Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio

Effect of noise factors in energy management of series plug-in hybrid electric vehicles

It has been demonstrated that charge depletion (CD) energy management strategies are more efficient choices for energy management of plug-in hybrid electric vehicles (PHEVs). The knowledge of drive cycle as a priori can improve the performance of CD energy management in PHEVs. However, there are many noise factors which affect both drivetrain power demand and vehicle performance even in identical drive cycles. In this research, the effect of each noise factor is investigated by introducing the concept of power cycle instead of drive cycle for a journey. Based on the nature of the noise factors, a practical solution for developing a power-cycle library is introduced. Investigating the predicted power cycle, an energy management strategy is developed which considers the influence of temperature noise factor on engine performance. The effect of different environmental and geographic conditions, driver behavior, aging of battery and other components are considered. Simulation results for a modelled series PHEV similar to GM Volt show that the suggested energy management strategy based on the driver power cycle library improves both vehicle fuel economy and battery health by reducing battery load and temperature.<br /

MODELING OF THERMAL DYNAMICS IN CHEVROLET VOLT GEN II HYBRID ELECTRIC VEHICLE FOR INTEGRATED POWERTRAIN AND HVAC OPTIMAL OPERATION THROUGH CONNECTIVITY

Integrated thermal energy management across system level components in electric vehicles (EVs) and hybrid electric vehicles (HEVs) is currently an under explored space. The proliferation of connected vehicles and accompanying infrastructure in recent years provides additional motivation to explore opportunities in optimizing thermal energy management in EVs and HEVs with the help of this newly available connected vehicle data. This thesis aims to examine and analyze the potential to mitigate vehicle energy consumption and extend usable driving range through optimal control strategies which exploit physical system dynamics via controls integration of vehicle subsystems. In this study, data-driven and physics-based models for heating, ventilation and air-conditioning (HVAC) are developed and utilized along with the vehicle dynamics and powertrain (VD\&PT) models for a GM Chevrolet Volt hybrid electric vehicle to enable co-optimization of HVAC and VD\&PT systems of HEVs. The information available in connected vehicles like driver schedules, trip duration and ambient conditions is leveraged along with the vehicle system dynamics to predict operating conditions of the vehicle under study. All this information is utilized to optimize the operation of an integrated HVAC and VD\&PT system in a hybrid electric vehicle to achieve the goal of reduced energy consumption. For achieving the goals outlined for this thesis, an integrated HVAC and VD\&PT model is developed and the various components, sub-systems and systems are validated against real world test data. Then, integrated relationships relevant to the thermal dynamics of an HEV are established. These relationships comprise the combined operational characteristics of the internal combustion (IC) engine coolant and the cabin electric heater for cabin heating, coordinated controls of IC engine using engine coolant and catalyst temperatures for cabin thermal conditioning in cold ambient conditions and the combined operational characteristics of the air-conditioning compressor for conditioning both cabin and high-voltage battery in an HEV. Next, these sub-system and system relationships are used to evaluate potential energy savings in cabin heating and cooling when vehicle\u27s operating schedule is known. Finally, an optimization study is conducted to establish an energy efficient control strategy which maximizes the HVAC energy efficiency whilst maintaining occupant comfort levels according to ASHRAE standards, all while improving the usable range of the vehicle relative to its baseline calibration. The mean energy savings in overall vehicle energy consumption using an integrated HVAC - Powertrain control strategy and a coordinated thermal management strategy proposed in this work are $3$\% and $14$\% respectively

Plug-in hybrid electric vehicle emissions impacts on control strategy and fuel economy

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, at the expense of increased tailpipe emissions due to multiple cold start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.The dissertation research focuses on the design of a vehicle supervisory control system for a pre-transmission parallel PHEV powertrain architecture. Energy management strategies are evaluated and implemented in a virtual environment for preliminary assessment of petroleum displacement benefits and rudimentary drivability issues. This baseline vehicle supervisory control strategy, developed as a result of this assessment, is implemented and tested on actual hardware in a controlled laboratory environment over a baseline test cycle. Engine cold start events are aggressively addressed in the development of this control system, which lead to enhanced pre-warming and energy-based engine warming algorithms that provide substantial reductions in tailpipe emissions over the baseline supervisory control strategy.The flexibility of the PHEV powertrain allows for decreased emissions during any engine starting event through powertrain torque shaping algorithms that eliminate high engine torque transients during these periods. The results of the dissertation research show that PHEVs do have the potential for substantial reductions in fuel consumption, while remaining environmentally friendly. Tailpipe emissions from a representative PHEV test platform have been reduced to acceptable levels through the development and refinement of vehicle supervisory control methods only. Impacts on fuel consumption are minimal for the emissions reduction techniques that are implemented, while in some cases, substantial fuel consumption reductions are observed

Linkage between knowledge management practices towards library user’s satisfaction at Malaysian University Libraries

Academic library services have begun to apply various knowledge management (KM) practices in the provision of library services. KM has been developed to enhance the use of organizational knowledge through practices and organizational learning. KM practices include the creation, capture and/or acquisition of knowledge, its retention and organization, its dissemination and re-use, and general responsiveness to the new knowledge. The focus of this research is the assessment of KM practices, particularly creation, acquisition, capture, sharing, recording and preservation, and their effects on Library User’s Satisfaction (LUS) in Malaysian university libraries. The objective of this research is the development of a model to enhance KM processes (i.e. Creation, acquisition, capturing, sharing, recording, and preserving) and to improve library users’ satisfaction. A quantitative approach in research methodology is employed (e.g. Questionnaire) for the purpose of generating new knowledge and understanding of library concerns. The findings of this research show that the overall KM practice at six Malaysian university libraries is at a high level. The findings from the structural model indicated that two KM processes, namely knowledge creation and acquisition, are not supported in terms of KM practices at Malaysian university libraries. Other KM processes, namely capturing, sharing, recording, and preserving are fully supported towards KM practices in the library. Hence, the major contribution of this research is a model, namely KM Practice-Library User’s Satisfaction (KMP-LUS) highlighting six KM processes based on strong Structural Equation Modeling (SEM) fit indices

Supervisory control of complex propulsion subsystems

Modern gasoline and diesel combustion engines are equipped with several subsystems with the goal to reduce fuel consumption and pollutant exhaust emissions. Subsystem synergies could be harnessed using the supervisory control approach. Look-ahead information can be used to potentially optimise power-train control for real time implementation. This thesis delves upon modelling the exhaust emissions from a combustion engine and developing a combined equivalent objective metric to propose a supervisory controller that uses look-ahead information with the objective to reduce fuel consumed and exhaust emissions. In the first part of the thesis, the focus is on diesel engine application control for emissions and fuel consumption reduction.\ua0Model of exhaust emissions in a diesel engine obtained from a combination of nominal engine operation and deviations are evaluated for transient drive cycles.\ua0The look ahead information as a trajectory of vehicle speed and load over time is considered.\ua0The supervisory controller considers a discrete control action set over the first segment of the trip ahead.\ua0The cost to optimise is defined and pre-computed off-line for a discrete set of operating conditions.\ua0A full factorial optimisation carried out off-line is stored on board the vehicle and applied in real-time.\ua0In a first proposal, the subsystem control of the after-treatment system comprising the lean NOx trap and the selective reduction catalyst is considered.\ua0As a next iteration, the combustion engine is added to the control problem.\ua0Simulation comparison of the controllers with the baseline controller offers a 1 % total fuel equivalent cost improvement while offering the flexibility to tailor the controller for different cost objective. In the second part of the thesis, the focus is on cold-start emissions control for modern gasoline engines.\ua0Emissions occurring when the engine is started until the catalyst is sufficiently warm, contribute to a significant proportion of tailpipe pollutant emissions.\ua0Electrically heated catalyst (EHC) in the three way catalyst (TWC) is a promising technology to reduce cold-start emissions where the catalyst can be warmed up prior to engine start and continued after start.\ua0A simulation framework for the engine, TWC with EHC with focus on modeling the thermal and chemical interactions during cold-start was developed.\ua0An evaluation framework with a proposed equivalent emissions approach was developed considering the challenges associated with cold-start emission control.\ua0An equivalent emission optimal post-heating time for the EHC is proposed that adapts to information which is available in a real-time on-line implementation.\ua0The proposed controller falls short of just 1 % equivalent emissions compared to the optimal case