), and a capacity increment curve. The battery charging curve reconstruction and state estimation method is adaptively updated with a change in a working state of the battery.

A battery charger and method is disclosed for detecting when a battery has a low state of health while simultaneously charging or maintaining the battery. A battery charger includes a processor; a non-transitory memory device; a power management device to receive an input power and to output a charging current; a pair of electrical conductors to electrically couple with a battery, and a display electrically coupled to the processor. The display being configured to indicate a bad battery indicator when the battery has a low state of health and whether the battery is good to start.

An illustrative battery charging device may identify a battery to be charged, and charge the identified battery using charge settings that are optimized for the identified battery. In some embodiments, the battery charging device may determine the optimized settings based on monitoring charging performance and discharge activities of the battery over time. The battery charging device may exchange data with a battery management service device, such as by exchanging battery health information, battery settings, and/or other data. The battery charging device may determine charge setting and times to charge a battery that is intended to power an unmanned aerial vehicle to complete a flight path.