Abstract: Systems and methods for estimating values of dynamic attributes of autonomous vehicles are disclosed. A first vehicle includes an inertial measurement unit (IMU) configured to measure a dynamic attribute (e.g., rate of change of vehicle yaw angle) and correlate the measured attribute with one or more input variables (e.g., values of steering angle commands). The correlated data is used to generate a model that can be used in a second vehicle to predict a dynamic attribute based at least in part on variable values input from the second vehicle. As a result, it is not necessary for the second vehicle to have an IMU.

Abstract: According to one embodiment, acceleration and deceleration profiles can be generated and applied dynamically when a condition exist necessitating a change in the velocity of an autonomously controlled vehicle. An acceleration or deceleration profile can be generated when conditions exist necessitating a change in velocity and according to a smoothing function and based on a set of control parameters such as a target velocity, a determined time to reach the target velocity, and a maximum rate of change for the velocity of the vehicle. The smoothing function can be non-linear and can produce a profile representing a curve having a lower rate of change in the velocity of the vehicle at a beginning and an end of the curve than in a middle of the curve. In this way, changes in velocity can be more gradual at a beginning and end of the change and more aggressive in the middle.

Abstract: Disclosed herein is a real-time nonlinear receding horizon control methodology for nonlinear battery management system. To increase the life of batteries, the problem is described as a tracking problem and is converted to a nonlinear on-line optimization problem. Different from the previous works, the nonlinear optimal problem is solved directly, without resorting to linearization techniques and/or iterative procedures. Based on the stabilized continuation method, the backward sweep algorithm is implemented to minimize the degradation as well as track the desired trajectory, and the optimal control strategy is integrated in real time. The stability of the systems is guaranteed provided that the horizon length is sufficiently long. Simulation results demonstrates the feasibility of the proposed scheme on nonlinear battery systems. When there are estimation error and model mismatch of the system, the proposed scheme still performs well.

Abstract: Disclosed herein is a real-time nonlinear receding horizon control methodology for nonlinear battery management system. To increase the life of batteries, the problem is described as a tracking problem and is converted to a nonlinear on-line optimization problem. Different from the previous works, the nonlinear optimal problem is solved directly, without resorting to linearization techniques and/or iterative procedures. Based on the stabilized continuation method, the backward sweep algorithm is implemented to minimize the degradation as well as track the desired trajectory, and the optimal control strategy is integrated in real time. The stability of the systems is guaranteed provided that the horizon length is sufficiently long. Simulation results demonstrates the feasibility of the proposed scheme on nonlinear battery systems. When there are estimation error and model mismatch of the system, the proposed scheme still performs well.

Abstract: According to one embodiment, acceleration and deceleration profiles can be generated and applied dynamically when a condition exist necessitating a change in the velocity of an autonomously controlled vehicle. An acceleration or deceleration profile can be generated when conditions exist necessitating a change in velocity and according to a smoothing function and based on a set of control parameters such as a target velocity, a determined time to reach the target velocity, and a maximum rate of change for the velocity of the vehicle. The smoothing function can be non-linear and can produce a profile representing a curve having a lower rate of change in the velocity of the vehicle at a beginning and an end of the curve than in a middle of the curve. In this way, changes in velocity can be more gradual at a beginning and end of the change and more aggressive in the middle.