Kabushiki Kaisha Toshiba

Toshiba Energy Systems & Solutions Corporation

Hisaaki Hatano

Kenichi Fujiwara

Takahiro Yamamoto

Masatake Sakuma

Koji Takazawa

Tomokazu Morita

Yoshinao Tatebayashi

Hideyuki Aisu

Takenori Kobayashi

Akihiko Nakase

Kaoru Koiwa

Kazuto Kubota

Kenji Mitsumoto

Yasuhiro Ogura

Takuro Moriyama

Kohei Maruchi

Yoshiyuki Isozaki

Chie Morita

Tomohiro Toyosaki

Yoshiyuki Sakamoto

Shun Egusa

There is provided a rechargeable battery evaluation device including: a charging controller configured to switch charging current of a rechargeable battery being charged with first current to second current; and a first state estimator configured to estimate a state of the rechargeable battery based on a feature of change in at least one of a voltage and a charging amount of the rechargeable battery due to switching from the charging current to the second current.

Information processing apparatus, method and system

According to one embodiment, the information processing device includes: a first state estimator configured to estimate a state of a target rechargeable battery based on: first data including a charge amount and voltage value of a rechargeable battery; information including a state of the rechargeable battery; and second data including a charge amount and voltage value of the rechargeable battery.

Information processing device, information processing method, non-transitory computer readable medium, and information processing system

A bias estimation apparatus according to an embodiment estimates a bias included in a measured values by each sensor. The bias estimation apparatus includes a reference model builder, a temporary bias generator, a corrected measured value calculator, a similarity calculator, a similarity selector, a score calculator, and an estimated bias determiner. The reference model builder builds a reference model of the measured value packs. The temporary bias generator generates a temporary bias pack. The corrected measured value calculator calculates corrected measured value packs. The similarity calculator calculates a similarity of each corrected measured value pack. The similarity selector selects a part of the similarities according to their values from among the similarities. The score calculator calculates a score based on the selected similarities. The estimated bias determiner determines an estimated bias which is an estimated value of the bias based on the score.

Bias estimation apparatus and method and failure diagnosis apparatus and method

According to one embodiment, an evaluation device for an energy storage device includes first processing circuitry and second processing circuitry. The first processing circuitry acquires data items including an amount of charge and a voltage value measured in a measurement period from the energy storage device, the energy storage device being charge-discharge-controlled according to charge-discharge command values. The second processing circuitry generates a representative data item of voltage values for amounts of charge, based on the data items acquired for the measurement period; and evaluates a degradation state of the energy storage device, based on a relative change between a plurality of the representative data items corresponding to a plurality of the measurement periods.

Evaluation device, evaluation method and evaluation system for energy storage device

According to one embodiment, an evaluation device includes first processing circuitry and second processing circuitry. The first processing circuitry calculates a feature value of an energy storage device based on voltage values measured from the energy storage device being subjected to charge-discharge-control according to charge-discharge command values. The second processing circuitry derives an evaluation function of a degradation state of the energy storage device according to a difference between a distribution of the charge-discharge command values and a reference distribution. The second processing circuitry evaluates the degradation state of the energy storage device, based on the evaluation function and the calculated feature value.

Evaluation device, energy storage system, evaluation method and non-transitory computer readable medium

According to one embodiment, a device for evaluating an energy storage device includes first processing circuitry and second processing circuitry. The first processing circuitry detects a plurality of times at which an amount of charge of the energy storage device satisfies a threshold condition, based on measurement data on the energy storage device in operation, selects a plurality of time intervals, calculates a charge-discharge efficiency in each of the time intervals based on charge-discharge command values belonging to the time interval, and divides a set of the charge-discharge efficiencies into a plurality of clusters and generates characteristic information on the clusters based on at least one of the measurement data and the charge-discharge command values. The second processing circuitry generates output data for displaying: transition data indicating transition of the charge-discharge efficiency belonging to the cluster; and characteristic information on the cluster.

Device and method for evaluating energy storage device and evaluation system

According to one embodiment, a sensor failure diagnosis device includes a drift estimator and a global rank determiner. The drift estimator estimates, based on data on measured values from sensors contained in a plurality of sensor groups including a sensor under inspection, whether or not drift failure that is steady deviation of a measured value from a true value has occurred in the sensor under inspection or a degree of the drift failure. The global rank determiner determines global ranks that are priorities of the plurality of sensor groups in the inspection based on a plurality of estimated results by the drift estimator.

Sensor failure diagnosis device, method and program

In one embodiment, a sensor assignment apparatus includes a measured value storage, a generator, a calculator, and a selector. The measured value storage stores measured values of a plurality of sensors. The generator generates combinations of sensors and arguments of a function to diagnose sensors by assigning sensors to its arguments. The calculator calculates evaluation values which are based on the function values. The function values are obtained by applying diagnostic functions to measured values for all combinations generated by the generator. The selector selects at least one combination from the resulting set of the generator by considering the evaluation values of combinations calculated by the calculator.

Sensor assignment apparatus and sensor diagnostic apparatus

According to an aspect of the present invention, there is provided with a data division apparatus which divides multi-dimensional data, including: a data input unit which inputs multi-dimensional data; a division plane candidate creator which creates a plurality of division plane candidates for dividing the multi-dimensional data; a data provisional division unit which provisionally divides the multi-dimensional data by using the division plane candidate to generate clusters; a model generator which generates models from the clusters; an evaluation value calculator which calculates an evaluation value on the basis of the generated models and the multi-dimensional data; a division candidate selector which compares evaluation values respectively corresponding to the division plane candidates and selects a division plane candidate having a highest evaluation value; and a data division unit which divides the multi-dimensional data by using the selected division plane candidate.

Data division apparatus, data division method and program

There is provided with a monitoring method of monitoring plural apparatuses each having a sensor, including: selecting two or more sample apparatuses out of the plural apparatuses;

Method and system for monitoring apparatus

A spatial data analysis apparatus includes a storage configured to store a record group of records each including a multi-dimensional spatial attribute containing a place and a description attribute regarding the spatial attribute; a divider configured to divide the record group into a plurality of virtual record groups corresponding to a plurality of places according to each of a plurality of conditions determined by the description attribute; a calculator configured to calculate a degree of spatial dispersion of records of each of the plurality of virtual record groups in the places to obtain a plurality of degrees of dispersion; and a selector configured to select from the conditions a condition corresponding to a virtual record group that indicates a lowest degree of dispersion among the degrees of dispersion. The divider re-divides the virtual record group into a plurality of re-divided virtual record groups, and the calculator computes a degree of spatial dispersion of records of each of the re-divided virtual record groups that are recursively generated by re-dividing recursively the virtual record group with the divider, to make a tree structure for determining a final division condition and a final virtual record group.

Spatial data analysis apparatus and spatial data analysis method

A data analyzing apparatus that stores geographic information and observation data including various information regarding an event. A data processing unit generates analyzing data by using the geographic information and the observation data, and a data analyzing unit derives a tendency of occurrence of the event based on the analyzing data. A prediction unit predicts a place where the tendency of occurrence of the event is high by using the tendency and the geographic information. An observation data input unit inputs new observation data, and an analyzing monitor unit controls the data processing unit to generate modified analyzing data by using the new observation data, the observation data and the geographic information, and readjusts the analyzed result based on the modified analyzing data. A health condition observer observes a health condition data, and the observation data input unit inputs the observation data including the health condition data.

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Yokohama, Japan

Hisaaki Hatano