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In the context of model-free optimization of dynamic nonlinear multiple-input-single-output (MISO) systems using extremum-seeking (ES), accurate and fast derivative estimation of the systems steady-state performance map is essential. This paper presents a generalized derivative estimator (DE) framework for unknown MISO static maps. To this extent, the map input is perturbed with sinusoidal dither signals with different frequencies. Using the proposed framework, the derivatives can be estimated up to an arbitrary order, for maps with an arbitrary number of inputs. Conditions on the dither frequencies are provided, which optimize the DE time-scale, such that derivative estimation is as fast as possible. Simulation examples are provided to demonstrate the effectiveness of the proposed framework.

For conventional buses, based on the decades of their operational knowledge, public transport companies are able to optimize their cost of operation. However, with recent trend in the usage of electric buses, cost optimal operation can become challenging. In this paper an offline optimal charging strategy is developed to minimize the energy cost. This is done by exploiting the periodicity and predictable operation of the city buses. For effective usage of the developed offline strategy, the actual energy demand of the electric bus must be known a-priori, which can be demanding. In order to address this issue, a predictor is designed. The neural network based predictor is able to estimate the energy demand for the next day. Using this, three different optimal charging strategies are implemented. Initially, only the operational constraints are considered to ensure the completion of a trip, later, a more involved problems consisting of battery state of charge (SoC) constraints and temperature constraints are included for the second and third optimization problems, respectively. All the three approaches result in significant energy cost minimization when compared to the non-optimal strategy of charging the electric bus to the full capacity at every available opportunity. Additionally, for the second and third formulations, namely, SoC and temperature constraints, by using a qualitative aging approach, some enhancements in the battery health is observed when compared to the non-optimal charging strategy.

Integrated Emission Management (IEM) is a supervisory control strategy that minimises operational costs (consisting of fuel and AdBlue) for diesel engines with an aftertreatment system, while satisfying emission constraints imposed by legislation. In most work on IEM, a suboptimal heuristic real-time implementable solution is used, which is based on Pontryagin's Minimum Principle (PMP). In this paper, we compute the optimal solution using both PMP and Dynamic Programming (DP). As the emission legislation imposes a terminal state constraint, standard DP algorithms are sensitive to numerical errors that appear close to the boundary of the feasible sets. Therefore, we propose two extensions to existing DP methods, which use an approximation of the forward reachable sets to reduce the grid size over time and an approximation of the backward reachable sets to avoid the aforementioned numerical errors. Using a simulation study of a cold-start World Harmonised Transient Cycle for a Euro-VI engine, we show that the novel extension to the DP algorithm yields the best approximation of the optimal cost, when compared to existing DP methods. Furthermore, we show that PMP yields almost the same results as DP, and that the real-time implementable solution only deviates approximately 0.08–0.16% from the optimal solution.

This paper presents an integrated energy and emission management strategy, called Integrated Powertrain Control (IPC), for an Euro-VI diesel engine with an electrified waste heat recovery system. This strategy optimizes the CO2–NOxCO2–NOx trade-off by minimizing the operational costs associated with fuel consumption, AdBlue dosage, and active particulate filter regeneration, while satisfying the tailpipe emission constraints. For comparison purposes, the proposed control strategy is applied to different powertrain configurations: with and without waste heat recovery (WHR) system and a WHR system equipped with a battery for energy storage. The potential of each studied configuration is evaluated over the World Harmonized Transient Cycle for cold-start and hot-start conditions. In comparison to the existing Euro VI engine without WHR system, it is shown in simulations that the optimal IPC strategy with an electrified WHR system and battery provides an additional 3.5% CO2 emission reduction and 19% particulate matter reduction, while satisfying the NOxNOx emission constraint.

Motivated by automotive emission legislations, a Virtual NOx sensor is developed. This virtual sensor consists of a realtime, phenomenological model that computes engine-out NOx by using the measured in-cylinder pressure signal from a single cylinder as its main input. The implementation is made on a Field Programmable Gate Array–Central Processing Unit architecture to ensure the NOx computation is ready at the end of the combustion cycle. The Virtual NOx sensor is tested and validated on an EURO-VI Heavy-Duty Diesel engine platform. The Virtual NOx sensor is proven to meet the accuracy of a production NOx sensor for steady-state conditions and has better frequency response compared to the production NOx sensor.

Motivated by automotive emission legislations, a Virtual NOx sensor is developed. This virtual sensor consists of a realtime, phenomenological model that computes engine-out NOx by using the measured in-cylinder pressure signal from a single cylinder as its main input. The implementation is made on a Field Programmable Gate Array–Central Processing Unit architecture to ensure the NOx computation is ready at the end of the combustion cycle. The Virtual NOx sensor is tested and validated on an EURO-VI Heavy-Duty Diesel engine platform. The Virtual NOx sensor is proven to meet the accuracy of a production NOx sensor for steady-state conditions and has better frequency response compared to the production NOx sensor.

One of the pending issues regarding Homogeneous Charge Compression Ignition (HCCI) engines is high load operation limit constrained by excessive pressure rise rates (PRRs). The present study investigates various measures to reduce combustion harness in a residual-affected HCCI engine. At the same time, the impact of those measures on efficiency and emissions is assessed. Experimental research was performed on a single cylinder engine equipped with a fully-flexible valvetrain mechanism and direct gasoline injection. The HCCI combustion mode with exhaust gas trapping was realized using negative valve overlap and fuel reforming, achieved via the injection of a portion of fuel during exhaust re-compression. Three measures are investigated for the PRR control under the same reference operating conditions, namely: (i) variable intake and exhaust valve timing, (ii) boost pressure adjustment and (iii) split fuel injection to control the amount of fuel injected for reforming. Variable exhaust valve timing enabled control of the amount of trapped residuals, and thus of the compression temperature. The reduction in the amount of trapped residuals, at elevated engine load, delays auto-ignition, which results in a simultaneous reduction of pressure rise rates and nitrogen oxides emissions. The effects of intake valve timing are much more complex, because they include the variability in the amount of intake air, the thermodynamic compression ratio as well as the in-cylinder fluid flow. It was found, however, that both early and late intake valve openings delay auto-ignition and prolong combustion. Additionally, the reduction of the amount of fuel injected during exhaust re-compression further delays combustion and reduces combustion rates. Intake pressure reduction has by far the largest effect on peak pressure reduction yet is connected with excessive NOx emissions. The research successfully identifies air-path and injection techniques, which allow for the control of combustion rates and emissions under elevated load regime, thus shorting the gap towards the real-world application of HCCI concepts.

This paper presents a battery peak power shaving strategy for battery electric buses. The developed strategy restricts the battery charge/discharge power when the propulsion power demand is high to avoid high deterioration of the battery capacity during operation. Without reducing the propulsion power, the developed strategy optimizes the power demand of the passenger cabin air conditioning system while guaranteeing the passenger temperature comfort. An equivalent consumption minimization strategy (ECMS) technique is extended to include the thermal comfort of the passenger in the energy management system of the bus. The developed strategy is analytical, real-time implementable and verified via hardware-in-the-loop test. Simulation results demonstrate the capability for satisfying the propulsion power demand and passenger temperature comfort while reducing the battery peak power (up to 20% during acceleration) to restrict the battery degradation significantly.

This brief presents a novel and time-efficient control design for modern heavy-duty diesel engines using a variable geometry turbine and an exhaust gas recirculation valve. The goal is to simultaneously and robustly achieve low fuel consumption and low emissions of nitrogen oxides (NOx) and particulate matter (PM). A new combination of three controlled outputs is used: 1) specific engine-out NOx emissions; 2) air-fuel equivalence ratio; and 3) the pressure difference between intake and exhaust manifold, which reflect NOx and PM emissions and fuel efficiency, respectively. It is shown that this combination allows for effective disturbance rejection and results in a well-conditioned system. An underactuated input-output system is formed, for which a linear feedback controller is designed. In addition to this feedback controller, a feedforward controller is implemented, which improves the torque response and lowers the PM emissions during fast changes in torque demand. The combined control system is suitable for the full range of speed and load variations. This new controller is tested experimentally on a modern heavy-duty engine running a hot world harmonized transient cycle and compared with a baseline controller. The new controller reduces the NOx and PM emissions by 3.9% and 11.7%, respectively, without a fuel penalty.

One of the pending issues regarding Homogeneous Charge Compression Ignition (HCCI) engines is high load operation limit constrained by excessive pressure rise rates (PRRs). The present study investigates various measures to reduce combustion harness in a residual-affected HCCI engine. At the same time, the impact of those measures on efficiency and emissions is assessed. Experimental research was performed on a single cylinder engine equipped with a fully-flexible valvetrain mechanism and direct gasoline injection. The HCCI combustion mode with exhaust gas trapping was realized using negative valve overlap and fuel reforming, achieved via the injection of a portion of fuel during exhaust re-compression. Three measures are investigated for the PRR control under the same reference operating conditions, namely: (i) variable intake and exhaust valve timing, (ii) boost pressure adjustment and (iii) split fuel injection to control the amount of fuel injected for reforming. Variable exhaust valve timing enabled control of the amount of trapped residuals, and thus of the compression temperature. The reduction in the amount of trapped residuals, at elevated engine load, delays auto-ignition, which results in a simultaneous reduction of pressure rise rates and nitrogen oxides emissions. The effects of intake valve timing are much more complex, because they include the variability in the amount of intake air, the thermodynamic compression ratio as well as the in-cylinder fluid flow. It was found, however, that both early and late intake valve openings delay auto-ignition and prolong combustion. Additionally, the reduction of the amount of fuel injected during exhaust re-compression further delays combustion and reduces combustion rates. Intake pressure reduction has by far the largest effect on peak pressure reduction yet is connected with excessive NOX emissions. The research successfully identifies air-path and injection techniques, which allow for the control of combustion rates and emissions under elevated load regime, thus shorting the gap towards the real-world application of HCCI concepts.

In the present paper, laboratory produced, animal origin biofuels (swine lard and turkey lard had been used as raw material for biofuel production) were evaluated in terms of physicochemical parameters and compared with commercial mineral diesel fuel and its mixture with rapeseed oil methyl esters. Basing on the results of physicochemical parameters assessment, mixtures containing 75% share of bio-component were preselected for further engine tests. The engine tests were performed on a medium-duty, turbocharged, Common Rail, Direct Injection (CRDI) diesel engine, using factory control maps. The scope of experiments included steady state measurements for two rotational speeds (1500 and 3000 RPM), for which full load sweep had been performed. Two injection strategies were utilized: single pulse (for 3000 RPM) and multi-pulse injection (1500 RPM). The research covered evaluation of engine performance parameters and full exhaust emissions. Furthermore, detailed combustion analysis was performed. The study confirmed that high quality fuel can obtained from waste fatty material. The mixtures containing up to 75% of bio-component are suitable for modern CRDI combustion engines, though slight deterioration of engine performance parameters can be expected. An average brake specific fuel consumption increased by 13% compared to reference, for biodiesel mixtures derived from animal fatty material. At the same time a 3% increase against rapeseed oil/diesel mixture was recorded. This was correlated with a minor reduction of brake fuel conversion efficiency, but the average drop didn't exceed 2% for all examined biodiesels. A significant reduction in exhaust gas emissions was observed, when comparing biofuel operation to reference diesel. The use of swine lard methyl ester/diesel mixture caused average reduction of hydrocarbon concentration (THC) by 13%, carbon monoxide (CO) by 22% and carbon dioxide (CO2) by 7%. The turkey biodiesel emission results were respectively: 9%, 20% and 6% reduced. NOx emission increased on average by 7% for both animal origin biofuels.

In the present paper, laboratory produced, animal origin biofuels (swine lard and turkey lard had been used as raw material for biofuel production) were evaluated in terms of physicochemical parameters and compared with commercial mineral diesel fuel and its mixture with rapeseed oil methyl esters. Basing on the results of physicochemical parameters assessment, mixtures containing 75% share of bio-component were preselected for further engine tests. The engine tests were performed on a medium-duty, turbocharged, Common Rail, Direct Injection (CRDI) diesel engine, using factory control maps. The scope of experiments included steady state measurements for two rotational speeds (1500 and 3000 RPM), for which full load sweep had been performed. Two injection strategies were utilized: single pulse (for 3000 RPM) and multi-pulse injection (1500 RPM). The research covered evaluation of engine performance parameters and full exhaust emissions. Furthermore, detailed combustion analysis was performed. The study confirmed that high quality fuel can obtained from waste fatty material. The mixtures containing up to 75% of bio-component are suitable for modern CRDI combustion engines, though slight deterioration of engine performance parameters can be expected. An average brake specific fuel consumption increased by 13% compared to reference, for biodiesel mixtures derived from animal fatty material. At the same time a 3% increase against rapeseed oil/diesel mixture was recorded. This was correlated with a minor reduction of brake fuel conversion efficiency, but the average drop didn't exceed 2% for all examined biodiesels. A significant reduction in exhaust gas emissions was observed, when comparing biofuel operation to reference diesel. The use of swine lard methyl ester/diesel mixture caused average reduction of hydrocarbon concentration (THC) by 13%, carbon monoxide (CO) by 22% and carbon dioxide (CO2) by 7%. The turkey biodiesel emission results were respectively: 9%, 20% and 6% reduced. NOx emission increased on average by 7% for both animal origin biofuels.

Purpose – The purpose of this paper is to study the performance and efficiency of two different permanent magnet (PM) machine rotor configurations under magnetic core saturation conditions. Design/methodology/approach – Since the accuracy of conventional analytical methods is limited under saturation conditions, a finite element model of the machine is built; which is used to predict the various losses over its operating range such as eddy current, hysteresis, copper and magnet losses. Using this model, the efficiency map of the machine is derived which is used to investigate its efficiency corresponding to a heavy vehicle drive cycle. The performance of two different rotor designs are studied and the efficiency of each design is compared under the considered drive cycle. Findings – It has also been proved that the performance advantage due to reluctance torque in the v-shaped interior PM (IPM) machine is offset by its core steel saturation at higher current/torque levels. The magnitude of iron losses in the IPM is higher than that in the surface PM (SPM) machine, however, the magnet loss in the SPM is higher than in the IPM. Originality/value – An investigation of the performance of the IPM design in comparison with the SPM∼design under magnetic saturation conditions is not known to the authors. Hence, in this paper, it will be determined if the assumed performance advantage of the IPM over the SPM still holds true under these conditions.

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.

This paper presents the modeling and control of a waste heat recovery system for a Euro-VI heavy-duty truck engine. The considered waste heat recovery system consists of two parallel evaporators with expander and pumps mechanically coupled to the engine crankshaft. Compared to previous work, the waste heat recovery system modeling is improved by including evaporator models that combine the finite difference modeling approach with a moving boundary one. Over a specific cycle, the steady-state and dynamic temperature prediction accuracy improved on average by 2% and 7%. From a control design perspective, the objective is to maximize the waste heat recovery system output power. However, for safe system operation, the vapor state needs to be maintained before the expander under highly dynamic engine disturbances. To achieve this, a switching model predictive control strategy is developed. The proposed control strategy performance is demonstrated using the high-fidelity waste heat recovery system model subject to measured disturbances from an Euro-VI heavy-duty diesel engine. Simulations are performed using a cold-start World Harmonized Transient cycle that covers typical urban, rural and highway driving conditions. The model predictive control strategy provides 15% more time in vapor and recovered thermal energy than a classical proportional-integral (PI) control strategy. In the case that the model is accurately known, the proposed control strategy performance can be improved by 10% in terms of time in vapor and recovered thermal energy. This is demonstrated with an offline nonlinear model predictive control strategy.

This paper presents the modeling and model validation for a modular two-phase heat exchanger that recovers energy in heavy-duty diesel engines. The model is developed for temperature and vapor quality prediction and for control design of the waste heat recovery system. In the studied waste heat recovery system, energy is recovered from both the exhaust gas recirculation line and the main exhaust line. Due to the similar design of these two heat exchangers, only the exhaust gas recirculation heat exchanger model is presented in this paper. Based on mass and energy conservation principles, the model describes the dynamics of two-phase fluid flow. Compared to other studies, the model is able to capture multiple phase transitions along the fluid flow by combining finite difference approach with moving boundary approaches. The developed model has low computational complexity, which makes it suitable for control design and real-time implementation.To validate the model, experiments are performed on a state-of-the-art Euro-VI heavy-duty diesel engine equipped with the waste heat recovery system. Simulation results show good accuracy, over the complete engine operating range, with average error below 4%. This is demonstrated on transitions between stationary operating points and on a dynamic response to a standard world harmonized transient cycle for both cold-start and hot-start conditions. cop. 2014 Elsevier Ltd.

Within electric and hybrid vehicles, batteries are used to provide/buffer the energy required for driving. However, battery performance varies throughout the temperature range specific to automotive applications, and as such, models that describe this behaviour are required. This paper presents a dynamic battery model describing the battery voltage based on current and temperature. A model identification method is presented and validation tests are performed. The results show that when temperature influences are included in the models, a significant increase in performance can be achieved. Modelling errors lower that 5% are achieved for validation tests conducted at -10 degrees Celsius, 0 degrees Celsius, and 10 degrees Celsius, whereas the models that do not consider temperature show errors up to 10%.

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. The development steps from off-line modelling, calibration and validation towards real-time on-line implementation and validation will be presented in great detail. The Virtual NOx sensor is validated on a state-of-the-art EURO-VI Heavy Duty Diesel Engine. Steady-state validation results show that the performance of the Virtual NOx sensor is similar to the physical NOx sensor. Transient load ramp tests and frequency response function evaluation both show improved transient response from the Virtual NOx sensor. Furthermore, the Virtual NOx sensor is successfully applied as substitute for the physical NOx sensor as input to the air path controller over a transient cycle. These validation results demonstrate the application of the Virtual NOx sensor for engine control and show the potential for improved transient emission control.

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. The tailpipe feedback module compares the signal from the cross-sensitive tailpipe NOx sensor to the modeled tailpipe sensor signal to estimate the measured and modeled NOx conversion and NH3 slip, without the need for an NH3 sensor. The difference between these measured and modeled quantities is used to adjust the dosing quantity to the aftertreatment system, thereby maintaining nominal performance of the aftertreatment system in the presence of disturbances. Simulation results are presented of the urea dosing control system covering a wide range of disturbances, demonstrating the robustness properties of the controlled system. The robust urea dosing control strategy was implemented on a rapid prototyping platform and tested on a state-of-the-art Euro VI engine and aftertreatment systems, confirming the expected performance and robustness properties.

The robustness of a new single-cylinder pressure sensor concept is experimentally demonstrated on a six-cylinder heavy-duty diesel engine. Using a single-cylinder pressure sensor and a crank angle sensor, this single-cylinder pressure sensor concept estimates the in-cylinder pressure traces in the remaining cylinders by applying a real-time, flexible crankshaft model combined with an adaptation algorithm. The single-cylinder pressure sensor concept is implemented on CPU/field-programmable gate array–based hardware. For steady-state engine operating conditions, the added value of the adaptation algorithm is demonstrated for cases in which a fuel quantity change or start of injection change is applied in a single, non-instrumented cylinder. It is shown that for steady-state and transient engine conditions, the cylinder pressure traces and corresponding combustion parameters, indicated mean effective pressure, peak cylinder pressure, and crank angle at 50% heat release, can be estimated with 1.2bar, 6.0bar, and 1.1CAD inaccuracy, respectively.

For conventional buses, based on the decades of their operational knowledge, public transport companies are able to optimize their cost of operation. However, with recent trend in the usage of electric buses, cost optimal operation can become challenging. In this paper an offline optimal charging strategy is developed to minimize the energy cost. This is done by exploiting the periodicity and predictable operation of the city buses. For effective usage of the developed offline strategy, the actual energy demand of the electric bus must be known a-priori, which can be demanding. In order to address this issue, a predictor is designed. The neural network based predictor is able to estimate the energy demand for the next day. Using this, three different optimal charging strategies are implemented. Initially, only the operational constraints are considered to ensure the completion of a trip, later, a more involved problems consisting of battery state of charge (SoC) constraints and temperature constraints are included for the second and third optimization problems, respectively. All the three approaches result in significant energy cost minimization when compared to the non-optimal strategy of charging the electric bus to the full capacity at every available opportunity. Additionally, for the second and third formulations, namely, SoC and temperature constraints, by using a qualitative aging approach, some enhancements in the battery health is observed when compared to the non-optimal charging strategy.

In the context of model-free optimization of dynamic nonlinear multiple-input-single-output (MISO) systems using extremum-seeking (ES), accurate and fast derivative estimation of the systems steady-state performance map is essential. This paper presents a generalized derivative estimator (DE) framework for unknown MISO static maps. To this extent, the map input is perturbed with sinusoidal dither signals with different frequencies. Using the proposed framework, the derivatives can be estimated up to an arbitrary order, for maps with an arbitrary number of inputs. Conditions on the dither frequencies are provided, which optimize the DE time-scale, such that derivative estimation is as fast as possible. Simulation examples are provided to demonstrate the effectiveness of the proposed framework.

Increasing demands for the reduction of exhaust emissions and the pursuit to reduce the use of fossil fuels require the search for new fuelling technologies in combustion engines. One of the most promising technologies is the multi-fuel compression ignition engine concept, in which a small dose of liquid fuel injected directly into the cylinder acts as the ignition inhibitor of the gaseous fuel. Achieving the optimum combustion process in such an engine requires the application of advanced control algorithms which require mathematical modelling support. In response to the growing demand for new simulation tools, a 0-D model of a dual-fuel engine was proposed and validated. The validation was performed in a broad range of engine operating points, including various speeds and load condition, as well as different natural gas/diesel blend ratios. It was demonstrated that the average model calculation error within the entire cycle did not exceed 6.2%, and was comparable to the measurement results cycle to cycle variations. The maximum model calculation error in a single point of a cycle was 15% for one of the complex (multipoint injection) cases. In other cases, it did not exceed 11%.

Increasing demands for the reduction of exhaust emissions and the pursuit to reduce the use of fossil fuels require the search for new fuelling technologies in combustion engines. One of the most promising technologies is the multi-fuel compression ignition engine concept, in which a small dose of liquid fuel injected directly into the cylinder acts as the ignition inhibitor of the gaseous fuel. Achieving the optimum combustion process in such an engine requires the application of advanced control algorithms which require mathematical modelling support. In response to the growing demand for new simulation tools, a 0-D model of a dual-fuel engine was proposed and validated. The validation was performed in a broad range of engine operating points, including various speeds and load condition, as well as different natural gas/diesel blend ratios. It was demonstrated that the average model calculation error within the entire cycle did not exceed 6.2%, and was comparable to the measurement results cycle to cycle variations. The maximum model calculation error in a single point of a cycle was 15% for one of the complex (multipoint injection) cases. In other cases, it did not exceed 11%.

Closed-loop combustion control helps to achieve precise fuel injection and robust engine performance against disturbances. The controller design complexity increases greatly with larger number of fuel injection pulses due to the coupled influence of changing individual pulse on the combustion process. In this work, we experimentally demonstrate the potential of a novel design methodology, which automatically computes a multivariable combustion controller. Using a one-cylinder test bench, we tested a single combustion controller in both the nominal and the disturbed operating condition with varied EGR valve position. The controller shows robust closed-loop stability and fast dynamical performance in both conditions as well as during the transition in between.

Natural gas - diesel, Reactivity Controlled Compression Ignition (RCCI) is currently one of the most promising combustion strategies for the next generation heavy-duty engines. A major issue to be addressed for this dual fuel concept to become practically applicable is its low combustion efficiency and associated high methane emissions in partial engine loads. Introducing gaseous fuel stratification (by direct injection) is considered to be beneficial for combustion efficiency increase. Yet, the improvement potential of such natural gas stratification in RCCI engines is unknown. Modeling studies are needed to investigate this methane reduction measure because they can provide more insight to the combustion process than measurements alone. The objective of this study is to assess the potential of RCCI with direct injected low reactivity fuel in terms of thermal efficiency and methane emissions. A broad range of measured, single point injected gas, operating points served as a basis for the simulation study. TNO’s in-house, multi-zone, chemical kinetics based model was validated for those points and used to generate simulation results for different natural gas stratification profiles. It is shown that gaseous fuel stratification in dual fuel RCCI helps to increase combustion efficiency and decrease methane and carbon monoxide emissions. At the same time, nitrogen oxides increase and (at some operating points) the emissions of non-methane hydrocarbons slightly increase too. The effect is strongest for the low load cases showing maximum improvement of combustion efficiency by 11 percentage point for in-extensive natural gas stratifications. The improvement potential reduces with increasing engine load. Thanks to the insights to the combustion process (given by the simulation results), explanations for the observed trends are provided, and important phenomena are identified that are associated with increased low reactivity fuel stratification.

Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[%] compared to conventional strategies for various battery capacities.

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. The development steps from off-line modelling, calibration and validation towards real-time on-line implementation and validation will be presented in great detail. The Virtual NOx sensor is validated on a state-of-the-art EURO-VI Heavy Duty Diesel Engine. Steady-state validation results show that the performance of the Virtual NOx sensor is similar to the physical NOx sensor. Transient load ramp tests and frequency response function evaluation both show improved transient response from the Virtual NOx sensor. Furthermore, the Virtual NOx sensor is successfully applied as substitute for the physical NOx sensor as input to the air path controller over a transient cycle. These validation results demonstrate the application of the Virtual NOx sensor for engine control and show the potential for improved transient emission control.

Closed-loop combustion control helps to achieve precise fuel injection and robust engine performance against disturbances. The controller design complexity increases greatly with larger number of fuel injection pulses due to the coupled influence of changing individual pulse on the combustion process. In this work, we experimentally demonstrate the potential of a novel design methodology, which automatically computes a multivariable combustion controller. Using a one-cylinder test bench, we tested a single combustion controller in both the nominal and the disturbed operating condition with varied EGR valve position. The controller shows robust closed-loop stability and fast dynamical performance in both conditions as well as during the transition in between.

The robustness of a new single-cylinder pressure sensor concept is experimentally demonstrated on a six-cylinder heavy-duty diesel engine. Using a single-cylinder pressure sensor and a crank angle sensor, this single-cylinder pressure sensor concept estimates the in-cylinder pressure traces in the remaining cylinders by applying a real-time, flexible crankshaft model combined with an adaptation algorithm. The single-cylinder pressure sensor concept is implemented on CPU/field-programmable gate array–based hardware. For steady-state engine operating conditions, the added value of the adaptation algorithm is demonstrated for cases in which a fuel quantity change or start of injection change is applied in a single, non-instrumented cylinder. It is shown that for steady-state and transient engine conditions, the cylinder pressure traces and corresponding combustion parameters, indicated mean effective pressure, peak cylinder pressure, and crank angle at 50% heat release, can be estimated with 1.2 bar, 6.0 bar, and 1.1 CAD inaccuracy, respectively.

Natural gas - diesel, Reactivity Controlled Compression Ignition (RCCI) is currently one of the most promising combustion strategies for the next generation heavy-duty engines. A major issue to be addressed for this dual fuel concept to become practically applicable is its low combustion efficiency and associated high methane emissions in partial engine loads. Introducing gaseous fuel stratification (by direct injection) is considered to be beneficial for combustion efficiency increase. Yet, the improvement potential of such natural gas stratification in RCCI engines is unknown. Modeling studies are needed to investigate this methane reduction measure because they can provide more insight to the combustion process than measurements alone. The objective of this study is to assess the potential of RCCI with direct injected low reactivity fuel in terms of thermal efficiency and methane emissions. A broad range of measured, single point injected gas, operating points served as a basis for the simulation study. TNO’s in-house, multi-zone, chemical kinetics based model was validated for those points and used to generate simulation results for different natural gas stratification profiles. It is shown that gaseous fuel stratification in dual fuel RCCI helps to increase combustion efficiency and decrease methane and carbon monoxide emissions. At the same time, nitrogen oxides increase and (at some operating points) the emissions of non-methane hydrocarbons slightly increase too. The effect is strongest for the low load cases showing maximum improvement of combustion efficiency by 11 percentage point for in-extensive natural gas stratifications. The improvement potential reduces with increasing engine load. Thanks to the insights to the combustion process (given by the simulation results), explanations for the observed trends are provided, and important phenomena are identified that are associated with increased low reactivity fuel stratification.

This paper presents an integrated vehicle model to simulate simultaneously the driver, powertrains, chassis, body, road condition, vehicle dynamics and the Active Suspension (AS) system with/without an energy harvesting module. The developed model is used to investigate the ride comfort and influences of energy harvesting AS system on the total energy consumption of battery Electric Vehicles (EVs) relative to EVs with a passive suspension system. Preliminary simulation results show that compared to EVs with a passive suspension system, the ones with AS system improve ride comfort, up to 31% reduction of the vehicle body acceleration RMS value, with an expense of higher energy consumption. This expense can be reduced to about 2.8% when using an energy harvesting AS system. Simulation results also demonstrate that the available energy for recuperation during the AS system operation is significant in relation to the regenerative braking energy of the propulsion system, up to approx. 70% on bumpy road surfaces.

Within hybrid electric vehicles (HEVs) predictive energy management strategies (EMSs) have the potential to reduce the fuel consumption compared to conventional EMSs, where the drive cycle is unknown. Typically, predictive EMSs require a future vehicle speed profile prediction. However, when prediction is inaccurate, the systems fuel reduction performance and robustness may be compromised. Among many influential factors, inaccurate prediction is mainly caused by uncertain dynamic traffic conditions, e.g. traffic and traffic lights. This paper develops a predictive EMS, which enhances the equivalent fuel consumption minimization strategy (ECMS) with real-time traffic flow data and traffic light position to maximize fuel reduction performance of HEVs. Moreover, a Monte Carlo approach is exploited to handle the traffic light uncertainty. Simulation results demonstrate the benefits of Monte Carlo approach in predictive EMS to enhance the robustness and fuel reduction performance up to 2-11[%] compared to conventional strategies for various battery capacities.

Heavy duty diesel vehicles compliant with current Euro VI／EPA13 emission limits employ aftertreatment systems based on DOC／DPF technology for soot and particulate matter reduction and SCR catalysts with urea dosing for NOx reduction．Traditionally，the majority of the control systems used for urea dosing are map based．However，increasing system complexity combined with real——world performance requirements are a strong motivation to switch to a model——based control approach．Firstly，this article describes a model—based design approach for aftertreatment control development．Focus is on urea dosing control for Euro VI level SCR systems．To achieve the legal emissions limits，including in—service conform ity over the vehicle lifetime，advanced model—based control strategies enable maximal NOx conversion in combination with minimum ammonia slip，while ensuring robustness against real—life disturbances．Simulation and experimental results of the control system are presented，which demonstrate the perform ance and robustness properties．Following this model-based approach，a concept study is perform ed to explore aftertreatment and control technologies to achieve ultra—low NOx emissions as will be imposed by regulatory bodies in the near future．It is shown that aftertreatment concepts with Passive N Adsorber and SCR on DPF are most promising．To optimize overall engine—aftertreatment performance．the modelbased control approach is extended towards Integrated Emission Management(IEM)．Based on the actual system state，this supervisory controller minimizes operating costs at each instant in time under all operating conditions．This is key for costoptimal and robust performance．

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. Model validation results will be shown demonstrating good model performance for both steady state and transient conditions. Following the validation of the off-line SCRoF model, the model was implemented on a dSPACE platform to demonstrate its real-time capability. The implementation on the dSPACE system is verified by comparing the online model signal outputs to the offline model. Further on, the model is compiled into object code and will be integrated into the ECM. The embedded model is characterized by a low memory footprint and a fast turnaround time.

Multi-pulse fuel injection in diesel engines enables a large flexibility in controlling the fueling profile, such that the engine performance can be maintained subject to disturbances, i.e., ambient condition changes and fuel injection deviations. Based on linearized systems, current fuel injection controller synthesis methods do not consider ambient condition changes as well as the nonlinear impact on the combustion process by varying the multi-pulse fuel injection profile. In this paper, we propose an infinity-norm Lyapunov function based controller synthesis method using a control-oriented quadratic engine model. With closed-loop stability and performance guarantees, the synthesis method explicitly considers the quadratic engine behavior and the influence of varying intake conditions. Using a data-based engine model that is fitted with measurements, we numerically demonstrate the reference regulation capability and the fast convergence rate of the fuel injection controller.

In this paper, multisine frequency response function identification is used for non-parametric modelling of the air path of a turbocharged diesel engine around a fixed operating point. The variable geometry turbine (VGT) and exhaust gas recirculation (EGR) valve are used as inputs. The nitrogen oxide emissions, air-fuel equivalence ratio, and pressure difference between the intake and exhaust manifold are the considered outputs. A time-efficient and accurate identification procedure for this input-output system is devised, analysed, and executed experimentally for a range of operating points. The analysis quantifies the individual effects of noise and nonlinearities and shows that the identified linear models capture the actual local behaviour with over 97.2% accuracy.

Design of high performance, low cost and clean propulsion systems requires multiple disciplines such as physics, mathematics, electrical engineering, mechanical engineering and specialisms like control engineering and safety. This paper details the program of EU FP7 Multi-ITN project ADvanced Electric Powertrain Technology (ADEPT) and presents a review of current research achievements of the recruited fellows. The ADEPT programme will provide a virtual research lab community from labs of European universities and industries. After finishing the ADEPT project, the know-how and expertise will also be opened to other research organizations or industry beyond those involved in the project. Special attention in this paper is given to ADEPT Virtual Library - collection of component level, sub-system level and system level components produced and maintained by the ADEPT Consortium. Functional Mock-up Interface (FMI) standard for model exchange and simulation is described and its connection with the ADEPT project is established.

Selective catalytic reduction (SCR) is a promising diesel aftertreatment technology that enables low nitrogen oxides $({rm NO}_{rm x})$ tailpipe emissions with relatively low fuel consumption. Future emission legislation is pushing the boundaries for SCR control systems to achieve high ${rm NO}_{rm x}$ conversion within a tailpipe ammonia $({rm NH}_{3})$ slip constraint, and to provide robustness to meet in-use compliance requirements. This work presents a new adaptive control strategy that uses an ammonia feedback sensor and an online ammonia storage model. Experimental validation on a 12-liter heavy-duty diesel engine with a 34-liter Zeolite SCR catalyst shows good performance and robustness against urea under- and over-dosage for both the European steady-state and transient test cycles. The new strategy is compared with a ${rm NO}_{rm x}$ sensor-based control strategy with cross-sensitivity compensation. It proved to be superior in terms of transient adaptation and taking an ${rm NH}_{3}$ slip constraint into account.

Energy Management Strategies for hybrid powertrains control the power split, between the engine and electric motor, of a hybrid vehicle, with fuel consumption or emission minimization as objective. Optimal control theory can be applied to rewrite the optimization problem to an optimization independent of time. Estimation of the Lagrange parameter, e.g., by feedback on the battery State-Of-Charge (SOC), can be used to arrive at a real-time implementable strategy. Nevertheless, it is still required to solve a nonconvex optimization problem with limited onboard computational power. This paper suggests to solve this optimization problem, offline, for different values of the Lagrange parameter, crankshaft rotational speed, and torque request. The resulting strategy is evaluated with simulations of a hybrid distribution truck on two different velocity trajectories. The influence of several control parameters is investigated also. © 2010 IFAC.

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. A novel feature introduced in this work is the addition of a feedback scheme to control the tailpipe NOx emissions through modification of the Lagrange multiplier in the optimal control algorithm. The Lagrange multiplier represents the penalty on tailpipe NOx emissions in the optimal control problem. This feature provides In-Service Conformity (ISC) in real-life operation and adds robustness, in terms of emissions, to system disturbances, such as production tolerances, system ageing and sensor or actuator errors. The IEM strategy is a generic toolkit and can incorporate additional optimization variables, provided their effects are accurately represented by the online models of the engine and aftertreatment system. This can be used to include additional degrees of freedom such as fuel injection timing, pressure, pre- and post-injections and power-splits for hybrid drivelines. IEM is capable of real-time implementation and has previously been operated on an engine testbed, showing up to 2% reduction in fuel consumption that can be translated into a corresponding CO2 emission reduction.

Design of high performance, low cost and clean propulsion systems requires multiple disciplines such as physics, mathematics, electrical engineering, mechanical engineering and specialisms like control engineering and safety. This paper details the program of EU FP7 Multi-ITN project ADvanced Electric Powertrain Technology (ADEPT) and presents a review of current research achievements of the recruited fellows. The ADEPT programme will provide a virtual research lab community from labs of European universities and industries. After finishing the ADEPT project, the know-how and expertise will also be opened to other research organizations or industry beyond those involved in the project. Special attention in this paper is given to ADEPT Virtual Library - collection of component level, sub-system level and system level components produced and maintained by the ADEPT Consortium. Functional Mock-up Interface (FMI) standard for model exchange and simulation is described and its connection with the ADEPT project is established.

Since the introduction of Euro IV legislation [1, 2], Selective Catalytic Reduction (SCR) technology using liquid urea injection is (one of) the primary methods for NOx reduction in many applications. Ammonia (NH3) is the reagent and key element for the SCR system and its control calibration to meet all operational requirements. TNO and Horiba are highly motivated to facilitate a correct interpretation and use of emissions measurement data. Different hypotheses were defined to investigate the impact of temperatures and flow rates on urea decomposition. These parameters are known to strongly affect the urea decomposition process, and thus, the formation of NH3. During a test campaign, different SCR catalyst feed gas conditions (mass flow, temperature, species and dosing quantities) were applied. Three Horiba FTIR gas analyzers were installed to simultaneously sample either all upstream or all downstream of the SCR brick. Both steady-state and dynamic responses were evaluated. When undecomposed urea is present, the application of sampling system conditioning above the thermolysis temperature of 133°C can result in additional conversion of urea into NH3. The NH3 concentration reading then does not correctly represent the in-situ condition. It is concluded that a lower sampling system temperature will result in the more accurate and physically sound NH3 measurement. This is an important observation to enable fundamental understanding or development of catalyst and control models. For tailpipe NH3 measurements on full-size SCR systems and typical urea dosing conditions, e.g. type approval measurements, same accuracies are expected for 113oC and 191oC sampling system conditioning.

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. A novel feature introduced in this work is the addition of a feedback scheme to control the tailpipe NOx emissions through modification of the Lagrange multiplier in the optimal control algorithm. The Lagrange multiplier represents the penalty on tailpipe NOx emissions in the optimal control problem. This feature provides In-Service Conformity (ISC) in real-life operation and adds robustness, in terms of emissions, to system disturbances, such as production tolerances, system ageing and sensor or actuator errors. The IEM strategy is a generic toolkit and can incorporate additional optimization variables, provided their effects are accurately represented by the online models of the engine and aftertreatment system. This can be used to include additional degrees of freedom such as fuel injection timing, pressure, pre- and post-injections and power-splits for hybrid drivelines. IEM is capable of real-time implementation and has previously been operated on an engine testbed, showing up to 2% reduction in fuel consumption that can be translated into a corresponding CO2 emission reduction.

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. Model validation results will be shown demonstrating good model performance for both steady state and transient conditions. Following the validation of the off-line SCRoF model, the model was implemented on a dSPACE platform to demonstrate its real-time capability. The implementation on the dSPACE system is verified by comparing the online model signal outputs to the offline model. Further on, the model is compiled into object code and will be integrated into the ECM. The embedded model is characterized by a low memory footprint and a fast turnaround time.

This paper presents an integrated energy and emission management strategy, called Integrated Power- train Control(IPC), for an Euro-VI diesel engine with an electrified waste heat recovery system. This strategy optimizes the CO – NOx 2 trade-off by minimizing the operational costs associated with fuel consumption, AdBlue dosage,and active particulate filter regeneration, while satisfying the tailpipe emission constraints.For comparison purposes, the proposed control strategy is applied to different power train configurations: with and without waste heat recovery (WHR) system and a WHR system equipped with a battery for energy storage. The potential of each studied configuration is evaluated over the World Harmonized Transient Cycle for cold-start and hot-start conditions. Incomparison to the existing Euro VI engine without WHR system, it is shown in simulations that the optimal IPC strategy with an electrified WHR system and battery provides an additional 3.5% CO2 emission reduction and 19% particulate matter reduction, while satisfying the NOx emission constraint.

This paper presents a battery peak power shaving strategy for battery electric buses. The developed strategy restricts the battery charge/discharge power when the propulsion power demand is high to avoid high deterioration of the battery capacity during operation. Without reducing the propulsion power, the developed strategy optimizes the power demand of the passenger cabin air conditioning system while guaranteeing the passenger temperature comfort. An equivalent consumption minimization strategy (ECMS) technique is extended to include the thermal comfort of the passenger in the energy management system of the bus. The developed strategy is analytical, real-time implementable and verified via hardware-in-the-loop test. Simulation results demonstrate the capability for satisfying the propulsion power demand and passenger temperature comfort while reducing the battery peak power (up to 20% during acceleration) to restrict the battery degradation significantly.