The future is automatic

WinGD TO INTRODUCE AI SELFCALIBRATING ENGINES BY 2026

WinGD has plans to gradually introduce fully self-tuning engines for its engine programme by the end of 2026, Peter Krähenbühl, WinGD’s Head of Digital Transformation & Technology tells The Motorship in an exclusive interview

Smart self-tuning technologies are expected to be progressively introduced on and around the company’s engine and energy management control system. The potential of the technology has been demonstrated already in other sectors as for example in the automotive industry, where engine control systems use closed-loop model predictive controls already.

Krähenbühl noted that WinGD’s research envisaged combining the optimisation of individual cylinder combustion, related components, and subsystems with a broader holistic approach to optimise the energy consumption of the engine and total propulsion system.

“Skilled engineers and operators are very good at achieving optimal outcomes for individual systems, but this approach will go beyond what any individual or team can achieve by allowing the operation of an entire system to be optimised in real-time,” Krähenbühl said.

The technology, which is described in ‘Self-tuning engines’ below, builds upon some of the detailed modelling work that the company has undertaken during the development of performance and diagnostic monitoring modules within its WiDE engine monitoring system, as well as the enhanced energy flow simulation platform that was developed during the development of WinGD’s integrated hybrid system.

The introduction of the system is expected to result in an incremental improvement in fuel efficiency for conventional diesel-fuelled engines, resulting in fuel consumption improvements of “at least 2-3%” without any other changes to the engine, Krähenbühl noted.

“More significant reductions” in fuel consumption are to be on offer for vessels that combine self-tuning engine technology with WinGD’s recently developed electrification solution, given the potential for the electrification system to contribute to the optimum operating point via peak shaving, or use engine free end driven generators instead of gensets, for example.

Further, the technology is also to make a significant contribution to WinGD’s dual-fuel engine programme, which has recently broadened its scope to include the introduction of ammonia and methanol engines by 2024 and 2025 respectively.

Compatibility with variable compression ratio

VCR technology introduces the ability to raise or lower the piston's top position to burn a reduced volume of fuel or lower it, creating a responsive compression ratio. The technology coincides with a separate advance, the replacement of mechanical cam-plate triggers with a sensor-based system, which will allow WinGD’s engines to monitor and adjust the piston’s position in relation to the cylinder liner.

The advances will broaden the range of control strategies available to WinGD engine designers.

“The introduction of this kind of flexibility [offered by selftuning approaches], allows you to start adjusting the engine to the combustion characteristics of different fuels,” Krähenbühl said. “Then, of course, if you can adjust the compression ratio, you can much better optimise both combustions [in dual-fuel engines]. And the [model-based control] would [open up] self-tuning by having feedback loops within some constraints.”

This would offer an elegant solution to the compromised adjustments and tunings operators experience today switching between different fuels.

Timeline for introduction

The introduction of model-based predictive control was likely to be more of a process than an event, Krähenbühl said, noting that the solution has already been introduced for some of the subsystems controlled by WinGD’s engine control system. Further systems were currently under development.

“For example, we're just completing the solution for the aftertreatment, because that's also one of the newer systems. So that should be available next year for testing, and then for roll out later.”

The model-based predictive control technology was being considered from the beginning for new solutions, such as hybrid, Krähenbühl said.

By progressively replacing existing subsystems with new model-based predictive controls, the expectation was that a fully self-tuning engine would be introduced “within the coming years”.

Ageing eff ects as a variable

The introduction of self-tuning technology would also allow WinGD to reflect engine-specific factors in its optimisation process. One such factor was the effect of ageing on subsystems, as existing optimisation approaches do not reflect the impact of deposits on the performance of an aftertreatment system, to take one example.

8 WinGD’s

next target is to introduce continuous optimization to its real time energy management controller

Further iterative improvements in the accuracy of modelbased systems could be expected in the future as WinGD’s understanding of the ageing effects on individual subsystems advances, Krähenbühl noted.

“The feedback from the field is very important in order to include these effects and to correctly model them.”

This is particularly important as the number of fuel types, hybrid configurations and aftertreatment solutions is expected to proliferate in the future. Long term trials for each potential configuration, reflecting potential variations in dual-fuel or multi-fuel use, without automated features is impractical.

Physical, semi-physical and data-driven models

WinGD’s existing approach uses a variety of different modelling approaches in its development work, ranging from physically-based models (best suited for simple systems where a good physical model can describe the system) through to data-driven systems (where it is too complex to describe the physics and a machine learning model is better suited) and combinations to utilize and combine the advantages of physical and mathematical approaches as semi-supervised-learning (SSL).

A hybrid semi-physical approach, in which physical data is integrated into a thermodynamic data driven engine model, has also been developed for use in performance and diagnostic monitoring modules within WinGD’s WiDE engine monitoring system.

While the processing of real-time inputs into highly detailed semi-physical or physical models has been too timeconsuming until now, WinGD expects its model-based control system to be able to update a simplified model and derive an optimisation response within the timeframe required for realtime optimisation. Advances in computer power and edge computing, in combination with the creation of simplified semi-physical thermodynamic models capable of integrating high speed sensor inputs have been key to this advance.

8 WinGD has based the

development of its data-driven model upon its existing highly detailed physical digital twin

Self-calibrating engines

Self-tuning engine technology is not a new technology, and the technology has been used for some time in the automotive sector. Peter Krähenbühl noted that there were two different areas of focus for self-calibrating engines.

The first – model-based calibration – makes use of the model to optimise engine calibration offline. This front-loading approach is compatible with either physical or data-driven models and will reduce the tuning time required for the engine. Such an approach can result in more consistent outcomes, as it reduces the human element during tuning.

The second approach – model-based control – connects a model of the engine to the engine software to the engine control unit. Using a feedback loop, the engine is continuously optimised. Krähenbühl added that rather than acting like a pre-setting, this model will control and adjust the system using inputs from the feedback loop to optimise the system for given constraints. However, the most interesting thing about this approach is that it can be applied to systems and subsystems on the engine

Example of aftertreatment

Krähenbühl discussed a selective catalytic reduction (SCR) aftertreatment system as an example of how a self-tuning engine would treat an individual system within an engine system. Modelling the efficiency of an aftertreatment system, based on a defined operating temperature at a certain flow from either a physical or a data-driven model, and then calculating how an engine’s NOx emission is influenced is a straightforward calculation. The value for the NOx is a standard output from an engine combustion physical or data-driven model.

By using the feedback loop within the model, it is then possible to model how the efficiency of the aftertreatment system can be changed by altering inputs. If the required emissions after the aftertreatment is set as a constraint, the feedback loop can be used to optimise operating costs by optimising the consumption of fuel and urea, for example. This would also have the advantage of reducing potential ammonia slip from excess urea consumption.

Controlling for multiple optimisation objectives

Krähenbühl continued by identifying how a model-based engine control system could use feedback loops to optimise performance against multiple optimisation priorities. The system could target the required emissions level to achieve compliance as a primary objective, and fuel consumption as a secondary priority. A third priority could include temperatures within the SCR, while risk tolerances or ageing effects could be added as a fourth.

“Rather than applying a rigid system that targets a certain engine NOx emission, and which requires a constant efficiency of the after treatment in these conditions, a model-based engine control system could target an optimal level within [these] defined constraints.”