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Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks

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journal contribution
posted on 2023-06-09, 03:06 authored by Colin Bennett, Julian DunneJulian Dunne, S Trimby, D Richardson
A recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics. This type of indirect sensing is important for cost effective closed-loop combustion control and for On-Board Diagnostics. The challenge addressed is to accurately predict cylinder pressure traces within the cycle under generalisation conditions: i.e. using data not previously seen by the network during training. This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion. The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines. The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability. Real data is obtained from a 1.125 litre, 3-cylinder, in-line, direct injection spark ignition (DISI) engine involving synchronised measurements of crank kinematics and cylinder pressure across a range of steady-state speed and load conditions. The paper shows that a RAGD-trained NARX network using both crank velocity and crank acceleration as input information, provides fast and robust training. By using the optimum epoch identified during RAGD training, acceptably accurate cylinder pressures, and especially accurate location-of-peak-pressure, can be reconstructed robustly under generalisation conditions, making it the most practical NARX configuration and recurrent training methodology for use on production engines.

Funding

Adaptive cylinder pressure reconstruction for production engines.; G0297; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/E03246X/1

Adaptive cylinder pressure reconstruction for production engines.; G0297; Jaguar Land Rover; EP/E03246X/1

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Mechanical Systems and Signal Processing

ISSN

0888-3270

Publisher

Elsevier

Volume

85

Page range

126-145

Department affiliated with

  • Engineering and Design Publications

Research groups affiliated with

  • Dynamics, Control and Vehicle Research Group Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2016-09-25

First Open Access (FOA) Date

2017-08-19

First Compliant Deposit (FCD) Date

2016-09-25

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