Active damping of driveline vibration in power-split hybrid vehicles based on model reference control

The power-split hybrid vehicles are widely used owing to its advantages of high efficiency and low emission. With the increasing of the requirements of the customer’s satisfaction nowadays, vibration control is getting progressively more attention. Since the transmission system components are elastic to some extent, meantime the torque of the motor responds quickly for a sudden acceleration, hence torsional vibration takes place, henceforth the passengers will suffer inconvenience attributable to this vibration. This paper studies the active control algorithm for the torsional vibration of the power-split hybrid powertrains under rapid acceleration and deceleration circumstances. Primarily, a dynamic modeling of the power-split hybrid vehicle is built, in addition the uncertainty caused by the variable transmission ratio is analyzed. Moreover, this paper proposes a method combining the model reference adaptive control and the pole configuration method to solve the torsional vibration active control problem with uncertainties. The reference model is constructed by pole configuration method. Based on the dynamic characteristics of the reference model, the model reference adaptive control is implemented and the torque ripple reduction of output shaft under the impact conditions is achieved. Furthermore, this paper designs a dynamic coordinated control algorithm combined with active vibration control strategy, which ensures the dynamic performance and comfort performance of the vehicle. Finally, the effectiveness of this active vibration control strategy under cyclic conditions is verified using Simulink simulation and hardware-in-loop simulation.     

插电式混合动力汽车模型预测控制策略研究

针对传统插电式混合动力汽车智能控制策略计算量大,难以实现实时最优控制的问题,提出了基于蓄电池充放电管理的插电式混合动力汽车预测控制策略.利用实测通勤插电式混合动力汽车车速信息,以蓄电池荷电状态为系统状态变量,以蓄电池充放电功率为系统控制变量,插电式混合动力汽车燃油消耗量最低为系统性能指标,设计了插电式混合动力汽车的模型预测控制智能优化算法,运用连续广义最小残量方法求解最优控制问题.在Matlab/Simulink与GT-POWER联合仿真平台上进行仿真,实验结果验证了所设计的模型预测控制算法不仅可以大幅度提高混合动力汽车的燃油经济性,而且能够满足实时控制的要求.     

混合动力电动汽车的跟车控制与能量管理

混合动力电动汽车(Hybrid electric vehicles, HEVs)的能量管理问题至关重要, 而混合动力电动汽车的跟车控制不仅涉及跟车效果与安全性, 也影响着能量的高效利用. 将HEVs的跟车控制与能量管理相结合, 提出一种基于安全距离的HEVs车辆跟踪与能量管理控制方法. 首先, 考虑坡度、载荷变动建立了HEVs车辆跟车系统的非线性模型, 并基于安全距离, 提出一种基于道路观测器的动态面控制(Dynamic surface control, DSC)进行车辆跟踪控制. 然后, 结合跟踪控制下工况循环, 采用滚动动态规划(Dynamic programming, DP)算法进行混合动力电动汽车能量实时优化控制. 最后, 通过仿真研究进行验证.     

插电式混合动力汽车车速预测及整车控制策略

本文针对插电式混合动力汽车(plug-in hybrid electric vehicle,PHEV)这一典型混杂系统,提出了一种基于车速预测的混合逻辑动态(mixed logical dynamical,MLD)模型预测控制策略.首先,通过对发动机和电动机能量消耗模型进行线性化,建立双轴并联插电式混合动力城市公交车的动力传动系统数学模型;其次,运用模糊推理进行驾驶意图分析,提出基于驾驶意图识别和历史车速数据相结合的非线性自回归(nonlinear auto-regressive models,NAR)神经网络车速预测方法进行未来行驶工况预测.然后,以最小等效燃油消耗为目标建立PHEV的混合逻辑动态模型,运用预测控制思想对车速预测时域内最优电机转矩控制序列进行求解.最后,通过仿真实验验证了本文所提出控制策略在特定的循环工况下与电动助力策略相比,能够提高燃油经济性.     

Model Predictive Control of Hybrid Electric Vehicles for Improved Fuel Economy

This brief proposes a model predictive control method using preceding vehicle information within hybrid electric vehicles' (HEVs') predictive cruise control system to improve car following performance and reduce fuel consumption. This paper adds two original contributions to the related literature. First, a real‐time optimization approach using Pontryagin's minimum principle with analytical methods rather than numerical iteration methods is proposed. Second, to compute the desired battery state of charge trajectory as a function of vehicle position, only the topographic profile of the future road segments must be known. Both the fuel economy and the driving profile are optimized using the proposed approach. Simulation results show that fuel economy using the proposed method is improved significantly.     

电动汽车复合储能系统能量管理策略及其 快速控制原型验证

纯电动汽车储能系统需同时满足高功率密度与高能量密度的要求,但现阶段单一储能单元往 往难以同时具备这两种特点。将高能量密度的锂电池与高功率密度的超级电容进行合理搭配,形成复 合储能系统,是解决以上问题的一个有效方案。该文以宝马 I3 纯电动汽车作为目标车型,设计了锂电 池/超级电容复合储能系统,并制定了一种基于规则的能量管理策略,综合考虑了外部工况要求、锂电 池与超级电容的荷电状态,自动规划工作模式,充分发挥各储能单元自身优势,在极端状况下可自动 启动保护模式;同时,基于快速控制原型的思想,设计搭建了以 dSPACE 为控制中心的复合储能系统 能量管理策略快速控制验证平台,搭配可编辑电力参数的外部电子负载设备,完成了能量管理策略的 半实物实验验证。实验结果表明,电动汽车锂电池/超级电容复合储能系统搭配合理的能量管理策略, 能够充分发挥锂电池的能量特性与超级电容的功率特性,更好地满足了现代纯电动汽车对续航里程与 动力性能的要求,同时可节约能源,在一定程度上起到延长储能系统使用周期的作用。     

Speed planning and energymanagement strategy of hybrid electric vehicles in a car-following scenario

The development of intelligent connected technology has brought opportunities and challenges to the design of energy management strategies for hybrid electric vehicles. First, to achieve car-following in a connected environment while reducing vehicle fuel consumption, a power split hybrid electric vehicle was used as the research object, and a mathematical model including engine, motor, generator, battery and vehicle longitudinal dynamics is established. Second, with the goal of vehicle energy saving, a layered optimization framework for hybrid electric vehicles in a networked environment is proposed. The speed planning problem is established in the upper-level controller, and the optimized speed of the vehicle is obtained and input to the lower-level controller. Furthermore, after the lower-level controller reaches the optimized speed, it distributes the torque among the energy sources of the hybrid electric vehicle based on the equivalent consumption minimum strategy. The simulation results show that the proposed layered control framework can achieve good car-following performance and obtain good fuel economy.     

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.     

混合动力电动汽车中利用决策树CART算法的能源管理方案

针对混合动力电动汽车(HEV)氮氧化物( )排放的问题,提出了一种基于决策树CART算法的柴油混合动力能源管理策略。首先,提出了一种结合决策树与回归树的分类算法(Classification and Regression Tress,CART),针对类别和变量特征,从一个或多个预测变量中预测出个例的趋势变化关系;然后,通过控制发动机和电动机之间的扭矩分配,引入了额外的自由度以调整从纯燃料经济性情况到纯 限制情况的优化权衡;最后,采用基于软件在环路和硬件在环仿真的方法,从而根据动力系统配置了解系统性能,并调整所提出的能源管理策略。实验结果表明,提出的柴油混合动力能源管理策略中, 的减少对燃料消耗的影响,且可以通过选择最佳工作点和限制发动机动力来限制 排放的潜力。相比其他几种较新的同类方案,提出的方案在同等燃料消耗的情况下 排放量更小,在燃料消耗略有下降的情况下,可以显着降低 。     

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.     

Explicit optimal control policy and its practical application for hybrid electric powertrains

This paper presents a simplified, yet realistic, model of a hybrid electric powertrain and derives the explicit solution of the optimal energy management. The explicit solution of this optimal control problem consists of simple rules that rely on powertrain parameters only. The simplified model is validated on a more complex model relying on measured data. Finally, a causal, real-time control strategy including anti-windup is presented. This strategy relies on the optimal control of the simplified model and is successfully evaluated on the complex model that relies on measured data.     

混合动力汽车模型预测控制策略研究

针对传统混合动力汽车控制方法无法实现实时最优控制的问题,提出了基于简化混合动力汽车系统模型的预测控制智能优化策略.通过将3自由度的系统模型简化为1自由度的系统模型,并采用连续广义最小残量方法求解模型预测控制问题.运用MATLAB/Simulink与GT-POWER联合仿真平台进行仿真,实验结果验证了系统模型简化的有效性,以及所设计的模型预测控制算法大幅度提高混合动力汽车的燃油经济性的能力和实时控制性能.     

Integrated vehicle dynamics management for distributed‐drive electric vehicles with active front steering using adaptive neural approaches against unknown nonlinearity

This paper proposes a new integrated vehicle dynamics management for enhancing the yaw stability and wheel slip regulation of the distributed‐drive electric vehicle with active front steering. To cope with the unknown nonlinear tire dynamics with uncertain disturbances in integrated control problem of vehicle dynamics, a neuro‐adaptive predictive control is therefore proposed for multiobjective coordination of constrained systems with unknown nonlinearity. Unknown nonlinearity with unmodeled dynamics is modeled using a random projection neural network via adaptive machine learning, where a new adaptation law is designed in premise of Lyapunov stability. Given the computational efficiency, a neurodynamic method is extended to solve the constrained programming problem with unknown nonlinearity. To test the performance of the proposed control method, simulations were conducted using a validated vehicle model. Simulation results show that the proposed neuro‐adaptive predictive controller outperforms the classical model predictive controller in tracking nominal wheel slip ratio, desired vehicle yaw rate and sideslip angle, showing its significance in vehicle yaw stability enhancement and wheels slip regulation.     

基于V模式开发插电式混合动力汽车整车热管理控制策略研究

为解决目前的环境污染和能源消耗的问题,插电式混合动力汽车的推广是一个重要的解决方案。插电式混合动力汽车整车控制软件开发是一个重要的核心技术得到了企业的重视和投入。基于模型的V模式开发流程是目前主流的软件开发流程,介绍了该开发流程的步骤,优势,并基于该开发模式进行了整车热管理控制策略的开发。     

Predictive and interactive controllers for solar absorption cooling systems in buildings

A predictive control approach is proposed for a solar powered hot water storage (SHWS) system which interacts with a simple thermal building control. The primary objective of this first controller is to optimize the use of the solar energy in order to ensure the cooling requirement of the building. The main difficulties are related to the presence of safety constraints and the nonlinearity as well as the hybrid nature of the system. The resulting optimization problem is simplified using various relaxations. The second controller is dedicated to the control of the building temperature. Using a model of the building thermal behavior, it sends its predicted operating profile to the SHWS controller. The performances of these two interacting controllers are illustrated by various simulations on a TRNSYS model of the building and its subsystems.     

Energy management for multi-microgrid system based on model predictive control

To reduce the computation complexity of the optimization algorithm used in energy management of a multi-microgrid system, an energy optimization management method based on model predictive control is presented. The idea of decomposition and coordination is adopted to achieve the balance between power supply and user demand, and the power supply cost is minimized by coordinating surplus energy in the multi-microgrid system. The energy management model and energy optimization problem are established according to the power flow characteristics of microgrids. A dual decomposition approach is imposed to decompose the optimization problem into two parts, and a distributed predictive control algorithm based on global optimization is introduced to achieve the optimal solution by iteration and coordination. The proposed method has been verified by simulation, and simulation results show that the proposed method provides the demanded energy to consumers in real time, and improves renewable energy efficiency. In addition, the proposed algorithm has been compared with the particle swarm optimization (PSO) algorithm. The results show that compared with PSO, the proposed method has better performance, faster convergence, and significantly higher efficiency.     

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.     

Predictive planning of optimal velocity and state of charge trajectories for hybrid electric vehicles

The combination of electric motors and internal combustion engines in hybrid electric vehicles (HEV) can considerably improve the fuel efficiency compared to conventional vehicles. In order to use its full potential, a predictive intelligent control system using information about impending driving situations has to be developed, to determine the optimal gear shifting strategy and the torque split between the combustion engine and the electric motor. To further increase fuel efficiency, the vehicle velocity can be used as an additional degree of freedom and the development of a predictive algorithm calculating good choices for all degrees of freedom over time is necessary.In this paper, an optimization-based algorithm for combined energy management and economic driving over a limited horizon is proposed. The results are compared with results from an offline calculation, which determine the overall fuel savings potential through the use of a discrete dynamic programming algorithm.     

Optimization of fuel consumption and NOx emission for mild HEV via hierarchical model predictive control

In this paper, we consider the fuel economy optimization problem for a mild hybrid electric vehicle (HEV) using hierarchical model predictive control. In the proposed algorithm, two problems are addressed: eco-driving and torque distribution. In the eco-driving problem, vehicle speed was controlled. Considering the reduction in fuel consumption and NOx emissions, the torque required to follow the target speed was calculated. Subsequently, in the torque distribution problem, the distribution between the engine and motor torques were calculated. In this phase, engine characteristics were considered. These problems differ in terms of time scales; therefore, a hierarchical model predictive control is proposed. Lastly, the numerical simulation results demonstrated the efficacy of this research.     

并联混合动力汽车能量管理系统的仿真研究

对于混合动力汽车而言,节能减排是促使其发展的主要原因,而能量管理策略是节能减排的关键技术,因此针对并联混合动力汽车的能量管理策略展开研究;首先运用ADVISOR电动汽车仿真软件,选用某款并联混合动力车型,并使用标准ECE_ECDU和UDDS循环工况来评估整车燃油经济性和污染物排放效果;然后,采用门限参数优化的方法对控制策略进行优化;最后对比优化前后不同循环工况仿真结果中汽车的燃油经济性和排放性能的变化,并分析了优化后的策略对汽车性能的影响;研究表明,所提出的优化方法使汽车在ECE_ECDU和UDDS循环工况中的每百公里油耗分别降低了8.45%和10%,有害气体HC、CO和NOX含量分别减少了5.88%和5.8%、12.24%和11.54%、8.55%和7.51%,进一步验证了优化策略的有效性。