Indiana University-Purdue University Indianapolis (IUPUI) / An increase in the awareness of environmental conservation is leading the automotive industry into the adaptation of alternatively fueled vehicles. Electric, Fuel-Cell as well as Hybrid-Electric vehicles focus on this research area with the aim to efficiently utilize vehicle powertrain as the first step. Energy and Power Management System control strategies play a vital role in improving the efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used in the given system. A kinematic mathematical model for Plug-in Hybrid Electric Vehicle (PHEV) has been developed in this study and is further optimized by determining optimal power management strategy for minimal fuel consumption as well as NOx emissions while executing a set drive cycle. A multi-objective optimization using weighted sum formulation is needed in order to observe the trade-off between the optimized objectives. Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the trade-off curve between fuel and NOx. In performing these optimizations, the control signal consisting of engine speed and reference battery SOC trajectory for a 2-hour cycle is used as the controllable decision parameter input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours, giving slightly less than 2.5 minutes per point, noting that the values used in the model are interpolated between the points for each time step. With the control signal consisting of 2 distinct signals, speed, and SOC trajectory, as 50 element time-variant signals, a multidimensional problem was formulated for the optimizer. Novel approaches to balance the optimizer exploration and convergence, as well as seeding techniques are suggested to solve the optimal control problem. The optimization of each involved individual runs at 5 different weight levels with the resulting cost populations being compiled together to visually represent with the help of Pareto front development. The obtained results of simulations and optimization are presented involving performances of individual components of the PHEV powertrain as well as the optimized PMS strategy to follow for a given drive cycle. Observations of the trade-off are discussed in the case of Multi-Objective Optimizations.

The new emission regulations for new trucks was made to decrease the CO2 emissions by 30% from 2020 to 2030. One of the solutions is hybridizing the truck powertrain with 48V or 600V that can recover brake energy with electrical machines and batteries. The control of this hybrid powertrain is key to increase fuel efficiency. The idea behind this approach is to combine two different power sources, an internal combustion engine and a battery driven electric machine, and use both to provide tractive forces to the vehicle. This approach requires a HEV controller to operate the power flow within the systems. The HEV controller is the key to maximize fuel savings which contains an energy management strategy. It uses the knowledge of the road profile ahead by GPS and maps, and strongly interacts with the control of the cruise speed, automated gear shifts, powertrain modes and state of charge. In this master thesis, the dynamic programming strategy is used as predictive energy management for hybrid electric truck in forward- facing simulation environment. An analysis of predictive energy management is thus done for receding and full horizon length on flat and hilly drive cycle, where fuel consumption and recuperation energy will be regarded as the primary factor. Another important factor to consider is the powertrain mode of the vehicle with different penalty values. The result from horizon study indicates that the long receding horizon length has a benefit to store more recuperative energy. The fuel consumption is decreased for all drive cycle in the comparison with existing Volvo’s strategy.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink, both the proposed ARBS and the established PRBS strategies are compared across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine effciency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.

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Electric transportation has been a well-studied research topic with electric ships gaining momentum. Ships can have a wide range in size from small cargo ships to military vessels. The benefits of electrification include meeting environmental sustainability goals and operational benefits in terms of flexibility and renewed operation. The power systems onboard a ship can be considered a microgrid, which is called a shipboard microgrid. This system poses unique challenges compared to land-based microgrids due to the resiliency requirements of being at sea. A control system for a hybrid- electric ship is proposed with both an energy storage system (ESS) and traditional diesel generators and gas turbines. This system balances economics with resilient control by calculating a baseline load distribution using the cost of operating each unit for the expected load profile. Additionally, the control system ensures that the generation capacity is available if the load does not follow the expected profile. To maintain flexibility, the system will redispatch the units as needed based on the actual load applied, while reducing the control efforts and maintaining the generation contingency. Therefore, the proposed shipboard microgrid control offers a control method that considers the cost of operation while maintaining the required standards of shipboard microgrid control. / Electrical and Computer Engineering

The use of an automatic transmission in pre-transmission parallel hybrid electric vehicles provides greater potential for powertrain optimization than conventional vehicles. By modifying the shift map, the transmission’s gear selection can be adjusted to reduce the energy consumption of the vehicle. A method for determining the optimal shift map for this hybrid vehicle has been implemented using global optimization and software-in-the-loop vehicle simulation. An analysis of the optimization has been performed using software-in-the-loop and hardware-in-the-loop simulation and evaluates two vehicle modes: regenerative braking active and regenerative braking disabled. The results of these two modes illustrate the successful implementation of the global optimization algorithm. However, the evaluation results raise practical concerns about implementing the optimized shift maps in a vehicle and illustrate a problem which must be overcome for future development.

Mississippi State University was selected for participation in the EcoCAR 3 Advance Vehicle Technology Competition. The team designed its architecture around the use of two UQM electric motors, and a Weber MPE 850cc turbocharged engine. To combine the three inputs into a singular output a custom gearbox was designed with seven helical gears. The gears were designed to handle the high torque and speeds the vehicle would experience. The use of this custom gearbox allows for a variety of control strategies. By optimizing the torque supplied by each motor, the overall energy consumption of the vehicle could be reduced. Additionally, studies were completed on the engine to understand the effects of injecting water into the engine’s intake manifold at 25% pedal request from 2000-3500 rpm. Overall, every speed showed an optimum at 20% water to fuel ratio, which obtained reductions in brake specific fuel consumption of up to 9.4%.

This thesis focuses on developing an intelligent energy management strategy for eco-driving in Connected and Autonomous Hybrid Electric Vehicles (CA-HEV's), which can be implemented in real-time. The strategy is divided into two layers, i.e. the upper level controller and the lower level controller. The upper level controller can be executed on the remote server. It is responsible for extracting the information from the driver about the trip and the vehicle information using the communication capabilities of the CA-HEV. The gathered information is then utilized by dynamic programming (DP), which is implemented in a bi-layer fashion to reduce the computation burden on the server. The outer layer of the DP algorithm and the optimal velocity trajectory and the inner layer optimizes the power distribution in the powertrain to minimize fuel consumption alongside maintaining charge balance conditions. These global optimal results are evaluated for an ideal environment without any traffic information. The lower level controller is responsible for real-time implementation on vehicles in the real world environment and is based on a well-accredited reinforcement learning (RL) strategy, i.e., Q-learning. The RL-based controller optimally distributes the power in a CA-HEV and maintains charge balance conditions. Furthermore, the RL-based controller is also trained on the remote server based on global optimal results obtained from the DP algorithm. The optimal parameter information is then resent to the vehicle's embedded controller for real-time implementation. Simulations are performed for Toyata Prius (2010) on MATLAB and Simulink, and road information is gathered from SUMO. Simulation results provide a comparative study between the global optimal and the RL-based controller. To validate the adaptiveness of the RL-based controller, it is also tested on two approximate real-world drivecycles and its performance is compared against global optimal results evaluated using DP. / Thesis / Master of Applied Science (MASc)

This master thesis project aims to complete and verify core functions of a test rig that is designed and built to emulate drive cycles for measuring the energy consumption of HEVs, especially a vehicle named ELBA from KTH Integrated Transport Research Lab (ITRL). To fulfill this goal, a simplified model is created for the test rig, whose involved parameters are identified through various experiments. Then the model is verified by both step voltage responses and sinusoidal current responses. Meanwhile, vehicle dynamics is modeled to calculate required resistance force for road slope emulation. Moreover, an existing method, vehicle equivalent mass, is utilized to compensate dynamic force of the vehicle body, enabling simulation of regenerative braking without an extra flywheel. Together with test rig’s model that is responsible for converting required resistance force to demanded current reference, the rig’s functions are completed and ready for final verification. As a result, the driver of the DC motor on the rig is found to has lower current limitation than required so that the rig is not able to entirely compensate dynamic force of the car. However, the feasibility of the principle is still proved by the tests. Based on the result, recommendations are given to solve the problem and achieve other improvements in the future. / Detta examensarbete syftar till att slutföra och verifiera kärnfunktioner i en testrigg som är designad och byggd för att emulera körcykler för att mäta energiförbrukningen för elhybridbilar, särskilt ett fordon som heter ELBA från KTH Integrated Transport Research Lab (ITRL). För att uppfylla detta mål skapades en förenklad modell för testriggen, vars parametrar identifieras genom olika experiment. Sedan verifieras modellen av både stegspänningssvar och sinusformade strömsvar. Under tiden modelleras fordonsdynamiken för att beräkna erforderlig motståndskraft för väglöpemulering. Samtidigt modelleras fordonsdynamiken för att beräkna den erforderliga motståndskraften för emulering av väglutningar. Dessutom används en befintlig metod, fordonsekvivalentmassa, för att kompensera fordonskroppens dynamiska kraft, vilket möjliggör simulering av regenerativ bromsning utan extra svänghjul. Tillsammans med testriggens modell som är ansvarig för att konvertera erforderlig motståndskraft till efterfrågad strömreferens, är riggens funktioner färdig och redo för slutlig verifiering. Som ett resultat har föraren av likström motorn på riggen visat sig ha lägre strömbegränsning än vad som krävs så att riggen inte helt kan kompensera bilens dynamiska kraft. Emellertid bevisas principens genomförbarhet fortfarande av testerna. Baserat på resultatet ges rekommendationer för att lösa problemet och uppnå andra förbättringar i framtiden.