The smart regenerative braking system in electric vehicles implements automatic control of the regeneration torque of motor to improve driver’s comfort and energy efficiency. To apply this system, the accurate prediction of the vehicle deceleration states is the preliminary step to reflect the driver’s behaviors. In this paper, we proposed a vehicle deceleration prediction model via deep neural network, which consists of a sequential recurrent neural network model with long-short term memory cell and a two-layer conventional neural network model. This model accommodates the physical constraint to designate the vehicle stop location in front of the traffic signals. The model is trained by vehicle experiment data with three drivers through the hyper-parameter optimization method. Using this model, the deceleration characteristics are characterized by two explicit parameters such that deceleration point, maximum point according to the initial slope and the shape of the braking profile. Using these two parameters as clustering variables through a K-means clustering method, the deceleration types are classified. These deceleration types to the input to the prediction model results in higher prediction accuracy of the vehicle states. The driving style of the three drivers at braking situations is analyzed according to the deceleration types as well.

中文翻译：

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制动条件下基于深度神经网络的车辆减速度预测

电动汽车中的智能再生制动系统实现了电动机再生转矩的自动控制，从而提高了驾驶员的舒适度和能源效率。为了应用该系统，准确预测车辆减速状态是反映驾驶员行为的初步步骤。本文提出了一种基于深度神经网络的车辆减速预测模型，该模型由具有长期短期记忆单元的顺序递归神经网络模型和两层常规神经网络模型组成。该模型适应了物理限制，以指定交通信号灯前面的停车位置。通过超参数优化方法，由三个驾驶员的车辆实验数据对模型进行训练。使用这个模型，减速特性的特征在于两个显式参数，例如减速点，根据初始斜率的最大点和制动曲线的形状。通过K-means聚类方法将这两个参数用作聚类变量，对减速类型进行分类。输入到预测模型的这些减速度类型导致车辆状态的较高预测精度。还根据减速类型分析了三个驾驶员在制动情况下的驾驶风格。输入到预测模型的这些减速度类型导致车辆状态的较高预测精度。还根据减速类型分析了三个驾驶员在制动情况下的驾驶风格。输入到预测模型的这些减速度类型导致车辆状态的较高预测精度。还根据减速类型分析了三个驾驶员在制动情况下的驾驶风格。