Abstract
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 consumption.
Highlights
In [19] we presented a method denoted recursive B-spline approximation (RBA) that iteratively adapts the coefficients of a B-spline function such that the function approximates data in the weighted least squares (WLS) sense
The output of the standard Fixed-Budget Kernel Recursive Least-Squares (FBKRLS) is zero for areas, in which no data points have occured yet. This property is undesired for trajectory optimization because it encourages nonlinear recursive B-spline approximation (NRBA) to favor high velocities or accelerations that are beyond the capabilities of the vehicle
In simulations we investigate the energy-saving potential using the automated longitudinal control (ALC) with different parameter settings compared to drives with manual longitudinal control (MLC) on the Weissach round (WR)
Summary
Current major automotive trends are automated driving, electric power train, shared mobility services, and connected mobility [1]. Continuously automated functions, that are activated by the driver intentionally and influence the vehicle directly These are usually comfort functions like cruise control (CC). This work deals with continuously automated functions for longitudinal control Such functions can contribute to increasing safety and comfort as well as to reducing energy consumption to different extents. ACC-like systems for automated longitudinal control (ALC) can determine an appropriate course of velocity, refered to as velocity trajectory, for the upcoming road section such that the vehicle automatically slows down if a curve is ahead. Due to the various degrees of freedom in the power train and the related constraints, the majority of such driver assistance systems determine the velocity trajectory by solving a multiobjective nonlinear high dimensional optimization problem with respect to goals like travel time, comfort, safety and energy consumption [5]. For a comprehensive review of motion planning techniques including the lateral dimension we refer to [16]
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