Browse Topic: Drive cycles

Items (394)

Synthesis and Validation of Multidimensional Driving Cycles

2021-01-0125

4/6/2021

Driving cycles are usually defined by vehicle speed as a function of time and they are typically used to estimate fuel consumption and pollutant emissions. Currently, certification driving cycles are mainly used for this purpose. Since they are artificially generated, the resulting estimates and analyzes can generally be biased. In order to address these shortcomings, recent research efforts have been directed towards development of statistically representative synthetic driving cycles derived from recorded real-world data. To this end, this paper focuses on synthesis of multidimensional driving cycles using the Markov chain-based method and particularly on their validation. The synthesis is based on Markov chain of fourth order, where the road slope is accounted, as well. The corresponding transition probability matrix is implemented in the form of a sparse matrix parameterized with a rich set of recorded city bus driving cycles. A wide collection of statistical features, including the frequency domain indicators, unique cross-correlation velocity-acceleration-slope indicators, and indicators related to bus stops at stations are considered for the purpose of driving cycle validation. To prove the synthesis method validity, a comparative statistical analysis of distributions of the nominated statistical features of synthetic and recorded driving cycles is carried out. Finally, a multi-criteria method of driving cycle validation based on lumped metrics is outlined and examined.

Topić, Jakov, Škugor, Branimir, Deur, Joško

Arrangement and Control Method of Cooperative Vehicle Platoon

2021-01-0113

4/6/2021

With the development of cellular communication technology and for the sake of reducing drag resistance, the multi-lane platoon technology will be more prosperous in the future. In this article, the cooperative vehicle platoon method on the public road is represented. The method’s architecture is mainly composed of the following parts: decision-making, path planning and control command generation. The decision-making uses the finite state machine to make decision and judgment on the cooperative lane change of vehicles, and starts to execute the lane change step when the lane change requirements are met. In terms of path planning, with the goal of ensuring comfort, the continuity of the vehicle state and no collision between vehicles, a fifth-order polynomial is used to fit every vehicle trajectory. In terms of control command generation module, a model predictive control algorithm is used to solve the multi-vehicle centralized optimization control problem. We use the two DOF vehicle model to simulate vehicle dynamics. The front wheel angle and acceleration or braking commands of multiple vehicles are optimized to ensure that the vehicle can well follow the trajectory of the vehicle which is calculated by the control command generation module. At the same time, the energy consumed by performing steering, acceleration and deceleration is also minimized. Finally, in the simulation process, we simulate one direction two lanes scenario. The result shows that the proposed method can effectively handle multi-lane platoon re-configuration scenario.