Predictive energy management of hybrid long-haul trucks

This paper presents a novel predictive control scheme for energy management in hybrid trucks that drive autonomously on the highway. The proposed scheme uses information from GPS together with information about the speed limits along the planned route to schedule the charging and discharging of the battery, the vehicle speed, the gear, and when to turn off the engine and drive electrically. The proposed control scheme divides the predictive control problem into three layers that operate with different update frequencies and prediction horizons. The top layer plans the kinetic and electric energy in a convex optimization problem. In order to avoid a mixed-integer problem, the gear and the switching decision between hybrid and pure electric mode are optimized in a lower layer in a dynamic program whereas the lowest control layer only reacts on the current state and available references. The benefits of the proposed predictive control scheme are shown by simulations between Frankfurt and Koblenz. The simulations show that the predictive control scheme is able to significantly reduce the mechanical braking, resulting in fuel reductions of 4% when allowing an over and under speed of 5 km/h.     

电动汽车用飞轮电池充放电控制系统研究

针对现有电池技术还不能满足电动汽车对电池系统能量密度和功率密度还有寿命的要求,本文结合蓄电池的高比能量和飞轮电池的高比功率,提出了基于蓄电池和飞轮电池的复合电源。建立了飞轮电池的充放电的数学模型,并提出了飞轮电池充放电的控制策略。飞轮充电过程中采用模糊-PI复合控制策略,放电过程中根据驱动电机需要的瞬时电流值来控制斩波器开关的通断。结果表明系统具有较好的动态特性。     

基于模糊逻辑算法的纯电动客车起步加速能力优化与再生制动控制

日益严重的环境问题促使城市交通向着清洁、高效和可持续的方向发展,同时也促进了新能源交通技术的推广和应用。随着电池和电机驱动技术的发展,纯电动客车也受到越来越多的关注。起步加速能力和可再生制动是纯电动公交车区别于传统内燃机车的两个方面。由于加速踏板信号响应与驱动电机响应较快,理论上纯电动客车的加速性能要优于传统内燃机车。再生制动是一种降低能耗、提高续驶里程的重要技术手段。文章基于模糊逻辑算法,设计了驱动扭矩控制策略对驱动工况下的纯电动客车起步加速能力进行优化。同时,针对纯电动客车制动工况设计了能量回收策略。结果表明,驱动扭矩控制策略可使纯电动客车起步加速时间从19.7 s减小至19.25 s,制动能量回收策略在中国典型城市公交路况下使能量消耗减少11%。     

信息融合架构下的新型再生制动控制策略

再生制动能够实现能量的回收利用,是电动汽车重要的工作模式之一.现有的制动力分配方案对蓄电池和电机的限制因素考虑不够充分,能量回收效率和制动效能较低.对此,提出一种基于信息融合架构下的新型再生制动控制策略.在蓄电池和电机限制因素的基础上,综合考虑增加电动车的续驶里程和制动时的舒适性、安全性等因素,对于电动汽车的不同行驶工况具有自适应性,能够实现能量高效回收;对车辆行驶速度和制动强度进行特征提取和制动模式分类,从而根据特征匹配结果切换制动模式.最后,通过搭建Matlab/Simulink整车动力学仿真模型,验证所提出控制策略的有效性和先进性.     

并联新能源汽车最优效率实时控制

为了提高并联混合动力汽车驱动系统的实时效率,降低燃油消耗,本文提出一种基于效率最优的协调控制策略.根据不同驱动模式下电池的充放电状态,建立了充放电状态下驱动系统的等效燃油消耗模型,在分析电池效率和发动机效率的基础上,得到驱动系统效率的统一表达式,进而通过建立不同功率需求不同荷电状态下系统最优效率的功率分配系数图谱,设计了系统效率最优的协调控制策略,协调控制策略根据优化的功率分配系数在发动机和电机间进行力矩分配,协调控制策略可以离线计算并实时执行.两种工况循环下的仿真结果表明效率最优控制策略能有效地提高混合动力系统实时效率和燃油经济性.     

Shortest path stochastic control for hybrid electric vehicles

When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP‐SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite‐horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time‐invariant causal state‐feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite‐horizon case, and for the same level of complexity as a discounted future‐cost controller, the shortest‐path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd.     

基于CVT的混合动力汽车建模与仿真

建立了基于无级变速器 (Continously Variable Transmission,CVT) 的前向并联式混合动力电动汽车动力系统模型,为了研究整车动力性、经济性,根据行驶动力学方程,采用极值原理和曲面拟合法对发动机台架试验得到的数据进行了多项式拟合,建立了发动机万有特性与最佳操作曲线(Optimal Operating Line,OOL) 模型,并建立了牵引用三相感应电机动力模型以及牵引蓄电池(State of Charge,SOC)模型.同时,提出了燃油消耗最低、蓄电池充放电平衡的能量分配控制策略,进行整车动力性仿真计算,仿真结果表明在保证循环结束电池充放电基本平衡的同时发动机燃油消耗最低,仿真试验对比结果验证了建立的模型的精确性.     

多能源电动汽车中的Fuzzy控制策略及仿真

多能源电动汽车的能量存储系统由锌空电池、镍氢电池和超大电容三种能量存储元件组成。锌空电池为负载提供基本能量。镍氢电池工作在中级能量区,并回收下坡和刹车过程中的能量。超大电容工作在尖峰负载区,为大加速度过程提供能量,在短时间内可以实现能量回收。该文在多能源电动汽车的模型基础上,针对能量管理系统(EMS)提出了一种模糊控制策略。EMS模糊控制策略的输入包括所需功率、镍氢电池的SOC和超大电容的SOC,模糊控制策略的输出包括三个能量存储元件的分配功率因子,每个输入和输出有不同的模糊量。仿真结果表明：模糊控制策略比简单查表控制策略在续驶里程、燃料经济性和效率等方面均有所改善。     

基于指数趋近律的车载复合储能系统全局滑模控制

针对电动汽车存在电池使用寿命和续航里程不足的问题,引入超级电容、电池和DC/DC变换器构成车载复合储能系统.基于五阶状态空间电路平均模型,提出一种基于指数趋近律的全局滑模(E-GSM)控制策略,并基于Lyapunov方法进行控制策略的稳定性分析.该策略包括一个全局滑模电流控制器(用于精确跟踪电池和超级电容电流参考值)和...     

A fast battery charging algorithm for an intelligent PV system with capability of on-line temperature compensation

This study presents a battery fast-charging mechanism for an intelligent generic photovoltaic (PV) system and also a pulse-charging method for the on-line temperature compensation. The fuzzy logic control (FLC) is adopted for fast maximum power point tracking (MPPT) of the PV system. Along with proposed battery charging algorithms, the controller presented in this study is named a fuzzy battery-managing controller (FBMC). The fast battery charging by this controller does not only prolong battery lifetime by restoring the maximum battery state of charge (SOC) in the shortest time but also with the temperature compensation. The designed charging algorithm consists of three different stages, namely constant current (CC), pulse charging and trickle charging. In the CC mode, the current at maximum power of the PV array is used for fast charging. The pulse charging mode is next adopted to contain temperature rise while maintaining relatively fast charging speed. To prevent battery damage by charging as battery capacity is close to its full status, 100 % SOC, the float charging mode is finally activated by further decreasing charging currents. Simulations are conducted via Powersim to validate the FBMC performance and the PV system model. The FBMC is next implemented by a DSP module (TMS320F2812) in order to adjust the switching duty cycle during operations of the buck converter. Finally, experimental results were compared with a general constant current and/or voltage method. The results show favorable performance of the propose charging method.     

电动汽车电-液并联ABS制动系统能量回收研究

为了提高续驶里程,针对某款越野车改装的电动汽车制动系统,提出一种基于ABS的电-液并联制动系统。此系统采用固定比例的前后轴制动力分配方式,结合恒定充电电流与最大回馈功率复合的再生制动控制方式,以基于滑移率的PID控制ABS系统来调节电、液制动力比例,在确保制动安全可靠的同时实现制动能量回收。根据上述理论建立数学模型,并利用AMESim和Simulink进行联合仿真,在3种典型工况下分析制动性能和能量回收效率。结果表明：基于ABS的电-液并联制动系统综合制动性能良好,且3种工况下的一次制动最小能量回收效率分别达到28%、28%和11%。     

蓄电池快速充电模糊控制技术的研究

由于电池充电时充电电流与充电时间的非线性,采用PID、PI等传统的控制方法对充电过程进行控制的效果不佳,该文在研究镍镉、镍氢电池充电特性的基础上,提出了利用模糊控制技术对充电过程进行智能控制的方法。文中详细介绍了充电模糊控制器的设计过程,并以XC164单片机为控制核心,设计了一种针对镍镉、镍氢电池的智能充电系统,实验结果表明,该系统可对电池的复杂充电过程进行最优控制,充电快速、效率高,充电安全,不会损坏电池或缩短电池寿命,实现了充电过程的智能化控制。     

Predictive energy management of a 4QT series-parallel hybrid electric bus

This paper presents a novel predictive control scheme for a series-parallel hybrid bus. The proposed scheme uses information from GPS together with a data record of the driving along the bus route to schedule the charging and discharging of the energy storage system. Switching between hybrid and pure electric mode is optimized in a receding horizon scheme based on a prediction model that reflects the uncertainty of the future driving.The benefits of the proposed predictive control scheme are shown by a simulation study on measured driving data along a bus route. The simulations show that the predictive control scheme achieves both lower fuel consumption and better control of the energy storage system than can be achieved with a non-predictive controller.     

Hardware-in-the-loop simulation for the design and verification of the control system of a series–parallel hybrid electric city-bus

Hybrid electric buses have been a promising technology to dramatically lower fuel consumption and carbon dioxide (CO₂) emission, while energy management strategy (EMS) is a critical technology to the improvements in fuel economy for hybrid electric vehicles (HEVs). In this paper, a suboptimal EMS is developed for the real-time control of a series–parallel hybrid electric bus. It is then investigated and verified in a hardware-in-the-loop (HIL) simulation system constructed on PT-LABCAR, a commercial real-time simulator. First, an optimal EMS is obtained via iterative dynamic programming (IDP) by defining a cost function over a specific drive cycle to minimize fuel consumption, as well as to achieve zero battery state-of-charge (SOC) change and to avoid frequent clutch operation. The IDP method can lower the computational burden and improve the accuracy. Second, the suboptimal EMS for real-time control is developed by constructing an Elman neural network (NN) based on the aforementioned optimal EMS, so the real-time suboptimal EMS can be used in the vehicle control unit (VCU) of the hybrid bus. The real VCU is investigated and verified utilizing a HIL simulator in a virtual forward-facing HEV environment consisting of vehicle, driver and driving environment. The simulation results demonstrate that the proposed real-time suboptimal EMS by the neural network can coordinate the overall hybrid powertrain of the hybrid bus to optimize fuel economy over different drive cycles, and the given drive cycles can be tracked while sustaining the battery SOC level.     

A control benchmark on the energy management of a plug-in hybrid electric vehicle

A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting–charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagin's minimum principle-derived optimization tool HOT.     

A μ-H∞ control strategy for decreasing torque fluctuation of electro-hydraulic integrated braking system in mode switching

Due to the difference of time-domain response between hydraulic braking and regenerative braking, as well as changes of equivalent parameters and operating parameters during braking mode switching, it is liable to cause torque fluctuation, which affects braking safety and vehicle ride comfort. First, the uncertainties of vehicle load and frictional coefficient model are investigated. Second, the hybrid system theory is applied to provide state transfer condition for mode switching strategy. Finally, the control strategy that utilizes regenerative braking torque to compensate for difference of the required braking torque is designed, and a new μ-H_∞ control algorithm through D-K iteration is presented to improve the robust performance. The proposed μ-H_∞ control strategy is examined under various braking situations, and the results indicate that (1) the μ-H_∞ controller have the advantage of robustness performance, the amplitude of regenerative braking is decreased by 6.14%, and the steady-state error of hydraulic braking is decreased by 5.26% over the H_∞, and (2) under the braking mode switching, the designed compensation control strategy has the performance of fast and accurate tracking of the desired torque, and the steady-state error does not exceed 3.5%.     

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.     

Fuzzy Adaptive Filtering-Based Energy Management for Hybrid Energy Storage System

Regarding the problem of the short driving distance of pure electric vehicles, a battery, super-capacitor, and DC/DC converter are combined to form a hybrid energy storage system (HESS). A fuzzy adaptive filtering-based energy management strategy (FAFBEMS) is proposed to allocate the required power of the vehicle. Firstly, the state of charge (SOC) of the super-capacitor is limited according to the driving/braking mode of the vehicle to ensure that it is in a suitable working state, and fuzzy rules are designed to adaptively adjust the filtering time constant, to realize reasonable power allocation. Then, the positive and negative power are determined, and the average power of driving/braking is calculated so as to limit the power amplitude to protect the battery. To verify the proposed FAFBEMS strategy for HESS, simulations are performed under the UDDS (Urban Dynamometer Driving Schedule) driving cycle. The results show that the FAFBEMS strategy can effectively reduce the current amplitude of the battery, and the final SOC of the battery and super-capacitor is optimized to varying degrees. The energy consumption is 7.8% less than that of the rule-based energy management strategy, 10.9% less than that of the fuzzy control energy management strategy, and 13.1% less than that of the filtering-based energy management strategy, which verifies the effectiveness of the FAFBEMS strategy.     

电动汽车低速电气制动防抱死功能的研究

研究电动汽车制动防抱死功能优化问题,电动汽车在冰雪路面上进行纯再生制动时,驱动轮极有可能抱死,从而造成车辆操纵稳定性下降。为解决上述问题,根据驱动电机在基速以下的调速特性,提出了调压调速型电气ABS模型。以单轮电动汽车模型为研究对象,设计了以车轮滑移率为控制目标的滑动模式防滑控制器。在Matlab/Simulink环境下建立了电气ABS仿真模型,仿真结果表明所建模型具有良好的稳定性;同时表明制动过程由初期的反接制动、为主体的中期再生制动及后期的反接制动构成;且制动精度明显高于传统ABS。研究结果对电动汽车再生制动系统的设计具有一定的参考价值。     

All-round two-wheeled quadrotor helicopters with protect-frames for air–land–sea vehicle (controller design and automatic charging equipment)

This paper presents a new all-round Air–Land–Sea Unmanned Aerial Vehicle (UAV) and the automatic battery charging device. The new UAV is a quadrotor helicopter with two rolling protect-frames, which not only can fly in the air but also can move on the ground, the wall, the ceiling, and the water in all directions. In this paper, firstly, the dynamic equation is derived of the new UAV, which is described by a set of nonlinear equations. Secondly, some tracking problems are considered in both the flight mode and the ground one under uncertain inertia parameters, and useful adaptive control systems based on the input–output linearization are proposed for the position tracking control. The design problem is reduced to that of a linear system, and the design method is simple and straightforward. Finally, a new automatic battery charging device is developed for the UAV. It is the unique point that the connection operation to the charging terminal is carried out by ground rolling motion without precise landing from the air. Consequently, even if in windy outdoor and narrow indoor space, the proposed method can complete the battery charge safely and surely.