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Self-learning energy management is a promising concept, which optimizes real-world system performance by automated, on-line adaptation of control settings. In this work, the potential of self-learning capabilities related to optimization is studied for energy management in Plug-in Hybrid Electric Vehicles (PHEV). These vehicles are of great interest for the transport sector, since they combine high fuel efficiency with last mile full-electric driving. We focus on a specific use case: PHEV operation through future Zero Emission (ZE) zones of cities. As a first step towards self-learning control, we introduce a novel, adaptive supervisory controller that combines modular energy and emission management (MEEM) and deals with varying constraints and system uncertainty. This optimal control strategy is based on Pontryagin’s Minimum Principle and maximizes overall energy efficiency.

A model-based control approach is proposed to give proper reference for the feed-forward combustion control of Partially Pre-mixed Combustion (PPC) engines. The current study presents a simplified first principal model, which has been developed to provide a base estimation of the ignition properties. This model is used to describe the behavior of a single-cylinder heavy-duty diesel engine fueled with a mix of bio-butanol and n-heptane (80vol% bio-butanol and 20 vol% n-heptane). The model has been validated at 8 bar gross Indicated Mean Effective Pressure (gIMEP) in PPC mode. Inlet temperature and pressure have been varied to test the model capabilities. First the experiments were conducted to generate reference points with BH80 under PPC conditions. And then CFD simulations were conducted to give initial parameter set up, e.g. fuel distribution, zone dividing, for the multi-zone model.

This paper focusses on the application of bioalcohols (ethanol and butanol) derived from seaweed in Heavy-Duty (HD) Compression Ignition (CI) combustion engines. Seaweed-based fuels do not claim land and are not in competition with the food chain. Currently, the application of high octane bioalcohols is limited to Spark Ignition (SI) engines. The Reactivity Controlled Compression Ignition (RCCI) combustion concept allows the use of these low carbon fuels in CI engines which have higher efficiencies associated with them than SI engines. This contributes to the reduction of tailpipe CO2 emissions as required by (future) legislation and reducing fuel consumption, i.e. Total-Cost-of-Ownership (TCO). Furthermore, it opens the HD transport market for these low carbon bioalcohol fuels from a novel sustainable biomass source. In this paper, both the production of seaweed-based fuels and the application of these fuels in CI engines is discussed.

CO2 regulations on heavy-duty transport are introduced in essentially all markets within the next decade, in most cases in several phases of increasing stringency. To cope with these mandates, developers of engines and related equipment are aiming to break new ground in the fields of combustion, fuel and hardware technologies. In this work, a novel diesel fuel injector, Delphi’s DFI7, is utilized to experimentally investigate and compare the performance of ramped injection rates versus traditional square fueling profiles. The aim is specifically to shift the efficiency and NOx tradeoff to a more favorable position. The design of experiments methodology is used in the tests, along with statistical techniques to analyze the data. Results show that ramped and square rates - after optimization of fueling parameters - produce comparable gross indicated efficiencies.

Reactivity Controlled Compression Ignition (RCCI) combines very high thermal efficiencies with ultra-low engine out NOx and PM emissions. Moreover, it enables the use of a wide range of fuels. As this pre-mixed combustion concept relies on controlled auto-ignition, closed-loop combustion control is essential to guarantee safe and stable operation under varying operating conditions. This work presents a coordinated air-fuel path controller for RCCI operation in a multi-cylinder heavy-duty engine. This is an essential step towards real-world application. Up to now, transient RCCI studies focused on individual cylinder control of the fuel path only. A systematic, model-based approach is followed to design a multivariable RCCI controller. Using the Frequency Response Function (FRF) method, linear models are identified for the air path and for the combustion process in the individual cylinders.

Complying to both the increasingly stringent pollutant emissions as well as (future) GHG emission legislation - with increased focus on in-use real-world emissions - puts a great challenge to the engine/aftertreatment control development process. Control system complexity, calibration and validation effort has increased dramatically over the past decade. A trend that is likely to continue considering the next steps in emission and GHG emission legislation. Control-oriented engine models are valuable tools for efficient development of engine monitoring and control systems. Furthermore, these (predictive) engine models are more and more used as part of control algorithms to ensure legislation compliant and optimized performance over the system lifetime. For these engine models, it is essential that simulation and prediction of system variables during non-nominal engine operation, such as non-standard ambient conditions, is well captured.

The Reactivity Controlled Compression Ignition concept for dual-fuel engines has multiple challenges of which some can be overcome using Variable Valve Actuation approaches. For various fuel combinations, the engine research community has shown that running dual-fuel engines in RCCI mode, improves thermal efficiency and results in ultra-low engine-out nitrous oxides and soot. However, stable RCCI combustion is limited to a certain load range, depending on available hardware. At low loads, the combustion efficiency can drop significantly, whereas at high loads, the maximum in-cylinder pressure can easily exceed the engine design limit. In this paper, three VVA measures to increase load range, improve combustion efficiency, and perform thermal management are presented. Simulation results are used to demonstrate the potential of these VVA measures for a heavy-duty engine running on natural gas and diesel.

The operation of a dual fuel combustion engine using combustion mode-switching offers the benefit of higher thermal efficiency compared to single-mode operation. For various fuel combinations, the engine research community has shown that running dual fuel engines in Reactivity Controlled Compression Ignition (RCCI) mode, is a feasible way to further improve thermal efficiency compared to Conventional Dual Fuel (CDF) operation of the same engine. In RCCI combustion, also ultra-low engine-out NOx and soot emissions have been reported. Depending on available hardware, however, stable RCCI combustion is limited to a certain load range and operating conditions. Therefore, mode-switching is a promising way to implement RCCI in practice on short term. In this paper, a model-based development approach for a dual fuel mode-switching controller is presented. Simulation results demonstrate the potential of this controller for a heavy-duty engine running on natural gas and diesel.

To meet future emission targets, it becomes increasingly important to optimize the synergy between engine and aftertreatment system. By using an integrated control approach minimal fluid (fuel and DEF) consumption is targeted within the constraints of emission legislation during real-world operation. In such concept, the on-line availability of engine-out NOx emission is crucial. Here, the use of a Virtual NOx sensor can be of great added-value. Virtual sensing enables more direct and robust emission control allowing, for example, engine-out NOx determination during conditions in which the hardware sensor is not available, such as cold start conditions. Furthermore, with use of the virtual sensor, the engine control strategy can be directly based on NOx emission data, resulting in reduced response time and improved transient emission control. This paper presents the development and on-line implementation of a Virtual NOx sensor, using in-cylinder pressure as main input.

This article describes a NOx sensor based urea dosing control strategy for heavy-duty diesel aftertreatment systems using Selective Catalytic Reduction. The dosing control strategy comprises of a fast-response, model-based ammonia storage control system in combination with a long-timescale tailpipe-feedback module that adjusts the dosing quantity according to current aftertreatment conditions. This results in a control system that is robust to system disturbances such as biased NOx sensors and variations in AdBlue concentrations. The cross-sensitivity of the tailpipe NOx sensor to ammonia is handled by a novel, smart signal filter that can reliably identify the contributions of NOx and NH3 in the tailpipe sensor signal, without requiring an artificial perturbation of the dosing signal.

SCR on Filter (SCRoF) is an efficient and compact NOX and PM reduction technology already used in series production for light-duty applications. The technology is now finding its way into the medium duty and heavy duty market. One of the key challenges for successful application is the robustness to real world variations. The solution to this challenge can be found by using model-based control algorithms, utilizing state estimation by physics-based catalyst models. This paper focuses on the development, validation and real time implementation of a physics-based control oriented SCRoF model. An overview of the developed model will be presented, together with a brief description of the model parameter identification and validation process using engine test bench measurement data. The model parameters are identified following a streamlined approach, focusing on decoupling the effects of deNOx and soot phenomena.

Heavy-duty diesel engines are used in a wide range of applications. For varying operating environments, the engine and aftertreatment system must comply with the real-world emission legislation limits. Simultaneously, minimal fuel consumption and good drivability are crucial for economic competitiveness and usability. Meeting these requirements takes substantial development and calibration effort, and complying with regulations results in a trade-off between emissions and fuel consumption. TNO's Integrated Emission Management (IEM) strategy finds online, the cost-optimal point in this trade-off and is able to deal with variations in operating conditions, while complying with legislation limits. Based on the actual state of the engine and aftertreatment system, an optimal engine operating point is computed using a model-based optimal-control algorithm.

Heavy-duty diesel engines are used in different application areas, like long-haul, city distribution, dump truck and building and construction industry. For these wide variety of areas, the engine performance needs to comply with the real-world legislation limits and should simultaneously have a low fuel consumption and good drivability. Meeting these requirements takes substantial development and calibration effort, where an optimal fuel consumption for each application is not always met in practice. TNO's Integrated Emission Management (IEM) strategy, is able to deal with these variations in operating conditions, while meeting legislation limits and obtaining on-line cost optimization. Based on the actual state of the engine and aftertreatment, optimal air-path setpoints are computed, which balances EGR and SCR usage.

Cylinder pressure-based combustion control is widely introduced for passenger cars. Benefits include enhanced emission robustness to fuel quality variation, reduced fuel consumption due to more accurate (multi-pulse) fuel injection, and minimized after treatment size. In addition, it enables the introduction of advanced, high-efficient combustion concepts. The application in truck engines is foreseen, but challenges need to be overcome related to durability, increased system costs, and impact on the cylinder head. In this paper, a new single cylinder pressure sensor concept for heavy-duty Diesel engines is presented. Compared to previous studies, this work focuses on heavy-duty Diesel powertrains, which are characterized by a relatively flexible crank shaft in contrast to the existing passenger car applications.

For natural gas (NG)-diesel RCCI, a multi-zonal, detailed chemistry modeling approach is presented. This dual fuel combustion process requires further understanding of the ignition and combustion processes to maximize thermal efficiency and minimize (partially) unburned fuel emissions. The introduction of two fuels with different physical and chemical properties makes the combustion process complicated and challenging to model. In this study, a multi-zone approach is applied to NG-diesel RCCI combustion in a heavy-duty engine. Auto-ignition chemistry is believed to be the key process in RCCI. Starting from a multi-zone model that can describe auto-ignition dominated processes, such as HCCI and PCCI, this model is adapted by including reaction mechanisms for natural gas and NOx and by improving the in-cylinder pressure prediction. The model is validated using NG-diesel RCCI measurements that are performed on a 6 cylinder heavy-duty engine.

Pedestrians and cyclists are the most unprotected road users and their injury risk in case of accidents is significantly higher than for other road users. The understanding of the influence and sensitivity between important variables describing a pedestrian crash is key for the development of more efficient and reliable safety systems. This paper reflects the related work carried out within the AsPeCSS project. The results summarized out of virtual and physical tests provide valuable information for further development. 1168 virtual and 120 physical tests were carried out with adult and child pedestrian headform as well as upper and lower legform impactors representatives of 4 different vehicle front geometries in a wide range of impact speeds, angles and locations. This test matrix was based on previous work carried out within the AsPeCSS project.

This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is considered to be standard for engine testing. The potential of the automated fit tool is demonstrated for two different heavy-duty diesel engines. This demonstrates that the combustion model and model fit methodology is not engine specific. Comparison of model and experimental results shows accurate prediction of in-cylinder peak pressure, IMEP, CA10, and CA50 over a wide operating range. This makes the model suitable for closed-loop combustion control development. However, NO emission prediction has to be improved.

Premixed combustion concepts like PCCI and RCCI have attracted much attention, since these concepts offer possibilities to reduce engine out emissions to a low level, while still achieving good efficiency. Most RCCI studies use a combination of a high-cetane fuel like diesel, and gasoline as low-cetane fuel. Limited results have been published using natural gas as low-cetane fuel; especially full scale engine results. This study presents results from an experimental study of diesel-CNG RCCI operation on a 6 cylinder, 8 l heavy duty engine with cooled EGR. This standard Tier4f diesel engine was equipped with a gas injection system, which used single point injection and mixed the gaseous fuel with air upstream of the intake manifold. For this engine configuration, RCCI operating limits have been explored. In the 1200-1800 rpm range, RCCI operation with Euro-VI engine out NOx and soot emissions was achieved between 2 and 9 bar BMEP without EGR.

This paper presents the identification and validation of a dynamic Waste Heat Recovery (WHR) system model. Driven by upcoming CO₂ emission targets and increasing fuel costs, engine exhaust gas heat utilization has recently attracted much attention to improve fuel efficiency, especially for heavy-duty automotive applications. In this study, we focus on a Euro-VI heavy-duty diesel engine, which is equipped with a Waste Heat Recovery system based on an Organic Rankine Cycle. The applied model, which combines first principle modeling with stationary component models, covers the two-phase flow behavior and the effect of control inputs. Furthermore, it describes the interaction with the engine on both gas and drivetrain side. Using engine dynamometer measurements, an optimal fit of unknown model parameters is determined for stationary operating points.

A new cost-based control strategy is presented that optimizes engine-aftertreatment performance under all operating conditions. This Integrated Emission Management strategy minimizes fuel consumption within the set emission limits by on-line adjustment of air management based on the actual state of the exhaust gas aftertreatment system. Following a model-based approach, Integrated Emission Management offers a framework for future control strategy development. This approach alleviates calibration complexity, since it allows to make optimal trade-offs in an operational cost sense. The potential of the presented cost-optimal control strategy is demonstrated for a modern heavy-duty Euro VI engine. The studied diesel engine is equipped with cooled EGR, Variable Geometry Turbocharger, and a DPF-SCR aftertreatment system.