Abstract: A method for brand identification of an electric vehicle to a charging station includes connecting a plug of the charging station to a socket of the electric vehicle, receiving a control pilot signal at the socket from the charging station, terminating the control pilot signal in a variable resistance in the electric vehicle, and toggling the variable resistance in a vehicle pattern that transitions between two charging state resistances. The charging station detects the vehicle pattern and maps the vehicle pattern to a vehicle brand of the electric vehicle. The method includes receiving electrical current to recharge the electric vehicle at the socket.

Abstract: A method of controlling a fuel battery system of the present disclosure is a method of controlling a fuel battery system, including a measurement process in which a power generation voltage at a predetermined current density of a fuel battery cell is measured, a first calculation process in which a poisoning rate of an electrode catalyst at the power generation voltage measured in the measurement process is calculated from a predetermined relationship between the power generation voltage at the predetermined current density and the poisoning rate of the electrode catalyst of the fuel battery cell, and a second calculation process in which a generation rate of hydrogen peroxide at the poisoning rate of the electrode catalyst calculated in the first calculation process is calculated from a predetermined relationship between the poisoning rate of the electrode catalyst and the generation rate of hydrogen peroxide of the fuel battery cell.

Abstract: In a method of estimating an internal degradation state of a degraded cell, a measurement system calculates a target capacity characteristic curve by differentiating current capacity by voltage on a target charge curve of a target battery. Further, the method obtains changes of parameters of different types based on a fitting operation that fits the curve and a reference capacity characteristic curve calculated from reference data. In the fitting operation, whichever of low current capacity regions and high current capacity regions of the curve and the curve have a stronger correlation are fit first and whichever of them have a weaker correlation are fit later.

Abstract: A battery diagnosis apparatus according to the present disclosure includes a voltage measurer for measuring a cell voltage of each of first to nth battery cells and a processor, wherein n is a natural number of 2 or greater. The processor determines an average cell voltage of each battery cell for a first rest period from a first time point to a second time point, based on the cell voltage of each battery cell measured a first number of times for the first rest period, determines an average cell voltage of each battery cell for a second rest period and diagnoses a fault of each battery cell based on the average cell voltage of each battery cell for the first rest period and the average cell voltage of each battery cell for the second rest period.

Abstract: According to an aspect, a battery system for controlling a charging current includes a temperature sensor configured to measure a temperature of a first battery, a state-of-charge (SoC) calculation engine configured to calculate an SoC value of the first battery, and a charge current controller configured to determine an adjusted current according to a charge profile based on the temperature and the SoC value, where the charge current controller is configured to transmit current data that indicates the adjusted current to a device.

Abstract: Disclosed is an estimation method for the safety state of a battery pack based on deep learning and consistency detection, including: acquiring battery parameters of each single battery in the battery pack in a charging process to be identified; calculating multiple groups of feature data according to the battery parameters; constituting a first matrix by the multiple groups of feature data, and calculating a covariance matrix of the first matrix; inputting the covariance matrix into a first trained fully connected layer, so as to extract principal components of the first matrix and obtain a second matrix; multiplying the first matrix and the second matrix to obtain a third matrix; and inputting the third matrix into a series-connected and trained multi-head self-attention layer and classification layer, to identify whether single battery consistency safety hazards exist in the charging process. This embodiment improves the accuracy of identification.

Abstract: A battery cell error determining apparatus including: a magnetic field measurement unit configured to measure a magnetic field generated by a current flowing in a battery cell, and a control unit configured to calculate a capacity of the battery cell by using a current calculated by the magnetic field measured by the magnetic field measurement unit. The control unit determines an error of the battery cell by using the capacity of the battery cell.

Abstract: A method for predicting a life includes the following. Obtain multiple cycle numbers of a battery and multiple capacity data corresponding to the cycle numbers. Obtain multiple sets of first fitting data by fitting the cycle numbers and the capacity data with a bi-exponential empirical model. Obtain multiple sets of second fitting data by fitting the cycle numbers and the capacity data with a box-cox transformation method. Determine a first capacity error according to the multiple sets of first fitting data and the multiple capacity data. Determine a second capacity error according to the multiple sets of second fitting data and the capacity data. Generate a battery-life prediction model according to the first capacity error, the second capacity error, the multiple sets of first fitting data, and the multiple sets of second fitting data. Determine an RUL of the battery according to a predetermined battery-capacity-value and the battery-life prediction model.

Abstract: A battery pack in accordance with an embodiment may include a battery management system (BMS) controlling the battery pack, the BMS includes an internal resistance value calculation module calculating an internal resistance value for each battery cell directly after the battery pack is charged or discharged for a predetermined time, a maximum internal resistance valued battery cell detection module detecting a battery cell having a biggest internal resistance value of the calculated internal resistance values, and a defective battery cell determination module determining a defective battery cell on the basis of a detected number of time each battery cell has the biggest internal resistance value.

Abstract: Energy storage cell qualification and related systems, methods, and devices are disclosed. A method of qualifying rechargeable battery cells includes taking measurements on the rechargeable battery cells, determining specific capacity distributions of the rechargeable battery cells as a function of a number of discharge cycles based on the measurements, determining one or more specific capacity thresholds to separate the specific capacity distributions of the rechargeable battery cells into two or more classifications, and qualifying the rechargeable battery cells into the two or more classifications based, at least in part, on the specific capacity distributions and the one or more specific capacity thresholds. A method of implementing rechargeable battery cells into product manufacturing and qualifying the rechargeable battery cells, and deploying those of the rechargeable battery cells qualified into a first classification of the two or more classifications into the product.

Abstract: In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may determine a historical discharge rate (dh) from multiple discharge rates a battery of an information handling system (IHS) while the IHS operates on electrical power provided by the battery; receive a current discharge rate (dc) of the battery; determine a full charge capacity correction factor value (Cfcc) associated with the battery; determine a weighting factor (?); determine a difference between a full charge capacity value (FCC) and Cfcc; determine ?·dh; determine (1??)·dc; determine a sum of dh·? and dc·?; determine a quotient value of FCC?Cfcc and ?·dh+(1??)·dc; scale the quotient value based at least on a relative state of charge of the battery to determine an estimated time remaining for the IHS to operate on the electrical power provided by the battery; and display the estimated time remaining.

Abstract: An apparatus for monitoring health of one or more cells in a battery includes a health monitoring system configured to measure voltage on one or more cells when a waveform is injected into the one or more cells. The waveform is measured at an end of a pulse for each current.

Abstract: A determination apparatus (10) includes: a criteria value acquisition unit (11) that acquires a criteria value which is a performance value of a storage battery at a criteria time; a target value acquisition unit (12) that acquires a target value which is the performance value of the storage battery at a determination time; a usage method data acquisition unit (13) that acquires usage method data indicating a usage method of the storage battery from the criteria time to the determination time; a predicted range computing unit (14) that computes a predicted range of the performance value at the determination time on the basis of the criteria value, the usage method data, and a time elapsed from the criteria time to the determination time; and a determination unit (15) that determines that the storage battery is not to be compensated if the target value is within the predicted.

Abstract: In order to estimate states of health of batteries storing electrical energy significantly indicated by battery capacity and battery internal resistance, by which the estimation of states of health of batteries, deteriorated over time usage, is automated and carried out without a need of any specific test such as a capacity test and the usage of data out of normal operation, it is proposed to collect data from normal operation of a battery storing electrical energy relating to battery-internal physical properties such as a terminal direct current IDC, a terminal direct voltage UDV and a battery cell temperature T due to battery measurements and the determination or estimation, based on this data and a battery model, of a model parameter by solving an optimization-/model parameter estimation-problem and minimizing a difference between the battery model and the battery measurements.

Abstract: A method for operating a central processing unit which is communicatively connected to a plurality of devices having electrical energy stores, includes determining a data-based state of health model which is trained to assign a state of health to an operating feature point which characterizes operation of a corresponding electrical energy store of the electrical energy stores of the plurality of devices and results from a plurality of operating features, determining operating feature points for the electrical energy stores of the plurality of devices, and selecting at least one of the operating feature points based on state uncertainties of the states of health of all operating feature points of the corresponding electrical energy store, such that the at least one operating feature point has a highest relevance for improving the state uncertainties of the operating feature points determined.

Abstract: In some embodiments, a battery management system is provided. The battery management system comprises a connector for electrically coupling a battery to the battery management system, at least one sensor configured to detect a battery state, a programmable chip configured to control at least one of charging and discharging of the battery, and a controller device. The controller device is configured to receive at least one battery state from the at least one sensor; provide the at least one battery state as input to a tanks-in-series model that represents the battery; and provide at least one output of the tanks-in-series model to the programmable chip for controlling at least one of charging and discharging of the battery.

Abstract: A charging apparatus for a vehicle where a terminal (1, FIG. 2) is connected to at least one kerbside power/data unit (9) to provide a power (4) and a data connection (5) to the power/data unit (9), the power/data unit (9) being connected to a nearby vehicle (17) to provide power to charge the vehicle (17) and receive data from the vehicle (17). The fact that the kerbside power/data unit (9) can charge a vehicle (17) using power supplied from a terminal (1, FIG. 2) and can transmit data from the vehicle (17) to the terminal (1, FIG. 2) provides the power and data requirements for connected autonomous vehicles at a kerbside location.

Abstract: Software-configurable battery monitoring and management systems and methods provide flexibility in selecting the location where the highest voltage in a block of cells is sensed so as to support control boards to connect to any number of cells. In certain embodiments, battery management is accomplished by measuring cell voltages in a block of cells in a battery stack, determining whether the battery stack comprises a bus bar, determining a sum of the individual cell voltages, and comparing the voltage at the top of a battery stack, including any bus bar, to the sum of the individual cell voltages to obtain a comparison result that may be used to perform a diagnostic procedure.

Abstract: Presented are control systems for operating rechargeable electrochemical devices, methods for making/using such systems, and vehicles with intelligent battery charging and charging behavior feedback capabilities. A method of operating a rechargeable battery includes an electronic controller receiving battery data from a battery sensing device indicative of a battery state of charge (SOC). Using this battery data, the controller determines a number of low SOC excursions at which the battery SOC is below a predefined low SOC threshold and a number of high SOC excursions at which the battery SOC exceeds a predefined high SOC threshold. The controller then determines if the number of low SOC excursions exceeds a predefined maximum allowable low excursions and/or the number of high SOC excursions exceeds a predefined maximum allowable high excursions. If so, the controller responsively commands a resident subsystem to execute a control operation that mitigates degradation of the rechargeable battery.

Abstract: A prediction method of battery health includes: obtaining an environment temperature, and a discharge capacity and an operating parameter of a battery; determining an estimated operating parameter in a preset temperature at the discharge capacity according to the environment temperature, the discharge capacity and the operating parameter; determining an estimated capacity of the battery according to the estimated operating parameter; and determining a health level of the battery according to the estimated capacity of the battery and a reference capacity, at the discharge capacity of the battery.

Abstract: A method for state-of-charge monitoring of an AC battery, in which the battery includes a central controller having a scheduler, measuring sensors and at least two battery modules. The at least two battery modules each have at least one energy storage element and at least two power semiconductor switches, which connect the respective battery module in series or in parallel or in bypass with another battery module. The battery is controlled by the central controller, and a respective switching state of the at least two battery modules is preset by the scheduler. The state-of-charge monitoring is implemented by a control program within the scheduler. During operation of the battery, a state of each individual energy storage element is monitored by virtue of a respective current flow at a respective energy storage element being determined using continued evaluation of measured values of preset battery parameters which are detected by measuring sensors.

Abstract: A battery system capable of avoiding an over charge with a simple sensor element comprises: a battery module including a battery pack A including a plurality of battery cells connected in series, and a battery pack B including a plurality of battery cells connected in series, connected in parallel, a monitoring unit configured to monitor a condition of the battery module, and a control unit configured to control at least a charge of the battery module. The monitoring unit includes: a first current sensor configured to measure a total current IA of the battery pack A, and a second current sensor configured to measure a total current IB of the battery pack B. The control unit calculates a determining value based on the total current IA and determines an occurrence of an over charge based on the determining value.

Abstract: A processor-implemented battery management method includes: estimating state information of a plurality of battery cells in a battery pack using a first battery state estimation model; determining whether state information of at least one of the plurality of battery cells is to be estimated using a second battery state estimation model; and estimating the state information of the at least one battery cell using the second model, in response to a result of the determining being that the state information of the at least one battery cell is to be estimated using the second model.

Abstract: An arrangement planning device includes: a collection unit that collects state information indicative of a present-time state of each of storage batteries; a prediction calculation unit that calculates a performance indicator of a degree of wear of each of the storage batteries from the state information; an amount-of-operation acquisition unit that obtains a required amount of operation of the storage batteries at each of places of use, the storage batteries being to be placed in the places of use; a policy acquisition unit that obtains an operation policy for each of the storage batteries; and a placement location determination unit that determines a placement location for each of the storage batteries from the places of use, wherein the placement location determination unit determines the placement location for each of the storage batteries based on the required amount of operation, the performance indicator, and the operation policy.

Abstract: A method for determining an internal-resistance based state-of-health of a battery in a device includes determining the internal-resistance based state-of-health of the battery using a hybrid state-of-health model including a physical ageing model and a correction model by (i) determining, based on an electrochemical battery model, a physical state-of-health according to one or more operating parameters of the battery using the physical ageing model, (ii) mapping operating features derived from the one or more operating parameters onto a correction parameter using the correction model that includes a data-based configuration, and (iii) applying the correction parameter to the physical state of health to determine the internal-resistance based state-of-health. The method further includes determining a modeled battery voltage, and determining a characteristic of the modeled battery voltage using an adaptation model.

Abstract: The present disclosure relates to an apparatus for estimating a battery free capacity, and more particularly, an apparatus for estimating a free capacity of a half cell of a battery. According to the present disclosure, it is possible to accurately estimate a free capacity of a half cell without inserting a reference electrode by revising an entire SOC region of a half cell by using an inflection point detected based on SOC-voltage data of a full cell and a half cell of the battery, respectively, and then estimating SOC-voltage data based on a SOC difference between the entire SOC regions before and after the revision.

Abstract: A method and a system of lithium battery state of charge (SOC) estimation based on second-order difference particle filtering belonging to the technical field of battery management are provided. The method includes the following steps: building a second-order RC battery model of a lithium battery; performing model parameterization by using a least squares algorithm with a forgetting factor; and generating an importance density function through a second-order central difference Kalman filtering (SCDKF) algorithm, improving a particle filtering algorithm to obtain a second-order difference particle filtering (SCDPF) algorithm, and performing SOC estimation on a lithium battery by using the SCDPF. The estimation method provided by the disclosure is accurate and has greater estimation accuracy than an unscented particle filtering algorithm (UPF), an unscented Kalman filtering algorithm (UKF), and an extended Kalman filtering algorithm (EKF). An SOC value of the lithium battery may thus be accurately estimated.

Abstract: In some embodiments, an electronic device displays one or more representations of power usage of the electronic device, including across various periods of time and subperiods of time within those periods. In some embodiments, the displayed information reflects power usage both for periods of the display being on and periods of the display being off. In some embodiments, the displayed information includes power usage attributed to various mobile applications running on the electronic device. In some embodiments, the electronic device displays recommendations to reduce the usage of power by the electronic device, which a user has the option of applying. In some embodiments, the electronic device displays prose insight into power usage, indicating causes of the power usage.

Abstract: This disclosure provides a method and an apparatus for correcting a state of health of a battery, a battery management system, and a storage medium, and relates to the field of battery technologies. The method includes: determining whether a current voltage value of a battery cell is within a correction OCV section of the battery cell; if yes, using the current voltage value of the battery cell as a correction voltage value; and acquiring an SOC correction value, and correcting a state of health (SOH) of the battery cell. According to the method, the apparatus, the battery management system, and the storage medium in this disclosure, reliability of an SOH correction result is improved, thereby providing accurate estimation of the state of health of the battery, a prolonged service life of the battery, and better user experience.

Abstract: Disclosed is a state estimation method for a power battery formation process based on convex space filtering, belonging to the technical field of power battery manufacturing. The method performs state estimation on a time delay system by a filtering method, and an iterative replacement method is provided for converting the state quantity at a time k to the state quantity at a time k?h and subsequent items, so as to combine time delay items, thereby avoiding the problem that the dimension is increased when a state matrix A and a state matrix Ah of a time-delay state quantity are subsequently combined into a new state matrix, and reducing the computation complexity and computation time in subsequent computations. Moreover, the estimation accuracy is also improved to a certain extent because of the cancellation of the same items in the iterative replacement.

Abstract: A battery monitoring device includes: an oscillator causing an AC signal to flow in the battery cell; a subtractor acquiring voltage fluctuation of the battery cell when the AC signal flows as a response signal; and a calculation unit calculating complex impedance. The calculation unit calculates the complex impedance based on a multiplication value X of the response signal and a first reference signal outputted in synchronization with the AC signal, and a multiplication value Y of the response signal and the second reference signal obtained by shifting the phase of the AC signal. The AC signal is a rectangular wave signal, the first reference signal is a rectangular wave signal outputted in synchronization with the AC signal, and the second reference signal is a rectangular wave signal, the phase of which is shifted so as not to be outputted overlapping with the first reference signal.

Abstract: Disclosed is a method and apparatus for measuring a battery state. The method includes determining an operational mode of a battery; measuring a battery state of the battery using an electrochemical model in response to the battery determined to be operating in a low rate mode, and measuring the battery state using a modified electrochemical model based on a characteristic of the battery, in response to the battery determined to be operating in a high rate mode.

Abstract: A data processing system includes: a host suitable for checking battery state information and determining a battery grade based on the battery state information; and a memory system suitable for storing information indicating the battery grade provided from the host, determining a method of performing a background operation based on the battery grade, and performing the background operation based on the determined method.

Abstract: The present disclosure relates to systems, devices, and methods for analyzing health of vehicle batteries. Vehicle batteries tend to degrade over time. The described systems, devices, and methods quantify this degradation (or quantify remaining health of the battery) by comparing average energy used to charge or discharge the battery by a charge level unit to a nominal quantity of energy used to charge or discharge a battery in optimal health by a charge level unit. Charge data for previous charge events of the vehicle battery can be used in the calculation, and can be filtered by identifying qualified charge events based on at least one of a number of metrics. Usage data for previous usage events of the vehicle battery can be used in the calculation, and can be filtered by identifying qualified usage events or subgroups of usage event based on at least one of a number of metrics.

Abstract: A processor-implemented method of estimating a state of a battery includes acquiring current information and voltage information of a battery; determining time interval values based on the acquired current information such that current integration values corresponding to the time variation values satisfy a condition; determining voltage values corresponding to the determined time interval values in the acquired voltage information; and determining state information of the battery based on the determined voltage values.

Abstract: A method is disclosed for determining the state of health of an electric battery that includes a plurality of battery cells. The method includes the steps of measuring the cell voltage of each individual cell of the plurality of battery cells, and analyzing each measured battery cell voltage to determine the state of health of the corresponding battery cell.

Abstract: An automatic driving executability determination unit restricts automatic driving when a charge amount determination unit (third determination unit) determines that a battery is in a low capacity state, and decides whether to restrict the automatic driving according to a determination result of a discharge performance determination unit (first determination unit) when a temperature determination unit (second determination unit) determines that the battery is in the low capacity state but a charge amount determination unit determines that the battery is not in the low capacity state.

Abstract: System, methods, and other embodiments described herein relate to improving the cycling of batteries by using data and a hierarchical Bayesian model (HBM) for predicting the cycle life of a cycling protocol. In one embodiment, a method includes classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols. The method also includes quantifying, using the class in a HBM, variability for the battery induced by the protocol. The method also includes predicting, using the HBM, an adjusted cycle life for the protocol according to the variability. The method also includes communicating the adjusted cycle life to operate the battery.

Abstract: A vehicle includes a traction battery and a controller. The controller is programmed to, in response to dynamic resistance and capacity of the traction battery for a drive cycle differing from dynamic resistance and capacity of the traction battery for a previous drive cycle by threshold percentages, controlling the traction battery to reduce lithium plating.

Abstract: Provided is a storage battery diagnostic device including: a positive/negative electrode OCV model function generation unit (106) configured to generate a positive electrode OCV model function and a negative electrode OCV model function for N storage batteries by a sum of OCV element functions; and a storage battery model function parameter group estimation unit (107), which is configured to generate a storage battery model function based on the positive electrode OCV model function and the negative electrode OCV model function, and to generate an evaluation function L indicating an error between the time-series data stored in the data storage unit (104) and time-series data on estimation data calculated by using the storage battery model function, to thereby calculate such an optimal group of estimation parameters of the storage battery model functions for the respective storage batteries as to minimize a value of the evaluation function L.

Abstract: Operation for performing diagnostics, such as vehicle diagnostics including short circuit and low impedance diagnostics during a high-voltage (HV) battery pre-charging a power-net of the vehicle and including insulation resistance monitoring diagnostics for measuring an insulation resistance between the power-net and another power-net, with reduced processing time includes measuring a physical parameter (voltage or current signal) as the parameter is being generated by a device-under-test to which the diagnostic process pertains. The diagnostic process requires a stable value of the parameter. The parameter variates while being generated during a beginning time and is stable while being generated during an ending time. While the parameter is being generated during the beginning time, a stable value of the parameter which the parameter will have during the ending time is predicted. The stable value of the parameter is predicted based on variation of measured values of the parameter during the beginning time.

Abstract: In a method for estimating a voltage of a battery a given electrochemical battery model is provided, wherein one parameter of the electrochemical battery model is an open circuit potential. The open circuit potential is linearized. The voltage of the battery is estimated by means of the electrochemical battery model with the linearized open circuit potential.

Abstract: A method for obtaining a residual electric quantity of a battery in an electronic device includes: obtaining a present electric quantity variation of a battery within a present detection period; obtaining a maximum discharge electric quantity of the battery corresponding to a number of charging and discharging within the present detection period; and obtaining a present residual electric quantity of the battery based on the maximum discharge electric quantity and the present electric quantity variation.

Abstract: A battery condition monitoring apparatus includes a measurer configured to measure a charging current which is an internal current of a converter and is charged into a sub-battery, a charging voltage of the converter, and an ambient temperature of a vehicle, an impedance calculator configured to an impedance of the sub-battery on basis of a voltage and a current each measured by the measurer, and a determiner configured to determine a condition of the sub-battery by using the ambient temperature of the vehicle and the impedance of the sub-battery.

Abstract: A power storage device includes: a cell; and a controller that: causes the cell to discharge from a full charge capacity to a set capacity that is set in advance, and executes a full charge capacity correction mode including a remaining capacity calculation operation and capacity consumption calculation operation. In the remaining capacity calculation operation, the controller: acquires a first voltage of the cell at the set capacity in response to the cell discharging from the full charge capacity to the set capacity, and calculates an actual remaining capacity of the cell based on the first voltage and one of a plurality of correlations between a voltage of the cell and a cell capacity.

Abstract: In the battery deterioration prediction system, a polarization calculation unit calculates negative and positive electrode polarizations from negative and positive electrode resistances, and an electrical current value flowing through the secondary battery. A CCP calculation unit calculates a negative electrode closed-circuit potential on the secondary battery negative electrode open circuit potential and the negative electrode polarization calculated by the polarization calculation unit, and calculates a positive electrode closed-circuit potential based on the secondary battery positive electrode open circuit potential and the positive electrode polarization calculated by the polarization calculation unit.

Abstract: A method and a system for identifying third-order model parameters of a lithium battery based on a likelihood function are provided, which relates to a method for estimating battery model parameters of a lithium battery under different temperatures, different system-on-chips (SOCs), and charge-discharge currents. The method includes the following steps. A third-order battery model of the lithium battery is established. A battery model output voltage Ud and a total battery current I under different temperatures, different SOCs, and charge-discharge currents are collected. The likelihood function is adopted to construct an identification model, and the collected data is substituted into the identification model to calculate the battery model parameters. Identified parameters are substituted into the third-order battery model to obtain a battery terminal voltage to be compared with a measured terminal voltage.

Abstract: A vehicle diagnostic system diagnoses a vehicle having a battery mounted thereon. The vehicle diagnostic system includes an instrument panel for showing a capacity retention of the battery, and an ECU that calculates the capacity retention to be displayed on the instrument panel. The ECU measures the full charge capacity of the battery each time external charging is completed, and, based on a result of the measurement, updates a capacity deterioration curve indicative of changes in over time of the capacity retention. When a display request for showing the capacity retention on the instrument panel is generated, the ECU calculates the capacity retention when the display request is generated by referring to the updated capacity deterioration curve.

Abstract: An information providing apparatus provides information concerning a lendable electric power supply device. More specifically, the information providing apparatus for providing information via a network in response to a request from a terminal, performs the following processes. The apparatus receives, from the terminal via the network, an information request concerning a lendable electric power supply device configured to supply electric power, acquires information concerning electric power that can be provided by the electric power supply device and a lending price of the electric power supply device by communicating with the electric power supply device via the network. The apparatus further edits, based on the acquired information, information to be provided to the terminal that has issued the information request, and transmits the edited information to the terminal that has issued the information request via the network.

Abstract: Provided are a battery management system, a battery management method, a battery pack and an electric vehicle. The battery management system includes a sensing unit to generate battery information indicating a current, a voltage and a temperature of a battery, and a control unit. The control unit determines a temporary estimate for a SOC in a current cycle using a time update process of an extended Kalman filter based on a previous estimate indicating a SOC in a previous cycle and the battery information. The control unit determines open circuit voltage (OCV) information based on the temporary estimate. The control unit determines a definitive estimate indicating the SOC in the current cycle using a measurement update process of the extended Kalman filter based on the temporary estimate, the OCV information and the battery information.