Cost of ownership-efficient hybrid electric vehicle powertrain sizing for multi-scenario driving cycles

During the last decade, hybrid electric vehicles have gained a presence in the automotive market. On the streets, in motorsports and in society, hybrid electric vehicles are increasingly common. Many manufacturers have become involved in hybrid electric vehicles, while others have hybrid electric vehicle projects in development. Thus, there is already a great variety of hybrid electric vehicles in production, from small microhybrid vehicles to range extenders. Although there are some hybrid electric vehicles designed for urban driving or luxury segments of the market, most of the market share is aimed to the same kind of use and driving, resulting in potentially subsized or oversized hybrid systems that could lead to inefficient use of the vehicle's fuel-saving capabilities in many situations. The present work studies the influence of the sizes of the powertrain components (i.e. the engine, the motor and the battery) on the fuel economy under different assumptions: city driving, highway driving and mixed driving. The utilized framework permits the calculation of the theoretically optimum powertrain sizes assuming a particular target. Different drivers and different traffic conditions are also evaluated. Finally, a long-term cost evaluation is carried out to estimate the optimal sizes of the hybrid electric vehicle powertrain as functions of the type of use of the vehicle throughout its life cycle.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been supported by Conselleria de Educacio Cultura i Esports de la Generalitat Valenciana through Project GV/2013/044 AECOSPH.Luján, JM.; Guardiola, C.; Pla Moreno, B.; Reig, A. (2016). Cost of ownership-efficient hybrid electric vehicle powertrain sizing for multi-scenario driving cycles. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 230(3):382-394. doi:10.1177/0954407015586333S382394230

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Modeling and Design Optimization of Plug-In Hybrid Electric Vehicle Powertrains

Hybrid electric vehicles (HEVs) were introduced in response to rising environmental challenges facing the automotive sector. HEVs combine the benefits of electric vehicles and conventional internal combustion engine vehicles, integrating an electrical system (a battery and an electric motor) with an engine to provide improved fuel economy and reduced emissions, while maintaining adequate driving range. By comparison with conventional HEVs, plug-in hybrid electric vehicles (PHEVs) have larger battery storage systems and can be fully charged via an external electric power source such as the electrical grid. Of the three primary PHEV architectures, power-split architectures tend to provide greater efficiencies than parallel or series systems; however, they also demonstrate more complicated dynamics. Thus, in this research project, the problem of optimizing the component sizes of a power-split PHEV was addressed in an effort to exploit the flexibility of this powertrain system and further improve the vehicle's fuel economy, using a Toyota plug-in Prius as the baseline vehicle. Autonomie software was used to develop a vehicle model, which was then applied to formulate an optimization problem for which the main objective is to minimize fuel consumption over standard driving cycles. The design variables considered were: the engine's maximum power, the number of battery cells and the electric motor's maximum power. The genetic algorithm approach was employed to solve the optimization problem for various drive cycles and an acceptable reduction in fuel consumption was achieved thorough the sizing process. The model was validated against a MapleSim model. This research project successfully delivered a framework that integrates an Autonomie PHEV model and genetic algorithm optimization and can be used to address any HEV parameter optimization problem, with any objective, constraints, design variables and optimization parameters.1 yea

Design optimisation and real-time energy management control of the electrified off-highway vehicle with artificial intelligence

Targeting zeros-emissions in transportation, future vehicles will be more energy-efficient via powertrain electrification. This PhD research aims to optimise an electrified off-highway vehicle to achieve the maximum energy efficiency by exploring new artificial intelligence algorithms. The modelling study of the vehicle system is firstly performed. Offline design optimisation and online optimum energy management control methodologies have been researched. New optimisation methods are proposed and compared with the benchmark methods. Hardware-in-the-Loop testing of the energy management controller has been carried out for validation of the control methods. This research delivers three original contributions: 1) Chaos-enhance accelerated particle swarm optimisation algorithm for offline design optimisation is proposed for the first time. This can achieve 200% higher reputation-index value compared to the particle swarm optimisation method. 2) Online swarm intelligent programming is developed as a new online optimisation method for model-based predictive control of the vehicle energy-flow. This method can save up to 17% energy over the rule-based strategy. 3) Multi-step reinforcement learning is researched for a new concept of ‘model-free’ predictive energy management with the capability of continuously online optimisation in real-world driving. It can further save at least 9% energy

Optimierung von Brennstoffzellen-Hybridfahrzeugen

The limited fossil fuel resources and the environmental concerns associated with burning those fossil fuels lie behind the increasing interest in hydrogen as a clean and sustainable alternative to fossil fuels, and in fuel cells as a clean converter of hydrogen into electrical energy especially in the transportation sector. Fuel cell hybrid vehicles (FCHVs) are characterized by the use of a fuel cell system (FCS) as the main power source and a battery, a supercapacitor or both as an energy storage system (ESS). Hybridizing the FCS with an ESS significantly improves the hydrogen economy, helps downsize the FCS, and resolves the issues related the long start-up time and slow dynamics of the FCS. The existence of multiple power sources in the powertrain gives rise to two important questions: How to coordinate the power contribution of the sources (i.e., power management strategy (PMS)), and how to size these sources in order to exploit the advantages of hybridization. The goal of this thesis is to develop a comprehensive framework for the optimization of PMS and size of FCHV powertrains. Depending on the type of ESS, three topologies are considered: fuel cell/ battery, fuel cell/ supercapacitor, and fuel cell/ battery/ supercapacitor. The PMS optimization is investigated on two levels; i.e., the vehicle level by simulation and the developed optimization algorithms are then validated on a small-scale test bench. When the driving cycle is known a priori, the off-line optimal PMS that globally minimizes the hydrogen consumption is calculated by two algorithms, namely, Dynamic Programming (DP) and Pontryagin’s Minimum Principle (PMP), and the two algorithms are compared. It has been found that PMP can be a superior approach for off-line optimization since it requires negligible computation resources without sacrificing the global optimality. The off-line optimal strategy is not real-time capable; hence, real-time strategies are designed and optimized while using the off-line optimal PMS as a benchmark. Special emphasize is put on the inclusion of multiple driving cycles, of different nature, in the optimization of the real-time PMS to increase its robustness. The sizing of the power sources of fuel cell/ battery and fuel cell/ supercapacitor hybrids considers hydrogen consumption and powertrain cost as two objectives and takes into account the drivability constraints such as top speed, gradeablity and acceleration time. The interesting designs (i.e., FCS size and ESS size), which represent the most efficient trade-off between the objectives, are then extracted and analyzed. The effect of battery aging on the optimal powertrain size is investigated by an Ampere-hour throughput model. It has been found that the battery aging leads to less efficient powertrain designs and the supercapacitor can become a more efficient option in comparison to batteries of poor lifetime.Die begrenzten fossilen Ressourcen und die Umweltsorgen, die mit der Verbrennung dieser fossilen Brennstoffe verbunden sind, stecken hinter dem steigenden Interesse am Wasserstoff als sauberer und nachhaltiger Alternative, und an Brennstoffzellen als sauberen Wandlern des Wasserstoffs in elektrische Energie, vor allem im Verkehrssektor. Ein Brennstoffzellen-Hybridfahrzeug (FCHV) verwendet ein Brennstoffzellensystem (FCS) als eine Hauptenergiequelle und eine Batterie, einen Superkondensator oder beide als Energiespeichersystem (ESS). Hybridisierung des FCS mit einem ESS verringert erheblich den Wasserstoffverbrauch, hilft das FCS zu verkleinern, und behebt das Problem der langen Anlaufzeit und der langsamen Dynamik des FCS. Die Existenz von mehreren Stromquellen im Antriebsstrang wirft zwei wichtige Fragen auf: Wie ist die Leistungsanforderung des Fahrzeugs zwischen den Quellen zu verteilen (d.h. Power-Management-Strategie (PMS)) und wie sind diese Quellen zu dimensionieren, um die Hybridisierung auszunutzen. Das Ziel dieser Arbeit ist es, einen umfassenden Rahmen für die Optimierung der PMS und Dimensionierung der Brennstoffzellen-basierten hybriden Antriebsstränge zu entwickeln. Abhängig von der Art des ESS werden drei Topologien berücksichtigt: Brennstoffzelle/ Batterie, Brennstoffzelle/ Superkondensator und Brennstoffzelle/ Batterie/ Superkondensator. Die PMS-Optimierung wird auf zwei Ebenen untersucht, und zwar die Fahrzeugebene durch Simulation und die Prüfstandsebene, worauf die entwickelten Optimierungsalgorithmen experimentell validiert werden. Wenn der Lastzyklus im Voraus bekannt ist, kann die offline optimale PMS, die den Wasserstoffverbrauch global minimiert, berechnet werden. Dazu werden die zwei Algorithmen, Dynamische Programmierung (DP) und Pontryagins Minimumprinzip (PMP), verglichen. Es wurde herausgefunden, dass das PMP ein überlegener Ansatz für die offline-Optimierung sein kann, da es viel weniger Rechenressourcen braucht, ohne die globale Optimalität zu opfern. Die offline optimale Strategie ist nicht echtzeitfähig, und deshalb werden Echtzeit-Strategien entworfen und optimiert, indem die offline optimale PMS als Maßstab verwendet wird. Beim Designen der echtzeitfähigen Strategien werden mehrere Fahrzyklen unterschiedlicher Natur beachtet, um die Robustheit der Strategien zu erhöhen. Die Dimensionierung der Stromquellen der Brennstoffzelle/ Batterie und Brennstoffzelle/ Superkondensator Hybriden betrachtet den Wasserstoffverbrauch und die Kosten des Antriebsstrangs als zwei Ziele. Es wird dabei die Fahrbarkeit, d.h. Höchstgeschwindigkeit, Steigfähigkeit und Beschleunigungszeit, berücksichtigt. Die interessanten Konfigurationen (FCS-Größe und ESS-Größe), die den effizientesten Kompromiss zwischen den Zielen darstellen, werden dann herausgefunden und analysiert. Die Wirkung der Batteriealterung auf die optimale Antriebsstrang-Größe wird durch ein Ampere-Stunden-Durchsatzmodell untersucht. Es wurde herausgefunden, dass die Batterie-Alterung weniger effiziente Antriebsstrang-Konfigurationen ergibt, und dass der Superkondensator eine effizientere Alternative zur Batterie sein kann, wenn er mit Batterien von schlechter Lebensdauer verglichen wird