Abstract: A method for estimating a present energy consumption of an electrically propelled vehicle powered by a propulsion battery. The method includes obtaining previous energy consumption values for a set of previous time instants, and a present drive pattern parameter value; estimating a present energy consumption based on a weighted moving average model fed with the energy consumption values, wherein, the weighted moving average model includes a modelled gain factor for each of at least a portion of the previous energy consumption values, where the modelled gain factors are modelled as a function of the drive pattern parameter.

Abstract: A method for estimating a present energy consumption of an electrically propelled vehicle powered by a propulsion battery. The method includes obtaining previous energy consumption values for a set of previous time instants, and a present drive pattern parameter value; estimating a present energy consumption based on a weighted moving average model fed with the energy consumption values, wherein, the weighted moving average model includes a modelled gain factor for each of at least a portion of the previous energy consumption values, where the modelled gain factors are modelled as a function of the drive pattern parameter.

Abstract: Disclosed is a method for estimating the maximum power of a battery, which can inexpensively perform an estimation of the maximum power of a battery in a relatively simple manner of using the internal resistance of the battery, which has a correlation with and a largest effect on the maximum power of the battery. The method includes the steps of: measuring an internal resistance and a temperature of the battery and estimating a state of charge, if an estimation of the maximum power of the battery is requested; and reading a value of the maximum power of the battery, which corresponds to the measured temperature, the estimated state of charge, and the measured internal resistance, from a table in which the internal resistances and the maximum powers of the battery are mapped according to the temperatures and states of charge.

Abstract: Disclosed is a method for estimating the maximum power of a battery, which can inexpensively perform an estimation of the maximum power of a battery in a relatively simple manner of using the internal resistance of the battery, which has a correlation with and a largest effect on the maximum power of the battery. The method includes the steps of: measuring an internal resistance and a temperature of the battery and estimating a state of charge, if an estimation of the maximum power of the battery is requested; and reading a value of the maximum power of the battery, which corresponds to the measured temperature, the estimated state of charge, and the measured internal resistance, from a table in which the internal resistances and the maximum powers of the battery are mapped according to the temperatures and states of charge.

Abstract: The prevent invention provides a device and a method for calculating a distance to empty of an electric vehicle which can reduce an initial error in estimating a distance to empty of an electric vehicle. The device and the method for calculating a distance to empty of an electric vehicle can provide more accurate DTE information from the start to the end of traveling by reducing the earlier error, in estimating the DTE from the amount of the presently remaining fuel (the amount of remaining capacity of a battery) of the electric vehicle.

Abstract: Disclosed is an apparatus and method for estimating a state of charge (SOC) in a battery, the apparatus including a detector unit; a soft computing unit for calculating and outputting a battery SOC estimation value by processing a current, a voltage and a temperature detected by the detector unit using a computing algorithm, which is a fuzzy algorithm implemented as a neural network, the soft computing unit storing the battery SOC estimation value in a memory, where the fuzzy algorithm has a form expressed as F=?(P,X)W, where ? is one of a fuzzy radial function, a radial basis function, and an activation function in the neural network, P is a learning parameter, X is an input, and W is a weight to be updated during learning.

Abstract: The present invention provides a technique for estimating a remaining capacity of a battery. In particular, the technique measures a current value, a voltage value and temperature of the battery and corrects the current value based on the voltage value. The corrected current value is then integrated and the result is used to calculate a current integration capacity by reflecting a charge/discharge efficiency accordingly. A battery forward voltage capacity is then determined from the measured current value, voltage value and temperature, and used to determine whether it is necessary to correct the current integration capacity. The current integration capacity is then converted into a remaining capacity, if it is determined that a correction is not necessary. If it is determined that a correction is necessary, the current integration capacity is corrected and then the corrected current integration capacity is converted into a remaining capacity.

Abstract: The present invention provides a method for estimating a distance to empty (DTE) from the present amount of remaining fuel (the amount of remaining electricity) in an electric vehicle. In particular, the present invention provides a method for estimating a remaining travel distance of an electric vehicle, which can efficiently and accurately estimate a remaining travel distance by setting the relationship of a DTE with a state of charge (SOC) of a battery which changes in real time under various load conditions, in consideration of the present travel pattern, in addition to the SOC of a battery and accumulated fuel efficiency for the amount of remaining fuel of the electric vehicle.

Abstract: Disclosed is a battery charging technique which prevents a battery from being deteriorated by controlling charging of the battery to a current rate appropriate for the current temperature environment in which the battery is currently charging at the time of charging the battery mounted in the vehicle to improve durability of the battery while at the same time allowing the battery to charge as rapidly as possible to improve the convenience of the vehicle.

Abstract: The prevent invention provides a device and a method for calculating a distance to empty of an electric vehicle which can reduce an initial error in estimating a distance to empty of an electric vehicle. The device and the method for calculating a distance to empty of an electric vehicle can provide more accurate DTE information from the start to the end of traveling by reducing the earlier error, in estimating the DTE from the amount of the presently remaining fuel (the amount of remaining capacity of a battery) of the electric vehicle.

Abstract: The present invention provides a method for estimating a distance to empty (DTE) from the present amount of remaining fuel (the amount of remaining electricity) in an electric vehicle. In particular, the present invention provides a method for estimating a remaining travel distance of an electric vehicle, which can efficiently and accurately estimate a remaining travel distance by setting the relationship of a DTE with a state of charge (SOC) of a battery which changes in real time under various load conditions, in consideration of the present travel pattern, in addition to the SOC of a battery and accumulated fuel efficiency for the amount of remaining fuel of the electric vehicle.

Abstract: Disclosed is an apparatus and method for estimating a state of charge (SOC) in a battery, in which the battery SOC is estimated using a fusion type soft computing algorithm, thereby accurately estimating the battery SOC in a high C-rate environment. The apparatus includes a detector unit for detecting current, voltage and temperature of a battery cell; and soft computing unit for outputting a battery SOC estimation value of processing the current, the voltage and the temperature detected by the detector unit using a radial function based on a neural network algorithm. Especially, the soft computing unit combines the neural network algorithm with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, and thereby adaptively updates the parameters of the neural network algorithm.

Abstract: Disclosed is a method and apparatus of estimating a state of health (SOH) of a battery using internal resistance, which has been found to act as a parameter exerting the greatest influence on the SOH of the battery. The method comprises the steps of: storing an SOH estimation table constructing SOH values corresponding to various values of internal resistance according to temperature and a state of charge (SOC) in a memory; performing measurement of the temperature and estimation of the SOC of the battery when a request is made to estimate the SOH; detecting the internal resistance value of the battery; and reading the SOH values corresponding to the measured temperature, the estimated SOC of the battery, and the detected internal resistance value of the battery from the SOH estimation table.

Abstract: Disclosed is a method for setting an initial SOC value, which initializes the SOC in consideration of not only the temperature change of the external environment of the battery but also the internal temperature change of the battery before the battery reaches a stable unloaded state, thereby enhancing the accuracy in the setup of the initial SOC value. The method includes: measuring internal temperatures of the battery and SOCs corresponding to voltages of the battery, which change according to the time passage after a loaded state is converted into an unloaded state, and constructing an SOC estimation table by using measured values, and storing the SOC estimation table; measuring the internal temperature and the voltage of the battery when estimation of an initial SOC value of the battery is required; and reading an SOC corresponding to the measured internal temperature and the voltage of the battery from the SOC estimation table.

Abstract: The present invention provides a method for estimating a remaining capacity of a battery. The method comprises: measuring a current value, a voltage value and temperature of the battery; correcting the current value based on the voltage value; calculating a current integration value by integrating the corrected current value and calculating a current integration capacity from the calculated current integration value by reflecting a charge/discharge efficiency; determining a battery forward voltage capacity from the measured current value, voltage value and temperature, and determining whether it is necessary to correct the current integration capacity based on the forward voltage capacity; converting the current integration capacity into a remaining capacity, if it is determined that it is not necessary to correct; and correcting, if it is determined that it is necessary to correct, the current integration capacity and converting the corrected current integration capacity into a remaining capacity.

Abstract: Disclosed are an apparatus and a method for estimating a state of charge of a battery representing a non-linear characteristic by using a neural network. The apparatus includes a sensing section for detecting current, voltage and a temperature from a battery cell, a neural network performing a neural network algorithm and a learning algorithm based on data of the current, voltage and temperature transmitted thereto from the sensing section and present time data, thereby outputting the SOC of the battery estimated through a final learning algorithm, and a comparator for comparing an output value of the neural network with a predetermined target value and making the neural network iteratively perform the learning algorithm if a difference between the output value of the neural network and the predetermined target value is out of a predetermined limit, thereby update the learning algorithm to generate the final learning algorithm.

Abstract: Disclosed is a method for structuring a comparative reference value used to estimate a residual charge (SOC; State of Charge) of a battery.

Abstract: A chip-type inductor comprising includes internal magnetic material, external magnetic material disposed on opposing sides of the internal magnetic material, and a conductor formed in a space between the internal and external magnetic material. The internal and external magnetic materials form a magnetic path along which magnetic flux of a magnetic field produced by current flowing along the conductor flows. According to at least one embodiment, the flow cross-sectional area of the magnetic flux in the internal magnetic material is at least substantially equal to a sum of the flow cross-sectional areas in the external magnetic materials.

Abstract: Disclosed is an apparatus and method for estimating a state of charge (SOC) in a battery, in which the battery SOC is estimated using a fusion type soft computing algorithm, thereby accurately estimating the battery SOC in a high C-rate environment. The apparatus includes a detector unit for detecting current, voltage and temperature of a battery cell; and soft computing unit for outputting a battery SOC estimation value of processing the current, the voltage and the temperature detected by the detector unit using a radial function based on a neural network algorithm. Especially, the soft computing unit combines the neural network algorithm with any one of a fuzzy algorithm, a genetic algorithm (GA), a cellular automata (CA) algorithm, an immune system algorithm, and a rough-set algorithm, and thereby adaptively updates the parameters of the neural network algorithm.

Abstract: Disclosed is a method for setting an initial SOC value, which initializes the SOC in consideration of not only the temperature change of the external environment of the battery but also the internal temperature change of the battery before the battery reaches a stable unloaded state, thereby enhancing the accuracy in the setup of the initial SOC value. The method includes: measuring internal temperatures of the battery and SOCs corresponding to voltages of the battery, which change according to the time passage after a loaded state is converted into an unloaded state, and constructing an SOC estimation table by using measured values, and storing the SOC estimation table; measuring the internal temperature and the voltage of the battery when estimation of an initial SOC value of the battery is required; and reading an SOC corresponding to the measured internal temperature and the voltage of the battery from the SOC estimation table.