Component control for the Zero Inertia powertrain

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Active damping of transient vibration in dual clutch transmission equipped powertrains: A comparison of conventional and hybrid electric vehicles

The purpose of this paper is to investigate the active damping of automotive powertrains for the suppression of gear shift related transient vibrations. Conventionally, powertrain vibration is usually suppressed passively through the application of torsional dampers in dual clutch transmissions (DCT) and torque converters in planetary automatic transmissions (AT). This paper presents an approach for active suppression of transient responses utilising only the current sensors available in the powertrain. An active control strategy for manipulating engine or electric machine output torque post gear change via a proportional-integral-derivative (PID) controller is developed and implemented. Whilst conventional internal combustion engine (ICE) powertrains require manipulation of the engine throttle, for HEV powertrains the electric machine (EM) output torque is controlled to rapidly suppress powertrain transients. Simulations for both conventional internal combustion engine and parallel hybrid vehicles are performed to evaluate the proposed strategy. Results show that while both the conventional and hybrid powertrains are both capable of successfully suppressing undesirable transients, the EM is more successful in achieving vibration suppression. © 2014 Elsevier Ltd

PHYSICS-BASED MODELING AND CONTROL OF POWERTRAIN SYSTEMS INTEGRATED WITH LOW TEMPERATURE COMBUSTION ENGINES

Low Temperature Combustion (LTC) holds promise for high thermal efficiency and low Nitrogen Oxides (NOx) and Particulate Matter (PM) exhaust emissions. Fast and robust control of different engine variables is a major challenge for real-time model-based control of LTC. This thesis concentrates on control of powertrain systems that are integrated with a specific type of LTC engines called Homogenous Charge Compression Ignition (HCCI). In this thesis, accurate mean value and dynamic cycleto- cycle Control Oriented Models (COMs) are developed to capture the dynamics of HCCI engine operation. The COMs are experimentally validated for a wide range of HCCI steady-state and transient operating conditions. The developed COMs can predict engine variables including combustion phasing, engine load and exhaust gas temperature with low computational requirements for multi-input multi-output realtime HCCI controller design. Different types of model-based controllers are then developed and implemented on a detailed experimentally validated physical HCCI engine model. Control of engine output and tailpipe emissions are conducted using two methodologies: i) an optimal algorithm based on a novel engine performance index to minimize engine-out emissions and exhaust aftertreatment efficiency, and ii) grey-box modeling technique in combination with optimization methods to minimize engine emissions. In addition, grey-box models are experimentally validated and their prediction accuracy is compared with that from black-box only or clear-box only models. A detailed powertrain model is developed for a parallel Hybrid Electric Vehicle (HEV) integrated with an HCCI engine. The HEV model includes sub-models for different HEV components including Electric-machine (E-machine), battery, transmission system, and Longitudinal Vehicle Dynamics (LVD). The HCCI map model is obtained based on extensive experimental engine dynamometer testing. The LTC-HEV model is used to investigate the potential fuel consumption benefits archived by combining two technologies including LTC and electrification. An optimal control strategy including Model Predictive Control (MPC) is used for energy management control in the studied parallel LTC-HEV. The developed HEV model is then modified by replacing a detailed dynamic engine model and a dynamic clutch model to investigate effects of powertrain dynamics on the HEV energy consumption. The dynamics include engine fuel flow dynamics, engine air flow dynamics, engine rotational dynamics, and clutch dynamics. An enhanced MPC strategy for HEV torque split control is developed by incorporating the effects of the studied engine dynamics to save more energy compared to the commonly used map-based control strategies where the effects of powertrain dynamics are ignored. LTC is promising for reduction in fuel consumption and emission production however sophisticated multi variable engine controllers are required to realize application of LTC engines. This thesis centers on development of model-based controllers for powertrain systems with LTC engines

Optimal control of a motor-integrated hybrid powertrain for a two-wheeled vehicle suitable for personal transportation

The present research aims to propose an optimized configuration of the motor integrated power-train with an optimal controller suitable for small power-train based two wheeler automobile which can increase the system level efficiency without affecting drivability. This work will be the foundation for realizing the system in a production ready vehicle for the two wheeler OEM TVS Motor Company in India. A detailed power-train model is developed (from first principles) for the scooter vehicle, which is powered by a 110 cc spark ignition (SI) engine and coupled with two types of transmission, a continuous variable transmission (CVT) and a 4-speed manual transmission (MT). Both models are capable of simulating torque and NOx emission output of the SI engine and dynamic response of the full power-train. The torque production and emission outputs of the model are compared with experimental results available from TVS Motor Company. The CVT gear ratio model is developed using an indirect method and an analytical model. Both types of powertrain models are applied to perform a simulated study of fuel consumption, NOx emission and drivability study for a particular vehicle platform. In the next stage of work, the mathematical model for a brush-less direct current machine (BLDC) with the drive system and Li-Ion battery are developed. The models are verified and calibrated with the experimental results from TVS Motor Company. The BLDC machine is integrated with both the CVT and MT powertrain models in parallel hybrid configurations and a drive cycle simulation is conducted for different static assist levels by the electrical machines. The initial test confirms the need of optimal sizing of the powertrain components as well as an optimal control system. The detailed model of the powertrain is converted to a control-oriented model which is suitable for optimal control. This is followed by multi-objective optimization of different components of the motor-integrated powertrain using a single function as well as Pareto-Optimal methods. The objective function for the multi-objective optimization is proposed to reduce the fuel consumption with battery charge sustainability with least impact on the increase of financial cost and weight of the vehicle. The optimization is conducted by a nested methodology that involves Particle Swarm Optimization and a Non-dominated sorting genetic algorithm where, concurrently, a global optimal control is developed corresponding to the multi-objective design. The global optimal controller is designed using dynamic programming. The research is concluded with an optimal controller developed using the hp-collocation method. The objective function of the dynamic programming method and hp-collocation method is proposed to reduce fuel consumption with battery charge sustainability.Open Acces

Influence of Architecture Design on the Performance and Fuel Efficiency of Hydraulic Hybrid Transmissions

Hydraulic hybrids are a proven and effective alternative to electric hybrids for increasing the fuel efficiency of on-road vehicles. To further the state-of-the-art this work investigates how architecture design influences the performance, fuel efficiency, and controllability of hydraulic hybrid transmissions. To that end a novel neural network based power management controller was proposed and investigated for conventional hydraulic hybrids. This control scheme trained a neural network to generalize the globally optimal, though non-implementable, state trajectories generated by dynamic programming. Once trained the neural network was used for online prediction of a transmission’s optimal state trajectory during untrained cycles forming the basis of an implementable controller. During hardware-in-the-loop (HIL) testing the proposed control strategy improved fuel efficiency by up to 25.5% when compared with baseline approaches. To further improve performance and fuel efficiency a novel transmission architecture termed a Blended Hydraulic Hybrid was proposed and investigated. This novel architecture improves on existing hydraulic hybrids by partially decoupling power transmission from energy storage while simultaneously providing means to recouple the systems when advantageous. Optimal control studies showed the proposed architecture improved fuel efficiency over both baseline mechanical and conventional hydraulic hybrid transmissions. Effective system level and supervisory control schemes were also proposed for the blended hybrid. In order to investigate the concept’s feasibility a blended hybrid transmission was constructed and successfully tested on a HIL transmission dynamometer. Finally to investigate controllability and driver perception an SUV was retrofitted with a blended hybrid transmission. Successful on-road vehicle testing showcased the potential of this novel hybrid architecture as a viable alternative to more conventional electric hybrids in the transportation sector

Analysis and Control of Multimode Combustion Switching Sequence.

Highly dilute, low temperature combustion technologies, such as homogeneous charge compression ignition (HCCI), show significant improvements in internal combustion engine fuel efficiency and engine-out NOx emissions. These improvements, however, occur at limited operating range and conventional spark ignition (SI) combustion is still required to fulfill the driver's high torque demands. In consequence, such multimode engines involve discrete switches between the two distinct combustion modes. Such switches unfortunately require a finite amount of time, during which they exhibit penalties in efficiency. Along with its challenges, the design of such a novel system offers new degrees of freedom in terms of engine and aftertreatment specifications. Prior assessments of this technology were based on optimistic assumptions and neglected switching dynamics. Furthermore, emissions and driveability were not fully addressed. To this end, a comprehensive simulation framework, which accounts for above-mentioned penalties and incorporates interactions between multimode engine, driveline, and three-way catalyst (TWC), has been developed. Experimental data was used to parameterize a novel mode switch model, formulated as finite-state machine. This model was combined with supervisory controller designs, which made the switching decision. The associated drive cycle results were analyzed and it was seen that mode switches have significant influence on overall fuel economy, and the issue of drivability needs to be addressed within the supervisory strategy. After expanding the analysis to address emissions assuming a TWC, it was shown that, in practice, HCCI operation requires the depletion of the TWC's oxygen storage capacity (OSC). For large OSCs the resulting lean-rich cycling nullifies HCCI's original efficiency benefits. In addition, future emissions standards are still unlikely to be fulfilled, deeming a system consisting of such a multimode engine and TWC with generous OSC unfavorable. In view of these difficulties, the modeling framework was extended to a mild hybrid electric vehicle (HEV) allowing a prolonged operation in HCCI mode with associated fuel economy benefits during city driving. Further analysis on how to reduce NOx while maintaining fuel economy resulted in a counterintuitive suggestion. It was deemed beneficial to constrain the HCCI operation to a small region, exhibiting lowest NOx, while reducing instead of increasing the OSC.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116660/1/snuesch_1.pd

Energy management for automotive power nets

Reducing fuel consumption has always been a major challenge to the automotive industry. Whereas first marketing aspects gave rise to innovative research, today the environmental regulations have become the main driving force behind new technologies. Historically, the research concentrated on improvements for the mechanical side of the vehicle. However, the introduction of Hybrid Electric Vehicles (HEV), where the propulsion power can also be delivered by an electric machine, definitely emphasizes the benefits of electro-mechanical solutions. With a secondary power source, the HEV can satisfy the vehicle power demand in various ways. An energy management (EM) strategy is needed to control this added freedom in a fuel-efficient way. At present, a broad range of EM strategies has been proposed in literature and several concepts have been implemented in series-production vehicles. Typically, the academic solutions focus on complex optimization techniques, arising from well defined mathematical problems. The engineering approach offers a sub-optimal strategy, based on heuristic rules. Nevertheless, both policies fail when the important vehicle characteristics for EM are not well understood. The main contribution of this thesis is to deduce a physical explanation of the EM problem for all HEV configurations, viz., the series-HEV, the parallel-HEV and the combined series/parallel-HEV. By having a good understanding of the vehicle properties of interest, it becomes possible to develop a model-based EM strategy that mimics the optimal solution, without the need for complex optimization routines, nor the necessity for having accurate predictions about the future driving cycle. The proposed causal strategy is directly suitable for on-line implementation in a vehicle. The primary goal of an EM strategy is to maximize the fuel efficiency of the vehicle. In practice, this requirement is often associated with operating the internal combustion engine (ICE) in its highest efficiency region. Nevertheless, this thesis reveals that this concept is only partially true. A better understanding of how to operate the ICE follows from two other properties: the slope of the fuel map and its fuel offset at idle speed. A formal optimization problem is formulated to prove that these properties also relate to a mathematical interpretation, and infer from the optimal solution. For all the HEV configurations mentioned above, a power-oriented vehicle model is derived. Next, a suitable EM strategy is proposed. This strategy originates from a non-causal global optimization, but through a physical understanding of the parameters of interest, it is translated into a causal on-line strategy. To cope with uncertainties in the future power demand, a feedback mechanism is added which automatically regulates the energy in the battery near a reference value. Contrary to standard control experience, this feedback control loop has a better performance if it incorporates a small bandwidth and a large tracking error. Simulation results for all HEVs demonstrate that the proposed EM strategy achieves a fuel economy which is almost equivalent to the optimal solution. Moreover, when the fuel costs for producing electric power are accurately known in advance, this strategy has the ability to further improve its performance. In practice, however, this requirement is inappropriate, since causality of the EM strategy is lost. An alternative methodology is presented to include road predictions into the causal EM strategy. By means of an electronic horizon, the prediction information is translated into a preferred reference trajectory for the energy stored in the battery. However, it will be demonstrated that the added value of having knowledge about the future driving cycle is limited, compared to the situation without prediction information. Finally, the EM concept can also be applied to the electric power net of vehicles with a traditional drive train, or micro HEVs. Here, the alternator takes the position of the electric machine. As a case-study, the EM strategy has been implemented in a Ford Mondeo vehicle. Vehicle experiments on a roller-dynamometer test-bench show that profits in fuel economy are achieved up to 2.6% for a typical driving cycle. Although the potential fuel benefits are limited for the vehicle under consideration, the return on investment is extremely high, since it requires primarily changes in the vehicle software