Hydraulic Hybrid Powertrain-In-the-Loop Integration for Analyzing Real-World Fuel Economy and Emissions Improvements

The paper describes the approach, addresses integration challenges and discusses capabilities of the Hybrid Powertrain-in-the-Loop (H-PIL) facility for the series/hydrostatic hydraulic hybrid system. We describe the simulation of the open-loop and closed-loop hydraulic hybrid systems in H-PIL and its use for concurrent engineering and development of advanced supervisory strategies. The configuration of the hydraulic-hybrid system and details of the hydraulic circuit developed for the H-PIL integration are presented. Next, software and hardware interfaces between the real components and virtual systems are developed, and special attention is given to linking component-level controllers and system-level supervisory control. The H-PIL setup allows imposing realistic dynamic loads on hydraulic pump/motors and accumulator based on vehicle driving schedule. Application of fast analyzers allows characterization of the impact of dynamic interactions in the propulsion system on engine-out emissions. Therefore, the H-PIL facility allows optimization of the hybrid system for both high-efficiency and low emissions. The impetus is provided by previous work showing that more than half of the soot emissions from a conventional diesel powertrain over the urban driving schedule can be attributed to transients. The setup includes a 6.4L V-8 International diesel engine, highly dynamic dynamometer, Radial piston pump/motors supplied by Bosch-Rexroth and dSPACE real-time environment with in-house developed simulation of the virtual vehicle.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89880/1/draft_01.pd

Energy and Emissions Conscious Optimal Following for Automated Vehicles with Diesel Powertrains

The emerging application of autonomous driving provides the benefit of eliminating the driver from the control loop, which offers opportunities for safety, energy saving and green house gas emissions reduction by adjusting the speed trajectory. The technological advances in sensing and computing make it realistic for the vehicle to obtain a preview information of its surrounding environment, and optimize its speed trajectory accordingly using predictive planning methods. Conventional speed following algorithms usually adopt an energy-centric perspective and improve fuel economy by means of reducing the power loss due to braking and operating the engine at its high fuel efficiency region. This could be a problem for diesel-powered vehicles, which rely on catalytic aftertreatment system to reduce overall emissions, as reduction efficiency drops significantly with a cold catalyst that would result from a smoother speed profile. In this work, control and constrained optimization techniques are deployed to understand the potential for and achieve concurrent reduction of fuel consumption and emissions. Trade-offs between fuel consumption and emissions are shown using results from a single objective optimal planning problem when the calculation is performed offline assuming full knowledge of the whole cycle. Results indicate a low aftertreatment temperature when energy-centric objectives are used, and this motivates the inclusion of temperature performance metric inside the optimization problem. An online optimal speed planner is then designed for concurrent treatment of energy and emissions, with a limited but accurate preview information. An objective function comprising an energy conscious term and an emissions conscious term is proposed based on its effectiveness of 1) concurrent reduction of fuel and emissions, 2) flexible balancing between the emphasis on fuel saving or emissions reduction based on performance requirements and 3) low computational complexity and ease of numerical treatment. Simulation results of the online optimal speed planner over multiple drive cycles are presented, and for the vehicle simulated in this work, concurrent reduction of fuel and emissions is demonstrated using a specific powertrain, when allowing flexible modification of the drive cycle. Hardware-in-the-loop experiment is also performed over the Federal Test Procedure (FTP) drive cycle, and shows up to 15% reduction in fuel consumption and 70% reduction in NOx emissions when allowing a flexible following distance. Finally, the stringent requirement of accurate preview information is relaxed by designing a robust re-formulation of the energy and emissions conscious speed planner. Improved fuel economy and emissions are shown while satisfying the constraints even in the presence of perturbations in the preview information. A Gaussian mixture regression-based speed prediction is applied to test the performance of the speed following strategy without assuming knowledge of the preview information. A performance degradation is observed in simulation results when using the predicted velocity compared with an accurate preview, but the speed planner preserves the capability to improve fuel and tailpipe emissions performance compared with a non-optimal controller.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170004/1/huangchu_1.pd

Model-Based Calibration and Control of Tailpipe Nitrogen Oxide Emissions in a Light-Duty Diesel Engine and Its Assessment through Model-In-The-Loop

The present paper investigates two different strategies for model-based calibration and control of tailpipe nitrogen oxide emissions in a light-duty 3.0 L diesel engine equipped with an aftertreatment system (ATS). The latter includes a diesel oxidation catalyst (DOC), a diesel particulate filter (DPF), and an underfloor selective catalytic reduction (SCR) device, in which the injection of diesel exhaust fluid (DEF), marketed as ‘AdBlue’, is also taken into account. The engine was modeled in the GT-SUITE environment, and a previously developed model-based combustion controller was integrated in the model, which is capable of adjusting the start of injection of the main pulse and the total injected fuel mass, in order to achieve desired targets of engine-out nitrogen oxide emissions (NOx) and brake mean effective pressure (BMEP). First, a model-based calibration strategy consisting of the minimization of an objective function that takes into account fuel consumption and AdBlue injection was developed and assessed by exploring different weight factors. Then, a direct model-based controller of tailpipe nitrogen oxide emissions was designed, which exploits the real-time value of the SCR efficiency to define engine-out NOx emission targets for the combustion controller. Both strategies exploit the model-based combustion controller and were tested through a Model-in-the-Loop (MiL) under steady-state and transient conditions. The advantages in terms of tailpipe NOx emissions, fuel consumption, and AdBlue injection were finally discussed

Feedback Control Strategies for Diesel Engine Emissions Compliance

Modern diesel engines are equipped with aftertreatment systems which are effective at reducing tailpipe hydrocarbon and oxides of nitrogen (NOx) emissions when the system’s catalysts are lit-off, meaning they are warmed-up to temperatures near 200 degrees Celsius. During engine cold-starts, combustion phasing retard is typically used to provide additional heat to the aftertreatment system to achieve faster light-off. Analysis of emissions cycle data has shown that improved heating during cold-starts could achieve further emission reductions, however combustion phasing retard heating strategies can be limited by combustion variability issues. Aftertreatment temperature issues can also occur after the engine is warmed-up, as real-world driving behaviors like extended idling and low-load operation can result in exhaust temperatures that are insufficient for maintaining catalyst light-off, resulting in emission increases. This thesis presents novel control solutions to achieve emissions reductions during cold-starts and real-world driving. For cold-start emissions, the concept of closed-loop variance control was analyzed and applied to combustion control, which enables more aggressive combustion phasing retard exhaust heating to achieve faster aftertreatment light-off while avoiding excessive combustion variability issues. Diesel combustion variability was characterized experimentally, and the data was used to identify feedback metrics. Conventional linear controls analysis and statistical theory were used to develop a better understanding of variance feedback control, and the understanding was applied to the engine problem. Closed-loop combustion variability control was performed during both steady-state and transient operation and enabled higher exhaust temperatures while avoiding excessive degradation of engine combustion. For real-world driving emissions, a model predictive control (MPC) framework was developed that uses long horizon engine speed and load preview along with onboard NOx measurements to control the engine for good fuel economy subject to emission constraints. To reduce computational complexity the controller output is a decision variable selecting between two engine calibrations, one with low brake-specific fuel consumption (BSFC) but high brake-specific NOx (or BSNOx), and one with high BSFC, low BSNOx, and increased exhaust heat to aid aftertreatment conversion efficiencies.The onboard NOx measurements are used to inform the optimization problem formulations, which include constraining NOx. based on windowed limits. Software-in-the-Loop (SIL) experimental results show that the controller has the ability to track a windowed emissions target, and appropriately responds to noise factors such as aftertreatment temperatures and emission rate errors.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169791/1/bieniekm_1.pd

Gear shift strategies for automotive transmissions

The development history of automotive engineering has shown the essential role of transmissions in road vehicles primarily powered by internal combustion engines. The engine with its physical constraints on the torque and speed requires a transmission to have its power converted to the drive power demand at the vehicle wheels. Under dynamic driving conditions, the transmission is required to shift in order to match the engine power with the changing drive power. Furthermore, a gear shift decision is expected to be consistent such that vehicle can remain in the next gear for a period of time without deteriorating the acceleration capability. Therefore, an optimal conversion of the engine power plays a key role in improving the fuel economy and driveability. Moreover, the consequences of the assumptions related to the discrete state variable-dependent losses, e.g. gear shifting, clutch slippage and engine starting, and their e¿ect on the gear shift control strategy are necessary to be analyzed to yield insights into the fuel usage. The ¿rst part of the thesis deals with the design of gear shift strategies for electronically controlled discrete ratio transmissions used in both conventional vehicles and Hybrid Electric Vehicles (HEVs). For conventional vehicles, together with the fuel economy, the driveability is systematically addressed in a Dynamic Programming (DP) based optimal gear shift strategy by three methods: i) the weighted inverse of the power re¬serve, ii) the constant power reserve, and iii) the variable power reserve. In addition, a Stochastic Dynamic Programming (SDP) algorithm is utilized to optimize the gear shift strategy, subject to a stochastic distribution of the power request, in order to minimize the expected fuel consumption over an in¿nite horizon. Hence, the SDP-based gear shift strategy intrinsically respects the driveability and is realtime implementable. By per¬forming a comparative analysis of all proposed gear shift methods, it is shown that the variable power reserve method achieves the highest fuel economy without deteriorating the driveability. Moreover, for HEVs, a novel fuel-optimal control algorithm, consist-ing of the continuous power split and discrete gear shift, engine on-o¿ problems, based on a combination of DP and Pontryagin’s Minimum Principle (PMP) is developed for the corresponding hybrid dynamical system. This so-called DP-PMP gear shift control approach benchmarks the development of an online implementable control strategy in terms of the optimal tradeo¿ between calculation accuracy and computational e¿ciency. Driven by an ultimate goal of realizing an online gear shift strategy, a gear shift map design methodology for discrete ratio transmissions is developed, which is applied for both conventional vehicles and HEVs. The design methodology uses an optimal gear shift algorithm as a basis to derive the optimal gear shift patterns. Accordingly, statis¬tical theory is applied to analyze the optimal gear shift pattern in order to extract the time-invariant shift rules. This alternative two-step design procedure makes the gear shift map: i) respect the fuel economy and driveability, ii) be consistent and robust with respect to shift busyness, and iii) be realtime implementation. The design process is ¿exible and time e¿cient such that an applicability to various powertrain systems con¿gured with discrete ratio transmissions is possible. Furthermore, the study in this thesis addresses the trend of utilizing the route information in the powertrain control system by proposing an integrated predictive gear shift strategy concept, consisting of a velocity algorithm and a predictive algorithm. The velocity algorithm improves the fuel economy in simulation considerably by proposing a fuel-optimal velocity trajectory over a certain driving horizon for the vehicle to follow. The predictive algorithm suc¬cessfully utilizes a prede¿ned velocity pro¿le over a certain horizon in order to realize a fuel economy improvement very close to that of the globally optimal algorithm (DP). In the second part of the thesis, the energetic losses, involved with the gear shift and engine start events in an automated manual transmission-based HEV, are modeled. The e¿ect of these losses on the control strategies and fuel consumption for (non-)powershift transmission technologies is investigated. Regarding the gear shift loss, the study ¿rstly ever discloses a perception of a fuel-e¿cient advantage of the powershift transmissions over the non-powershift ones applied for commercial vehicles. It is also shown that the engine start loss can not be ignored in seeking for a fair evaluation of the fuel economy. Moreover, the sensitivity study of the fuel consumption with respect to the prediction horizon reveals that a predictive energy management strategy can realize the highest achievable fuel economy with a horizon of a few seconds ahead. The last part of the thesis focuses on investigating the sensitivity of an optimal gear shift strategy to the relevant control design objectives, i.e. fuel economy, driveability and comfort. A singu¬lar value decomposition based method is introduced to analyze the possible correlations and interdependencies among the design objectives. This allows that some of the pos¬sible dependent design objective(s) can be removed from the objective function of the corresponding optimal control problem, hence thereby reducing the design complexity

Model Predictive Climate Control for Connected and Automated Vehicles

Emerging connected and automated vehicle (CAV) technologies are improving vehicle safety and energy efficiency to the next level and creating unprecedented opportunities and challenges for the control and optimization of the vehicle systems. While previous studies have been focusing on improving the fuel efficiency via powertrain optimizations, vehicle thermal management and its interaction with powertrain control in hot and cold weather conditions have not been fully explored. For light-duty vehicles, the power used by the climate control system usually represents the most significant thermal load. It has been shown that the thermal load imposed by the climate control system may lead to dramatic vehicle range reduction, especially for the vehicles with electrified powertrains. Besides its noticeable impact on vehicle range reduction, the performance of the climate control system also has a direct influence on occupant comfort and customer satisfaction. Aiming at reducing the energy consumption and improving the occupant thermal comfort (OTC) level for the automotive climate control system, this dissertation takes air conditioning (A/C) system as an example and is dedicated to developing practical A/C management strategies for electrified vehicles. In particular, the proposed strategies leverage the predictive information enabled by the CAV technologies such as the traffic and weather predictions. There are three novel MPC-based A/C management strategies developed in this dissertation, the hierarchical optimization, the precision cooling strategy (PCS), and the combined energy and comfort optimization (CECO). They can be differentiated by their OTC assumptions, robustness considerations, and implementation complexities on the testing vehicle. In the hierarchical optimization, a two-layer hierarchical MPC (H-MPC) scheme is exploited for potential integration between the A/C and the powertrain systems of an HEV. This hierarchical structure handles the timescale difference between power and thermal systems as well as the uncertainties associated with long prediction horizon. Comprehensive simulation results over different driving cycles have demonstrated the energy saving potentials of efficient A/C energy management, which is attributes to leveraging the vehicle speed sensitivity of the A/C system efficiency. In terms of the comfort metric, the average cabin air temperature is applied. In contrast to this hierarchical optimization, PCS and CECO utilize the simpler single-layer MPC structure assuming accurate predictive information. They are focusing on formulating more practical OTC metrics and the implementation on the testing vehicle. Specifically, the PCS renders the simplest control-oriented model structure and its energy benefits are validated based on an industrial-level A/C system model. The proposed PCS exploits a more practical comfort metric, DACP, which directly motivates the design of an off-line eco-cooling strategy, which coordinates the A/C operation with respect to the vehicle speed. Vehicle-level energy saving is confirmed according to repeatable vehicle experiments. Finally, the CECO strategy considers a comprehensive OTC model, PMV, and combines the energy and comfort optimizations simultaneously. Further energy saving and OTC improvement can be achieved by explicitly leveraging both traffic and weather predictive information.PHDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153481/1/autowang_1.pd

Evaporator Modeling and an Optimal Control Strategy Development of an Organic Rankine Cycle Waste Heat Recovery System for a Heavy Duty Diesel Engine Application

The Organic Rankine Cycle (ORC) has proven to be a promising technology for Waste Heat Recovery (WHR) systems in heavy duty diesel engine applications. However, due to the highly transient heat source, controlling the working fluid flow through the ORC system and maximizing the heat recovery is a challenge for real time application. To that end, this research resulted in the following main developments. The first new development is in the area of heat exchanger modeling. The heat exchanger is a key component within the WHR system and it governs the dynamics of the complete ORC system. The heat exchanger model is extended using a thermal image data to improve its phase length prediction capability. It’s shown that the new identified empirical equations help improve the phase length estimation by 43% over a set of transient experiments. As a result, the model can be used to develop an improved control oriented moving boundary model and to provide insights into evaporator design. The second new development is the advancement of the control design of an ORC system. With advanced knowledge of the heat source dynamics, there is potential to enhance power optimization from the WHR system through predictive optimal control. The proposed approach in this this dissertation is a look-ahead control strategy where, the future vehicle speed is predicted utilizing road topography and V2V connectivity. The forecasted vehicle speed is utilized to predict the engine speed and torque, which facilitates estimation of the engine exhaust conditions used in the ORC control model. In the simulation study, a reference tracking controller is designed based on the Model Predictive Control (MPC) methodology. Two variants of Non-linear MPC (NMPC) are evaluated: an NMPC with look-ahead exhaust conditions and a baseline NMPC without the knowledge of future exhaust conditions. Simulation results show no particular improvement to working fluid superheat tracking at the evaporator outlet via the look-ahead strategy for a drive cycle. However, the look-ahead control strategy does provide a substantial reduction in system control effort via dampening the heavily transient working fluid pump actuation, enhancing pump longevity, health, and reducing pump power consumption. This reduction in pump actuation helps the NMPC with preview to maintain the superheat lower than the NMPC without this feature for certain frequency of the exhaust conditions. Overall, NMPC with preview feature can help reduce parasitic losses, like pump power and improve power generation. The third development addresses the modeling errors and measurement inaccuracies for NMPC implementation. NMPC is inherently a state feedback system and for that reason an Extended Kalman Filter (EKF) is used to estimate unmeasurable states inside the ORC evaporators based on exhaust gas and working fluid temperatures. Since it is not realistic to expect that the system model will perfectly describe the behavior of the evaporator dynamics in all operating conditions, the estimator is therefore augmented with a disturbance model for offset free MPC tracking. Simulation study shows that the augmented system is perfectly capable of discarding the model errors and rejecting the measurement inaccuracies. Moreover, experimental validation confirms that no steady state error is observed during online implementation of the augmented EKF. Finally, experimental validation of the designed NMPC control strategy was conducted. The performance of the NMPC was evaluated on a heavily transient drive cycle, as well as on a sinusoidal generated heat signals. Both experimental and simulated sinusoidal exhaust condition shows that evaporator under consideration inherently helps attenuate the fluctuating exhaust conditions due to its thermal inertia especially for heat signals of shorter time periods. However for slow changing exhaust conditions, a slower rate of change of working fluid flow helps in inhibiting temperature overshoot at the evaporator outlet