Robust control for electric vehicle powertrains

This paper considers the application of robust control methods ($mu$- and ${rm H}_{infty}$-synthesis) to the speed and acceleration control problem encountered in electric vehicle powertrains. To this end, we consider a two degree of freedom control structure with a reference model. The underlying powertrain model is derived and combined into the corresponding interconnected system required for $mu$- and ${rm H}_{infty}$-synthesis. The closed-loop performance of the resulting controllers are compared in a detailed simulation analysis that includes nonlinear effects. It is observed that the $mu$-controller offers performance advantages in particular for the acceleration control problem, but at the price of a high-order controller.     

Integrated torque vectoring and power management framework for electric vehicles

An integrated vehicle control framework is presented, which uses torque vectoring across independently driven wheels for control. The approach is general in nature, but is particularly well suited for electric vehicles due to increased control bandwidth. The novel algorithm optimizes wheel torque outputs in real time, constraining against power management, traction control, chassis configuration, actuator limits, and fault-case limitations. The structure is modular, and designed to adapt for differing vehicles with minimal re-tuning. Simulation and experimental results are provided for a modified electric SUV platform, under a range of dynamic maneuvers in 4WD, FWD, and RWD modes.     

A charging-scheme decision model for electric vehicle battery swapping station using varied population evolutionary algorithms

This paper proposes a new battery swapping station (BSS) model to determine the optimized charging scheme for each incoming Electric Vehicle (EV) battery. The objective is to maximize the BSS’s battery stock level and minimize the average charging damage with the use of different types of chargers. An integrated objective function is defined for the multi-objective optimization problem. The genetic algorithm (GA), differential evolution (DE) algorithm and three versions of particle swarm optimization (PSO) algorithms have been implemented to solve the problem, and the results show that GA and DE perform better than the PSO algorithms, but the computational time of GA and DE are longer than using PSO. Hence, the varied population genetic algorithm (VPGA) and varied population differential evolution (VPDE) algorithm are proposed to determine the optimal solution and reduce the computational time of typical evolutionary algorithms. The simulation results show that the performances of the proposed algorithms are comparable with the typical GA and DE, but the computational times of the VPGA and VPDE are significantly shorter. A 24-h simulation study is carried out to examine the feasibility of the model.     

Relaxed static stability based on tyre cornering stiffness estimation for all-wheel-drive electric vehicle

A novel dynamics control approach for all-wheel-drive electric vehicle (EV), relaxed static stability (RSS) approach is proposed with two advantages. Firstly, it allows vehicle lateral dynamics system to be inherent unstable to improve configuration flexibility. Secondly, handling performance could be improved based on closed-looped pole assignment with additional yaw moment. In this paper, basic control framework of RSS is proposed, including ‘Desired Pole Location’, ‘Pole Assignment’ and ‘Tyre Cornering Stiffness Estimation’ modules. The tyre cornering stiffness is estimated online to improve the robustness of the controller. The experiments based on an EV testbed show the performance and efficiency of RSS.     

基于模板技术的汽车多体动力学建模研究

针对当前汽车动力学仿真软件建模复杂度高、模型结构不易理解、模板开放性不高等缺点,提出开放的模板化建模技术.将汽车动力学模型分解为相对独立的子系统并抽象为基本模板,采用“基本模板—结构模板—模板实例化”的架构,用XML描述模板,从而实现模板的开放性,构建汽车动力学模型的树状层级模板库和实例库,结合基于实例推理的方法生成模型.在此基础上开发了汽车整车建模软件,并以某重型汽车起重机为例说明了该技术的应用.     

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.     

Combined state of charge estimator for electric vehicle battery pack

Ah counting is not a satisfactory method for the estimation of the state of charge (SOC) of a battery, as the initial SOC and coulomb efficiency are difficult to measure. To address this issue, an equivalent coulomb efficiency is defined and a new SOC estimation method, denoted as “KalmanAh”, is proposed. This method uses the Kalman filtering method to correct for the initial value used in the Ah counting method. A Ni/MH battery test, consisting of 8.08 continuous federal urban driving schedule (FUDS) cycles, is carried out to verify the method. The SOC estimation error is 2.5% when compared with the real SOC obtained from a discharge test. This compares favorably with an estimation error of 11.4% when using Ah counting.     

基于分段PWM占空比输出的电动汽车电动机控制系统设计

为加快电动汽车在负载量变化时所表现出来的电量响应速率,从而降低执行电动机控制指令所需的电能消耗量,设计基于分段PWM占空比输出的电动汽车电动机控制系统。根据带存贮电容功率变换器的连接状态,调试功率变换器驱动电路、档位与油门给定输入电路的响应形式,联合DSP控制板与软启动模块,确保滑模转矩控制器不出现过量负载的情况,从而将电动汽车电动机控制系统的核心设备元件组合起来,完成硬件应用环境的搭建。在此基础上,研究PWM 占空比控制原理,利用开关磁阻电机数学模型,确定脉冲行为成因,并将整个脉冲行为区间规划成多个分段结构,完成基于分段PWM占空比输出电动机控制行为分析,结合各级应用设备,实现电动汽车电动机控制系统的设计。实验结果显示,在分段PWM占空比输出原理的作用下,无论负载量增大或减小,电动汽车所表现出来的电量响应速率均能保持相对较高的数值水平,能够将电动机控制指令执行所需的电能消耗量保持在理想数值区间之内,符合实际应用需求。     

A flexible distributed framework for realising electric and plug-in hybrid vehicle charging policies

Motivated by the problems of charging a number of electric vehicles via limited capacity infrastructure, this article considers the problem of individual load adjustment under a total capacity constraint. For reasons of scalability and simplified communications, distributed solutions to this problem are sought. Borrowing from communication networks (AIMD algorithms) and distributed convex optimisation, we describe a number of distributed algorithms for achieving relative average fairness whilst maximising utilisation. We present analysis and simulation results to show the performance of these algorithms. In the scenarios examined, the algorithm's performance is typically within 5% of that achievable in the ideal centralised case, but with greatly enhanced scalability and reduced communication requirements.     

Integrated Dynamics Control and Energy Efficiency Optimization for Overactuated Electric Vehicles

A large number of studies have been conducted on the dynamics control of electric vehicles or on the optimization of their energy efficiency but few studies have looked at both of these together. In this study, an integrated dynamics control and energy efficiency optimization strategy is proposed for overactuated electric vehicles, where the control of both longitudinal and lateral dynamics is dealt with while the energy efficiency is optimized. First, considering the trade‐off between control performance and energy efficiency, criteria are defined to categorize the vehicle motion status as linear pure longitudinal motion and non‐linear motion or turning motion. Then different optimization targets are developed for different motion status. For the pure linear longitudinal motion and cornering motion, the energy efficiency and vehicle dynamics performance are equally important and a trade‐off control performance between them needs to be achieved. For the non‐linear turning motion, vehicle handling and stability performance are the primary concerns, and energy efficiency is a secondary target. Based on the defined targets, the desired longitudinal and lateral tyre forces and yaw moment are then optimally distributed to the wheel driving and steering torques. Finally numerical simulations are used to verify the effectiveness of the proposed strategies. The simulation results show that the proposed strategies can provide good dynamics control performance with less energy consumption.     

高速铁道车辆悬挂系统参数化建模、优化与仿真分析

为了协调高速铁道车辆的运动稳定性与曲线通过性能之间的矛盾,本文采用多目标优化方法对一种高速铁道车辆的关键悬挂参数进行了优化处理.采用多体动力学技术建立了某型高速铁道车辆62个自由度的动力学模型,模型考虑了轮轨接触几何非线性、轮轨蠕滑非线性和阻尼非线性等.采用ADAMS-Matlab联合仿真对车辆悬挂系统进行参数化改造,使弹簧刚度和阻尼系数均可调.采用基于遗传算法的多目标优化方法对悬挂参数进行优化,使车辆模型能同时满足3种动力学指标.对比优化前后模型的动力学性能可以发现:模型的运动稳定性和曲线通过性能得到显著提高,虽然运行平稳性有小幅降低,但仍能保持在优良的工作状态.     

Hardware-in-the-loop validation of a power management strategy for hybrid powertrains

Previously, a hybrid powertrain management strategy was developed that controls the power sources based on frequency content, mitigating aggressive engine transients. This article presents a hardware-in-the-loop validation of this strategy, with a real engine and battery integrated into a diesel hybrid electric vehicle simulation, thereby allowing for a realistic evaluation of fuel economy, soot emissions, and battery life. Considering an aggressive drive cycle and a state-of-charge regulation strategy as a benchmark, the frequency-based strategy yields 5.9% increase in fuel economy, 62.7% decrease in soot emissions, and 23% reduction in effective Amp-hours processed, which should yield an increase in battery life.     

基于Modelica的电动汽车电池建模与仿真

为了实时仿真电动汽车电池组的动态性能,应用Modelica多领域建模语言建立描述电池组的PNGV等效电路模型.通过电池的动态充放电性能实验标定电池组模型的电阻、电容等参数,并利用Dymola多领域仿真软件进行性能参数求解.将该电池模型应用在电动汽车整车的仿真分析中,实验结果表明,该模型准确度高,能实时地仿真电动汽车的动力性能.该电池组建模方法实现简单且便于扩展,可以为电动汽车的产品设计、系统性能分析提供有效的解决办法.     

增程式电动汽车控制策略的优化研究

针对增程式电动汽车恒功率控制策略中发动机工作点难以选择的问题,运用一种基于多目标遗传算法的优化方法,以百公里油耗和排放指标为优化目标,利用AVL CRUISE和Matlab/Simulink软件联合建立增程式电动汽车整车动态性能仿真分析模型,针对NEDC工况和FTP75工况进行恒功率控制策略下发动机工作点优化,仿真结果显示,优化后的发动机工作点有效改善了百公里油耗和尾气排放量。该优化方法可以减少设计者调试和选择电动汽车增程器发动机工作点的时间,具有良好的实用价值。     

A predictive discrete event approach for the optimal charging of electric vehicles in microgrids

Distributed generation, renewables (RES), electric vehicles (EVs), storage systems and microgrids are increasing widespread all over the world owing to the necessity of applying policies for sustainable development. In particular, the progressive shift from traditional vehicles to EVs is considered as one of the key measures to achieve the objective of a significant reduction in the emission of pollutants, especially in urban areas. One of the major problem to be solved to make EVs a viable solution for the sustainable mobility is the development of effective facilities for vehicles. In this context, besides to technological aspects, one of the most important issues is the definition of fair and efficient policies for the sequencing and scheduling of the vehicle charging. In fact, scheduling problems are widely recognized as representing one of the most challenging class of optimization problems. Besides, the additional presence of specific features concerning vehicle charging systems (like controllable execution times, presence of intermittent energy sources, etc.) make even more difficult the vehicle charging problem. In this framework, despite the fact that optimization problems regarding energy systems are generally considered within a discrete-time setting, in this paper a discrete event approach is proposed. The reasons for this choice are essentially two. The first one is the necessity of containing the number of the decision variables, which grows beyond reasonable values when a small-time discretization step is chosen. The second is the impossibility of an accurate tracking of process and events using a discrete-time approach.The considered optimization problem regards the charging of a series of vehicles by a charging station that is integrated in a microgrid. Such a microgrid includes also renewable and traditional energy sources, storage systems and a local load. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the cost related to the use of fossil fuels, and the overall tardiness of the services provided to the customers. The effectiveness of the proposed approach is tested on a real case study.     

电力电子技术在电动车驱动系统中的应用

现今世界上节能和环境保护正日益受到重视,因此电动汽车技术的发展步伐正在加速。本文论述了电动汽车技术的现状以及电动机、电力电子技术的快速发展所产生的影响,介绍了电力电子技术在电动车驱动系统中的具体应用,最后就电力电子技术在电力驱动方面将来的发展趋势进行了展望。     

Integrated stability and traction control for electric vehicles using model predictive control

In this paper, an integrated vehicle and wheel stability control is developed and experimentally evaluated. The integrated structure provides a more accurate solution as the output of the stability controller is not altered by a separate unit, therefore its optimality is not compromised. Model predictive control is used to find the optimal control actions. The proposed control scheme can be applied to a wide variety of vehicle driveline and actuation configurations such as: four, front and rear wheel drive systems. Computer simulations as well as experiments are provided to show the effectiveness of the proposed control algorithm.     

Fault-tolerant traction control of electric vehicles

This paper investigates a new traction control approach that requires neither chassis velocity nor information about tire-road conditions. Plant fault subject to the uncertainties of the mathematical model and slightly sensor fault are concerned. For general traction control of vehicles, the variation of model behavior may break down the steering stability if the chassis velocity is not monitored. This paper presents a fault-tolerant approach based on the maximum transmissible torque estimation (MTTE) scheme which has the ability to prevent electric vehicles from skidding. A PI-type disturbance observer is employed to enhance the steering stability of the MTTE approach. This proposed approach does not require both the differentiator and the inversion of the controlled plant. Finally, illustrated examples are given for evaluating the fault-tolerant performance and feasibility of the presented anti-slip strategy.     

基于强度Pareto进化算法的有约束并联混合动力汽车多目标优化

将混合动力系统多目标优化问题转化为单目标优化问题进行求解需要设置权系数。为避免设置权系数,研究基于强度Pareto进化算法（SPEA2）的有约束并联式混合动力电动汽车(PHEV)参数优化方法。该方法基于Pareto支配性原理判定候选方案的优劣,采用ADVISOR仿真PHEV,并将仿真所得的燃油消耗量与污染物排量作为候选方案的目标值。实验结果表明,该方法所获得的控制策略与传动系统参数,在提高PHEV工作效率、整车性能及降低燃油消耗与污染物排放等方面效果显著。     

Agent-oriented modeling of the dynamics of biological organisms

In this paper, the agent-oriented modeling perspective to cope with biological complexity is discussed. Three levels of dynamics can distinguished and related to each other: dynamics of externally observable agent behavior, dynamics of internal agent processes, and dynamics of multi-agent organisations. This paper addresses the first two. Basic agent concepts to describe externally observable agent behavior are introduced. In the context of two case studies on animal behavior and cell functioning, it is shown how these concepts can be used to specify dynamic properties. In addition, a number of basic agent concepts to describe an agent’s internal processes are introduced. Also, these concepts are illustrated for specification of dynamic properties in the two case studies. Furthermore, the relationships between dynamic properties of externally observable behavior and dynamic properties of internal agent processes are addressed and illustrated for the animal and cell case studies.