An apparatus comprising an interface, a memory and a processor. The interface may be configured to receive sensor data samples during operation of a vehicle. The memory may be configured to store the sensor data samples over a number of points in time. The processor may be configured to analyze the sensor data samples stored in the memory to detect a pattern. The processor may be configured to manage an application of brakes of the vehicle in response to the pattern.

Electrically powered vehicles may be equipped with both mechanical braking systems and regenerative braking systems. Regenerative braking systems improve vehicle efficiency by returning a portion of the energy lost in deceleration to the battery of the electrically powered vehicle. An electrically powered vehicle controller that provides collision avoidance functionality can maximize the energy returned to the battery of the electrically powered vehicle by maximizing the use of regenerative braking for collision avoidance. A first braking mode can include only regenerative braking for objects greater than the minimum regenerative stopping distance. A second braking mode can include composite braking using both mechanical and regenerative braking. The electrically powered vehicle controller determines the maximum regenerative braking level at least based on data provided battery charge level or battery state sensors.

A method of controlling the hybrid vehicle in which electric power of the battery and electric power generated by an electric generator are supplied to a drive device, a running load of the drive motor is estimated on the basis of the driver's requirement, and a first distance to empty that allows for running in a state where the estimated running load is fulfilled is calculated on the basis of an amount of charge remaining in the battery and an amount of fuel remaining used to drive the fuel cell. Then, a required running distance is estimated on the basis of the driver's requirement, and, on the basis of the first distance to empty and the required running distance, a necessary energy replenishment operation is notified to the driver.

A power management system includes a sensor interface that receives sensor data samples during operation of a vehicle. A storage device stores the sensor data samples for multiple points in time along a route segment traveled by the vehicle. One or more processors analyze the sensor data samples to detect a historical pattern of the vehicle. The one or more processors determine time efficient operational parameters for the vehicle in response to a destination and an estimated travel time to the destination. The estimated travel time may be based on predicted conditions of the vehicle indicated by the historical pattern. The time efficient operational parameters may be selected to decrease the estimated travel time. At least one of the sensor data samples may include telemetry data.

A controller is applied to a vehicle including an engine (11) and a motor generator (12 and 13) as power sources of the vehicle and a battery (20) that transfers power with the motor generator. The controller charges the battery with a regeneration power that is a power regenerated by the motor generator when the vehicle is decelerated. The controller includes a SOC prediction unit (39, 50 and 205) to predict a SOC indicating a remaining capacity of the battery in a scheduled travel route of the vehicle, based on a predicted result of a road grade and a vehicle speed in the scheduled travel route, a discharge control unit (39, 52, 206, 208 and 301 to 303) to execute a discharge increasing control to previously increase a discharge quantity of the battery to prevent the battery from becoming in a saturation state based on a predicted SOC that is the SOC predicted by the SOC prediction unit, when the discharge control unit determines that the battery becomes in the saturation state where the battery cannot be charged with the regeneration power based on the predicted SOC, a determination unit (39 and 105 to 109) to determine whether a behavior of the predicted SOC shifts from a behavior of an actual SOC or determine whether a SOC shift factor occurs, after a start of the discharge increasing control, where the SOC shift factor is a vehicle control or an environment change that predicts the behavior of the predicted SOC shifts from the behavior of the actual SOC, and a correction unit (39, 110, 201 to 209 and 301 to 303) to correct the discharge increasing control by executing a prediction of the SOC in the scheduled travel route again, when the determination unit determines that the behavior of the predicted SOC shifts from the behavior of the actual SOC or determines that the SOC shift factor occurs.

The configuration includes: a motor for driving a driving wheel (rear wheel) provided to a body; a slip ratio calculating section for calculating a slip ratio while the lawn mower is traveling; a friction coefficient calculating section for calculating a friction coefficient of a ground on which the lawn mower is traveling; a determination section for determining whether the ground on which the lawn mower is traveling is a lawn based on a relationship between the slip ratio and the friction coefficient and a drive controlling section for controlling the motor based on a determination result from the determination section. This configuration allows suitable driving operability for both of a case where the ground on which a lawn mowers traveling is a lawn and a case where the ground is other than a lawn.

The invention relates to a system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.

A battery management system for a vehicle, a battery management method thereof and a vehicle including the same, which can vary a state of charge (SOC) of a battery in accordance with a location of a recharging station are disclosed. The battery management system includes: a position information acquisition unit for acquiring position information of a recharging station and the vehicle; a storage for storing a hilly area recharging station list comprising position information of hilly area recharging stations and learned full SOC values; and a controller for controlling the position information acquisition unit and the storage. The controller checks whether a recharging station where recharging of the vehicle is executed is a hilly area recharging station, and restricts a full SOC in order to avoid restriction of regenerative braking according to downhill road travel, thereby enhancing fuel economy and safety.

A vehicle includes a traction battery and a controller. The controller, responsive to a command to enter a highway, confirmation the traction battery can output power to accelerate the vehicle to enter the highway via an entrance at a same speed as traffic on the highway in a vicinity of the entrance, and an increase in temperature beyond a threshold of the traction battery predicted to occur on the highway due to expected commands to maintain the same speed on the highway with power from the traction battery, increases cooling of the traction battery prior to entering the highway.

The invention relates to a system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.