Next Generation HEV Powertrain Design Tools: Roadmap and Challenges

Hybrid electric vehicles (HEVs) represent a fundamental step in the global evolution towards transportation electrification. Nevertheless, they exhibit a remarkably complex design environment with respect to both traditional internal combustion engine vehicles and battery electric vehicles. Innovative and advanced design tools are therefore crucially required to effectively handle the increased complexity of HEV development processes. This paper aims at providing a comprehensive overview of past and current advancements in HEV powertrain design methodologies. Subsequently, major simplifications and limits of current HEV design methodologies are detailed. The final part of this paper defines research challenges that need accomplishment to develop the next generation HEV architecture design tools. These particularly include the application of multi-fidelity modeling approaches, the embedded design of powertrain architecture and on-board control logic and the endorsement of multi-disciplinary optimization procedures. Resolving these issues may indeed remarkably foster the widespread adoption of HEVs in the global vehicle market

Design and control of the energy management system of a smart vehicle

This thesis demonstrates the design of two high efficiency controllers, one non-predictive and the other predictive, that can be used in both parallel and power-split connected plug-in hybrid electric vehicles. Simulation models of three different commercially available vehicles are developed from measured data for necessary testing and comparisons of developed controllers. Results prove that developed controllers perform better than the existing controllers in terms of efficiency, fuel consumption, and emissions

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

Investigation of a novel coaxial power-split hybrid powertrain for mining trucks

© 2018 by the authors. Due to the different working conditions and specification requirements of mining trucks when compared to commercial passenger vehicles, better fuel efficiency of mining trucks could lead to more significant economic benefits. Therefore, investigating a hybrid transmission system becomes essential. A coaxial power-split hybrid powertrain system for mining trucks is presented in this paper. The system is characterized as comprising an engine, a generator (MG1), a motor (MC2), two sets of planetary gears, and a clutch (CL1). There are six primary operation modes for the hybrid system including the electric motor mode, the engine mode, the hybrid electric mode, the hybrid and assist mode, the regenerative mode, and the stationary charging mode. The mathematical model of the coaxial power-split hybrid system is established according to the requirements of vehicle dynamic performance and fuel economy performance in a given driving cycle. A hybrid vehicle model based on a rule-based control strategy is established to evaluate the fuel economy. Compared with the Toyota Hybrid System (THS) and the conventional mechanical vehicle system using a diesel engine, the simulation results based on an enterprise project indicate that the proposed hybrid system can enhance the vehicle's fuel economy by 8.21% and 22.45%, respectively, during the given mining driving cycle. The simulation results can be used as a reference to study the feasibility of the proposed coaxial hybrid system whose full potential needs to be further investigated by adopting non-causal control strategies

REAL-TIME PREDICTIVE CONTROL OF CONNECTED VEHICLE POWERTRAINS FOR IMPROVED ENERGY EFFICIENCY

The continued push for the reduction of energy consumption across the automotive vehicle fleet has led to widespread adoption of hybrid and plug-in hybrid electric vehicles (PHEV) by auto manufacturers. In addition, connected and automated vehicle (CAV) technologies have seen rapid development in recent years and bring with them the potential to significantly impact vehicle energy consumption. This dissertation studies predictive control methods for PHEV powertrains that are enabled by CAV technologies with the goal of reducing vehicle energy consumption. First, a real-time predictive powertrain controller for PHEV energy management is developed. This controller utilizes predictions of future vehicle velocity and power demand in order to optimize powersplit decisions of the vehicle. This predictive powertrain controller utilizes nonlinear model predictive control (NMPC) to perform this optimization while being cognizant of future vehicle behavior. Second, the developed NMPC powertrain controller is thoroughly evaluated both in simulation and real-time testing. The controller is assessed over a large number of standardized and real-world drive cycles in simulation in order to properly quantify the energy savings benefits of the controller. In addition, the NMPC powertrain controller is deployed onto a real-time rapid prototyping embedded controller installed in a test vehicle. Using this real-time testing setup, the developed NMPC powertrain controller is evaluated using on-road testing for both energy savings performance and real-time performance. Third, a real-time integrated predictive powertrain controller (IPPC) for a multi-mode PHEV is presented. Utilizing predictions of future vehicle behavior, an optimal mode path plan is computed in order to determine a mode command best suited to the future conditions. In addition, this optimal mode path planning controller is integrated with the NMPC powertrain controller to create a real-time integrated predictive powertrain controller that is capable of full supervisory control for a multi-mode PHEV. Fourth, the IPPC is evaluated in simulation testing across a range of standard and real-world drive cycles in order to quantify the energy savings of the controller. This analysis is comprised of the combined benefit of the NMPC powertrain controller and the optimal mode path planning controller. The IPPC is deployed onto a rapid prototyping embedded controller for real-time evaluation. Using the real-time implementation of the IPPC, on-road testing was performed to assess both energy benefits and real-time performance of the IPPC. Finally, as the controllers developed in this research were evaluated for a single vehicle platform, the applicability of these controllers to other platforms is discussed. Multiple cases are discussed on how both the NMPC powertrain controller and the optimal mode path planning controller can be applied to other vehicle platforms in order to broaden the scope of this research

Design of Power Split Hybrid Powertrains with Multiple Planetary Gears and Clutches.

Fuel economy standards for automobiles have become much tighter in many countries in the past decades. Hybrid electric vehicles (HEVs), as one of the most promising solutions to take on these challenging standards, have been successful in the US market. In the last few years, an observed trend is to use multiple planetary gears with multiple operating modes to further improve vehicle fuel economy and driving performance. Most work in existing literature on HEV design and optimization has been based on specific configurations, rather than exhaustively searching through all possible configurations. This limitation arises from the large size of the design space–millions to trillions of possible topological candidates. In this dissertation, a systematic design methodology is presented, which enables the exhaustive search of multi-mode powertrain systems. As a first step, a systematic analysis has been performed for all 12 single PG configurations with multiple operating modes enabled by clutch operation. The Dynamic Programming (DP) technique is used to solve the optimal energy management problems for each design candidate. For multi-mode HEVs with multiple PGs, an automated modeling and mode classification methodology is developed, which makes it possible to exhaustively search all possible designs. General mode shift mechanisms are studied, while mode shift cost is evaluated using Dijkstra’s algorithm, which identifies the optimal mode shift path. For each candidate, the optimal control problem needs to be solved so that all designs can be compared based on their best possible execution. A fast and near-optimal energy management strategy is proposed. The comparison results show that it is up to 10,000 times faster than DP while achieving similar performance. To ensure acceptable launching performance of the design candidates, a fast and optimal acceleration performance test procedure is developed, which can be used to determine optimal control inputs and mode shift schedule. Combining all proposed methodologies produces a systematic and optimal design procedure. Optimization results show that the exhaustive search design method is able to identify dozens of better designs than the production hybrid vehicle models available in today’s market.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116659/1/xiaowuz_1.pd

Overview of Sensitivity Analysis Methods Capabilities for Traction AC Machines in Electrified Vehicles

© 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.A robust design in electrified powertrains substantially helps to enhance the vehicle's overall efficiency. Robustness analyses come with complexity and computational costs at the vehicle level. The use of sensitivity analysis (SA) methods in the design phase has gained popularity in recent years to improve the performance of road vehicles while optimizing the resources, reducing the costs, and shortening the development time. Designers have started to utilize the SA methods to explore: i) how the component and vehicle level design options affect the main outputs i.e. energy efficiency and energy consumption; ii) observing sub-dependent parameters, which might be influenced by the variation of the targeted controllable (i.e. magnet thickness) and uncontrollable (i.e. magnet temperature) variables, in nonlinear dynamic systems; and iii) evaluating the interactions, of both dependent, and sub-dependent controllable/uncontrollable variables, under transient conditions. Hence the aim of this study is to succinctly review recent utilization of SA methods in the design of AC electric machines (EM)s used in vehicle powertrains, to evaluate and discuss the findings presented in recent research papers while summarizing the current state of knowledge. By systematically reviewing the literature on applied SAs in electrified powertrains, we offer a bibliometric analysis of the trends of application-oriented SA studies in the last and next decades. Finally, a numerical-based case study on a third-generation TOYOTA Prius EM will be given, to verify the SA-related findings of this article, alongside future works recommendations.Peer reviewe

A New Powertrain Architecture: From Electromagnetic-Structural Dynamics to Platooning

Electrification and vehicle-to-vehicle connectivity have become two of the major areas of vehicle development in recent years. Electrified vehicles show significant advantages because of their high performance in fuel economy and low emissions compared to conventional vehicles. Although hybrid electric vehicle (HEV) development has resulted in a variety of powertrain architectures, novel high-performance powertrain solutions with fewer components and low cost remain an important need. In addition, common HEV configurations use small internal combustion engines, which can suffer from high torque fluctuations detrimental for NVH performance and safety. Advanced powertrains that absorb these fluctuations efficiently are needed. This thesis presents a novel HEV powertrain architecture without any planetary gears or clutches. Using physics-based component model, a proof-of-concept powertrain model is implemented and demonstrated ability to remove over 99.5% torque fluctuation and fulfill vehicle driving demands. A comprehensive design and control optimization for the novel powertrain is performed. A single utility function is designed by combining multiple objectives, and is tuned using the Pareto front of the novel powertrain performance to obtain different optimal powertrain designs. Optimal novel powertrain designs show comparable performance with optimal designs of commercially available power-split benchmark powertrains. Torque fluctuations in HEVs may result in electromagnetic-structural (EMS) phenomena within the electric machines of the powertrain. Periodic forces generated by permanent magnets or windings and other disturbances to the EM device can lead to excitation of specific structural resonances due to EMS coupling. Existing EMS models are usually 2D and do not capture the EMS coupling. Thus, a model that accurately and efficiently captures EMS phenomena is required. To capture the EMS phenomena, displacement-dependent EM forces are introduced in the modal space to the structural dynamics of electric machines. Both linear and nonlinear approximations of EM forces are calculated using high-fidelity FEA models, forming a reduced-order model (ROM) with EMS coupling, namely the EMS ROM. The dynamics of the EMS ROM is similar to a damped dynamical system governed by Mathieu's equation, which exhibits parametric excitation. The EMS ROM is used to compute the stability transition threshold for the parametric excitation. Parametric resonance peaks are revealed in the responses from an unstable device with EMS. In addition, a frequency shift of the primary resonance peak caused by (nonlinear) EM force harmonics is detected. Time-domain analyses using the high-fidelity FEA model confirm the EMS phenomena and accuracy of the EMS ROM. Multiple vehicles, each with an advanced powertrain can be used in platoons to enhance fuel economy, road capacity, and safety compared to a single vehicle. Studies that focus on platooning usually do not focus on task-based longitudinal planning and do not capture detailed powertrain operations, which impact the control and energy consumption of the overall platoon. In this thesis, multiple vehicles, each equipped with the novel powertrain, are investigated when they form a platoon and drive on a specified path. The drive schedule and vehicle controllers are optimized to minimize the total energy consumption of the platoon. Energy optimization requires an integrated vehicle-following model and a high-fidelity powertrain model. In addition, component-level, vehicle-level, and platoon-level constraints are applied. Parametric studies are performed for both homogeneous and heterogeneous platoons. Optimization is shown to effectively reduce the maximum headway error by an order of magnitude and enhance energy saving of 17% to 37%.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166142/1/albertyi_1.pd