In:
International Journal of Engine Research, SAGE Publications, Vol. 23, No. 9 ( 2022-09), p. 1503-1521
Abstract:
Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.
Type of Medium:
Online Resource
ISSN:
1468-0874
,
2041-3149
DOI:
10.1177/14680874211019345
Language:
English
Publisher:
SAGE Publications
Publication Date:
2022
detail.hit.zdb_id:
2030603-9
detail.hit.zdb_id:
2030240-X
