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

STUDY OF CONTROL SCHEMES FOR SERIES HYBRID-ELECTRIC POWERTRAIN FOR UNMANNED AERIAL SYSTEMS

Hybrid-Electric aircraft powertrain modeling for Unmanned Aerial Systems (UAS) is a useful tool for predicting powertrain performance of the UAS aircraft. However, for small UAS, potential gains in range and endurance can depend significantly on the aircraft flight profile and powertrain control logic in addition to the subsequent impact on the performance of powertrain components. Small UAS aircraft utilize small-displacement engines with poor thermal efficiency and, therefore, could benefit from a hybridized powertrain by reducing fuel consumption. This study uses a dynamic simulation of a UAS, representative flight profiles, and powertrain control logic approaches to evaluate the performance of a series hybrid-electric powertrain. Hybrid powertrain component models were developed using lookup tables of test data and model parameterization approaches to generate a UAS dynamic system model. These models were then used to test three different hybrid powertrain control strategies for their ability to provide efficient IC engine operation during the charging process. The baseline controller analyzed in this work does not focus on optimizing fuel efficiency. In contrast, the other two controllers utilize engine fuel consumption data to develop a scheme to reduce fuel consumption during the battery charging operation. The performance of the powertrain controllers is evaluated for a UAS operating on three different representative mission profiles relevant to cruising, maneuvering, and surveillance missions. Fuel consumption and battery state of charge form two metrics that are used to evaluate the performance of each controller. The first fuel efficiency-focused controller is the ideal operating line (IOL) strategy. The IOL strategy uses performance maps obtained by engine characterization on a specialized dynamometer. The simulations showed the IOL strategy produced average fuel economy improvements ranging from 12%-15% for a 30-minute mission profile compared to the baseline controller. The last controller utilizes fuzzy logic to manage the charging operations while maintaining efficient fuel operation where it produced similar fuel saving to the IOL method but were generally higher by 2-3%. The importance of developing detailed dynamic system models to capture the power variations during flight with fuel-efficient powertrain controllers is key to maximizing small UAS hybrid powertrain performance in varying operating conditions

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

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

Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning

Real-time applications of energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are the harshest requirements for researchers and engineers. Inspired by the excellent problem-solving capabilities of deep reinforcement learning (DRL), this paper proposes a real-time EMS via incorporating the DRL method and transfer learning (TL). The related EMSs are derived from and evaluated on the real-world collected driving cycle dataset from Transportation Secure Data Center (TSDC). The concrete DRL algorithm is proximal policy optimization (PPO) belonging to the policy gradient (PG) techniques. For specification, many source driving cycles are utilized for training the parameters of deep network based on PPO. The learned parameters are transformed into the target driving cycles under the TL framework. The EMSs related to the target driving cycles are estimated and compared in different training conditions. Simulation results indicate that the presented transfer DRL-based EMS could effectively reduce time consumption and guarantee control performance.Comment: 25 pages, 12 figure

MODEL-BASED CONTROL OF HYBRID ELECTRIC POWERTRAINS INTEGRATED WITH LOW TEMPERATURE COMBUSTION ENGINES

Powertrain electrification including hybridizing advanced combustion engines is a viable cost-effective solution to improve fuel economy of vehicles. This will provide opportunity for narrow-range high-efficiency combustion regimes to be able to operate and consequently improve vehicle’s fuel conversion efficiency, compared to conventional hybrid electric vehicles (HEV)s. Low temperature combustion (LTC) engines offer the highest peak brake thermal efficiency reported in literature, but these engines have narrow operating range. In addition, LTC engines have ultra-low soot and nitrogen oxides (NOx) emissions, compared to conventional compression ignition and spark ignition (SI) engines. This dissertation concentrates on integrating the LTC engines (i) in series HEV and extended range electric vehicle (E-REV) architectures which decouple the engine from the drivetrain and allow the ICE to operate fully in a dedicated LTC mode, and (ii) a parallel HEV architecture to investigate optimum performance for fuel saving by utilizing electric torque assist level offered by e-motor. An electrified LTC-SI powertrain test setup is built at Michigan Technological University to develop the powertrain efficiency maps to be used in energy management control (EMC) framework. Three different types of Energy Management Control (EMC) strategies are developed. The EMC strategies encompass thermostatic rule-based control (RBC), offline (i.e., dynamic programing (DP) and pontryagin’s minimum principal (PMP)), and online optimization (i.e., model predictive control (MPC)). The developed EMC strategies are then implemented on experimentally validated HEV powertrain model to investigate the powertrain fuel economy. A dedicated single-mode homogeneous charge compression ignition (HCCI) and reactivity controlled compression ignition (RCCI) engines are integrated with series HEV powertrain. The results show up to 17.7% and 14.2% fuel economy saving of using HCCI and RCCI, respectively in series HEV compared to modern SI engine in the similar architecture. In addition, the MPC results show that sub-optimal fuel economy is achieved by predicting the vehicle speed profile for a time horizon of 70 sec. Furthermore, a multi-mode LTC-SI engine is integrated in both series and parallel HEVs. The developed multi-mode LTC-SI engine enables flexibility in combustion mode-switching over the driving cycle, which helps to improve the overall fuel economy. The engine operation modes include HCCI, RCCI, and SI modes. The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations. In the parallel HEV architecture, the results for the UDDS driving cycle show the maximum benefit of the multi-mode LTCSI engine is realized in the mild electrification level, where the LTC mode operating time increases dramatically from 5.0% in Plug-in Hybrid Electric Vehicle (PHEV) to 20.5% in mild HEV

A policy-oriented vehicle simulation approach for estimating the CO2 emissions from Hybrid Light Duty Vehicles

Pollutants emissions and fuel economy tests for passenger cars differ from region to region of the world, since different driving condition and vehicle fleet characterize different geographical areas. In particular, the European type approval procedure for passenger cars uses as reference cycle the New European Driving Cycle (NEDC), which is nowadays not representative of real driving conditions. Therefore, the European Commission has planned to introduce the Worldwide Harmonized Light Duty Test Procedure (WLTP) from September 2017. As a consequence, the CO2 emissions target should be adapted, since the current 2020 goals are based on NEDC assessment. The European Commission and the Joint Research Centre (JRC) are therefore developing a simulation tool called CO2MPAS (CO2 Module for Passenger and commercial vehicles Simulation) for the correlation of CO2 emissions from WLTP to NEDC, which will be used for the type approval of European passenger cars from 2017, avoiding expensive duplicate test campaigns for car manufactures. However, the implementation of CO2MPAS has so far involved solely conventional light duty vehicles. Within this context, a research project has been carried out in closed collaboration between Politecnico di Torino and JRC for the development of CO2MPAS for Hybrid Electric Vehicles (HEVs) and Plug-In Hybrid Electric Vehicles (PHEVs). The correlation model is based on a unique simplified physical approach, which should be able to detect the powertrain behavior along the NEDC cycle from the physical measurements along the new driving cycle, estimating with a good accuracy the CO2 emissions (within ± 3 g/km)