A system is provided for vehicle range prediction. The system determines a change in mass to a vehicle while driving. Additionally, the system calculates a vehicle load in response to determining the change in mass and adjusts a vehicle range in response to calculating the vehicle load. The vehicle range is indicative of a distance in which the vehicle is predicted to travel with a remaining fuel. The adjusted vehicle range is based on the vehicle load.

In one aspect, a method for a cloud-based computing system may include training, using test data, machine learning models to predict a remaining useful life of each cell of a battery pack of a vehicle. The method may include using a rule-based evaluator to determine first scores for the machine learning models, using a machine learning based metric evaluator to determine second scores for the machine learning models, using a model selection inference engine to select, based on the first and second scores for the machine learning models, a machine learning model to use to predict the remaining useful life of each cell of the battery pack of the vehicle, and transmitting, to a processing device of the vehicle, the selected machine learning model and parameters to predict the remaining useful life of each cell of the battery pack of the vehicle.

A method for controlling electrical connection of at least two battery packs (202, 203) of an energy storage System (200) of a vehicle (201) to a common load during operation of the vehicle, each one of the battery packs being connectable to the load via at least one respective switching device, the method comprising: —receiving measurement data relating to current operating conditions of the energy storage System, —based on at least the measurement data, estimating at least one battery state of each one of the battery packs, wherein said at least one battery state is at least one of an open circuit voltage and a state of charge, —based on the estimated at least one battery state of each one of the battery packs, controlling electrical connection of each battery pack to the load via the at least one respective switching device.

A solution is disclosed for a state estimation system and method configured for a hyperloop vehicle. Further, the state estimation system provides an estimate of the future position and/or orientation of the hyperloop vehicle such that the hyperloop vehicle can maintain safe, efficient flight during a journey. The state estimation system utilizes a number of sensors to gather data in order to perform state estimation using a Kalman filter. The state estimation is then sent to a motion execution controller such that the state estimation may be translated into commands for engines disposed throughout the hyperloop vehicle such that the position and/or orientation may be reached by hyperloop vehicle.

The present disclosure relates to a method and apparatus for determining a state of charge (SOC) of a battery and a battery management system (BMS), so as to resolve a problem such as inaccurate estimation of the SOC. The method includes: acquiring state data of the battery, where the state data comprises current data and voltage data; determining each element parameter value in an equivalent circuit model of the battery based on the equivalent circuit model, error information, battery characteristic data, and the state data by using a recursive least square (RLS) prediction model; and determining an estimated value of the SOC of the battery based on the element parameter value in the equivalent circuit model, the state data, and the battery characteristic data according to a technique of an observer.

Techniques regarding parameterizing energy consumption of an electric vehicle are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a vehicle state estimation component that determines an operating condition experienced by a vehicle while traveling a route. Further, the system can comprise an energy consumption component that parametrizes an amount of energy expended by the vehicle while traveling the route based on a loss table that is populated with an energy consumption value derived from historic operation of the vehicle at the operating condition.

A vehicle air conditioning apparatus is provided that can extend the mileage of a vehicle by reducing the power consumed by the operation of a compressor and a heater. When a required quantity of heating Q_req is acquired, the minimum power sharing ratio between quantity of heat release Q_hpof a water-refrigerant heat exchanger 22 and quantity of heat release Q_htrof a water heater 32 is calculated, which allows the power consumption W_total to be minimized, and a compressor 21 and the water heater 32 is operated based on the result of the calculation.

A computer-implemented method is introduced for predicting a residual service life of vehicle batteries of a fleet of electric vehicles. In the method, parameters of the vehicle batteries are measured during the operation of the electric vehicles and transmitted to a server; a conditional probability is determined that the residual service life of a specific vehicle battery undershoots a predefined limit value at a point in time lying in the past; and the residual service life of vehicle batteries of the fleet is predicted as a function of the conditional probability.

A method for performance analysis and use management of a battery module is disclosed, wherein the battery module includes a multitude of interconnected battery cells and a battery management system with a plurality of dedicated analysis/control units (ACUs) that analyze performance of the battery module, the ACUs being assigned to individual battery cells and/or battery blocks of battery module. The method includes measuring current and voltage of one or more of an individual battery cell and a battery block; calculating a charge removal from the one or more of the individual battery cell and the battery block; calculating a loading charge of the one or more of the individual battery cell and the battery block; determining the remaining charge of the one or more of the individual battery cell and the battery block; and failure monitoring of the one or more of the individual battery cell and the battery block.

A battery charging device includes a battery pack including a plurality of battery cells connected in series, and a control unit. The control unit is configured to change a series connection of the battery cells in response to a variation in output voltage of a solar battery.