Battery prognostics and health management for electric vehicles under industry 4.0

Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead before EVs can establish themselves as the dominant force in the global automotive market. Concerns such as range anxiety, battery aging, and safety issues remain significant challenges.

State of Health Estimation of LiFePO_(4) Batteries for Battery Management Systems

When considering the mechanism of the batteries,the capacity reduction at storage(when not in use)and cycling(during use)and increase of internal resistance is because of degradation in the chemical composition inside the batteries.To optimize battery usage,a battery management system(BMS)is used to estimate possible aging effects while different load profiles are requested from the grid.This is specifically seen in a case when the vehicle is connected to the net(online through BMS).During this process,the BMS chooses the optimized load profiles based on the least aging effects on the battery pack.The major focus of this paper is to design an algorithm/model for lithium iron phosphate(LiFePO4)batteries.The model of the batteries is based on the accelerated aging test data(data from the beginning of life till the end of life).The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery.By the analysis of the test data,the complete trend of the battery aging and the factors on which the aging is depending on is identified,the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing.The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures.A Linear and non-linear model-based approach is used based on statistical data.The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks.Self-adaptive characteristic map using a lookup table was also used.The nonlinear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

A review of data-driven whole-life state of health prediction for lithium-ion batteries:Data preprocessing,aging characteristics,algorithms,and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.

Boosting battery state of health estimation based on self-supervised learning

State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

Towards Long Lifetime Battery:AI-Based Manufacturing and Management

Technologies that accelerate the delivery of reliable battery-based energy storage will not only contribute to decarbonization such as transportation electrification,smart grid,but also strengthen the battery supply chain.As battery inevitably ages with time,losing its capacity to store charge and deliver it efficiently.This directly affects battery safety and efficiency,making related health management necessary.Recent advancements in automation science and engineering raised interest in AI-based solutions to prolong battery lifetime from both manufacturing and management perspectives.This paper aims at presenting a critical review of the state-of-the-art AI-based manufacturing and management strategies towards long lifetime battery.First,AI-based battery manufacturing and smart battery to benefit battery health are showcased.Then the most adopted AI solutions for battery life diagnostic including state-of-health estimation and ageing prediction are reviewed with a discussion of their advantages and drawbacks.Efforts through designing suitable AI solutions to enhance battery longevity are also presented.Finally,the main challenges involved and potential strategies in this field are suggested.This work will inform insights into the feasible,advanced AI for the health-conscious manufacturing,control and optimization of battery on different technology readiness levels.

Battery Modeling: A Versatile Tool to Design Advanced Battery Management Systems

Fundamental physical and (electro) chemical principles of rechargeable battery operation form the basis of the electronic network models developed for Nickel-based aqueous battery systems, including Nickel Metal Hydride (NiMH), and non-aqueous battery systems, such as the well-known Li-ion. Refined equivalent network circuits for both systems represent the main contribution of this paper. These electronic network models describe the behavior of batteries during normal operation and during over (dis) charging in the case of the aqueous battery systems. This makes it possible to visualize the various reaction pathways, including convention and pulse (dis) charge behavior and for example, the self-discharge performance.

Review on Lithium-ion Battery PHM from the Perspective of Key PHM Steps

Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.

A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve

The estimation of State of Health(SOH)for battery packs used in Electric Vehicles(EVs)is a complex task with significant importance,accompanied by several challenges.This study introduces a data-fusion model approach to estimate the SOH of battery packs.The approach utilizes dual Gaussian Process Regressions(GPRs)to construct a data-driven and non-parametric aging model based on charging-based Aging Features(AFs).To enhance the accuracy of the aging model,a noise model is established to replace the random noise.Subsequently,the statespace representation of the aging model is incorporated.Additionally,the Particle Filter(PF)is introduced to track the unknown state in the aging model,thereby developing the data-fusion-model for SOH estimation.The performance of the proposed method is validated through aging experiments conducted on battery packs.The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation,with maximum errors less than 1.5%.Compared to conventional techniques such as GPR and Support Vector Regression(SVR),the proposed method exhibits higher estimation accuracy and robustness.

一种钠离子电池的健康状态估计方法

钠离子电池因其经济性和材料来源丰富而成为有巨大潜力的储能设备。准确评估电池健康状态对于确保其高效、安全运行至关重要。结合循环神经网络和扩展卡尔曼滤波技术,提出一种新颖的健康状态估计框架。利用循环神经网络对时间序列数据的处理能力为健康状态估计提供强大的支持,而扩展卡尔曼滤波则用于确保状态估计的鲁棒性。通过对3个钠离子电池的试验验证,该方法显示了出色的估计效果,其中估计值与实际值的平均绝对误差约为1.79%,均方根误差约为1.38%,模型拟合度高达96.28%。此研究不仅提供了一种钠离子电池健康状态的高效估计方法,还为实际应用中的电池管理和维护提供了宝贵的参考。

基于健康特征筛选与GWO-LSSVM的锂电池健康状态预测

锂电池健康状态(SOH)预测是电池管理系统(BMS)最重要的功能之一,准确有效地预测锂电池SOH可有效提升设备利用率,保证系统稳定性。为了提高预测准确度,本文提出一种基于健康特征筛选与灰狼优化算法(GWO)-最小二乘支持向量机(LSSVM)的锂电池SOH预测方法,首先采用灰色关联分析(GRA)法计算每个健康特征相对于锂电池SOH的灰色关联度,并将灰色关联度进行排序,确定SOH预测的主要健康特征;然后针对LSSVM模型参数需靠人为经验选择的问题,采用寻优性能较好的灰狼优化算法对其进行优化选择并构建GWO-LSSVM模型;最后基于NASA数据集,对模型进行训练和测试,并与其他3种模型的预测结果进行对比,对比结果证明了本文所提方法的有效性。

锂离子电池在数据中心的安全应用

介绍锂离子电池(以下简称“锂电池”)的工作原理及其在数据中心应用的优势和局限性;阐述国家标准和团体标准针对锂电池在数据中心应用的消防安全隐患提出的相关要求;分析数据中心UPS系统应用铅酸电池与锂电池的特性差异、UPS配合铅酸电池与配合锂电池在电池管理方面的差异,以及UPS备用锂电池与电化学储能的区别;总结锂电池在数据中心安全应用的前景和挑战,并展望未来的研究方向。

铅蓄电池绿色工厂设计

为了实现铅蓄电池行业健康、可持续发展的绿色生产之路,总结了铅蓄电池绿色工厂设计经验,对标《绿色工厂评价通则》,汇总建筑工程、工艺设备、公用设备、环保系统、能源系统、管理系统和资源投入等设计要点,介绍了铅蓄电池绿色工厂的设计要点,为铅蓄电池行业绿色工厂设计提供参考。

基于深度学习的电池健康状态监测与预测系统设计

文章旨在设计一套能够实时监测锂离子电池健康状态并进行准确预测的系统。通过整合改进的完全自适应噪声集合经验模态分解(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,ICEEMDAN)信号分解算法、支持向量回归(Support Vector Regression,SVR)算法以及长短期记忆(Long Short-Term Memory,LSTM)网络模型,构建了一个综合性的电池健康管理系统。通过对锂离子电池进行恒流恒压充电、恒流放电以及阻抗测量等,利用所获取的数据进行预处理、分解及模型训练。结果显示,所提出的系统能够有效预测电池的容量、健康状态及剩余使用时间,与实际数据符合度较高。该研究为电池健康管理领域的发展提供了有效参考,具有一定的理论和应用价值。

Experimental Study on Thermal Management of Cylindrical Li-ion Battery with Flexible Microchannel Plates

A novel thermal management system of cylindrical Li-ion battery with the liquid cooling in flexible microchannel plate was established in the study. The experiments were conducted with R141 b in flexible microchannel plates. The cooling system with the flexible aluminum microchannels can effectively transfer heat from battery to the cooling refrigerant R141 b based on flow boiling. A battery module with five cells along flow channel was chosen to study the effects of contact surface area and mass flux on the thermal performance and electrochemical characteristics in the experiments. Three types of structure with different contact areas were studied and their performances were compared with the experiments without cooling structures. The experiments were carried out at the same discharge rate with the inlet mass flow rates of 0–10 kg/h. For the inlet mass flow rate of 5.98 kg/h, the surface temperature and temperature uniformity of battery were the best, and the output voltage and capacity of batteries were higher than those under other mass flow rates. With given inlet mass flow rates, the five series cells exhibited different electrochemical performances, including output voltage and discharge capacity, due to the different refrigerant flow states in the microchannels. Finally, an optimal design was presented with thermal performances, macroscopic electrochemical characteristics, inlet mass flow rates and cooling performance taken into consideration.

Cost-efficient Thermal Management for a 48V Li-ion Battery in a Mild Hybrid Electric Vehicle

The 48V mild hybrid system is a cost-efficient solution for original equipment manufacturers to meet increasingly stringent fuel consumption requirements.However,hybrid functions such as auto-stop/start and brake regeneration are unavailablewhen a 48V battery is at very low temperature because of its limited charge and discharge capability.Therefore,it is important to develop cost-efficient thermal management to warm-up the battery of a 48V mild hybrid electric vehicle(HEV)to recover hybrid functions quickly in cold climate.Following the model-based“V”process,we first define the requirements and then design different mechanisms to heat a 48V battery.Afterward,we build a 48V battery model in LMS AMESim and conduct co-simulation with simplified battery management system and hybrid control unit algorithms in MATLAB Simulink for analysis.Finally,we carry out a series of vehicle experiments at low temperature and observe the effect of heating to validate the design.Both simulation results and experimental data show that a cold 48V battery placed in a cabin with hot air can be heated effectively in the developed“Enhanced Generator Mode with 48V Battery”mode.The entire design is in a newly developed software that cyclically charges and discharges a 48V battery for quick warm-up in cold temperature without needing any additional hardware such as a heater,making it a cost-efficient solution for HEVs.

基于多尺度数据融合的锂电池健康状态评估

针对锂电池健康状态评估问题,提出一种以人工神经网络为核心多尺度数据融合框架下的锂电池健康状态评估方法.选取内阻、充电电压样本熵和等压降放电时间作为典型特征参数,建立3层分布式人工神经网络对特征参数进行多尺度融合计算,以计算拟合输出结果作为评估健康状态的参考值,并通过美国国家航空航天局(national aeronautics and space administration,NASA)试验数据集进行验证.结果表明:提出的评估方法能够基于锂电池充放电测量数据和解算特征参数,利用多尺度数据融合框架迅速迭代收敛,完成锂电池健康状态评估拟合;该评估方法的计算结果与测试平台实测值相比,平均误差小于3%,评估性能衰退趋势与实际劣化趋势一致.

锂电池健康状态均衡技术综述

锂电池健康状态(State of Health,SOH)均衡技术是电池管理系统(Battery Management System,BMS)的关键技术之一。实现锂电池SOH均衡可使系统内所有锂电池同时达到报废标准,降低锂电池维护和更换费用,提高锂电池容量利用率。文中对SOH定义和不均衡影响因素进行介绍,指出影响SOH均衡的因素。从主动均衡、被动均衡和复合均衡三个角度出发,对目前发表的锂电池SOH均衡方案进行分类和总结。重点分析现有主动、被动和复合SOH均衡方案原理、优缺点及面临的问题。同时指出锂电池SOH均衡技术未来发展及改进方向,以期实现锂电池SOH均衡技术突破。

Numerical and Experimental Investigation on the Performance of Battery Thermal Management System Based on Micro Heat Pipe Array

Battery thermal management is very crucial for the safe and long-term operation of electric vehicles or hybrid electric vehicles.In this study,numerical simulation method is adopted to simulate the temperature field of Li-ion battery cell and module.It is proved that the maximum temperature and maximum temperature difference of battery cell and module increase with the increase of charge/discharge rate(C-rate)of the battery.For battery module,it can reach a maximum temperature of 61.1℃at a C-rate of 2 under natural convection condition with the ambient temperature of 20.0℃.A battery thermal management system based on micro heat pipe array(BTMS-MHPA)is deeply investigated.Experiments are conducted to compare the cooling effect on the battery module with different cooling methods,which include natural cooling,only MHPA,MHPA with fan.The maximum temperature of battery module which is cooled by MHPA with a fan is 43.4℃at a C-rate of 2,which is lower than that in the condition of natural cooling.Meanwhile,the maximum temperature difference was also greatly reduced by the application of MHPA cooling.The experimental results confirm that the feasibility and superiority of the BTMS-MHPA for guaranteeing the working temperature range and temperature uniformity of the battery.

基于模型的锂离子电池SOC及SOH估计方法研究进展

电池管理系统是保证锂离子电池高效、安全运行的重要手段。在电池管理系统功能中,电池状态估计,特别是荷电状态(state of charge,SOC)估计和健康状态(state of health,SOH)估计至关重要。SOC/SOH不仅与全生命周期内电池安全运行直接相关,也是其他功能有效实现的必要前提。本文围绕模型类电池状态估计方法,综述了国内外在锂离子电池模型构建、SOC及SOH估计方法方面的研究进展;指出了模型类状态估计方法存在的难点和局限,提出了今后研究重点。