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The thesis is focused on remitting lead-acid segments of partial charge mode which simulates the conditions in HEV. The experimental cells were submitted to different pressures on the electrode system. The main aim of the thesis was to minimize the irreversible sulphating of the negative electrodes, which are in the PSoC regime limiting in the overall life of lead-acid accumulators. All cells were submitted to measurement of the negative electrode potentials, resistance of active materials, contact resistance of the grid – the active material and measurements of pressure changes during three PSoC cycles.

This Master’s thesis deals with the issue of lead-acid batteries, which are used in hybrid electric vehicles. The lead-acid batteries works in mode PSoC. In this mode occurs to degradation mechanisms at negative electrodes. These degradation mechanisms reduce the battery life. The practical part of Master’s thesis describes the production and a compilation of experimental cells and experimental part examines the characteristics of lead-acid batteries with the pressure to the electrode system.

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.

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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 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.

Energy-efficient powertrain components and advanced vehicle control strategies are two effective methods to promote the potential of hybrid electric vehicles (HEVs). Aiming at hybrid system efficiency improvement, this thesis presents a comprehensive review of energy-efficient hybrid powertrain specific engines and proposes three improved energy management strategies (EMSs), from a basic non-adaptive real-time approach to a state-of-the-art learning-based intelligent approach. To evaluate the potential of energy-efficient powertrain components in HEV efficiency improvement, a detailed discussion of hybrid powertrain specific engines is presented. Four technological solutions, i.e., over-expansion cycle, low temperature combustion mode, alternative fuels, and waste heat recovery techniques, are reviewed thoroughly and explicitly. Benefits and challenges of each application are identified, followed by specific recommendations for future work. Opportunities to simplify hybrid-optimized engines based on cost-effective trade-offs are also investigated. To improve the practicality of HEV EMS, a real-time equivalent consumption minimization strategy (ECMS)-based HEV control scheme is proposed by incorporating powertrain inertial dynamics. Compared to the baseline ECMS without such considerations, the proposed control strategy improves the vehicle drivability and provides a more accurate prediction of fuel economy. As an improvement of the baseline ECMS, the proposed dynamic ECMS offers a more convincing and better optimal solution for practical HEV control. To address the online implementation difficulty faced by ECMS due to the equivalence factor (EF) tuning, a predictive adaptive ECMS (A-ECMS) with online EF calculation and instantaneous power distribution is proposed. With a real-time self-updating EF profile, control dependency on drive cycles is reduced, and the requirement for manual tuning is also eliminated. The proposed A-ECMS exhibits great charge sustaining capabilities on all studied drive cycles with only slight increases in fuel consumption compared to the basic non-adaptive ECMS, presenting great improvement in real-time applicability and adaptability. To take advantage of machine learning techniques for HEV EMS improvement, a deep reinforcement learning (DRL)-based intelligent EMS featuring the state-of-the-art asynchronous advantage actor-critic (A3C) algorithm is proposed. After introducing the fundamentals of reinforcement learning, formulation of the A3C-based EMS is explained in detail. The proposed algorithm is trained successfully with reasonable convergence. Training results indicate the great learning ability of the proposed strategy with excellent charge sustenance and good fuel optimality. A generalization test is also conducted to test its adaptability, and results are compared with an A-ECMS. By showing better charge sustaining performance and fuel economy, the proposed A3C-based EMS proves its potential in real-time HEV control. / Thesis / Doctor of Philosophy (PhD)

The automotive industry is inevitably experiencing a paradigm shift from fossil fuels to electric powertrain with significant technological breakthroughs in vehicle electrification. Emerging hybrid electric vehicles were one of the first steps towards cleaner and greener vehicles with a higher fuel economy and lower emission levels. The energy management strategy in hybrid electric vehicles determines the power flow pattern and significantly affects vehicle performance. Therefore, in this thesis, a learning-based strategy is proposed to address the energy management problem of a hybrid electric vehicle in various driving conditions. The idea of a deep recurrent neural network-based energy management strategy is proposed, developed, and evaluated. Initially, a hybrid electric vehicle model with a rule-based supervisory controller is constructed for this case study to obtain training data for the deep recurrent neural network and to evaluate the performance of the proposed energy management strategy. Secondly, due to its capabilities to remember historical data, a long short-term memory recurrent neural network is designed and trained to estimate the powertrain control variables from vehicle parameters. Extensive simulations are conducted to improve the model accuracy and ensure its generalization capability. Also, several hyper-parameters and structures are specifically tuned and debugged for this purpose. The novel proposed energy management strategy takes sequential data as input to capture the characteristics of both driver and controller behaviors and improve the estimation/prediction accuracy. The energy management controller is defined as a time-series problem, and a network predictor module is implemented in the system-level controller of the hybrid electric vehicle model. According to the simulation results, the proposed strategy and prediction model demonstrated lower fuel consumption and higher accuracy compared to other learning-based energy management strategies. / Thesis / Master of Applied Science (MASc)