Control systems integration for enhanced vehicle dynamics

This paper deals with improving comfort and handling for a ground vehicle through the coordinated control of different active systems available in passenger cars, e.g., electronic stability control, active roll control and engine torque control. The authors first describe separate control systems, each with its logic, showing advantages and limits, then propose various possible integrations, aiming at exploiting the benefits of a coordinated approach. Finally, the proposed control logics are tested on a vehicle model: simulation results prove the effectiveness of the approach in improving vehicle response during typical handling maneuver

Automated longitudinal control based on nonlinear recursive B-spline approximation for battery electric vehicles

This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to MLC. On the chosen reference route the ALC saves up to 3.4% energy compared to MLC at same average velocity, and achieves a 2.6% higher average velocity than MLC at the same energy consumptio

Model Predictive Control for Grid Scale PV and Battery

Model Predictive Control (MPC) is a control technique that uses prediction data to optimize costs over a given predictive horizon. There are many papers that use this technique to optimize cost in a substantially loaded microgrid, but these techniques are not feasible for utility-scale PV+Storage facility. In this study, MPC is used to optimize the cost for a utility-scale PV+Storage facility, by adding a factor of a possible curtailment. The thesis also presents the various factors that the MPC has in that utility size grid. These factors include line losses, net yield, and curtailment