Various embodiments are provided for controlling an autonomous car. The embodiments acquire meeting information indicating a place of a meeting and a time of the meeting. A remaining time between the time of the meeting and a current time is calculated. A waiting time for waiting in a waiting place by the autonomous car and a traveling time for traveling to the place of the meeting by the autonomous car are determined. The waiting and traveling times are determined to minimize a total cost and to make a total time within the remaining time. The total cost includes a cost of the traveling and a cost of the waiting. The total time includes the waiting and traveling times. A route along which the autonomous car is to travel to the place of the meeting within the traveling time is determined.

A diagnosis system includes: a charging control part that controls, upon connection between a vehicle and a charger provided at an outside of the vehicle, charging from the charger to a secondary battery provided on the vehicle; and a derivation part that derives a degradation degree of the secondary battery based on a change amount of a charging rate of the secondary battery and an integration value of a charging current of the secondary battery, wherein the charging control part determines, based on a remaining time until a next departure time of the vehicle, whether specific charging in which a charging pause period is provided in a middle of a charging time of the secondary battery is performed.

A system for operating an electric-vehicle charging station is proposed. The system is capable of preventing a general vehicle other than an electric vehicle from being parked in a parking station where the charging module for charging the electric vehicle is installed, preventing an electric vehicle from being parked in a charging station at a higher level than necessary, and eliminating the need for the driver of a subsequent vehicle to wait for charging in the charging station when there is a vehicle that is in a charging state in the charging station.

Systems and methods are provided for dynamically selecting a control policy from among several available control policies for controlling an energy system having multiple controllable assets. The performance of the selected control policy is monitored and a different control policy may be deployed in its place if the different control policy has a higher chance of providing better performance given the current control environment. Thus, as the control environment changes, the control policy that controls the power system may also be changed in an adaptive manner. In this way, the control policies may be changed as the control environment changes to provide an improved real-time performance compared to the use of a single control policy.

A device and a method for controlling an SOC of a battery of a hybrid vehicle are provided. The device includes at least one processor, and the processor sets a battery SOC at a time of terminating operation of the vehicle by activating a SOC setting function when a destination is set. The processor further determines whether the set battery SOC is equal to or less than a SOC chargeable with a high efficiency, and performs battery SOC control such that a battery SOC reaches the set battery SOC at a time of arrival at the destination of the vehicle when the set battery SOC is equal to or less than the SOC chargeable with the high efficiency.

An electric vehicle charging station network includes multiple electric vehicle charging stations belonging to multiple charging station hosts. Each host controls one or more charging stations. A charging station network server provides an interface that allows each of the hosts to define one or more pricing specifications for charging electric vehicles on one or more of their electric vehicle charging stations belonging to that host. The pricing specifications are applied to the charging stations such that a cost of charging electric vehicles using those charging stations is calculated according to the pricing specifications.

A method of controlling scheduled charging is provided. The method includes calculating a primary scheduled charge time based on an expected charge time consumption and an expected primary scheduled charge start time and calculating an expected temperature for the primary scheduled charge time based on the primary scheduled charge time and charger temperature information or climate information. A corrected expected charge time consumption is calculated based on the calculated expected temperature and a secondary scheduled charge time is calculated based on the corrected expected charge time consumption. A vehicle battery is charged based on the secondary scheduled charge time.

An information processing apparatus for controlling an autonomous vehicle that travels between points includes a controller. The controller is configured to determine, based on the current time and a predetermined time, to charge, to replace, or neither to charge nor replace a battery, mounted in the autonomous vehicle, at a first point when the autonomous vehicle located at the first point is to travel by the predetermined time to a second point that is the next destination.

Systems and methods are provided for dynamically selecting a control policy from among several available control policies for controlling an electric vehicle fleet charging system. A control policy may take into account fluctuating local renewable generation and/or time of use electricity pricing. The performance of the selected control policy is monitored and a different control policy may be deployed in its place if the different control policy has a higher chance of providing better performance given the current control environment. Thus, as the control environment changes, the control policy that controls the power system may also be changed in an adaptive manner. In this way, the control policies may be changed as the control environment changes to provide an improved real-time performance compared to the use of a single control policy.

A method for predicting the availability of a charging station for a vehicle with a driver assistance system is provided. The method includes establishing an expected time of arrival of the vehicle at the charging station LS, and determining an expected availability of the charging station at the time of arrival, wherein at least one user feedback about the correctness of a prediction of the availability of the charging station in the past is taken into account. Moreover, a driver assistance system for carrying out the method is provided.