System and Thermal Modeling of Hydraulic Hybrids: Thermal Characteristics Analysis

Hybrid vehicles have become a popular alternative to conventional powertrain architectures by offering improved fuel efficiency along with various other environmental benefits. Among them, hydraulic hybrid vehicles (HHVs) have several benefits, which make it the superior technology for certain applications over other types of hybrid vehicles, such as lower component costs, more environmentally friendly construction materials, higher power densities, and more regenerative energy available from braking. There have been various studies on HHVs, such as energy management optimization, control strategies for various system configurations, the effect of system parameters on the hybrid system, and proposals for novel hybrid architectures. One area not been thoroughly covered in the past is a detailed modeling and examination of the thermal characteristics for HHVs due to a difficulty of describing the rapid thermal transients in the unsteady state systems. In this dissertation, a comprehensive system and thermal modeling has been studied for hydraulic hybrid transmissions (HHTs). The main motivation behind developing a thermal model of HHTs is to gain a deeper understanding of the system’s thermal performance, and key influencing factors, without relying on experimental data. This will enable HHVs to be designed more efficiently by identifying and addressing potential issues with transmission’s thermal performance prior to hardware testing. Since there exists no thermal study on HHVs in the past, a thermal modeling method has been introduced, which can be applicable to hydraulic hybrid architectures. A thermal modeling methodology based on a novel numerical scheme and accurate theoretical description has been developed in order to capture the rapid thermal transient in the hydraulic system under unsteady state conditions. The model has been applied to a series HHT and validated with measured data from the hardware-in-the-loop (HIL) test rig with a standard driving cycle, FTP-72. In addition, the proposed thermal modeling methodology has been used to analyze and optimize the cooling system of a novel HHV architecture, which is implemented in a sport utility vehicle (SUV) in Maha Fluid Power Research Center. The modeling results have been compared with the measured data while driving the vehicle. In both studies, the simulation results have shown a good correlation with the experimental data in terms of the overall trends and variation ranges. The goal of the developed model is the application to the system and thermal issues in HHVs, such as thermal stability analysis, management of the cooling system, packaging and hydraulic component optimization, and evaluation of thermal characteristics of different architectures. As an advanced topic of this research, thermal management of an open and a closed circuit hydraulic hybrid systems has been studied by simulation. The comparison results show a potential to a better thermal management for the open circuit systems with smaller heat exchangers, as well as less power consumption with incorporation of smaller charge pumps compared to the closed circuit systems. In the future, the developed comprehensive system and thermal modeling method can be applied to different advanced topics, such as analysis of performance and thermal characteristics, systems and components optimization, and systems evaluation with different external conditions, for different hydraulic hybrid systems

Real-Time Stochastic Predictive Control for Hybrid Vehicle Energy Management.

This work presents three computational methods for real time energy management in a hybrid hydraulic vehicle (HHV) when driver behavior and vehicle route are not known in advance. These methods, implemented in a receding horizon control (aka model predictive control) framework, are rather general and can be applied to systems with nonlinear dynamics subject to a Markov disturbance. State and input constraints are considered in each method. A mechanism based on the steady state distribution of the underlying Markov chain is developed for planning beyond a finite horizon in the HHV energy management problem. Road elevation information is forecasted along the horizon and then merged with the statistical model of driver behavior to increase accuracy of the horizon optimization. The characteristics of each strategy are compared and the benefit of learning driver behavior is analyzed through simulation on three drive cycles, including one real world drive cycle. A simulation is designed to explicitly demonstrate the benefit of adapting the Markov chain to real time driver behavior. Experimental results demonstrate the real time potential of the primary algorithm when implemented on a processor with limited computational resources

Topological analysis of powertrains for refusecollecting vehicles based on real routes – Part I: Hybrid hydraulic powertrain

In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on the simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this first part, a characterization of a standard route is performed, analyzing the average power consumption and the most frequent working points of an internal combustion engine (ICE) in real routes. This information is used to define alternative powertrain architectures. A hybrid hydraulic powertrain architecture is proposed and modelled. The proposed powertrain model is executed using two different control algorithms, with and without predictive strategies, with data obtained from real routes. A calculation engine (an algorithm which runs the vehicle models on real routes), is presented and used for simulations. This calculation engine has been specifically designed to analyze if the different alternative powertrain delivers the same performance of the original ICE. Finally, the overall performance of the different architectures and control strategies are summarized into a fuel and energy consumption table, which will be used in the second part of this paper to compare with the different architectures based on hybrid electric powertrain. The overall performance of the different architectures indicates that the use of a hybrid hydraulic powertrain with simple control laws can reduce the fuel consumption up to a 14 %.Postprint (author's final draft

Topological analysis of powertrains for refusecollecting vehicles based on real routes – Part II: Hybrid electric powertrain

In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this second part, three different hybrid electric powertrain architectures are proposed and modeled. These architectures are based on the use of fuel cells, ultracapacitors, and batteries. A calculation engine, which is specifically designed to estimate energy consumption, respecting the original performance as the original internal combustion engine (ICE), is presented and used for simulations and component sizing. Finally, the overall performance of the different architectures (hybrid hydraulic, taken from the first paper part, and hybrid electric, estimated in this second part) and control strategies are summarized in a fuel and energy consumption table. Based on this table, an analysis of the different architecture performance results is carried out. From this analysis, a technological evolution of these vehicles in the medium- and long terms is proposed.Postprint (author's final draft

Influence of Architecture Design on the Performance and Fuel Efficiency of Hydraulic Hybrid Transmissions

Hydraulic hybrids are a proven and effective alternative to electric hybrids for increasing the fuel efficiency of on-road vehicles. To further the state-of-the-art this work investigates how architecture design influences the performance, fuel efficiency, and controllability of hydraulic hybrid transmissions. To that end a novel neural network based power management controller was proposed and investigated for conventional hydraulic hybrids. This control scheme trained a neural network to generalize the globally optimal, though non-implementable, state trajectories generated by dynamic programming. Once trained the neural network was used for online prediction of a transmission’s optimal state trajectory during untrained cycles forming the basis of an implementable controller. During hardware-in-the-loop (HIL) testing the proposed control strategy improved fuel efficiency by up to 25.5% when compared with baseline approaches. To further improve performance and fuel efficiency a novel transmission architecture termed a Blended Hydraulic Hybrid was proposed and investigated. This novel architecture improves on existing hydraulic hybrids by partially decoupling power transmission from energy storage while simultaneously providing means to recouple the systems when advantageous. Optimal control studies showed the proposed architecture improved fuel efficiency over both baseline mechanical and conventional hydraulic hybrid transmissions. Effective system level and supervisory control schemes were also proposed for the blended hybrid. In order to investigate the concept’s feasibility a blended hybrid transmission was constructed and successfully tested on a HIL transmission dynamometer. Finally to investigate controllability and driver perception an SUV was retrofitted with a blended hybrid transmission. Successful on-road vehicle testing showcased the potential of this novel hybrid architecture as a viable alternative to more conventional electric hybrids in the transportation sector

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

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

Electrified Powertrains for a Sustainable Mobility: Topologies, Design and Integrated Energy Management Strategies

This Special Issue was intended to contribute to the sustainable mobility agenda through enhanced scientific and multi-disciplinary knowledge to investigate concerns and real possibilities in the achievement of a greener mobility and to support the debate between industry and academic researchers, providing an interesting overview on new needs and investigation topics required for future developments

Trends and Hybridization Factor for Heavy-Duty Working Vehicles

Reducing the environmental impact of ground vehicles is one of the most important issues in modern society. Construction and agricultural vehicles contribute to pollution due to their huge power trains, which consume a large amount of petrol and produce many exhaust emissions. In this study, several recently proposed hybrid electric architectures of heavy-duty working vehicles are presented and described. Producers have recently shown considerable attention to similar research, which, however, are still at the initial stages of development. In addition, despite having some similarities with the automotive field, the working machine sector has technical features that require specific studies and the development of specific solutions. In this work, the advantages and disadvantages of hybrid electric solutions are pointed out, focusing on the greater electromechanical complexity of the machines and their components. A specific hybridization factor for working vehicles is introduced, taking into account both the driving and the loading requirements in order to classify and compare the different hybrid solutions