A method for thermal preconditioning at least one thermal buffer in a thermal system of a vehicle, the thermal system being a rechargeable energy storage system, RESS, and/or an energy transformation system comprising fuel cells. The method includes providing scheduled operational information of the thermal buffer, the scheduled operational information comprising a scheduled initialization time and scheduled operational load of the thermal buffer, determining whether the thermal buffer is in need of cooling or heating in order to reach a pre-determined temperature level, preconditioning the thermal buffer in accordance with the scheduled operational information such that the thermal buffer is thermally preconditioned by cooling or heating to the pre-determined level in accordance with the scheduled operational load at a time in accordance with the scheduled initialization time.

Methods and apparatuses for accurately estimating an internal temperature of a secondary battery with a charging rate SOC and a charge/discharge state taken into account are provided. A method may include a method of estimating an internal temperature (T) of a secondary battery for a vehicle, which includes a change determination process to be repeatedly performed and an internal temperature estimation process to be repeatedly performed.

Embodiments of this application provide a battery management system (BMS) wake-up method, a BMS wake-up apparatus, and a storage medium, and pertains to the field of battery safety technologies. In this application, before a BMS hibernates, a wake-up threshold corresponding to first sampling information is determined based on an obtained extremum of the first sampling information of a battery cell. After the BMS hibernates, a sampling chip collects second sampling information of the battery cell when the BMS is hibernating, and transmits a wake-up signal to the BMS when it is determined that the second sampling information of the battery cell reaches the wake-up threshold. In this way, the BMS can be woken up in a timely manner by the wake-up signal, and therefore the BMS can find battery safety problems in a timely manner.

A battery temperature control apparatus for electric vehicles, the battery temperature control apparatus including a temperature measurement unit configured to measure a temperature of a battery mounted in an electric vehicle, a display unit configured to display state information of the battery based on temperature information of the battery measured by the temperature measurement unit, and a controller configured to perform control such that a preconditioning function to maintain the temperature of the battery at a predetermined optimum temperature is selectively turned ON/OFF by a user based on the state information displayed on the display unit.

In one aspect, computer-implemented method may include receiving, from a cloud-based computing system, one or more machine learning model parameters that are configured to enable predicting a remaining useful life of each cell of a battery pack of a vehicle. The method may include loading, into memory of a processing device at the vehicle, the one or more machine learning model parameters, receiving data comprising one or more measurements and one or more user battery usage profiles, and based on the data, executing a trained machine learning model with the one or more parameters to input the data and to output the remaining useful life of each cell of the battery pack.

The disclosure set forth herein is directed to battery devices and methods therefor. More specifically, embodiments of the instant disclosure provide a battery electrode that comprises both intercalation chemistry material and conversion chemistry material, which can be used in automotive applications. There are other embodiments as well.

A temperature control device for temperature control of a battery system has at least one battery subsystem. The temperature control device has a temperature control line for conducting a temperature control fluid and a pump device for generating a flow of the temperature control fluid in the temperature control line at least in a first flow direction. The temperature control line has at least one temperature control section which can be thermally conductively connected to the at least one battery subsystem for supplying and/or discharging thermal energy to or from the battery subsystem.

Power supply system mounted in electric vehicle includes voltage measurement unit that measures a voltage of each of a plurality of cells to ensure both safety of an electric vehicle and convenience of a user. Current measurement unit therein measures a current flowing through the plurality of cells. Temperature measurement unit therein measures a temperature of the plurality of cells. Controller therein determines a current limit value defining an upper limit of a current for suppressing cell deterioration and ensuring safety based on the voltage, the current, and the temperature of each of the plurality of cells measured by voltage measurement unit, current measurement unit, and temperature measurement unit respectively, and that notifies a higher-level controller in electric vehicle of the determined current limit value.

An apparatus and method for electrochemical energy storage for high-power and high-energy autonomous applications, including autonomous electric vehicles having remote active drive cycle monitoring and/or governance and thermal management control, are described. For autonomous vehicles, the apparatus includes: at least one high-power, low-energy density tertiary storage battery having low cost, and designed to wear and be replaceable; at least one high energy density core battery; at least one intermediate power and energy density secondary battery for buffering the load on the core battery; and a battery controller. The autonomous vehicle energy requirement and consumption rate are provided in such a manner that performance degradation over the life of the system is reduced.

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.