Refine Your Search

Search Results

Viewing 1 to 6 of 6
Technical Paper

A Method of Acceleration Order Extraction for Active Engine Mount

2017-03-28
2017-01-1059
The active engine mount (AEM) is developed in automotive industry to improve overall NVH performance. The AEM is designed to reduce major-order signals of engine vibration over a broad frequency range, therefore it is of vital importance to extract major-order signals from vibration before the actuator of the AEM works. This work focuses on a method of real-time extraction of the major-order acceleration signals at the passive side of the AEM. Firstly, the transient engine speed is tracked and calculated, from which the FFT method with a constant sampling rate is used to identify the time-related frequencies as the fundamental frequencies. Then the major-order signals in frequency domain are computed according to the certain multiple relation of the fundamental frequencies. After that, the major-order signals can be reconstructed in time domain, which are proved accurate through offline simulation, compared with the given signals.
Technical Paper

Active launch vibration control of power-split hybrid electric vehicle considering nonlinear gear backlash

2021-04-06
2021-01-0667
The backlash between engaging components in a driveline is unavoidable, especially when the gear runs freely and collides with the backlash, the impact torque generated increases the vibration amplitude. The power-split hybrid electric vehicle generates output torque only from the traction motor during launching process. The nonlinear backlash can greatly influence the driveability of the driveline due to the rapid response of the traction motor and the lack of the traditional clutches and torsional shock absorbers in the powertrain. This paper focuses on the launch vibration of the power-split hybrid electric vehicle, establishes a nonlinear driveline model considering gear backlash, including a engine, two motors, a Ravigneaux planetary gear set, a reducer, a differential, a backlash assembly, half shafts and wheels.
Technical Paper

Novel research for energy management of hybrid electric vehicles with dual motors based on Pontryagin’s minimum principle optimized by reinforcement learning

2021-04-06
2021-01-0726
The hybrid electric vehicles with dual-motor and multi-gear structure can realize multiple operation modes such as series, parallel, hybrid and etc. The traditional rule-based energy management strategy mostly selects some of the modes (such as series and parallel) to construct the energy management strategy. Although this method is simple and reliable, it can’t fully exert the full potential of this structure considering both economy and driving performance. Therefore, it is very important to study the algorithm which can exert the maximum potential of the multi-degree-of-freedom structure. In this paper, a new RL-PMP algorithm is proposed, which does not divide the operation modes, and explores the optimal energy allocation strategy to the maximum extent according to the economic and drivability criteria within the allowable range of the characteristics of the power system components.
Technical Paper

Monocular camera object detection method of autonomous vehicle based on lightweight deep learning

2021-04-06
2021-01-0192
Object detection is an important visual content of autonomous vehicle, the traditional detecting methods usually cost a lot of computational memory and elapsed time. This paper proposes to use lightweight deep convolutional neural network (MobilenetV3-SSDLite) to carry out object detection task of autonomous vehicle. Simulation analysis based on the proposed method is implemented, the feature layer obtained after h-swish activation function in the first convolution layer of the 13th bottle neck module in MobilenetV3 is taken as the first effective feature layer, and the feature layer before pooling and convolution of the antepenultimate layer in MobilenetV3 is taken as the second effective feature layer, these two feature layers extracted from the MobilenetV3 network are used to replace the two feature layers of VGG16 in the original SSD network, thus the model construct work is completed.
Technical Paper

Reward Function Design via Human Knowledge Graph and Inverse Reinforcement Learning for Intelligent Driving

2021-04-06
2021-01-0180
Motivated by applying artificial intelligence technologies into automobile industry, reinforcement learning is becoming more and more popular in the community of intelligent driving. Reward function is one of the important factors affecting reinforcement learning, and its design principle is highly dependent on the functional characteristics of agent. The agent studied in this paper has the ability of perception, decision-making and motion-control, who is aimed to be the assistant or substitute of human in the field of driving in latest future. Therefore, this paper firstly analyzes the characteristics of human excellent driving behavior by induction, constructs knowledge graph, and takes it as the basic design principle of reward function to guide agent to form safe driving consciousness.
Technical Paper

Comparison between different modelling methods of secondary path to maximize control effect for active engine mounts

2021-04-06
2021-01-0668
Active engine mount (AEM) is an effective approach which can optimize the noise, vibration and harshness (NVH) performance of vehicles. The filtered-x-least-mean-squares (FxLMS) algorithm is widely applicated for vibration attenuation in AEMs. However, the performance of FxLMS algorithm can be deteriorated without an accurate secondary path estimation. First, this paper models the secondary path using finite impulse response (FIR) model, infinite impulse response (IIR) model and back propagation (BP) neuron network model and the model errors of which are compared to determine the most accurate and robust modeling method. After that, the influence of operation frequency on accuracy of the secondary path model is analyzed through simulation approach. Then, the impact of reference signal mismatch on the control effect is demonstrated to study the robustness of FxLMS algorithm.
X