A hybrid vehicle control method controls a hybrid vehicle having an electric power source, a vehicle electrical equipment and drive motor to which electric power is supplied from the electric power source. The vehicle electrical equipment and the drive motor are electrically connected to the electric power source via at least a shared harness. When a temperature of the harness is equal to or greater than a predetermined temperature, upper limit values of electric power supplied from the electric power source to the vehicle electrical equipment and the drive motor are both reduced, and a degree of reduction in the upper limit value for the vehicle equipment is greater than a degree of reduction in the upper limit value for the drive motor.

A control system (208) for controlling a power inverter (214) of a vehicle (10), the vehicle comprising a first torque source (202), the power inverter for an electric machine (216) coupled to the first torque source, and a second torque source (212), the control system comprising one or more controllers (300), wherein the control system is configured to: enable (412) the power inverter to transition from an active state to a standby state in dependence on the first torque source being inactive while the second torque source is active (400), wherein the standby state is configured to inhibit quiescent electrical current draw by the power inverter; receive information on which an activation condition depends, the activation condition configured to cause at least activation of the first torque source; determine (404, 408) that a predictive condition for a requirement of the activation condition is satisfied, in dependence on the received information; and request (410) the power inverter to transition from the standby state to an active state in dependence on satisfaction of the predictive condition.

Embodiments described herein improve fuel economy by controlling a vehicle powertrain based on a predicted vehicle velocity. The vehicle velocity is predicted based on vehicle-to-vehicle data when a prediction horizon is a longer prediction horizon and the vehicle velocity is predicted based on historical drive cycle data when the prediction horizon is a shorter prediction horizon. A time duration of the shorter prediction horizon is shorter than the time duration of the longer prediction horizon. A plurality of drive cycles are established for both the longer and the shorter prediction horizons using a neural network. A shorter prediction horizon drive cycle uses nonlinear autoregressive exogenous model neural networks and the longer prediction horizon drive cycle uses two layer feedforward neural networks. The predicted vehicle velocity is determined from a similar drive cycle of the plurality of drive cycles of either the shorter and/or the longer prediction horizon drive cycles.

The disclosed computer-implemented method may include tracking personal mobility vehicle batteries. In some embodiments, the method may track and maintain battery power for personal mobility vehicles to help to ensure that there are personal mobility vehicles with sufficient charge available to perform the needed transportation tasks within a dynamic transportation network. In some examples, a swappable battery for a personal mobility vehicle may communicate with a dynamic transportation management system and provide information about current and/or historical charge information. In some examples, the method may use the current state of charge and/or historical charge information to predict the performance of the battery. Based on the predicted performance, the method may predict the range of a personal mobility vehicles with the battery and/or a lifespan of the battery and make matching decisions accordingly. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed computer-implemented method may include automated signaling for networked personal mobility vehicles (PMVs). In some embodiments, a system may identify a PMV in use by a transportation requestor taking a trip. The system may also predict one or more actions of the PMV based on navigational data for the trip. Additionally, the system may select one or more signals corresponding to a predicted action of the PMV. In some embodiments, the system may determine, based on a current state of the PMV, that the predicted action of the PMV will be initiated within a time frame. Furthermore, the system may send a start command to the PMV to initiate a signal in response to determining the predicted action will be initiated. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed computer-implemented method may include improving safety in operating personal mobility vehicles. The method may track and/or control personal mobility vehicles associated with dynamic transportation networks. The method may improve safety related to PMV operation by taking advantage of the various sources and types of information related to PMV operation that are available in the dynamic transportation network. Other methods, systems, and computer-readable media are disclosed.

Personal mobility vehicles, their various components, methods and systems for controlling, using, tracking, and/or interacting with personal mobility vehicles, and methods and systems for integrating personal mobility vehicles within dynamic transportation networks so that a personal mobility vehicle (PMV) can be used in combination with a vehicle of a transportation provider to efficiently complete a transportation request are discussed. For example, a PMV may be used in combination with a lane-constrained vehicle to improve travel time between two locations in situations where the time it may take for a lane-constrained vehicle to reach the starting location may be affected by traffic congestion at the starting location. The PMV may transport a transportation requestor from a starting location to an intermediate location away from the traffic congestion to then transfer to a lane-constrained vehicle for the remainder of the trip.

The disclosed computer-implemented method may include matching transportation requests to personal mobility vehicles. A dynamic transportation network may incorporate different types of vehicles, such as bicycles and/or scooters. Certain vehicles may have advantages over other vehicles in certain contexts but be disadvantageous in others. For example, a dynamic transportation matching system may match a user transporting a bulky package with a basket-equipped bike rather than a scooter without a basket. Moreover, the dynamic transportation matching system may account for a wide variety of other factors, including but not limited to route features, ambient conditions, and vehicle status when matching a transportation requestor to a specific vehicle. Moreover, some systems may account for vehicle wear-and-tear, battery power levels, operational status, etc. to avoid matching users vehicles that would be unable to fulfill a transportation request. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed computer-implemented method may include routing personal mobility vehicles based on road or path conditions. In some embodiments, trip routing for personal mobility vehicles participating in a dynamic transportation network may leverage road condition map data gathered from personal mobility vehicle sensors to evaluate potential routes for personal mobility vehicles. In some examples, the method may account for the type and/or characteristics of the personal mobility vehicle when evaluating a potential route. In some examples, the method may account for user preferences when evaluating a potential route. The method may also make matching decisions for a dynamic transportation matching system and/or personal mobility vehicle distribution decisions for the dynamic transportation network based on the conditions of prospective routes. Various other methods, systems, and computer-readable media are also disclosed.

The disclosed computer-implemented method may include determining the allocation of personal mobility vehicles. By monitoring personal mobility vehicles and determining, based on sensor data from the personal mobility vehicles, the current usage status of the personal mobility vehicles, a dynamic transportation matching system may improve the user experience of transportation requestors relinquishing custody of personal mobility vehicles. In addition, the dynamic transportation matching system may reduce transfer time between personal mobility vehicles and other modes of transportation and/or may improve the availability of personal mobility vehicles across a dynamic transportation network. Various other methods, systems, and computer-readable media are also disclosed.