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Volume 8 Issue 1
Jan.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(1): 169-178
Mohammad Al-Sharman, David Murdoch, Dongpu Cao, Chen Lv, Yahya Zweiri, Derek Rayside and William Melek, "A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 169-178, Jan. 2021. doi: 10.1109/JAS.2020.1003474
Citation: Mohammad Al-Sharman, David Murdoch, Dongpu Cao, Chen Lv, Yahya Zweiri, Derek Rayside and William Melek, "A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 169-178, Jan. 2021. doi: 10.1109/JAS.2020.1003474
shu

A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach

doi: 10.1109/JAS.2020.1003474
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    Abstract
  • In today’s modern electric vehicles, enhancing the safety-critical cyber-physical system (CPS)’s performance is necessary for the safe maneuverability of the vehicle. As a typical CPS, the braking system is crucial for the vehicle design and safe control. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. In this paper, a sensorless state estimation technique of the vehicle’s brake pressure is developed using a deep-learning approach. A deep neural network (DNN) is structured and trained using deep-learning training techniques, such as, dropout and rectified units. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.

     

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    Highlights

    • A sensorless novel deep-learning-based algorithm is developed for brake pressure state estimation of an electric vehicle.
    • This state estimation technique uses current DL techniques and functions, such as dropout and ReLU to provide overfitting-free models of the state estimator.
    • The implementation of the proposed network is based on experimental data acquired using a real experimental vehicle testing environment.
    • Compared with conventional training methods, the proposed approach demonstrates more accurate brake pressure state estimation with RMSE errors of 0.048 MPa.
    • The proposed deep learning structure is expandable, hence, it can estimate other EV states in urban and high-way environments.

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