Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet

Modelling and Co-simulation of hybrid vehicles: A thermal management perspective

Thermal management plays a vital role in the modern vehicle design and delivery. It enables the thermal analysis and optimisation of energy distribution to improve performance, increase efficiency and reduce emissions. Due to the complexity of the overall vehicle system, it is necessary to use a combination of simulation tools. Therefore, the co-simulation is at the centre of the design and analysis of electric, hybrid vehicles. For a holistic vehicle simulation to be realized, the simulation environment must support many physical domains. In this paper, a wide variety of system designs for modelling vehicle thermal performance are reviewed, providing an overview of necessary considerations for developing a cost-effective tool to evaluate fuel consumption and emissions across dynamic drive-cycles and under a range of weather conditions. The virtual models reviewed in this paper provide tools for component-level, system-level and control design, analysis, and optimisation. This paper concerns the latest techniques for an overall vehicle model development and software integration of multi-domain subsystems from a thermal management view and discusses the challenges presented for future studies

Optimal Power Management Based on Q-Learning and Neuro-Dynamic Programming for Plug-in Hybrid Electric Vehicles

Energy optimization for plug-in hybrid electric vehicles (PHEVs) is a challenging problem due to its system complexity and various constraints. In this research, we present a Q-learning based in-vehicle model-free solution that can robustly converge to the optimal control. The proposed algorithms combine neuro-dynamic programming (NDP) with future trip information to effectively estimate the expected future energy cost (expected cost-to-go) for a given vehicle state and control actions. The convergence of those learning algorithms is demonstrated on both fixed and randomly selected drive cycles. Based on the characteristics of these learning algorithms, we propose a two-stage deployment solution for PHEV power management applications. We will also introduce a new initialization strategy that combines optimal learning with a properly selected penalty function. Such initialization can reduce the learning convergence time by 70%, which has huge impact on in-vehicle implementation. Finally, we develop a neural network (NN) for the battery state-of-charge (SoC) prediction, rendering our power management controller completely model-free.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/140754/1/Chang Liu Final Dissertation.pdfDescription of Chang Liu Final Dissertation.pdf : Dissertatio

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

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

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

Plug in hybrid electric vehicle energy management system for real world driving

The energy management system (EMS) of hybrid electric vehicle controls the operation of two power plants; electric machine/battery and typically engine. Hence the fuel economy and emissions of hybrid vehicles strongly depend on the EMS. It is known that considering the future trip demand in devising an EMS control strategy enhance the vehicle and component performances. However existing such acausal EMS cannot be used in real time and would require prior knowledge of the trip vehicle speed profile (trip demand). Therefore rule based EMS which considers instantaneous trip demand in devising a control strategy are used. Such causal EMS are real time capable and simple in design. However rule based EMS are tuned for a set of driving cycles and hence their performance is vulnerable in real world driving. The research question is “How to design a real time capable acausal EMS for a plug in hybrid electric vehicle (PHEV) that can adapt to the uncertainties of real world driving”. In the research, the design and evaluation of a proposed EMS to deal and demonstrate in scenarios expected in real world driving respectively were considered. The proposed rule based acausal EMS is formulated over the estimated vehicle trip energy and driving information. Vehicle trip energy is the electric (battery) energy required to meet the trip demand estimated using known driving information. Driving information that can be considered are driver style, route distance and road types like urban and extra urban, with traffic as a sub function. Unlike vehicle speed, vehicle trip energy is shown to be relatively less dynamic in real world driving. For the proposed EMS evaluation, a commonly used parallel PHEV model was simulated. For driving information EMS was not integrated to a navigation system but manually defined. Evaluation studies were done for a driver, and traffic was not considered for simplicity. In the thesis, vehicle performance and credentials for real world applicability (real time capability and adaptability) of the proposed acausal EMS are demonstrated for various scenarios in real world driving; varied initial SOC, sequence of road types, trip distance and trip energy estimation. Over the New European Driving Cycle (NEDC) the proposed EMS vehicle performance is compared to a conventional rule based EMS. The proposed EMS fuel economy improvement is up to 11% with 5 times fewer number of engine stop-starts. Similarly in the validation study, with no prior knowledge of trip vehicle speed profile, the fuel economy improvement is up to 29% with 7 times fewer number of engine stop-starts. The simulation duration of the proposed EMS is as good as conventional rule based EMS. Hence the proposed EMS is potentially real time capable. The proposed EMS can adapt to a wide variation in trip energy (±15%) estimation and still perform better than the conventional rule based EMS. The proposed EMS can tolerate variation in trip demand estimation and no prior knowledge of trip vehicle speed profile is required, unlike other acausal EMS studies in the literature. A new PHEV EMS has been formulated. Through simulation it has been seen to deliver benefit in vehicle performance and real world applicability for varied scenarios as expected in real world driving. The key new step was to use vehicle trip energy in the formulation, which enabled rule based EMS to be acausal and potentially real time capable