Dynamic energy management for a novel hybrid electric system based on driving pattern recognition

This paper focuses on the dynamic energy management for Hybrid Electric Vehicles (HEV) based on driving pattern recognition. The hybrid electric system studied in this paper includes a one-way clutch, a multi-plate clutch and a planetary gear unit as the power coupling device in the architecture. The powertrain efficiency model is established by integrating the component level models for the engine, the battery and the Integrated Starter/Generator (ISG). The powertrain system efficiency has been analyzed at each operation mode, including electric driving mode, driving and charging mode, engine driving mode and hybrid driving mode. The mode switching schedule of HEV system has been designed based on static system efficiency. Adaptive control for hybrid electric vehicles under random driving cycles with battery life and fuel consumption as the main considerations has been optimized by particle swarm optimization algorithm (PSO). Furthermore, driving pattern recognition based on twenty typical reference cycles has been implemented using cluster analysis. Finally, the dynamic energy management strategy for the hybrid electric vehicle has been proposed based on driving pattern recognition. The simulation model of the HEV powertrain system has been established on Matlab/Simulink platform. Two energy management strategies under random driving condition have both been implemented in the study, one is knowledge-based and the other is based on driving pattern recognition. The model simulation results have validated the control strategy for the hybrid electric vehicle in this study in terms of drive pattern recognition and energy management optimization.     

Routing a mix of conventional,plug-in hybrid,and electric vehicles

We introduce an electric vehicle routing problem combining conventional, plug-in hybrid, and electric vehicles. Electric vehicles are constrained in their service range by their battery capacity, and may require time-consuming recharging operations at some specific locations. Plug-in hybrid vehicles have two engines, an internal combustion engine and an electric engine using a built-in rechargeable battery. These vehicles can avoid visits to recharging stations by switching to fossil fuel. However, this flexibility comes at the price of a generally higher consumption rate and utility cost.To solve this complex problem variant, we design a sophisticated metaheuristic which combines a genetic algorithm with local and large neighborhood search. All route evaluations, within the approach, are based on a layered optimization algorithm which combines labeling techniques and greedy evaluation policies to insert recharging stations visits in a fixed trip and select the fuel types. The metaheuristic is finally hybridized with an integer programming solver, over a set partitioning formulation, so as to recombine high-quality routes from the search history into better solutions. Extensive experimental analyses are conducted, highlighting the good performance of the algorithm and the contribution of each of its main components. Finally, we investigate the impact of fuel and energy cost on fleet composition decisions. Our experiments show that a careful use of a mixed fleet can significantly reduce operational costs in a large variety of price scenarios, in comparison with the use of a fleet composed of a single vehicle class.     

Heuristics and lower bounds for minimizing fuel consumption in hybrid-electrical vehicles

In hybrid electric vehicles, the electrical powertrain system has multiple energy sources that it can gather power from to satisfy the propulsion power requested by the vehicle at each instant. This paper focusses on the minimization of the fuel consumption of such a vehicle, taking advantage of the different energy sources. Based on global optimization approaches, the proposed heuristics find solutions that best split the power requested between the multi-electrical sources available. A lower bounding procedure is introduced to validate the quality of the solutions. Computational results show a significant improvement over previous results from the literature in both the computing time and the quality of the solutions.     

A stochastic approach for the optimization of open-loop engine control systems

The operation of sensors and actuators in engine control systems is always affected by errors, which are stochastic in nature. In this paper it is shown that, because of the non-linear interactions between engine performance and control laws in an open-loop engine control system, these errors can give rise to unexpected deviations of control variables, fuel consumption and emissions from the optimal values, which are not predictable in an elementary way.A model for vehicle performance evaluation on a driving cycle is presented, which provides the expected values of fuel consumption and emissions in the case of stochastic errors in sensors and actuators, utilizing only steady-state engine data.The stochastic model is utilized to obtain the optimal control laws; the resultant non-linear constrained minimization problem is solved by an Augmented Lagrangian approach, using a Quasi-Newton technique. The results of the stochastic optimization analysis indicate that significant reductions in performance degradation may be achieved with respect to the solutions provided by the classical deterministic approach.     

Charge depleting range dynamic strategy with power feedback considering fuel-cell degradation

A charge depleting range dynamic strategy considering the fuel-cell degradation is proposed for the plug-in fuel cell hybrid electric vehicles in this paper, to seek the excellent economy performance with various driving ranges. The proposed strategy is developed by means of incorporating an adaptive equivalent factor into equivalent consumption minimization strategy with power feedback control. Firstly, the mathematical modeling of the proposed strategy is formulated to adjust the equivalent factor corresponding to the battery state of charge, which may vary with the forthcoming trip distance to dominate the expected energy consumption. The fuel cell voltage decay rate is applied to the feedback control of the restricted fuel-cell power, which is designed as the degradation model of fuel cell and integrated with the strategy. Secondly, the realization process of the charge depleting range dynamic strategy with restricted power feedback control model is a new attempt to design the real-time control method. Finally, the proposed strategy is validated by a numerical model developed by using the MATLAB/Simulink software and its effectiveness was evaluated by comparing it with the equivalent consumption minimization strategy. A sensitivity analysis is also conducted to study the effects of different trip distances. The effectiveness of power feedback control in fuel cell durability is validated through a hardware-in-the-loop experiment. The verified results demonstrate the influence of the equivalent factor on the control process, which makes the proposed strategy possible to provide significant improvement in the economy performance and fuel-cell durability.     

Battery state-of-charge estimator using the SVM technique

State-of-charge (SOC) is the equivalent of a fuel gauge for a battery pack in an electric vehicle. Determining the state-of-charge becomes an important issue in all battery applications including electric vehicles (EV), hybrid electric vehicles (HEV) or portable devices. The aim of this innovative study is to estimate the SOC of a high capacity lithium iron phosphate (LiFePO₄) battery cell from an experimental data-set obtained in the University of Oviedo Battery Laboratory (UOB Lab) using support vector machine (SVM) approach. The SOC of a battery cannot be measured directly and must be estimated from measurable battery parameters such as current, voltage or temperature. An accurate predictive model able to forecast the SOC in the short term is obtained. The agreement of the SVM model with the experimental data-set confirmed its good performance.     

Extended Fuzzy C-Means and Genetic Algorithms to Optimize Power Flow Management in Hybrid Electric Vehicles

The need for personal transportation must be harmonized by considering the impact of so huge number of vehicles on the environment. The adoption of hybrid electric vehicles can provide a sensible improvement from an environmental viewpoint, but at the same time makes more difficult the definition and implementation of the overall powertrain control mechanism. In fact, powertrain control problems are known to be very complex due to conflicting requirements, and this difficulty augments in case of hybrid electric vehicles. Most of the features of the future hybrid electric vehicles are enabled by a new energy flow management unit designed to split the instantaneous power demand between the internal combustion engine and the electric motor, ensuring both an efficient power supply and reduced emissions. Classic approaches that rely on static thresholds, optimized on a fixed drive cycle, cannot face the high dynamicity and unpredictability of real-life drive conditions. The need to actually control a real vehicle stimulates the research of innovative methodologies for the real-time identification of the operating points of each energy source. This paper is framed into this context: after a brief discussion about a non-conventional formalization of the energy flows problem based on a multiobjective function, a knowledge-based control system for splitting the vehicle's power demand between the engine and motor is presented. The proposed approach exploits a fuzzy clustering criterion that combined with a genetic algorithm, permits to achieve better results, both in terms of a reduced computational effort and an improved efficiency of the control system over various driving cycles. To validate the proposed approach, simulation tests and comparisons with other energy management strategies are discussed.     

Fuzzy-hybrid modelling of an Ackerman steered electric vehicle

Physical system modelling with known parameters together with 2-D or high order look-up tables (obtained from experimental data), have been the preferred method for simulating electric vehicles. The non-linear phenomena which are present at the vehicle tyre patch and ground interface have resulted in a quantitative understanding of this phenomena. However, nowadays, there is a requirement for a deeper understanding of the vehicle sub-models which previously used look-up tables. In this paper the hybrid modelling methodology used for electric vehicle systems offers a two-stage advantage: firstly, the vehicle model retains a comprehensive analytical formulation and secondly, the ‘fuzzy’ element offers, in addition to the quantitative results, a qualitative understanding of specific vehicle sub-models. In the literature several hybrid topologies are reported, sequential, auxiliary, and embedded.In this paper, the hybrid model topology selected is auxiliary and within the same hybrid model, the first paradigm used is the vehicle dynamics together with the actuator/gearbox system. The second paradigm is the non-linear fuzzy tyre model for each wheel. In particular, conventional physical system dynamic modelling has been combined with the fuzzy logic type-II or type-III methodology. The resulting hybrid-fuzzy tyre models were estimated for a-priori number of rules from experimental data. The physical system modelling required the available vehicle parameters such as the overall mass, wheel radius and chassis dimensions. The suggested synergetic fusion of the two methods, (hybrid-fuzzy), allowed the vehicle planar trajectories to be obtained prior to the hardware development of the entire vehicle. The strength of this methodology is that it requires localised system experimental data rather than global system data. The disadvantage in obtaining global experimental data is the requirement for comprehensive testing of a vehicle prototype which is both time consuming process and requires extensive resources. In this paper the authors have proposed the use of existing experimental rigs which are available from the leading automotive manufacturers. Hence, for the ‘hybrid’ modelling, localised data sets were used. In particular, wheel-tyre experimental data were obtained from the University tyre rig experimental facilities. Tyre forces acting on the tyre patch are mainly responsible for the overall electric vehicle motion. In addition, tyre measurement rigs are a well known method for obtaining localised data thus allowing the effective simulation of more detailed mathematical models. These include, firstly, physical system modelling (conventional vehicle dynamics), secondly, fuzzy type II or III modelling (for the tyre characteristics), and thirdly, electric drive modelling within the context of electric vehicles. The proposed hybrid model synthesis has resulted in simulation results which are similar to piece-wise ‘look-up’ table solutions. In addition, the strength of the ‘hybrid’ synthesis is that the analyst has a set of rules which clearly show the reasoning behind the complex development of the vehicle tyre forces. This is due to the inherent transparency of the type II and type III methodologies. Finally, the authors discussed the reasons for selecting a type-III framework. The paper concludes with a plethora of simulation results.     

Modelling of Heat Transfer and Vehicle Dynamics for Thermal Load Reduction by Hypersonic Flight Optimization

Heat input reduction by appropriate, optimal trajectory control is considered for the range cruise and the return-to-base cruise of a hypersonic vehicle propelled by a turbo/ram jet engines combination. A mathematical model is developed for describing the unsteady heat transfer through the thermal protection system. This model is coupled to the model of the dynamics of the vehicle. An efficient optimization technique is applied for constructing a solution for the two cruise problems. The results show that significant heat input reductions can be achieved with only a small penalty in fuel consumption.     

The two-echelon capacitated electric vehicle routing problem with battery swapping stations: Formulation and efficient methodology

In this paper, we present a two-echelon capacitated electric vehicle routing problem with battery swapping stations (2E-EVRP-BSS), which aims to determine the delivery strategy under battery driving range limitations for city logistics. The electric vehicles operating in the different echelons have different load capacities, battery driving ranges, power consumption rates, and battery swapping costs. We propose an integer programming formulation and a hybrid algorithm that combines a column generation and an adaptive large neighborhood search (CG-ALNS) to solve the problem. We conducted extensive computational experiments, demonstrate the applicability of the proposed model, and show the efficiency of the CG-ALNS algorithm. In addition, we explore the interplay between battery driving range and the effectiveness of vehicle emission reduction through sensitivity analysis.     

Integrated design and optimization of a complex nonlinear hybrid electric powertrain including simulations in time domain

This paper deals with the design and optimization of hybrid electric powertrains. Therefore basic relations of the behavior of hybrid electric powertrain systems and the controller design are introduced. Based on models of typical hybrid electric system components principal optimization approaches with respect to performance parameters like efficiency, availability, lifetime, etc. are shown. Hereby an optimization algorithm based on a global optimization technique is applied. Using the example of a fuel cell based hybrid electric powertrain system the approaches are introduced and compared to each using time-domain simulations integrated in optimization algorithms. The results show that both approaches are appropriate to design the system as well as the controllers. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Locating Hybrid Fuel Cell–Turbine Power Generation Units under Uncertainty

Hybrid gas turbine–solid oxide fuel cell power generation has the potential to create a positive economic and environmental impact. Annually, the U.S. spends over $235 billion on electricity, and electric utilities emit 550 million metric tons of carbon. The integration of distributed hybrid generation can reduce these emissions and costs through increased efficiencies. In this paper, a model is presented that minimizes the costs of distributed hybrid generation while optimally locating the units within the existing electric infrastructure. The model utilizes data from hybrid generation modules, and includes uncertainty in customer demand, weather, and fuel costs.     

Routing vehicles to minimize fuel consumption

We consider a generalization of the capacitated vehicle routing problem known as the cumulative vehicle routing problem in the literature. Cumulative VRPs are known to be a simple model for fuel consumption in VRPs. We examine four variants of the problem, and give constant factor approximation algorithms. Our results are based on a well-known heuristic of partitioning the traveling salesman tours and the use of the averaging argument.     

Hybrid modeling approach for vehicle frame coupled with nonlinear dampers

The vehicle frame system comprises frame structure and nonlinear dampers. In order to investigate the effects of frame flexibility and nonlinear hysteresis, a hybrid modeling approach for vehicle frame coupled with nonlinear dampers will be proposed. Before that, a complex model for nonlinear damper is developed consisting of knowledge-based model and support vector machine (SVM) model. The frame structure is modeled by FEM where the SVM complex model of damper is embedded in. Thus a hybrid model for vehicle frame system is established and successfully validated via a dummy vehicle riding in different conditions. The results show that the hybrid model can capture the nonlinear dynamic characteristics accurately. The hybrid model can also provide a basis for structural design with the existing of FEM model.     

Hybrid or electric vehicles? A real options perspective

This paper investigates the decision of an automaker concerning the alternative promotion of a hybrid vehicle (HV) and a full electric vehicle (EV). We evaluate the HV project by considering the option to change promotion from the HV to the EV in the future. The results not only extend previous findings concerning American options on multiple assets, but also include several new implications. One notable observation is that increased market demand for EVs can accelerate the promotion of the HV because of the embedded option.     

带时间窗偏好的同时配集货且需求可拆分车辆路径问题

针对带时间窗偏好的同时配集货且需求可拆分车辆路径问题,最小化派遣成本、理货成本、时间窗惩罚成本以及油耗成本之和,建立数学模型。设计混合遗传变邻域搜索算法求解问题,在算法中引入时空距离的理念,首先用最近邻插入法和Logistic映射方程生成初始种群;然后利用变邻域搜索算法的深度搜索能力优化算法;提出自适应搜索策略,平衡种群进化所需的广度和深度;设计拆分准则,为各客户设置不同的拆分服务量;提出确定车辆最优出发时间的时差推移法,减少车辆在客户处的等待时间;最后通过多组算例验证本文模型和算法的有效性。     

Online powermanagement optimization of a fuel cell-based hybrid electric powertrain

In this paper an online optimization approach of the powermanagement of a fuel cell-based hybrid electric powertrain system is desribed. Therefore two online optimization algorithms of the powermanagement control parameters are introduced and compared experimentally. The first one deals with parameters rating the performance of the system with respect to efficiency, availability, and lifetime. Using a pre-defined load profile the optimal parameters can be determined for a given powermanagement. The second approach is based on the statistical properties of a predicted and online-updated measured load profile defining the actual load profile characteristics. Depending on this load profile and with the knowledge of the design parameters of the system appropriate adaptive powermanagement parameters are determined. It becomes clear that the performance of the system with respect to the mentioned system properties can be improved by these approaches. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Trajectory planning for unmanned aerial vehicles: a network optimization approach

This paper presents a new approach for trajectory planning of air vehicles. It considers scenarios with risk areas and forbidden zones and takes into account the maneuverability of the air vehicle. It is flexible as to allow different kinds of objective functions such as minimizing risk, flight path length or flight time, and allows to implement constraints on fuel consumption or other resources. Additionally, it can incorporate waypoints to be passed by the air vehicle with or without specified overflight directions. The method includes planning of one-way and return trips. The underlying model is based on a discretization of the airspace into a non-regular network. Every path in the network corresponds to a flyable trajectory which means that the trajectory is within the performance limits of the air vehicle. The generation of the network is done non-deterministically. One of the main benefits of the model is that one can make use of standard network optimization techniques.     

A hybrid feedback control strategy for autonomous waypoint transitioning and loitering of unmanned aerial vehicles

We consider the problem of autonomously controlling a fixed-wing aerial vehicle to visit a neighborhood of a pre-defined waypoint, and when nearby it, loiter around it. To solve this problem, we propose a hybrid feedback control strategy that unites two state-feedback controllers: a transit controller capable of steering or transitioning the vehicle to nearby the waypoint and a loiter controller capable of steering the vehicle about a loitering radius. The aerial vehicle is modeled on a level flight plane with system performance characterized in terms of the aerodynamic, propulsion, and mass properties. Thrust and bank angle are the control inputs. Asymptotic stability properties of the individual control algorithms, which are designed using backstepping, as well as of the closed-loop system, which includes a hybrid algorithm uniting the two controllers, are established. In particular, for this application of hybrid feedback control, Lyapunov functions and hybrid systems theory are employed to establish stability properties of the set of points defining loitering. The analytical results are confirmed numerically by simulations.