State of charge estimation for Li-ion battery based on model from extreme learning machine

Lithium-ion (Li-ion) battery state of charge (SOC) estimation is important for electric vehicles (EVs). The model-based state estimation method using the Kalman filter (KF) variants is studied and improved in this paper. To establish an accurate discrete model for Li-ion battery, the extreme learning machine (ELM) algorithm is proposed to train the model using experimental data. The estimation of SOC is then compared using four algorithms: extended Kalman filter (EKF), unscented Kalman filter (UKF), adaptive extended Kalman filter (AEKF) and adaptive unscented Kalman filter (AUKF). The comparison of the experimental results shows that AEKF and AUKF have better convergence rate, and AUKF has the best accuracy. The comparison from the radial basis function neural network (RBF NN) model also verifies that the ELM model has lighter computation load and smaller estimation error in SOC estimation process. In general, the performance of Li-ion battery SOC estimation is improved by the AUKF algorithm applied on the ELM model.     

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.     

基于免疫遗传算法的动力电池SOC估计研究

锂离子动力电池SOC（电池荷电状态）难以直接测量且由于高度非线性所导致估计误差较大。为了减少动力电池SOC估计误差,提高估算精度。在分析了锂离子动力电池电压、温度、电流和放电电量对电池SOC影响后,提出一种新颖的免疫遗传算法（Immune Genetic Algorithm,IGA）和BP神经网络相结合的锂离子动力电池SOC值联合估计方法,该方法首次使用在锂离子动力电池SOC值估计中,采用新颖的免疫遗传算法通过对BP神经网络进行参数寻优,优化网络结构模型,增强神经网络自适应学习效率。通过仿真和动力电池实际工况下实验,结果表明使用新颖的联合估计算法提高了网络的运行效率和电池SOC值估计精度,估计均方根误差控制在2%以内,验证了这一联合估计算法的可行性和有效性,解决了动力电池SOC值估计误差较大的问题。     

基于工况特性的电动汽车蓄电池组SOC与SOH在线智能识别与评价方法研究

蓄电池组SOC和SOH是电动汽车电池管理和能量管理的关键参数,受单体电池特性、蓄电池组一致性和均衡技术等因素影响,不易建立准确计算模型.基于电动汽车日常行驶工况统计特性提出一种改进的Ah积分法计算蓄电池组SOC和SOH,该方法采用工况容量与等效工况电流根据Peukert方程实现稳态容量修正,同时采用模糊逻辑实现放电率波动对容量的动态修正;提出采用单体统计特性建立状态评价矩阵表征蓄电池组状态的全面评价方法;最后通过对比仿真计算分析验证了所提方法的合理性和实用性.     

锂离子电池荷电状态估计: 非线性观测器方法

电池荷电状态(state of charge,SOC)的准确估计是电动汽车有效实施能量管理的基本前提和安全高效运行的重要保障.为降低电池系统因迟滞效应和非线性因素对SOC估计产生的不利影响,本文基于Lipschitz非线性系统观测器设计理论,提出了一类电池SOC估计新方法.基于该新方法设计的观测器具有结构简单,估计性能好等优点.首先根据电池等价电路模型给出电池系统的数学描述,进而利用脉冲放电实验数据计算出电池系统各参数值,然后利用线性矩阵不等式方法求解出观测器增益矩阵,最后利用城市道路循环(urban dynamometer driving schedule,UDDS)工况测试验证了观测器系统具有良好的跟踪性能.     

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.     

基于Thevenin模型和自适应卡尔曼的SOC估算研究

精确的荷电状态(SOC)值在电池的应用开发中具有重要的意义.选择合适的滤波算法是精确估算的前提.由于扩展卡尔曼滤波(EKF)中噪声的给定值与实际工况下噪声的统计特性不符,导致估算精度低.为提高SOC估算精度,构建能准确反映锂电池工作特性的Thevenin电路模型.在此基础上,构建状态方程和观测方程,提出自适应卡尔曼滤波...     

Centroid based Multilayer Perceptron Networks

In this study we investigate a hybrid neural network architecture for modelling purposes. The proposed network is based on the multilayer perceptron (MLP) network. However, in addition to the usual hidden layers the first hidden layer is selected to be a centroid layer. Each unit in this new layer incorporates a centroid that is located somewhere in the input space. The output of these units is the Euclidean distance between the centroid and the input. The centroid layer clearly resembles the hidden layer of the radial basis function (RBF) networks. Therefore the centroid based multilayer perceptron (CMLP) networks can be regarded as a hybrid of MLP and RBF networks. The presented benchmark experiments show that the proposed hybrid architecture is able to combine the good properties of MLP and RBF networks resulting fast and efficient learning, and compact network structure.     

Least costly energy management for series hybrid electric vehicles

Energy management of plug-in hybrid electric vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user – the combined costs of fuel, grid energy and battery degradation – is here proposed. A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters. The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign.     

基于路况预测的PHEV能量管理策略

为了提高插电式混合动力汽车(plug-in hybrid electric vehicle, PHEV)的燃油经济性,减少排放,提出了基于路况预测的PHEV能量管理策略;首先,建立PHEV系统结构并在此基础上依据动力电池SOC(State of charge)变化规律定义了3种PHEV基本工作模式;然后,设计路况识别模糊控制器对当前行驶路况进行识别并预测;最后,根据预测的路况类型结合合理规划的动力电池SOC的曲线约束,制定PHEV能量管理策略;仿真结果表明,该能量管理策略能够较好的使动力电池SOC保持在设定的参考轨迹附近,提高燃油经济性,减少排放。     

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...     

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...     

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.     

混合动力汽车Ni—MH电池SOC估算算法的研究

电池的荷电状态SOC（state—of—charge）,对于混合动力汽车的电池管理系统来说是一个非常重要的参数。文章介绍了当前常用的一些SOC的估算方法,并分析了这些方法存在的一些局限性。着重研究了基于卡尔曼滤波器估算SOC的算法,并在MATLAB下进行了仿真。     

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.     

基于模糊神经网络优化扩展卡尔曼滤波的锂离子电池荷电状态估计

电池荷电状态(state of charge,SOC)的精确估计是判断电池是否过充或过放的重要依据,是电动汽车安全、可靠运行的重要保障.传统基于扩展卡尔曼滤波(extended Kalman filter,EKF)的SOC估计方法过度依赖于精确的电池模型,并且要求系统噪声必须服从高斯白噪声分布.为解决上述问题,基于模糊神经网络(fuzzy neural network,FNN)建立模型误差预测模型,并藉此修正扩展卡尔曼滤波测量噪声协方差,以实现当模型误差较小时对状态估计进行测量更新,而当模型误差较大时只进行过程更新.仿真和实验结果表明,该算法能有效消除由于模型误差和测量噪声统计特性不确定而引入的SOC估计误差,误差在1.2%以内,并且具有较好的收敛性和鲁棒性,适用于电动汽车的各种复杂工况,应用价值较高.     

装甲车辆铅酸蓄电池荷电状态辨识模型的研究

针对装甲车辆铅酸蓄电池的特殊使用状况以及非线性特性,提出了一种新的基于最小二乘支持向量机的蓄电池荷电状态辨识模型,估测电池静置状态的剩余容量;通过对三种核函数的仿真分析,确定了径向基函数作为模型核函数,并将模型与其他模型的辨识速度和精度进行了比较分析;仿真结果表明该模型具有更好的整体评估性能,对装甲车辆铅酸蓄电池的容量估测有很好的实用性。     

直流微网混合储能系统优化控制策略

和单一储能相比,蓄电池/超级电容混合储能系统可以满足微网多样化的要求.在独立直流微网系统中,根据优先使用功率密度较高的超级电容、减少蓄电池频繁大电流充放电的原则,提出了一种混合储能系统优化控制策略.加入延迟环节延迟蓄电池出力,由超级电容迅速平抑系统功率波动;根据超级电容实时荷电状态和充放电状态,采用模糊控制器调整延迟时间,在保证超级电容安全运行的前提下实现其充分利用;蓄电池作为持续供能设备,采用基于其端电压的多滞环电流控制方法,对蓄电池充放电过程进行优化,减少高频充放电电流切换造成的损伤.仿真实验结果验证了控制策略的有效性.     

基于DSP的电动汽车锂电池荷电状态估算的研究与实现

锂电池荷电状态(SOC)的准确估算是电动汽车能源管理的关键技术。为了提高锂电池SOC的估算精度,将无迹卡尔曼滤波(UKF)应用于锂电池SOC估算,以减小拓展卡尔曼滤波(EKF)简单线性化带来的误差。搭建电池检测系统的硬件平台,以TMS320F28335型数字信号处理器(DSP)为主控芯片(MCU),实现电压、电流、温度的检测及UKF算法,并设计了相关的电池测试实验。实验结果表明,UKF可以实时估算锂电池SOC,估算误差在4%以内,高于传统的拓展卡尔曼滤波(EKF)。