Implementation Of Fuzzy Logic Control Into An Equivalent Minimization Strategy For Adaptive Energy Management Of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Electric vehicles have been introduced by the industry, showing promising signs of reducing emissions production in the automotive sector. However, many consumers may be hesitant to purchase fully electric vehicles due to several uncertainty variables including available charging stations. Hybrid electric vehicles (HEVs) have been introduced to reduce problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% regardless of starting SOC. Recommendations for modification of the fuzzy logic controller are made to produce additional fuel economy and charge sustaining benefits from the parallel hybrid vehicle model

A COMPARATIVE ANALYSIS OF COASTDOWN TESTING METHODS FROM AN ELECTRIC DRIVE UNIT ENGAGEMENT PERSPECTIVE USING A STUDENT-DESIGNED PARALLEL HYBRID ELECTRIC VEHICLE

Coastdown testing and road load determination are pivotal parts of the automotive design process. Vehicle manufacturers and independent companies perform and analyze road loads determined through a coastdown or similar method to determine a vehicle’s road load for modeling and EPA certification. For a traditional coastdown, the vehicle’s drivetrain must be disconnected through a clutch between the engine and the transmission while traveling at a high rate of speed to place the vehicle in neutral. This changes for hybrid and electric vehicles. Some hybrid, and most electric, vehicles delivered to customers do not have this clutch action to grant the luxury of a traditional neutral. In fact, when coasting, most hybrid or electric vehicles, the electric drivetrain is charging the battery through regenerative braking with negative torque commands. Vehicle manufacturers can successfully disconnect the electric motors and drive units to perform a traditional coastdown with no negative torque. This disconnection is important to isolate the rolling resistance and air resistance without need to account for losses in the electric drive unit. This research aims to test and analyze hybrid and electric vehicle coastdowns where this disconnect is not possible/provided by the manufacturer. The conditions tested enable a hybrid or electric vehicle to coast as intended from the factory with the negative torque through the electric drive unit to recapture energy. The goal of this research is to provide methodology and results to justify testing a hybrid or electric vehicle with its electric drive unit clutch engaged. The testing for this thesis was performed on a 2019 Chevrolet Blazer that West Virginia University’s EcoCAR team converted into a P4 parallel hybrid electric vehicle with an internal combustion engine on the front axle and an electric drive unit on the rear. This vehicle’s electric drive unit has a clutch that can be disconnected through the team-implemented controls system. To test two post-processing methods to account for the forces of the drive unit outlined by an independent testing organization, the vehicle was subjected to 4 different coastdown conditions. The first condition was a traditional coastdown with the transmission from the engine to the front axle in neutral (all conditions had the transmission in neutral) and the electric drive unit disconnected. The second condition had the electric drive unit engaged with no torque commanded. The last two conditions were the regenerative braking conditions with a “low” torque condition of -200 Nm and the other was a “high” torque condition of -400 Nm. The two regenerative braking condition results were adjusted through two post-processing methods to account for the forces of the drive unit. The second coastdown condition is not statistically the same as the traditional coastdown condition at low vehicle speeds and served as the control for the final two. There all small differences between the two results but none that exceed one standard deviation of the control. Only one post-processing method was viable for the lower regenerative braking torque condition and the calculated road load matches the control. For the higher regenerative braking torque condition, the methods match the road load at vehicle velocities above 15 m/s. Below this velocity, they failed to match the control’s road load

Energy Management

Forecasts point to a huge increase in energy demand over the next 25 years, with a direct and immediate impact on the exhaustion of fossil fuels, the increase in pollution levels and the global warming that will have significant consequences for all sectors of society. Irrespective of the likelihood of these predictions or what researchers in different scientific disciplines may believe or publicly say about how critical the energy situation may be on a world level, it is without doubt one of the great debates that has stirred up public interest in modern times. We should probably already be thinking about the design of a worldwide strategic plan for energy management across the planet. It would include measures to raise awareness, educate the different actors involved, develop policies, provide resources, prioritise actions and establish contingency plans. This process is complex and depends on political, social, economic and technological factors that are hard to take into account simultaneously. Then, before such a plan is formulated, studies such as those described in this book can serve to illustrate what Information and Communication Technologies have to offer in this sphere and, with luck, to create a reference to encourage investigators in the pursuit of new and better solutions

Practice and Innovations in Sustainable Transport

The book continues with an experimental analysis conducted to obtain accurate and complete information about electric vehicles in different traffic situations and road conditions. For the experimental analysis in this study, three different electric vehicles from the Edinburgh College leasing program were equipped and tracked to obtain over 50 GPS and energy consumption data for short distance journeys in the Edinburgh area and long-range tests between Edinburgh and Bristol. In the following section, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation is proposed to accurately estimate state of charge (SOC), which is vital for safe operation and efficient management of lithium-ion batteries. A coupled-inductor DC-DC converter with a high voltage gain is proposed in the following section to match the voltage of a fuel cell stack to a DC link bus. Finally, the book presents a review of the different approaches that have been proposed by various authors to mitigate the impact of electric buses and electric taxis on the future smart grid

Hybrid Electric Mobility: Design Considerations for Energy Storage Systems and Fuel Economy Optimization of Shared Semi-Autonomous Vehicles

Recent trends show that consumers are shifting away from individually owned personal vehicles, and towards shared mobility solutions. Those who do own a vehicle are opting for those that are more fuel efficient and environmentally friendly. This thesis examines the design and vehicle development process of a shared semi-autonomous hybrid electric SUV to be deployed in a carsharing application, effectively addressing both established trends within the automotive industry and personalized transportation. The thesis directs the reader through each step of the vehicle design process starting with Target Market analysis, Component sizing, selection, and layout, and powertrain modeling and optimization, while highlighting how the primary design objective of fuel economy maximization guided the overall vehicle design. Ultimately, this thesis aims to serve as a reference and knowledge transfer document to be used by members of the University of Waterloo Alternative Fuels Team as it pertains directly to the design of Energy Storage System utilized in the development vehicle, and therefore should be read by those involved with the project

Integration of dual-clutch transmissions in hybrid electric vehicle powertrains

This dissertation presents a study focused on exploring the integration of Dual-Clutch Transmissions (DCTs) in Hybrid Electric Vehicles (HEVs). Among the many aspects that could be investigated regarding the electrification of DCTs, research efforts are undertaken here to the development of control strategies for improving vehicle dynamic performance during gearshifts and the energy management of HEVs. In the first part of the dissertation, control algorithms for upshift and downshift maneuvers are developed for a Plug-in Hybrid Electric Vehicle (PHEV) architecture in which an electric machine is connected to the output of the transmission, thus obtaining torque filling capabilities during gearshifts. Promising results, in terms of the vehicle dynamic performance, are obtained for the two transmission systems analyzed: Hybrid Automated Manual Transmission (H-AMT) and Hybrid Dual-Clutch Transmission (H-DCT). On the other hand, the global optimal solution to the energy management problem for a PHEV equipped with a DCT is found by developing a detailed Dynamic Programing (DP) formulation. The main control objective is to reduce the fuel consumption during a driving mission. Based on the DP results, a novel real-time implementable Energy Management Strategy (EMS) is proposed. The performance of such controller, in terms of the overall fuel usage, is close to that of the optimal solution. Furthermore, the developed approach is shown to outperform a well-known causal strategy: Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). One of the main aspects that differentiates the EMSs proposed here to those presented in previous works is the introduction of a model to estimate the energy consumption during gearshifts in DCTs. Thus, this dissertation illustrates how through the electrification of powertrains equipped with DCTs both the vehicle dynamic performance and the energy consumption can be improved

Supervisory model predictive control of building integrated renewable and low carbon energy systems

To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Design and energy management of aircraft hybrid electric propulsion system.

This thesis investigates the design and development of a Hybrid Electric Propulsion System (HEPS) for aircraft. The main contributions of the study are the multi-objective system sizing and the two energy optimization algorithms. First, the system sizing method is employed to design the hybrid electric propulsion system for a prototype aircraft. The sized hybrid propulsion system can ensure that no significant performance is sacrificed and the fuel economy is improved. The novel approach in this work is a new non-dominated sorting algorithm for the Non-dominated Sorting Genetic Algorithm (NSGA). The new algorithm can improve the time complexity of non-dominated sorting process. The optimized hybrid aircraft can save up to 17% fuel, achieve higher cruising speed and rate of climb. It is concluded that the optimal results are more sensitive to the variation of battery energy density than other parameters. Next, the main components of the HEPS are modelled for example. The engine model provides an insight into the inherent relationship between the throttle command and the output torque. Regarding the d-q model of motor/generator, the estimation of torque loss at steady state is achieved using the efficiency map from experiments. The application of Shepherd model leads to the straightforward parameter identification. In this research, both non-causal and causal energy management strategies for HEPS are investigated. The main novelty when studying convex optimization is the proposal of a new lossless convexification, which simplifies the formation of the convexified problem, and the proof of equality between the original problem and convexified problem. The introduced variable—battery internal energy, is proposed to convexify the battery model. The first test case verifies that the convex relaxation does not sacrifice the optimality of the solution nor does the variable change lose the original bounds. Also, the optimal control from convex optimization is demonstrated to be robust to a disturbance in power demand. Comparison with the benchmark optimization—dynamic programming, shows that convex optimization achieves a minimal objective value with much less optimization time. Most significant is that the convexification reduces the optimization computation time to a level compatible with implementation in practical application. In causal control, the main focus is to extend the original Equivalent Consumption Minimization Strategy (ECMS) with the fuzzy control. The proposed algorithm can maintain the battery State of Charge (SoC) in a desirable range, without the requirement of off-line estimation of equivalence factor. By comparing with non-causal control—dynamic programming, the test cases validates that the fuzzy based ECMS succeeds in converting the non-causal optimization, with little sacrifice of the optimality of the solution. In other words, the prior-knowledge of flight mission is not a pre- requisite, and the fuzzy based ECMS can achieve the sub-optimal control for on-line implementation. The fuzzy based ECMS is also validated to outperform the adaptive ECMS, since it can reduce the computation time of optimization and save more fuel usage. The theoretical relationship between the equivalence factor of ECMS and the co-state variable of Hamiltonian function is also demonstrated in this thesis. The convex optimization and fuzzy based ECMS are combined to complete a flight mission with several sub-tasks. Each task has different power and SoC requirements. The test case demonstrates that only the combination of non-causal and causal optimization can satisfy the various constraints and requests of the test scenario. Compared with the engine-only powered aircraft, the hybrid powered aircraft saves 18.7% on fuel consumption. Furthermore, the hybrid propulsion system has better efficiency since it integrates the high efficient electric powertrain.PhD in Aerospac