Abstract: The processing part is configured to calculate an elapsed time from an inspection date or installation date of a power transmission apparatus installed at a location of occurrence of an abnormality to a date of occurrence of an abnormality based on the inspection information and information on occurrence of an abnormality relating to the location of occurrence of an abnormality and date of occurrence of the abnormality where the power transfer efficiency from the power transmission apparatus to the power reception apparatus received from the mobile object through the communication part becomes less than a predetermined value, and is configured to judge an operating state of the power reception apparatus based on the elapsed time.

Abstract: An abnormality judgment apparatus provided with a processing part, a communicating part able to communicate with a mobile object provided with a power reception apparatus receiving power wirelessly transmitted from a power transmission apparatus installed on a road, and a storage part storing power reception history information received from the mobile object. The processing part calculates a probability of occurrence of an abnormality in the power reception apparatus of the mobile object based on the power reception history information and detects a presence of an abnormality occurring location where the power transfer efficiency becomes less than a predetermined value and judges that an abnormality has occurred in the power transmission apparatus installed at the abnormality occurring location when an abnormality occurring location is detected and the probability becomes less than a predetermined judgment threshold value.

Abstract: The processor of the processing unit of the abnormality determination device is configured to calculate a number ratio between (i) a number of mobile units having the power transmission efficiency less than a predetermined value and (ii) a number of mobile units having the power transmission efficiency greater than or equal to the predetermined value for each power reception location, based on the power reception history information of the plurality of mobile units stored in the storage unit, and determine an abnormality of the power transmitting device or the power receiving device of a mobile unit among the plurality of mobile units, based on the number ratio for each power reception location.

Abstract: A control system for a hybrid vehicle which includes an internal combustion engine and an electric motor and whose drive mode is switchable between an electric vehicle mode and a hybrid vehicle mode includes: an on-board learning unit mounted on the hybrid vehicle and configured to perform a learning action; a position determination unit configured to determine whether the hybrid vehicle is located in a low emission area where operation of the internal combustion engine is supposed to be restricted; and a learning control unit configured to at least partially stop the learning action of the on-board learning unit when determination is made that the hybrid vehicle is located in the low emission area.

Abstract: The offer device includes a processor configured to offer candidate vehicles for reservation to a user through a user terminal, and determine a possibility of an area in which operation of an internal combustion engine is banned or restricted being included in a planned running route of the user. The processor is configured to determine an offer mode of the candidate vehicles for reservation based on the possibility.

Abstract: A CO2 recovery system used in a vehicle includes a CO2 recovery device recovering CO2 contained in inflowing gas; and a flow rate control device controlling flow rates of gases present in a plurality of different regions of the vehicle flowing into the CO2 recovery device. The gases present at the plurality of different regions include at least any two among air at an outside of the vehicle, air at an inside of the vehicle, and exhaust gas discharged from a body of an internal combustion engine of the vehicle.

Abstract: The machine learning device includes a predicting part configured to use a machine learning model to predict predetermined information, an updating part configured to update the machine learning model, and a part information acquiring part configured to detect replacement of a vehicle part and acquire identification information of the vehicle part after replacement. The updating part is configured to receive a new machine learning model trained using training data sets corresponding to the vehicle part after replacement from a server and apply the new machine learning model to the vehicle, if a vehicle part relating to input data of the machine learning model is replaced with a vehicle part of a different configuration.

Abstract: A vehicle 100 comprises a fuel tank for storing fuel, a fueling port for supplying the fuel tank with fuel, a CO2 recovery device configured to recover CO2, a CO2 collection port for collecting CO2 from the CO2 recovery device, and a single openable lid configured to cover both the fueling port and the CO2 collection port.

Abstract: A ridesharing management system which enables a plurality of users using a ridesharing service to share a vehicle as passengers, wherein the vehicle is comprised of a hybrid vehicle, and a boundary is established between the inside of an engine restricted zone in which operation of an internal combustion engine is restricted and the outside of the engine restricted zone. It is predicted whether the SOC amount of the battery will become less than a setting value during travel in the engine restricted zone if traveling through a stop in the engine restricted zone and a stop outside the engine restricted zone due to a pick-up request or drop-off request from a user. A route order that does not result in the SOC amount of the battery becoming less than the setting value during travel in the engine restricted zone is suggested.

Abstract: A model updating apparatus updates a machine learning model for outputting a value of an output parameter associated with a device when a value of the input parameter associated with the device is input. The model updating apparatus has a processor, which is configured to: acquire a learning data set used for updating the machine learning model; identify a plurality of model candidates in which at least one of an algorithm and a hyperparameter is different from each other; calculate an estimate accuracy of each of the model candidates, by using the learning data set; and update the machine learning model to a model corresponding to a model candidate with the highest estimation accuracy among the model candidates. The model candidate at the time of a current update identified by the processor includes the model candidate at the time of the previous update having the highest estimation accuracy.

Abstract: A machine learning system comprises a learning planning part 81 configured to create a learning plan of a neural network model, a data set creating part 82 configured to create training data sets, a learning part 83 configured to perform learning of the neural network model using the training data sets when the vehicle is stopped, and a stopping period predicting part 84 configured to predict stopping periods of the vehicle. The data set creating part is configured to allocate the training data sets to a plurality of learning regions determined based on respective ranges of the plurality of input parameters. The learning part is configured to perform learning of the neural network model for each learning region. The learning planning part is configured to allocate the plurality of learning regions to the stopping periods so that learning of the neural network model is not suspended during the stopping periods.

Abstract: A control system for a vehicle having a CO2 capturing device configured to capture CO2 certainly from gas streams. The CO2 captured by the CO2 capturing device is desorbed from the CO2 capturing device by an energy available in the vehicle. A controller is configured to discharge the CO2 captured by the CO2 capturing device into the recovery station by energy delivered from the recovery station to the CO2 capturing device when the energy available in the vehicle is less than a predetermined value.

Abstract: The machine learning device includes a predicting part configured to use a machine learning model to predict predetermined information, an updating part configured to update the machine learning model, and a part information acquiring part configured to detect replacement of a vehicle part and acquire identification information of the vehicle part after replacement. The updating part is configured to receive a new machine learning model trained using training data sets corresponding to the vehicle part after replacement from a server and apply the new machine learning model to the vehicle, if a vehicle part relating to input data of the machine learning model is replaced with a vehicle part of a different configuration.

Abstract: A control device mounted in a vehicle in which at least one controlled part is controlled based on an output parameter obtained by inputting input parameters to a learned model using a neural network, provided with a parked period predicting part predicting future parked periods of the vehicle and a learning plan preparing part preparing a learning plan for performing relearning of the learned model during the future parked periods based on results of prediction of the future parked periods.

Abstract: A vehicle provided with a rechargeable battery, a charging port connected to a charging cable for supplying the battery with electric power of an outside power source, a CO2 recovery device for recovering CO2, a CO2 collection port connected to a collection hose for collecting CO2 from the CO2 recovery device, and a single openable lid covering both the charging port and the CO2 collection port.

Abstract: An alarm device adapted for a hybrid vehicle equipped with an internal combustion engine and an electric motor is disclosed. A restriction zone, in which the operation of the internal combustion engine should be restricted, is defined in advance within a zone in which the vehicle can move. An alarm is issued by an alarm device in response to determining that the SOC of a battery of a vehicle, which is about to enter the restriction zone, is lower than a predetermined threshold value.

Abstract: An abnormality determination apparatus includes a processing unit, a communication unit configured to communicate with a plurality of mobile bodies and a storage unit configured to store power reception history information received from each of the mobile body. The power reception history information includes a power receiving location of the received power received by the power receiving device, and a power transmission efficiency calculated based on the received power, or the received power. The processing unit is configured to extract information of the power receiving location where the power transmission efficiency is less than a predetermined value from the power reception history information of the plurality of the mobile bodies stored in the storage unit, and is configured to determine an abnormality of the power transmission device based on the information of the extracted power receiving location.

Abstract: A vehicle control device including processor being configured to, when driving sections are present inside a restricted region, extract as a restricted driving section from among driving sections present inside the restricted region a driving section through which it is projected the vehicle will be driven in a restricted time period in which operation of internal combustion engines is restricted, and prepare a driving plan able to drive through the restricted driving section in the EV mode.

Abstract: A machine learning device training a learning model unique to a vehicle is provided with: a processor configured to use training data sets including values of state parameters detected by detectors provided at the vehicle, to train the learning model; and if an abnormality occurs in values of a state parameter detected by a detector, acquire values of the state parameter, where an abnormality has occurred, detected by another vehicle under conditions matching detection conditions when the values of the state parameter included in the training data sets were detected by the detector. If an abnormality occurs in values of the state parameter detected by the detector, the training part uses training data sets including values acquired from another vehicle by the parameter value acquiring part, instead of the values of the state parameter where an abnormality has occurred detected by the detector, to train the leaning model.

Abstract: A boundary is at an engine drive restricted zone where operation of the internal combustion engine is restricted. An adjusting device adjusts of at least one of the temperature inside the cabin using electric power, the humidity inside the cabin using electric power, or the air pollution degree inside the cabin. In response to a prediction that (1) a host vehicle will enter the inside of the engine drive restricted zone and that (2a) the time period for the host vehicle to reach the boundary is within a preset time period or that (2b) the distance between the host vehicle and the boundary is within a preset distance, the value of at least one of the temperature inside the cabin, the humidity inside the cabin, and the air pollution degree inside the cabin is reduced before the host vehicle enters the inside of the engine drive restricted zone.