Ultracapacitor Heavy Hybrid Vehicle: Model Predictive Control Using Future Information to Improve Fuel Consumption

This research is concerned with the improvement in the fuel economy of heavy transport vehicles through the use of high power ultracapacitors in a mild hybrid electric vehicle platform. Previous work has shown the potential for up to 15% improvement on a hybrid SUV platform, but preliminary simulations have shown the potential improvement for larger vehicles is much higher. Based on vehicle modeling information from the high fidelity, forward-looking modeling and simulation program Powertrain Systems Analysis Toolkit (PSAT), a mild parallel heavy ultracapacitor hybrid electric vehicle model is developed and validated to known vehicle performance measures. The vehicle is hybridized using a 75kW motor and small energy storage ultracapacitor pack of 56 Farads at 145 Volts. Among all hybridizing energy storage technologies, ultracapacitors pack extraordinary power capability, cycle lifetime, and ruggedness and as such are well suited to reducing the large power transients of a heavy vehicle. The control challenge is to effectively manage the very small energy buffer (a few hundred Watt-hours) the ultracapacitors provide to maximize the potential fuel economy. The optimal control technique of Dynamic Programming is first used on the vehicle model to obtain the \u27best possible\u27 fuel economy for the vehicle over the driving cycles. A variety of energy storage parameters are investigated to aid in determining the best ultracapacitor system characteristics and the resulting effects this has on the fuel economy. On a real vehicle, the Dynamic Programming method is not very useful since it is computationally demanding and requires predetermined vehicle torque demands to carry out the optimization. The Model Predictive Control (MPC) method is an optimization-based receding horizon control strategy which has shown potential as a powertrain control strategy in hybrid vehicles. An MPC strategy is developed for the hybrid vehicle based on an exponential decay torque prediction method which can achieve near-optimal fuel consumption even for very short prediction horizon lengths of a few seconds. A critical part of the MPC method which can greatly affect the overall control performance is that of the prediction model. The use of telematic based \u27future information\u27 to aid in the MPC prediction method is also investigated. Three types of future information currently obtainable from vehicle telematic technologies are speed limits, traffic conditions, and traffic signals, all of which have been incorporated to improve the vehicle fuel economy

Energy Management of a Battery-Ultracapacitor Hybrid Energy Storage System in Electric Vehicles

Electric vehicle (EV) batteries tend to have accelerated degradation due to high peak power and harsh charging/discharging cycles during acceleration and deceleration periods, particularly in urban driving conditions. An oversized energy storage system (ESS) can meet the high power demands; however, it suffers from increased size, volume and cost. In order to reduce the overall ESS size and extend battery cycle life, a battery-ultracapacitor (UC) hybrid energy storage system (HESS) has been considered as an alternative solution. In this work, we investigate the optimized configuration, design, and energy management of a battery-UC HESS. One of the major challenges in a HESS is to design an energy management controller for real-time implementation that can yield good power split performance. We present the methodologies and solutions to this problem in a battery-UC HESS with a DC-DC converter interfacing with the UC and the battery. In particular, a multi-objective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. This optimization problem is numerically solved for standard drive cycle datasets using Dynamic Programming (DP). Trained using the DP optimal results, an effective real-time implementation of the optimal power split is realized based on Neural Network (NN). This proposed online energy management controller is applied to a midsize EV model with a 360V/34kWh battery pack and a 270V/203Wh UC pack. The proposed online energy management controller effectively splits the load demand with high power efficiency and also effectively reduces the battery peak current. More importantly, a 38V-385Wh battery and a 16V-2.06Wh UC HESS hardware prototype and a real-time experiment platform has been developed. The real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the real-time controller design for the battery-UC HESS. A battery State-of-Health (SoH) estimation model is developed as a performance metric to evaluate the battery cycle life extension effect. It is estimated that the proposed online energy management controller can extend the battery cycle life by over 60%

A distributed real-time power management scheme for shipboard zonal multi-microgrid system

The increasing demands of reducing fuel consumption for marine transportation have motivated the use of high fuel efficiency power plants and the development of power management systems (PMS). Current studies on shipboard PMS are mostly categorized as centralized, which are easy to be implemented and able to converge to the global optimum solutions. However, centralized techniques may suffer from the high computational burden and single-point failures. Considering the future trends of marine vessels toward zonal electrical distribution (ZED), distributed PMS are becoming an alternative choice. To achieve the ship high fuel-efficiency operation under high fluctuated propulsion loads, a real-time distributed PMS is developed in this paper that can acquire as good fuel economy as centralized PMS, but with faster computing speed. With a combination of filter-based, rule-based, and optimization-based approaches in a highly computationally efficient manner, the distributed PMS is constructed based on three layers that guarantees not only high fuel efficiency, but also sufficient energy reservation in different sailing modes and even in faulty conditions. Convergence tests and multiple case studies are conducted to prove the effectiveness of the proposed PMS in terms of fast convergence speed, improved fuel efficiency, and enhanced resilience.Peer ReviewedPostprint (published version

Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the feasibility of the model. In addition, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation

Power and energy management of multiple energy storage systems in electric vehicles

This dissertation contributes to the problem description of managing power and energy of multiple energy sources for electric vehicle power system architectures. The area of power and energy management in the application domain of electric vehicles is relatively new and encompasses several different disciplines. Primarily, the challenges in electric vehicles having multiple energy storage systems lies in managing the energy expenditure, determining the proportional power splits and establishing methods to interface between the energy systems so as to meet the demands of the vehicle propulsion and auxiliary load requirements. In this work, an attempt has been made to provide a new perspective to the problem description of electric vehicle power and energy management. The overall approach to the problem borrows from the basic principles found in conventional management methodology. The analogy between well-known hierarchical management concepts and power and energy management under timing constraints in a general task-graph is exploited to form a well-defined modular power and energy management implementation structure. The proposed methodology permits this multidisciplinary problem to be approached systematically. The thesis introduces a modular power and energy management system (MPEMS). Operation of the M-PEMS is structured as tri-level hierarchical process shells. An Energy Management Shell (EMS) handles the long-term decisions of energy usage in relation to the longitudinal dynamics of the vehicle while processes within a Power Management Shell (PMS) handles the fast decisions to determine power split ratios between multiple energy sources. Finally, a Power Electronics Shell (PES) encompasses the essential power interfacing circuitry as well as the generation of low-level switching functions. This novel framework is demonstrated with the implementation of a power and energy management system for a dual-source electric vehicle powered by lead acid batteries and ultracapacitors. A series of macro simulations of the energy systems validated against practical tests were performed to establish salient operating parameters. These parameters were then applied to the M-PEMS design of a demonstrator vehicle to determine both the general effectiveness of a power and energy management scheme and to support the validity of the new framework. Implementation of the modular blocks that composes the entire system architecture is described with emphasis given to the power electronics shell infrastructure design. The modular structure approach is design-implementation oriented, with the objective of contributing towards a more unified description of the electric vehicle power and energy management problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modeling, control, and design of hybrid electrical and thermal energy storage systems

Advances in power density, energy storage technology, and thermal management are crucial to the increased electrification of vehicles, including those with high ramp rate loads such as heavy construction and military vehicles. In this thesis, a hybrid electro-thermal energy storage system is introduced which offers a power-dense electro-thermal energy storage solution for future electrified vehicles. This energy storage system includes energy-dense batteries and power-dense ultracapacitors for electrical energy storage, and PCM thermal energy storage modules and coolant loops for thermal energy storage. Multi-domain graph-based modeling techniques are used to facilitate modeling, control, and design optimization of the energy storage system. Graph-based models capture multi-domain dynamics in a unified framework. A heuristic control strategy is used, which seeks to protect the energy storage elements while maintaining references. Sizing and control parameters of the electro-thermal energy storage system are optimized using a graph-based optimization framework. Optimized designs demonstrate significant reductions in size while retaining a high level of performance, leading to improvements in power density. A multi-domain optimization formulation is compared to optimization subroutines which individually optimize parameters pertaining the electrical and thermal domains. Additionally, the multi-domain sizing and control optimization study is compared to a similar study in which the control parameters are not optimized. The results accentuate the importance of considering multi-domain dynamics as well as control in the design process for dynamic systems

Design of Energy Management Strategies for a Battery-Ultracapacitor Electric Vehicle

The battery pack is the most expensive component in electric vehicles. Electric vehicles are prone to accelerated battery degradation due to the high charging/discharging cycles and high peak power demand. One solution to this issue would be increasing the battery capacity to meet the high energy requests. However, increasing the battery size is not reasonable due to the high cost and volume. An alternative solution is integrating other energy storage systems into the vehicle powertrain. The additional energy storage system highlights an energy management strategy to distribute the power among onboard energy storage systems effectively. Energy management systems incorporate different strategies classified based on their computational time, implementability in real-time, and measurable performance to be optimized. This thesis considers the case study of Chevy Spark model year 2015 with a hybrid energy storage system including battery and ultracapacitor. First, an overview of diffrent energy storage systems is presented, followed by a review of different hybrid energy storage' configurations. Second, energy management strategies are categorized into three main classifications: rule-based, optimization-based, and data-based algorithms. Third, the selected vehicle model with an embedded rule-based energy management strategy is developed in MATLAB Simulink, and battery performance is validated against available real-world data. Optimal power distribution among battery and ultracapacitor is achieved through an offline global optimal algorithm in chapter 5 in a way to improve battery life. Finally, optimal results are used as a training dataset for an online data-based energy management strategy. Results prove the strategy's effectiveness by improving battery life by an average of 16% compared to the rule-based and 12% difference from the globally optimal strategy on various driving conditions. The proposed energy management strategy provides near-optimal performance while it is real-time implementable and does not need to have beforehand knowledge of driving cycles

Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles

Razavian, R. S., Taghavipour, A., Azad, N. L., & McPhee, J. (2012). Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles. International Journal of Electric and Hybrid Vehicles, 4(3), 260. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2012.050501We propose a real-time optimal controller that will reduce fuel consumption in a series hybrid electric vehicle (HEV). This real-time drive cycle-independent controller is designed using a control-oriented model and Pontryagin's minimum principle for an off-line optimisation problem, and is shown to be optimal in real-time applications. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions, but the amount of information is reduced. Although the controller design procedure explained here is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to find the supervisory controller for a series HEV with an ultra-capacitor. To evaluate the performance of the model-based controller, it is coupled to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. The simulation results show that the simplified control-oriented model is accurate enough in predicting real vehicle behaviour, and final fuel consumption can be reduced using the model-based controller. Such a reduction in HEVs fuel consumption will significantly contribute to nationwide fuel saving.The authors would like to thank the Natural Sciences and Engineering Research Council (NSERC) of Canada, Toyota, and Maplesoft for their support of this research

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies