Browsing by Author "Liu, Teng"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Levenberg-Marquardt backpropagation training of multilayer neural networks for state estimation of a safety critical cyber-physical system
    (IEEE, 2017-11-24) Lv, Chen; Xing, Yang; Zhang, Junzhi; Na, Xiaoxiang; Li, Yutong; Liu, Teng; Cao, Dongpu; Wang, Fei-Yue
    As an important safety critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer Artificial Neural Networks (ANN) with Levenberg-Marquardt Backpropagation (LMBP) training algorithm. Firstly, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of backpropagation, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.
  • Thumbnail Image
    ItemOpen Access
    Parallel driving in CPSS: a unified approach for transport automation and vehicle intelligence
    (IEEE, 2017-09-15) Wang, Fei-Yue; Zheng, Nan-Ning; Cao, Dongpu; Martinez, Clara Marina; Li, Li; Liu, Teng
    The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems U+0028 CPSS U+0029 framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space, considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon U+0028 iHorizon U+0028 and its applications are also presented towards parallel horizon. The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.
  • Thumbnail Image
    ItemOpen Access
    Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle
    (IEEE, 2017-08-14) Liu, Teng; Hu, Xiaosong; Li, Shengbo Eben; Cao, Dongpu
    This paper presents a predictive energy management strategy for a parallel hybrid electric vehicle (HEV) based on velocity prediction and reinforcement learning (RL). The design procedure starts with modeling the parallel HEV as a systematic control-oriented model and defining a cost function. Fuzzy encoding and nearest neighbor approaches are proposed to achieve velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities of power demand. To determine the optimal control behaviors and power distribution between two energy sources, a novel RL-based energy management strategy is introduced. For comparison purposes, the two velocity prediction processes are examined by RL using the same realistic driving cycle. The look-ahead energy management strategy is contrasted with shortsighted and dynamic programming based counterparts, and further validated by hardware-in-the-loop test. The results demonstrate that the RL-optimized control is able to significantly reduce fuel consumption and computational time.