电动汽车锂离子电池荷电状态估算方法研究综述

锂离子电池具有循环寿命长、能量密度高、自放电率低、环境污染小等优点,在电动汽车产业中得到广泛应用.电动汽车中的电池管理系统(BMS)可以维护和监测电池状态,确保电池的安全性和可靠性.电池荷电状态(SoC)表示电池中剩余的电量,是BMS的重要参数之一,实时精确的SoC估算可以延长电池寿命,保障行驶安全.然而锂离子电池是一个高度复杂的非线性时变系统,电池寿命、环境温度、电池自放电等许多未知因素均会对估算精度造成影响,使估算难度大大增加.为了满足不同条件下对锂离子电池SoC精确、快速、实时估算的要求,需要对SoC估计算法进行进一步研究与改进.近年来已有相关文献对锂离子电池SoC的估算方法进行了综述,然而已有相关综述对估算方法的总结不够全面且缺少流程表达.该文首先介绍了锂离子电池的工作原理,阐述了影响电池SoC估算的因素;其次,通过总结最新的研究成果对电池SoC估算方法进行了归纳分析,根据各类算法的不同特性将其分为查表法、安时积分法、基于模型的方法、数据驱动的方法以及混合方法五大类,说明了各类估算方法的主要特征并对模型或算法的优缺点进行综合的比较和讨论;最后,对电动汽车中锂离子电池SoC估算方法的未来发展方向做出展望.     

电动汽车锂离子电池模型参数辨识和荷电状态估算

针对电动汽车锂离子电池荷电状态在线估算准确率低、实时性差等问题,文中建立一种精确在线估算荷电状态的有效方法,采用MAFF-RLS和EKF对荷电状态进行估算。建立锂离子电池的等效电路模型,将MAFF-RLS应用在电池等效电路模型的参数辨识上,可以有效在线辨识模型参数。在模型参数辨识的基础上,将辨识出的模型参数作为荷电状态估算的输入,采用EKF估算动力电池实时荷电状态。经过实验仿真发现,采用MAFF-RLS和EKF联合估算荷电状态能够提高估算精确度,估算误差仅在2%以内。     

一种改进型锂离子电池SOC估算方法

为提高锂离子电池荷电状态(SOC)的估算精度，首先建立锂离子电池的较精确的二阶 RC等效模型，根据模型建立 状态空间方程，其次介绍电池充放电时电压电流采集方法，根据采集的数据，运用扩展卡尔曼滤波算法(EKF)，对锂离子电池的荷电状态进行估算。结果表明，该数据的采集方法具有很高的精度，用扩展卡尔曼滤波算法估算的结果与真实值较为接近，精确度可达4％，对噪声有很强的抑制作用。     

电动车用NiMH电池SoC预测方法的探讨

NiMH电池因具有高比能量、高功率、长寿命及宽使用温度范围等优点，成为电动汽车和混合电动汽车上很有希望的动力电源。NiMH电池SoC的预测在EV和HEV上是非常必要的，它表明了还有多少能量存储在电动汽车中。综合目前国内外情况，介绍了镍氢电池的建模及SoC预测方法的进展，分析了各种方法的适用情况。     

动力电池荷电状态估算方法的研究

由于电池本身特性决定了电池电量的预测成为电动汽车开发的一个难点。但目前各种方法都难以精确地测量蓄电池的剩余电量,并以此计算电动汽车蓄电池的荷电状态（SOC）。在对目前常用的剩余电量计量方法分析基础上,提出了一种基于开路电压法和安时法复合的估算方法,然后利用卡尔曼滤波估计递推算法对蓄电池SOC进行实时估算,并在MATLAB下进行了仿真,实现了电池荷电状态（SOC）的精确估算。     

基于神经网络与UKF结合的锂离子电池组SOC估算方法

锂离子电池组作为电动汽车的主要动力能源,对荷电状态的准确估计是电动汽车的关键技术之一。准确的SOC估计,对锂离子电池组的寿命维持及电动汽车的行车安全,具有十分重要的意义。基于此设计一种基于神经网络与无迹卡尔曼滤波器(UKF)相结合的SOC估算方法,既克服了UKF需要等效电池组电路模型的缺点,也能显著减小神经网络估算方法的最大误差。该实验数据来源于高级车辆仿真器(ADVISOR2002)基于实际工况的仿真结果,经实验数据证明,该方法具有有效性和实用性。     

电动汽车估算策略综述

近年来,由于混合动力和纯动力电动汽车能有限减少燃料消耗,减少对进口石油的依赖和温室气体排放而在全球范围内受到广泛关注。电动汽车的全面性成功主要取决于他们所创建的子系统的性能。为了提高这些子系统的性能,对其参数的估算精度要求非常之高。此外,估算策略在电池系统和车辆能量管理上发挥了重要作用。有少数的估算策略相关的综述,但只关注于电池的核电状态(SOC)和健康状态(SOH)的估计。文章对混合动力和纯动力电池汽车的各种估计策略做了全面综述,对现有的估计策略进行分类,并阐述每个估计策略中使用的不同方法。     

一种基于预测开路电压估算SOC初值的方法

为解决电动汽车动力电池 SOC初值估算问题,文章以锂离子动力电池为对象,进行了脉冲放电实验,拟合了锂离子动力电池开路电压与 SOC函数关系式。对七阶Thevenin等效电池模型进行了参数辨识,预测了锂离子电池开路电压,将预测的开路电压代入开路电压与 SOC函数关系式进行了 SOC初值的估计。通过仿真实验,得出 SOC 初值估计误差为0.1321%。文中 SOC初值估算精度优于市场上通用的电池容量检测仪精度,验证了预测开路电压估算 SOC初值方法的可行性。     

基于Freescale单片机的电池管理系统设计

为了实现电动汽车电池的实时监控,在研究了锂离子电池特点的基础上,提出了一种用于混合动力汽车的分布式电池管理系统。其中,硬件系统包括电源模块、基于Freescale系列单片机的主控制模块和子模块、均衡模块以及CAN总线通信模块等;软件系统包括基于下溢中断的数据采集与处理、SOC估算、均衡处理和CAN通信等任务。     

基于FBG储能锂离子电池的温度与应变监测技术的研究进展

锂离子电池因其高能量密度、环保性和长循环寿命等优点，近年来已广泛用于各种储能应用。然而，储能电池运行安全问题频繁发生，稳定性和可靠性等方面仍需改进。目前，检测锂离子电池温度和应变的方法主要包括热电偶温度传感器、电阻温度传感器和电阻应变计等。然而，这些方法存在一些限制，例如不能全面测量电池整体温度，以及传感器尺寸较大，难以嵌入电池内部。在此基础上介绍了几种基于FBG的储能锂离子电池温度与应变监测技术，同时比较了它们之间的优点和缺点，最后总结并展望了FBG在锂离子电池温度与应变监测中的发展前景。     

FPGA-based design of advanced BMS implementing SoC/SoH estimators

Energy storage system, usually a battery, become essential part for all electric drive vehicles such as hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV) and electric vehicle (EV) in the coming decades. These energy storage systems include Li-ion batteries, Ni-MH batteries, lead-acid batteries and ultra-capacitors. An accurate Battery Management System (BMS) is highly demanded integrated system in all electric derive vehicles to ensure the optimum use of an energy storage system. The battery's state monitoring & evaluation, charge control and cell balancing are the important features of any BMS. However, due to unavailability of inaccurate battery's state-of-charge (SoC)/state-of-health (SoH) estimators and uncertainty of battery's performance, new approaches of BMS design are under development to control batteries optimally and hence, the vehicle performance. In addition, most of the existing BMSs either do not provide SoH at all or provide it as a function of capacity degradation over the battery usage. This research paper presents the field-programmable gate array (FPGA) - based Advanced BMS design using MATLAB-to-FPGA design flow. The Advanced BMS design provides the combined estimation of both SoC and SoH of a rechargeable battery. This research paper also summarizes the Neuro-Fuzzy & statistical models implemented in Advanced BMS for accurate estimation of battery's SoC & SoH respectively. Further, this research paper presents the selection of suitable FPGA and its hardware realization implementing Advanced BMS. Finally, the experimental results are confirmed by simulation and synthesis of its register transfer level (RTL) design. FPGA-based Advanced BMS would provide the best chip solution for a generalized BMS with benefits of low Non-recurring engineering (NRE) cost, low power consumption, high speed of operation, large reconfigurable logic and large data storage capacity.     

Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC

Lithium-ion batteries are widely used as power sources in various portable electronics, hybrid electric vehicles, aeronautic and aerospace engineering, etc. To ensure an uninterruptible power supply, the remaining useful life (RUL) prediction of lithium-ion batteries has attracted extensive attention in recent years. This paper proposed an improved unscented particle filter (IUPF) method for lithium-ion battery RUL prediction based on Markov chain Monte Carlo (MCMC). The method uses the MCMC to solve the problem of sample impoverishment in UPF algorithm. Additionally, the IUPF method is proposed on the basis of UPF, so it can also suppress the particle degradation existing in the standard PF algorithm. In this work, the IUPF method is introduced firstly. Then, the capacity data of lithium-ion batteries are collected and the empirical capacity degradation model is established. The proposed method is used to estimate the RUL of lithium-ion battery. The RUL prediction results demonstrate the effectiveness and advantage.     

Battery remaining useful life prediction at different discharge rates

Lithium-ion batteries are widely used in hybrid electric vehicles, consumer electronics, etc. As of today, given a room temperature, many battery prognostic methods working at a constant discharge rate have been proposed to predict battery remaining useful life (RUL). However, different discharge rates (DDRs) affect both usable battery capacity and battery degradation rate. Consequently, it is necessary to take DDRs into consideration when a battery prognostic method is designed. In this paper, we propose a discharge-rate-dependent battery prognostic method that is able to track usable battery capacity affected by DDRs in the process of battery degradation and to predict RUL at DDRs. An experiment was designed to collect accelerated battery life testing data at DDRs, which are used to investigate how DDRs influence usable battery capacity, to design a discharge-rate-dependent state space model and to validate the effectiveness of the proposed battery prognostic method. Results show that the proposed battery prognostic method can work at DDRs and achieve high RUL prediction accuracies at DDRs.     

Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model

Prediction of lithium-ion batteries remaining useful life (RUL) plays an important role in battery management system (BMS) used in electric vehicles. A novel approach which combines empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA) model is proposed for RUL prognostic in this paper. At first, EMD is utilized to decouple global deterioration trend and capacity regeneration from state-of-health (SOH) time series, which are then used in ARIMA model to predict the global deterioration trend and capacity regeneration, respectively. Next, all the separate prediction results are added up to obtain a comprehensive SOH prediction from which the RUL is acquired. The proposed method is validated through lithium-ion batteries aging test data. By comparison with relevance vector machine, monotonic echo sate networks and ARIMA methods, EMD-ARIMA approach gives a more satisfying and accurate prediction result.     

国内电动车用动力锂离子电池现状

锂离子电池具有比能量高、比功率大、使用寿命长(循环性能)、工作范围宽等特点,已经应用于纯电动汽车和混合动力汽车中。总结了国内电动车用动力锂离子电池的标准,以及电池结构、正极材料、负极材料、电解液、隔膜、电池管理系统等方面的研究现状,并对国内电动车用动力锂离子电池产业化现状和面临的技术难题进行了阐述。     

基于物联网的锂动力电池智能综合管理系统

锂动力电池是一种应用非水电解质溶液，并由锂合金或锂金属作为负极材料的电池，具有绿色环保、轻便、高能量密度、使用寿命长等特点。近年来，锂动力电池被广泛应用于电动工具、电动自行车等领域，并逐步应用到电动车辆与混合动力车领域。但是，原有的锂动力电池监控方法局限于电池组节点保护层面，无法保证将信息传输至监控平台。为提高电动汽车的电池智能综合管理系统的智能化与实时性水平，文中提出以物联网技术为基础，设计了一款基于物联网的锂动力电池智能综合管理系统，其以全面感知、获取锂动力电池的实时数据，并通过智能综合管理系统对相关数据的计算与分析，实现对锂动力电池的智能化控制。文中以构建物联网背景下的锂动力电池智能综合管理系统为目标，首先分析了锂动力电池智能综合管理系统的基本结构，然后从系统物理层、系统网络层、系统应用层等层面出发，研究了锂动力电池智能综合管理系统的相关设计。     

A new method to estimate the state of charge of the green battery

Green batteries have attracted great attention due to the characteristics of its high performance and non-pollution. In order to understand the working condition of the batteries and get a better estimation effect on the state of charge (SoC), the following works had been done in NMC18650 lithium ion battery. Firstly, the hybrid pulse power characteristic (HPPC) test was carried out on the battery with different currents. The extended Kalman filter (EKF) was used to estimate the SoC of the battery based on combined model and Thevenin model whose parameters were identified in advance; furthermore, the estimation results of the two models were compared. Secondly, an improved open circuit voltage (OCV) based method was proposed. Its improvements were as follows: the changes of OCV on battery were recorded during the current interruption, and it was assumed that the OCV had been restored to a certain degree if the change of OCV did not exceed 0.001 V in 10 s. Finally, two new improved methods were proposed based on the combined model, and the estimation effects of the above methods were compared under dynamic condition. The results showed that the accuracy of the Thevenin model was slightly higher than that of the combined model, and the accuracies of the two improved methods were both improved. Especially the second improved method had the least error and the best adaptability; the maximum error under dynamic conditions was 3.07%, and the average error was less than 1%, which only accounted for 22.46% of the un- improved. The improved OCV based method proposed in this study is applied to the SoC estimation of batteries, which greatly improves the accuracy of the estimation; moreover, the method is easy to implement and suitable for estimating SoC in real time.     

一种改进的电动汽车锂电池SOC算法

本文提出了一种改进的安时积分法来估算电动汽车电池剩余电量(SOC)的方法。在大量实验基础上,分析了影响安时积分法估算精度的参数,采用最小二乘法建立了数学模型以及实现了各参数修正因子,经实验表明,此方法提高了电池SOC计算的精度,达到了电动汽车的应用要求。     

Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model

The prediction of Remaining useful life (RUL) and the estimation of State of health (SOH) are extremely important issues for operating performance of Lithium-ion (Li-ion) batteries in the Battery management system (BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network (WNN) with Unscented particle filter (UPF) model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform (DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries' lifespan.