嵌入式燃料电池车锂离子动力电池管理系统

锂离子动力电池是燃料电池汽车的辅助动力源,合理地对锂离子电池进行管理对燃料电池汽车起了至关重要的作用。文章基于模块化的思想,从燃料电池汽车的整车性能需求出发对车用锂离子电池管理系统的功能结构进行了划分,设计出了相应的硬件系统,并在此基础上,为了保证系统的稳定性和实时性,基于嵌入式实时操作系统μC/OS-II进行了软件系统设计。将管理系统用于实车运行,取得了比较理想的效果。     

A decoupled controller for fuel cell hybrid electric power split

The power management of a hybrid system composed of a fuel cell, a battery and a DC/DC power converter is developed. A decoupled control strategy is proposed, aimed at balancing the power flow between the stack and the battery and avoiding electrochemical damage due to low oxygen concentration in the fuel cell cathode. The controller is composed of two components. The first controller regulates the compressor, and as consequence the oxygen supplied to the cathode, via a classic proportional–integral controller. The second controller optimally manages the current demanded by the fuel cell and battery via linear-quadratic control strategy acting on the converter. The closed-loop performance has been tested both in simulation and in real-time simulation using a microprocessor for the controller.     

A reactive scheduling and control framework for integration of renewable energy sources with a reformer-based fuel cell system and an energy storage device

The main technological barrier in relying solely on renewable energy resources is that the sources such as wind and solar are highly intermittent in availability and result in uncertainty in demand satisfaction. This paper focuses on the integration of these uncertain renewable energy sources along with relatively deterministic energy sources such as reformer based fuel cell and battery. The power mix scenario between these multiple renewable energy sources along with the reformer based fuel cell system, coupled with an energy storage option is envisaged in this paper to ensure undisrupted power supply, to combat the possible intermittent nature of these renewable sources. An appropriate scheduling layer which provides a detailed plan of the optimum contribution of the various available power sources is considered over one week (7 days) duration. A model predictive control (MPC) scheme is deployed at the lower level control layer that receives a measurement of the possible fluctuations or uncertainties in the renewable power sources and maintains a smooth operation of the power generation system through appropriate decisions on generation via the reformer based fuel cell or by exploiting the battery storage, to ensure a delay-free delivery of power to the external load. During real-time operation of the plant, due to the uncertainties in the contribution from solar and wind sources, the power demanded from the fuel cell and the battery is varied accordingly by the MPC layer to meet the overall power demand. The performance of the designed MPC to maintain a smooth delivery of power in both the absence and presence of uncertainties in the renewable energy sources, with and without a reactive feedback between the scheduling and control layers, is illustrated using case studies.     

Real‐time optimal energy management for a fuel cell/battery hybrid system

An energy management strategy is proposed for a class of fuel cell/battery hybrid systems. In such hybrid systems, a fuel cell system is the main power source, and a lithium‐ion battery is the auxiliary power source. In order to manage the system power at the next moment in a reasonable way, a load current filter with bounded estimation errors is designed to estimate the load current. Then, a real‐time optimal energy management algorithm is proposed to optimize economy consumption of the hybrid system. By taking current change rate of the fuel cell and the state of charge into consideration and taking reasonable model simplifications, the optimization problem can be described as a quadratic programming problem. Then a general purpose solver is proposed to solve the quadratic programming problem based on the alternating direction method of multipliers. The efficiency of the proposed solver is much faster than computing interior point method or active set method. Simulation results in MATLAB/SIMULINK are carried out to validate the significant effectiveness and efficiency of the proposed management strategy.     

Control and real-time optimization of an automotive hybrid fuel cell power system

A real-time optimization method is used to find the point of maximum fuel efficiency of a hybrid fuel cell power system, in an automotive application. The controller performs as the master control in cascade with lower level controls. The lower level controls distribute the power between the ultra-capacitor and the fuel cell, during transients, to find the optimum operating point of the power system. The ultra-capacitor is used to protect fuel cell health and improve phase characteristics of the system. In simulations, the controller is able to find the optimum operating point for the hybrid system without requiring previous knowledge of the system dynamics or explicit representation of the optimization function.     

Control strategies for high-power electric vehicles powered by hydrogen fuel cell,battery and supercapacitor

Problems relating to oil supply, pollution, and green house effects justify the need for developing of new technologies for transportation as a replacement for the actual technology based on internal combustion engines (ICE). Fuel cells (FCs) are seen as the best future replacement for ICE in transportation applications because they operate more efficiently and with lower emissions. This paper presents a comparative study performed in order to select the most suitable control strategy for high-power electric vehicles powered by FC, battery and supercapacitor (SC), in which each energy source uses a DC/DC converter to control the source power and adapt the output voltage to the common DC bus voltage, from where the vehicle loads are supplied.Five different controls are described for this kind of hybrid vehicles: a basic control based on three operation modes of the hybrid vehicle depending on the state of charge (SOC) of the battery (operation mode control); a control strategy based on control loops connected in cascade, whose aim is to control the battery and SC SOC (cascade control); a control based on the technique of equivalent fuel consumption, called equivalent consumption minimization strategy (ECMS); and two based on control techniques very used nowadays, the first one of them is a fuzzy logic control and the second one is a predictive control. These control strategies are tested and compared by applying them to a real urban street railway. The simulation results reflect the optimal performance of the presented control strategies and allow selecting the best option for being used in this type of high-power electric vehicles.     

基于PMP算法的HEV能量优化控制策略

针对常用混合动力汽车（Hybrid electric vehicle,HEV）中锂离子电池在功率波动较大时难以满足需求,以及单个驱动周期内HEV燃油能耗大且能量不能很好回收等问题,研究采用锂离子电池和超级电容器混合储能系统（Lithium-ion battery and super-capacitor hybrid energy storage system,Li-SC HESS）与内燃机共同驱动HEV运行.结合比例积分粒子群优化算法（Particle swarm optimization-proportion integration,PSO-PI）控制器和Li-SC HESS内部功率限制管理办法,提出一种改进的基于庞特里亚金极小值原理（Pontryagin's minimum principle,PMP）算法的HEV能量优化控制策略,通过ADVISOR软件建立HEV整车仿真模型,验证该方法的有效性与可行性.仿真结果表明,该能量优化控制策略提高了HEV跟踪整车燃油能耗最小轨迹的实时性,节能减排比改进前提高了1.6%~2%,功率波动时减少了锂离子电池的出力,进而改善了混合储能系统性能,对电动汽车关键技术的后续研究意义重大.     

Hybrid fuel cell and battery tramway control based on an equivalent consumption minimization strategy

This paper focuses on describing a control strategy for a real surface tramway powered by a hybrid system based on fuel cell and battery. This tramway, called Metro Centro, serves the centre of Seville, a city in Spain. Currently, it operates as catenary-powered tramway.The configuration and modeling of all principal components of the hybrid system are briefly described. The models, implemented in MATLAB-Simulink environment, have been designed from commercially available components. The implemented control is based on an equivalent consumption minimization strategy. It allows a suitable energy management of the hybrid system, minimizing the hydrogen consumption.     

Operation mode control of a hybrid power system based on fuel cell/battery/ultracapacitor for an electric tramway

This paper focuses on describing a control strategy for a real tramway, in Zaragoza (Spain), whose current propulsion system is to be replaced by a hybrid system based on fuel cell (FC) as primary energy source and batteries and ultracapacitors (UCs) as secondary energy sources. Due to its slow dynamic response, the FC needs other energy sources support during the starts and accelerations, which are used as energy storage devices in order to harness the regenerative energy generated during brakings and decelerations. The proposed energy management system is based on an operation mode control, which generates the FC reference power, and cascade controls, which define the battery and UC reference powers in order to achieve a proper control of the DC bus voltage and states of charge (SOC) of battery and UC. The simulations, performed by using the real drive cycle of the tramway, show that the proposed hybrid system and energy management system are suitable for its application in this tramway.     

Disturbance rejection control of a fuel cell power plant in a grid-connected system

Interface of a fuel cell plant to power grid is challenging because of the high nonlinearities of the fuel cell plant and the power conditioning system (PCS). This paper focuses on the control of grid-connected solid-oxide fuel cell (SOFC) power plant that is subject to varying load and uncertain network parameters. To this end, Active Disturbance Rejection Control (ADRC) is utilized to improve the performance of the PCS consisting of a dc-dc converter and a dc-ac inverter. ADRC is used in the dc-dc converter to stabilize the dc link voltage and yield a robust performance against the nonlinearity. Used in the dc-ac inverter, ADRC eliminates the steady-state error and is insensitive to the high-frequency noise. Simulation results show that, for grid current control, ADRC achieves a more robust performance than the conventional proportional-integral (PI) controller. Moreover, the total harmonic distortions (THDs) of the output current controlled by ADRC are always below 5% in spite of the variation in the load demand and network parameters.     

Adaptive controller for tracking power profile in a fuel cell powered automobile

In this paper, a model reference adaptive controller is designed using the Lyapunov method, for tracking a time varying power profile in an automobile powered by a fuel cell. The adaptability of the controller is tested by implementing the controller on different power profiles which simulate actual power requirement of different road conditions. The performance of the adaptive controller is compared with a conventional PID controller and it is observed that the adaptive controller has superior performance.     

Model predictive control of a DC–DC converter for battery emulation

A battery emulator is used instead of a real traction battery to supply an electric motor inverter on a test bed for hybrid and electric powertrains under deterministic conditions. The use of virtual battery models eliminates the need for expensive battery prototypes. Virtual battery properties such as chemistry, state of charge or state of health can be changed instantly. However, the control of a battery emulator is a challenging problem. Very fast reference tracking is required for battery impedance emulation but motor inverters act as constant power loads that have a negative influence on the tracking performance and can even lead to instability. In this paper an MPC strategy is proposed for solving this problem. Scheduling of local controllers is utilized in order to handle the nonlinear and destabilizing load. Furthermore, a fast algorithm is presented that allows real-time MPC subject to input and output constraints. Experimental results obtained with a battery emulator supplying an electric drive inverter demonstrate the performance of the stabilizing controller.     

Hybrid Model Predictive Power Management of A Fuel Cell‐Battery Vehicle

This paper considers optimal power management of a fuel cell‐battery hybrid vehicle (FCHV) powertrain having three distinct modal configurations (modes): electric motor propelling/battery discharging, propelling/charging, and generating/charging. Each mode has a distinct set of dynamics and constraints. Using component dynamical/algebraic models appropriate to power flow management, the paper develops a supervisory‐level switched system model as an interconnection of subsystems. Given the model, the paper sets forth a hybrid model predictive control strategy based on a minimization of a performance index (PI) that trades off tracking and fuel economy in each operational mode. Specifically, the PI trades off velocity tracking error, battery state of charge variance, and electric drive and hydrogen fuel usages while penalizing frictional braking to encourage regenerative braking. The optimization is performed using an embedded system model and collocation with matlab 's fmincon to compute mode switches and continuous time controls. The methodology avoids the computational complexity of alternate approaches based on, e.g., mixed integer programming. Projection methods for approximating the switched system solution from the embedded solution are empirically evaluated. To demonstrate the methodology, an example of a FCHV is simulated using three standard velocity driving profiles: a sawtooth profile with a hill climb, the EPA urban dynamic driving schedule, and the New European Driving Cycle. Also, drive cycle fuel usage is compared to that from the Equivalent Consumption Minimization Strategy.     

燃料电池汽车最优氢耗马尔科夫决策控制

本文基于马尔科夫决策过程提出一种燃料电池汽车最优等效氢燃料消耗控制策略.控制策略以部分观测量为基础,以马尔科夫转移概率矩阵为条件,采用基于蒙特卡洛马尔科夫(MCMC)算法的Metropolis-Hastings采样方法,获得平均奖励输出,进而通过最优氢燃料消耗代价函数的优化以控制在氢燃料电池系统和动力电池系统间进行能量分配.该策略避免了目前燃料电池汽车控制策略过度依赖未来需求功率的预测以及预测模型的准确性.在建立燃料电池汽车动力模型,燃料电池系统和动力电池系统模型的基础上,进行了包含自学习系统、基于MH采样的平均奖励过滤系统以及控制选择输出系统的控制策略设计.通过仿真和实验结果表明基于马尔科夫决策控制策略的有效性.     

Combined passivity based control and optimal control for energy management of fuel cell/battery hybrid system

The energy management of hybrid electric vehicles is becoming an interesting topic for many researchers. Furthermore, the wise choice of the energy management strategy allows not only the best distribution of the power between the used sources, but also it allows reduction of consumption, increase in the lifetime of the sources, and improves the autonomy of the hybrid electric vehicle. The autonomy is guaranteed by the optimization of the embedded sources. In this study, the hybrid system consists of combining the fuel cell as the main source with the battery as the auxiliary source. The novelty of the proposed energy management strategy for the studied hybrid system is the combination between interconnection and damping assignment‐passivity based control and the Hamiltonian Jacobi Bellman method. The stability proof is given and the efficiency of the proposed strategy is proved by the experimental work, where the obtained results show the good and adequate results to the proposed scenario.     

Adaptive coordinated control of engine speed and battery charging voltage

In this paper, the control problem of auxiliary power unit (APU) for hybrid electric vehicles is investigated.An adaptive controller is provided to achieve the coordinated control between the engine speed and the battery charging voltage. The proposed adaptive coordinated control laws for the throttle angle of the engine and the voltage of the powerconverter can guarantee not only the asymptotic tracking performance of the engine speed and the regulation of the battery charging voltage, but also the robust stability of the closed loop system under external load changes. Simulation results are given to verify the performance of the proposed adaptive controller.     

Adaptive coordinated control of engine speed and battery charging voltage

In this paper, the control problem of auxiliary power unit （APU） for hybrid electric vehicles is investigated. An adaptive controller is provided to achieve the coordinated control between the engine speed and the battery charging voltage. The proposed adaptive coordinated control laws for the throttle angle of the engine and the voltage of the power-converter can guarantee not only the asymptotic tracking performance of the engine speed and the regulation of the battery charging voltage, but also the robust stability of the closed loop system under external load changes. Simulation results are given to verify the performance of the proposed adaptive controller.     

燃料电池温度模糊自适应控制

如何控制燃料电池温度性能是燃料电池的一个重要问题。首先基于模糊辨识建模方法建立质子交换膜燃料电池温度性能的T-S模型。模型结构简单,精度高,方便地应用于质子交换膜燃料电池系统控制中。其次针对该模型设计电堆温度的模糊自适应控制器。最后在Matlab平台进行仿真,模糊自适应控制器在较大幅度变化的系统参数下都得到较好的控制性能,证明模糊自适应控制系统具有很好的鲁棒性和良好的控制品质,能够满足质子交换膜燃料电池温度控制系统的要求。     

安全操控下5 kW固体氧化物燃料电池电堆预测控制器设计

基于5 kW固体氧化物燃料电池(SOFC)电堆,考虑建模仿真—2温度层模型在模型精度与复杂度上做了更好的折中,可以更有效地应用于控制器设计.本文首先对2温度层模型在常用稳态工作点附近采用泰勒级数展开,获得其状态空间方程.然后考虑其安全操作特性,设计了两种带约束的预测控制器:即面向SOFC电堆的快速负载跟踪与燃料亏空控制器与面向SOFC电堆温度安全的控制器.重点分析了不同切换速率工况下的温度及其梯度、功率以及燃料亏空特性,使得系统在快速进行功率跟踪的同时工作在安全范围以内.结果发现随着电流调节速率的增大,跟踪过程虽然加快,但其存在安全风险也相应增大;此外,安全指标相对避免燃料亏空指标而言,对电流调节速率的要求更加苛刻,在控制器设计时必须综合进行考虑.