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

Hybrid vehicle assessment. Phase 1: Petroleum savings analysis

The results of a comprehensive analysis of near term electric hybrid vehicles are presented, with emphasis on their potential to save significant amounts of petroleum on a national scale in the 1990s. Performance requirements and expected annual usage patterns of these vehicles are first modeled. The projected U.S. fleet composition is estimated, and conceptual hybrid vehicle designs are conceived and analyzed for petroleum use when driven in the expected annual patterns. These petroleum consumption estimates are then compared to similar estimates for projected 1990 conventional vehicles having the same performance and driven in the same patterns. Results are presented in the form of three utility functions and comparisons of sevral conceptual designs are made. The Hybrid Vehicle (HV) design and assessment techniques are discussed and a general method is explained for selecting the optimum energy management strategy for any vehicle mission battery combination. Conclusions and recommendations are presented, and development recommendations are identified

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles has accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. The design and implementation of an optimized control strategy is a complex challenge. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. Real-time strategies incorporate methods such as drive cycle prediction algorithms, parameter feedback, driving pattern recognition algorithms, etc. The goal of this work is to use a previously defined strategy which has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS) [1], which uses an equivalence factor to define the control strategy. The equivalence factor essentially defines the torque split between the electric motor and internal combustion engine. Consequently, the equivalence factor greatly affects fuel economy. An equivalence factor that is optimal (with respect to fuel economy) for a single drive cycle can be found offline – with a priori knowledge of the drive cycle. The RBF ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data are used to train the RBF ANN, each set contains characteristics from a different drive cycle. Each drive cycle is characterized by 9 parameters. For each drive cycle, the optimal equivalence factor is determined and included in the training data. The performance of the RBF ANN is evaluated against the fuel economy obtained with the optimal equivalence factor from the ECMS. For the majority of drive cycles examined, the RBF ANN implementation is shown to produce fuel economy values that are within +/- 2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF ANN is that it does not require a priori drive cycle knowledge and is able to be implemented real time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF ANN could be improved to produce better results across a greater array of driving conditions

Electromobility Studies Based on Convex Optimization DESIGN AND CONTROL ISSUES REGARDING VEHICLE ELECTRIFICATION

This article presents a framework to study design tradeoffs in the search for electromobility solutions based on approximate modeling of the power flows in the powertrain as a function of component sizes. An important consequence of the modeling assumptions is that the optimal energy management and component sizes can be computed simultaneously in a convex program, which means that competing designs can be evaluated in an objective way, avoiding the influence of a separate control system design. The fact that the optimization problem is convex allows large problems to be solved with moderate computational resources, which can be exploited by, for example, running optimizations over very long driving cycles. The problem formulation also admits design decisions for the charging infrastructure to be included in the optimization

Battery States Monitoring and its Application in Energy Optimization of Hybrid Electric Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

Modeling and Control Strategy for Hybrid Electrical Vehicle

This chapter reviews the developments and configurations of hybrid electrical vehicles. A classic model for a parallel hybrid electrical vehicle is chosen and modeled. Model predictive controllers and simulations for this vehicle model are applied to control the vehicle speed and power to check the ability of the system to handle the transitional period for the automatic clutch engagement from the electrical driving to the internal combustion engine (ICE) driving. The chapter produces potential model predictive control considerations to achieve the optimal real-time control actions subject to the vehicle physical constraints. The new system can be applied for electronic control units in real hybrid vehicle powertrains

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

Three-phase modular permanent magnet brushless machine for torque boosting on a downsized ICE vehicle

The paper describes a relatively new topology of 3-phase permanent magnet (PM) brushless machine, which offers a number of significant advantages over conventional PM brushless machines for automotive applications, such as electrical torque boosting at low engine speeds for vehicles equipped with downsized internal combustion engine (ICEs). The relative merits of feasible slot/pole number combinations for the proposed 3-phase modular PM brushless ac machine are discussed, and an analytical method for establishing the open-circuit and armature reaction magnetic field distributions when such a machine is equipped with a surface-mounted magnet rotor is presented. The results allow the prediction of the torque, the phase emf, and the self- and mutual winding inductances in closed forms, and provide a basis for comparative studies, design optimization and machine dynamic modeling. However, a more robust machine, in terms of improved containment of the magnets, results when the magnets are buried inside the rotor, which, since it introduces a reluctance torque, also serves to reduce the back-emf, the iron loss and the inverter voltage rating. The performance of a modular PM brushless machine equipped with an interior magnet rotor is demonstrated by measurements on a 22-pole/24-slot prototype torque boosting machine

A Study on the Integration of a High-Speed Flywheel as an Energy Storage Device in Hybrid Vehicles

The last couple of decades have seen the rise of the hybrid electric vehicle as a compromise between the outstanding specific energy of petrol fuels and its low-cost technology, and the zero tail-gate emissions of the electric vehicle. Despite this, considerable reductions in cost and further increases in fuel economy are needed for their widespread adoption. An alternative low-cost energy storage technology for vehicles is the high-speed flywheel. The flywheel has important limitations that exclude it from being used as a primary energy source for vehicles, but its power characteristics and low-cost materials make it a powerful complement to a vehicle's primary propulsion system. This thesis presents an analysis on the integration of a high-speed flywheel for use as a secondary energy storage device in hybrid vehicles. Unlike other energy storage technologies, the energy content of the flywheel has a direct impact on the velocity of transmission. This presents an important challenge, as it means that the flywheel must be able to rotate at a speed independent of the vehicle's velocity and therefore it must be coupled via a variable speed transmission. This thesis presents some practical ways in which to accomplish this in conventional road vehicles, namely with the use of a variator, a planetary gear set or with the use of a power-split continuously variable transmission. Fundamental analyses on the kinematic behaviour of these transmissions particularly as they pertain to flywheel powertrains are presented. Computer simulations were carried out to compare the performance of various transmissions, and the models developed are presented as well. Finally the thesis also contains an investigation on the driving and road conditions that have the most beneficial effect on hybrid vehicle performance, with a particular emphasis on the effect that the road topography has on fuel economy and the significance of this

Supervisory Controller Validation For A Plug-In Parallel-Through-The-Road Hybrid Electric Vehicle By Software-In-The-Loop Testing

The goal of this research is to develop an operational supervisory controller for Wayne State University Hybrid Warriors\u27 hybrid electric vehicle architecture that can be transitioned easily to a hardware-in-the-loop testing environment for the 2011-2014 EcoCAR2 competition. It serves to demonstrate how model-based design, specifically software-in-the-loop testing, is effective for the initial steps in design, verification, and validation of a supervisory control strategy. Overall, the supervisory controller aims to meet all safety and functional requirements while reducing fuel consumption. The thesis starts by presenting a plug-in parallel-through-the-road architecture and its powertrain hardware components. Next, characteristics and capabilities of all significant powertrain components are explained along with the implementation of the vehicle plant model. Initial stages and preparations for the development of supervisory controller begin with applying the Design Failure Mode and Effects Analysis and identifying the functional vehicle requirements. Control strategies implemented within the supervisory controller are discussed in detail. Finally, results from the software-in-the-loop testing as well as safety critical fault mitigation are shown, to demonstrate the end product of a supervisory controller that has reached a high level of functionality and safety and therefore is ready for hardware-in-the-loop testing. Outlines are provided for extending the current work into next phases of hardware-in-the-loop testing, optimization using vehicle-in-the-loop results, and special applications such as cold-start