Simultaneous Optimization Of Supervisory Control And Gear Shift Logic For A Parallel Hydraulic Hybrid Refuse Truck Using Stochastic Dynamic Programming

The power management controller of a hybrid vehicle orchestrates the operation of onboard energy sources, namely engine and auxiliary power source with the goal of maximizing performance objectives such as the fuel economy. The paper focuses on optimization of the power management strategy of the refuse truck with parallel hydraulic hybrid powertrain. The high power density of hydraulic components and high charging/discharging efficiency of accumulator with no power constraint make hydraulic hybrid an excellent choice for heavy-duty stop and go application. Two power management strategies for a parallel hydraulic hybrid refuse truck are compared; heuristic and stochastic dynamic programming based optimal controller. For designing a SDP based controller, an infinite horizon problem is setup with power demand from driver modeled as random Markov process. The objective is to maximize system level efficiency by optimizing (i) the power split between engine and hydraulic propulsion unit, and (ii) gear shift schedule. This combines the optimization of powertrain parameters with power management design.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89878/1/draft_01.pd

Power management optimisation for hybrid electric systems using reinforcement learning and adaptive dynamic programming

This paper presents an online learning scheme based on reinforcement learning and adaptive dynamic programming for the power management of hybrid electric systems. Current methods for power management are conservative and unable to fully account for variations in the system due to changes in the health and operational conditions. These conservative schemes result in less efficient use of available power sources, increasing the overall system costs and heightening the risk of failure due to the variations. The proposed scheme is able to compensate for modelling uncertainties and the gradual system variations by adapting its performance function using the observed system measurements as reinforcement signals. The reinforcement signals are nonlinear and consequently neural networks are employed in the implementation of the scheme. Simulation results for the power management of an autonomous hybrid system show improved system performance using the proposed scheme as compared with a conventional offline dynamic programming approach

Power Quality Improvement in Fuel Cell Based Hybrid Power System using STATCOM

This paper presents the power quality improvement in a microgrid, supplying a nonlinear and unbalanced load using fuzzy logic controller based static synchronous compensator (STATCOM). The microgrid contains a hybrid combination photovoltaic (PV), fuel cell (FC) along with the battery energy storage system (BESS) where a decentralized power sharing algorithm is used to control its power management. Here, STATCOM is used as a compensator for the hybrid system to reduce the harmonics in the voltage and current profile at the point of common coupling (PCC). The current reference is generated taking into account, the active and reactive power to be supplied by the micro-source which is connected to the compensator. Depending on the power requirement of the nonlinear load, the proposed control scheme can change modes of operation without any external communication interfaces. The proposed control scheme can even compensate system unbalance caused by the single-phase micro sources and load changes. The efficacy of the proposed power quality improvement control and method in such a microgrid is validated through simulation in MATLAB/SIMULINK software with detailed dynamic models of the micro-sources and power electronic converters

Smart power management of a hybrid photovoltaic/wind stand-alone system coupling battery storage and hydraulic network

An off-grid energy system based on renewable photovoltaics (PV) and wind turbines (WT) generators is coupled via converters to electric and hydraulic networks. The electric network is composed of consumers and of a battery bank for electrical storage,while the hydraulic part is made of motor-pumps and hydraulic tanks for water production and desalination. Both battery and water tanks are used to optimize the power management of both electric and hydraulic subsystems by ensuring electric load demand and by reducing at the same time water deficit following the operation of the renewable intermittent source. Thus, both electric and hydraulic subsystems are strongly coupled in terms of energy making necessary to manage the power flows provided by renewable sources to optimize the overall system performance. In this paper, two kinds of management strategies are then compared in the way they share the hybrid power sources between the storage devices (battery and tanks) and the electrical/hydraulic loads. The first approach deals with an “uncoupled power management” in which the operation of electrical and hydraulic loads does not depend on the state of the intermittent renewable sources: in particular, hydraulic pumps are operated only taking account of water demand and tank filling but without considering power sources. On the contrary, given the available power produced by the sources, the second class of strategy (i.e. the “coupled management strategy”) consists of a “smart” power sharing between the electrical and hydraulic networks with regard to the battery SOC and the tank L1 and L2. A dynamic simulator of the hybrid energy system has been developed and tested using a MATLAB environment. The system performance is shown under the two investigated approaches (uncoupled vs coupled). Several tests are carried out using real meteorological data of a remote area and a practical load demand profile. The simulation results show that the “coupled strategy” clearly outperforms the classical “uncoupled” management strategies

Real-time control strategies for hybrid vehicles issued from optimization algorithm

International audienceThis paper focuses on a mild-hybrid city car (Smart), equipped with a starter-alternator, where the kinetic energy in the braking phases can be recovered to be stored in a supercapacitor, and re-used later via the electric motor. The additional traction power allows to downsize the engine and still fulfill the power requirements. Moreover, the engine can be turned off in idle phases. The optimal control problem of the energy management between the two power sources is solved for given driving cycles by a classical dynamic programming method. From dynamic models of the electric motor and supercapacitor a quasistatic model of the whole system is derived and used in the optimization. The real time control law to be implemented on the vehicle is derived from the resulting optimal control strategies

Hybrid fuzzy PI controlled multi-input DC/DC converter for electric vehicle application

Power electronic interface with its effective control scheme plays a major role in the utilization of energy sources for electric vehicle application. For this purpose, a hybrid fuzzy PI based control scheme for a multiple input converter (MIC) topology is proposed. The proposed hybrid fuzzy PI controller includes a conventional PI controller at steady state and fuzzy PI at transient state. Also, the proposed control design helps in tracking a predefined speed profile to have complete realization of electric vehicle. Detailed simulation study and performance comparisons with conventional controller are performed. The results show that the developed control scheme is robust providing bidirectional power management, fast tracking capability with less steady state error, better dynamic response by enhancing the flexibility and proper utilization of energy sources. Simulation in MATLAB/SIMULINK environment is carried out to verify the performance of the multi-input converter with the developed control scheme. An experimental set-up is constructed to validate the same

Power converters and controllers for UPS applications with backup PEM fuel cell

This paper studies the practical cost-effective DC/DC converter, DC/AC inverter and AC/DC rectifier for an uninterruptible power supply (UPS) system with backup proton exchange membrane fuel cell (PEMFC). Furthermore, a comprehensive controller for the PEMFC is designed according to the change of the load, while the energy storage elements, such as battery and ultracapacitor, are chosen in order to compensate the slow dynamic response of PEMFC and to meet the sudden peak load energy demand. The designed power converters can supply high quality power with flexible conversion functions, leading to the establishment of reliable power management for UPS applications. Finally, a suitable control strategy and technique, capable of coping with the change of the load for PEMFC and realizing the energy managements of UPS hybrid system, is implemented. The performances of the proposed power converters and controllers are evaluated by experimental results, showing that the developed UPS system with backup PEMFC and battery power sources is suitable for industry applications. © 2008 IEEE

Dynamic modeling of a hybrid electric system based on an anion exchange membrane fuel cell

The main topic of this paper is the dynamic modeling of a hybrid energy system based on an anion exchange membrane fuel cell (AEMFC). The objective was to develop a dynamic model, able to describe the system behavior and the response of components to sudden load changes. The overall model design includes other different subsystems, represented by electric dipoles or double dipoles. In addition to the two power sources, AEMFC and battery pack, there is a DC-DC buck converter, interjected between them, whose function is to decrease AEMFC voltage to bus voltage set by a battery pack and to smooth this voltage, thereby serving the load properly. Above all, there is a step time-varying dc load and a power management block, for the managing of energy flows through the system. The whole model was implemented in Matlab–Simulink environment. The main outputs of DC-DC buck converter and battery dynamic models were validated respectively with the data of other equally detailed reference simulation models and with experimental data found in the literature. In this paper, the design of a discrete intervals power management strategy to manage the connection/disconnection of the AEMFC was made and the hybrid managed energy system calculation code was used to evaluate the time trends of AEMFC and battery pack electric powers, of battery pack state of charge, of DC-DC buck converter and entire hybrid system efficiencies for different initial battery pack states of charge and different step time-varying dc loads

Real-time Power Management of Hybrid Power Systems in All Electric Ship Applications.

Motivated by the need for achieving flexible shipboard arrangement and meeting future on-board power demand, the concept of all-electric ships (AES) has been pursued. The integrated power systems enable this initiative by providing a common electrical platform for the propulsion and ship-service loads and are a classic example of hybrid power systems (HPS). In order to leverage the complementary dynamic characteristics of the diverse sources, effective power management (PM) is essential to coordinate the sources and energy storage to achieve efficient power generation and fast load following. Although extensive research has been done on the PM of hybrid land vehicles for commercial applications, this problem for shipboard military applications remains largely unaddressed, leading to its exclusive focus in this dissertation. While HPS brings in many opportunities for power management, there are many associated challenges for systems used in military applications since both performance as well as survivability criteria have to be satisfied. While the on-demand goal for the power management problem makes real-time control a key requirement, leveraging the look-ahead opportunities for the shipboard missions makes it difficult to attain this goal. Furthermore, the nonlinearity and the complexity of hybrid power systems, make the optimal control of HPS challenging. In this dissertation, we address real-time power management for the AES and general hybrid power systems targeting military applications. The central theme of this work is the development of power management schemes with real-time computational efficiency by exploring HPS dynamic properties, for improved performance (namely fuel economy and fast load following) during normal mode conditions as well as increased survivability during component failure. A reduced order dynamic HPS model and a scaled test bed is developed as a numerical tool for controller design and validation. The power management (PM) schemes for both normal as well as failure mode conditions are proposed and implemented on a real-time simulator which demonstrated the real-time performance of the proposed method. While the normal mode PM leverages the complementary dynamic characteristics of the HPS for real-time look-ahead control and performance, the failure mode PM uses a reference governor approach for real-time constraint enforcement.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77863/1/gseenuma_1.pd