A review of intelligent road preview methods for energy management of hybrid vehicles

Due to the shortage of fuel resources and concerns of environmental pressure, vehicle electrification is a promising trend. Hybrid vehicles are suitable alternatives to traditional vehicles. Travelling information is essential for hybrid vehicles to design the optimal control strategy for fuel consumption minimization and emissions reduction. In general, there are two ways to provide the information for the energy management strategy (EMS) design. First is extracting terrain information by utilizing global positioning system (GPS) and intelligent transportation system (ITS). However, this method is difficult to be implemented currently due to the computational complexity of extracting information. This leads to the second method which is predicting future vehicle speed and torque demand in a certain time horizon based on current and previous vehicle states. To support optimal EMS development, this paper presents a comprehensive review of prediction methods based on different levels of trip information for the EMS of hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV)

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process

Predictive Energy Management in Connected Vehicles: Utilizing Route Information Preview for Energy Saving

This dissertation formulates algorithms that use preview information of road terrain and traffic flow for reducing energy use and emissions of modern vehicles with conventional or hybrid powertrains. Energy crisis, long term energy deficit, and more restrictive environmental protection policies require developing more efficient and cleaner vehicle powertrain systems. An alternative to making advanced technology engines or electrifying the vehicle powertrain is utilizing ambient terrain and traffic information in the energy management of vehicles, a topic which has not been emphasized in the past. Today\u27s advances in vehicular telematics and advances in GIS (Geographic Information System), GPS (Global Positioning Systems), ITS (Intelligent Transportation Systems), V2V (Vehicle to Vehicle) communication, and VII (Vehicle Infrastructure Integration ) create more opportunities for predicting a vehicle\u27s trip information with details such as the future road grade, the distance to the destination, speed constraints imposed by the traffic flow, which all can be utilized for better vehicle energy management. Optimal or near optimal decision-making based on this available information requires optimal control methods, whose fundamental theories were well studied in the past but are not directly applicable due to the complexity of real problems and uncertainty in the available preview information. This dissertation proposes the use of optimal control theories and tools including Pontryagin minimum principle, Dynamic Programming (DP) which is a numerical realization of Bellman\u27s principle of optimality, and Model Predictive Control (MPC) in the optimization-based control of hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and conventional vehicles based on preview of future route information. The dissertation includes three parts introduced as follows: First, the energy saving benefit in HEV energy management by previewing future terrain information and applying optimal control methods is explored. The potential gain in fuel economy is evaluated, if road grade information is integrated in energy management of hybrid vehicles. Real-world road geometry information is taken into account in power management decisions by using both Dynamic Programming (DP) and a standard Equivalent Consumption Minimization Strategy (ECMS), derived using Pontryagin minimum principle. Secondly, the contribution of different levels of preview to energy management of plug-in hybrid vehicles (PHEVs) is studied. The gains to fuel economy of plug-in hybrid vehicles with availability of velocity and terrain preview and knowledge of distance to the next charging station are investigated. Access to future driving information is classified into full, partial, or no future information and energy management strategies for real-time implementation with partial future preview are proposed. ECMS as well as Dynamic Programming (DP) is systematically utilized to handle the resulting optimal control problems with different levels of preview. We also study the benefit of future traffic flow information preview in improving the fuel economy of conventional vehicles by predictive control methods. According to the time-scale of the preview information and its importance to the driver, the energy optimization problem is decomposed into different levels. In the microscopic level, a model predictive controller as well as a car following model is employed for predictive adaptive cruise control by stochastically forecasting the driving behavior of the lead car. In the macroscopic level, we propose to incorporate the estimated macroscopic future traffic flow information and optimize the cost-to-go by utilizing a two-dimension Dynamic Programming (2D-DP). The algorithm yields the optimal trip velocity as the reference velocity for the driver or a low level controller to follow. Through the study, we show that energy use and emissions can be reduced considerably by using preview route information. The methodologies discussed in this dissertation provide an alternative mean for the automotive industry to develop more efficient and environmentally friendly vehicles by relying mostly on software and information and with minimal hardware investments

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

Combined design and control optimization of hybrid vehicles

Hybrid vehicles play an important role in reducing energy consumption and pollutant emissions of ground transportation. The increased mechatronic system complexity, however, results in a heavy challenge for efficient component sizing and power coordination among multiple power sources. This chapter presents a convex programming framework for the combined design and control optimization of hybrid vehicles. An instructive and straightforward case study of design and energy control optimization for a fuel cell/supercapacitor hybrid bus is delineated to demonstrate the effectiveness and the computational advantage of the convex programming methodology. Convex modeling of key components in the fuel cell/supercapactior hybrid powertrain is introduced, while a pseudo code in CVX is also provided to elucidate how to practically implement the convex optimization. The generalization, applicability, and validity of the convex optimization framework are also discussed for various powertrain configurations (i.e., series, parallel, and series-parallel), different energy storage systems (e.g., battery, supercapacitor, and dual buffer), and advanced vehicular design and controller synthesis accounting for the battery thermal and aging conditions. The proposed methodology is an efficient tool that is valuable for researchers and engineers in the area of hybrid vehicles to address realistic optimal control problems

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

Hybrid and Electric Vehicles Optimal Design and Real-time Control based on Artificial Intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

Pontryagin's Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus

To improve computational efficiency of energy management strategies for plug-in hybrid electric vehicles (PHEVs), this paper proposes a stochastic model predictive controller (MPC) based on Pontryagin’s Minimum Principle (PMP), which differs from widely used dynamic programming (DP)-based predictive methods. First, short-time speed forecasting is achieved using a Markov chain model, based on real-world driving cycles. The PMP- and DP-based MPCs are compared under four preview horizons (5 s, 10 s, 15 s and 20 s), and the results show that the computational time of the DP-MPC is almost four times of that in the PMP-MPC. Moreover, the influence of predication horizon length on computational time and energy consumption is examined. Given a preview horizon of 5 s, the PMP-MPC holds a total energy consumption cost of 7.80 USD and computational time per second of 0.0130 s. When the preview horizon increases to 20 s, the total cost is 7.77 USD with the computational time per second increasing to 0.0502 s. Finally, DP, PMP, and rule-based strategies are contrasted to the PMP-MPC method, further demonstrating the promising performance and computational efficiency of the proposed methodology

A methodology and a tool for evaluating hybrid electric powertrain configurations

This paper describes a methodology for automatic optimisation of hybrid electric powertrains. This methodology is developed and implemented in a tool, CAPSimO, and the paper is written in the form of describing the tool. Given the user inputs, which are dynamic vehicle model, driving cycle and optimisation criterion, the tool first produces a simplified powertrain model in a form of static maps, before dynamic programming is used to find an optimal power split which minimises the chosen criterion. The tool does not require that the vehicle model is transparent, which makes it possible to work on models hidden for intellectual property reasons. The paper presents two examples of powertrain evaluation, in terms of fuel consumption, for a parallel and a parallel-series powertrain