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 (ANNs) with Levenberg-Marquardt backpropagation (LMBP) training algorithm. First, 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 BP, 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.

中文翻译：

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Levenberg__arquardt 用于安全关键网络物理系统状态估计的多层神经网络反向传播训练


制动系统作为重要的安全关键信息物理系统（CPS），对于电动汽车的安全运行至关重要。制动压力的准确估计对于汽车CPS设计和控制具有重要意义。本文开发了一种基于多层人工神经网络（ANN）和 Levenberg-Marquardt 反向传播（LMBP）训练算法的电动汽车制动压力概率估计方法。首先，说明了所提出的用于制动压力估计的多层人工神经网络的高级架构。然后，介绍用于训练前馈神经网络（FFNN）的标准反向传播（BP）算法。基于BP的基本概念，提出了一种更高效的LMBP方法训练算法。接下来，在标准行驶循环下在底盘测功机上进行实车测试。收集车辆和动力总成系统的实验数据，并选择用于FFNN训练收集的特征向量。最后，使用测量的车辆数据对开发的多层神经网络进行训练，评估制动压力估计的性能并与其他可用的学习方法进行比较。实验结果验证了所提出的基于人工神经网络的真实减速场景下制动压力估计方法的可行性和准确性。