Heat transfer enhanced inorganic phase change material compositing carbon nanotubes for battery thermal management and thermal runaway propagation mitigation

Developing technologies that can be applied simultaneously in battery thermal management(BTM)and thermal runaway(TR)mitigation is significant to improving the safety of lithium-ion battery systems.Inorganic phase change material(PCM)with nonflammability has the potential to achieve this dual function.This study proposed an encapsulated inorganic phase change material(EPCM)with a heat transfer enhancement for battery systems,where Na_(2)HPO_(4)·12H_(2)O was used as the core PCM encapsulated by silica and the additive of carbon nanotube(CNT)was applied to enhance the thermal conductivity.The microstructure and thermal properties of the EPCM/CNT were analyzed by a series of characterization tests.Two different incorporating methods of CNT were compared and the proper CNT adding amount was also studied.After preparation,the battery thermal management performance and TR propagation mitigation effects of EPCM/CNT were further investigated on the battery modules.The experimental results of thermal management tests showed that EPCM/CNT not only slowed down the temperature rising of the module but also improved the temperature uniformity during normal operation.The peak battery temperature decreased from 76℃to 61.2℃at 2 C discharge rate and the temperature difference was controlled below 3℃.Moreover,the results of TR propagation tests demonstrated that nonflammable EPCM/CNT with good heat absorption could work as a TR barrier,which exhibited effective mitigation on TR and TR propagation.The trigger time of three cells was successfully delayed by 129,474 and 551 s,respectively and the propagation intervals were greatly extended as well.

Managing the surge:A comprehensive review of the entire disposal framework for retired lithium-ion batteries from electric vehicles

Anticipating the imminent surge of retired lithium-ion batteries(R-LIBs)from electric vehicles,the need for safe,cost-effective and environmentally friendly disposal technologies has escalated.This paper seeks to offer a comprehensive overview of the entire disposal framework for R-LIBs,encompassing a broad spectrum of activities,including screening,repurposing and recycling.Firstly,we delve deeply into a thorough examination of current screening technologies,shifting the focus from a mere enumeration of screening methods to the exploration of the strategies for enhancing screening efficiency.Secondly,we outline battery repurposing with associated key factors,summarizing stationary applications and sizing methods for R-LIBs in their second life.A particular light is shed on available reconditioning solutions,demonstrating their great potential in facilitating battery safety and lifetime in repurposing scenarios and identifying their techno-economic issues.In the realm of battery recycling,we present an extensive survey of pre-treatment options and subsequent material recovery technologies.Particularly,we introduce several global leading recyclers to illustrate their industrial processes and technical intricacies.Furthermore,relevant challenges and evolving trends are investigated in pursuit of a sustainable end-of-life management and disposal framework.We hope that this study can serve as a valuable resource for researchers,industry professionals and policymakers in this field,ultimately facilitating the adoption of proper disposal practices.

电动汽车BMS从控板热分析及散热优化

针对电动汽车电池管理系统从控板服役过程中因温度过高和不均影响整车动力性、安全性问题,本文基于CFD理论,采用Icepak软件建立并验证了某商用BMS从控板热分析模型。首次在车载服役条件下,基于热分析模型开展了温度场分析和热均匀性优化研究。BMS从控板热仿真分析表明,均衡模块及供电模块因局部积热温度均超过BMS的设计温度限值60℃,整个BMS从控板最大温差为21.0℃。为此,进一步开展了BMS从控板散热路径分析,并通过改变均衡电阻间距、布局、PCB基材以及增设导热硅胶垫进行散热优化设计。提高了BMS从控板的散热能力,使BMS从控板的最高温度控制在设计规定值60℃以下,同时整个电路板的温差降为6.9℃,提高了BMS从控板在实际车载服役条件下的安全性和可靠性,可望为BMS从控板热设计与优化提供理论方法。

Research on Performance Optimization of Liquid Cooling and Composite Phase Change Material Coupling Cooling Thermal Management System for Vehicle Power Battery

The serpentine tube liquid cooling and composite PCM coupled cooling thermal management system is designed for 18650 cylindrical power batteries,with the maximum temperature and temperature difference of the power pack within the optimal temperature operating range as the target.The initial analysis of the battery pack at a 5C discharge rate,the influence of the single cell to cooling tube distance,the number of cooling tubes,inlet coolant temperature,the coolant flow rate,and other factors on the heat dissipation performance of the battery pack,initially determined a reasonable value for each design parameter.A control strategy is used to regulate the inlet flow rate and coolant temperature of the liquid cooling system in order to make full use of the latent heat of the composite PCM and reduce the pump’s energy consumption.The simulation results show that the maximum battery pack temperature of 309.8 K and the temperature difference of 4.6 K between individual cells with the control strategy are in the optimal temperature operating range of the power battery,and the utilization rate of the composite PCM is up to 90%.

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.

A Hydrogen Iron Flow Battery with High Current Density and Long Cyclability Enabled Through Circular Water Management

The hydrogen-iron(HyFe)flow cell has great potential for long-duration energy storage by capitalizing on the advantages of both electrolyzers and flow batteries.However,its operation at high current density(high power)and over continuous cycling testing has yet to be demonstrated.In this article,we discuss our design and demonstration of a water-management strategy that supports high current and long-cycling performance of a HyFe flow cell.Water molecules associated with the movement of protons from the iron electrode to the hydrogen electrode are sufficient to hydrate the membrane and electrode at a low current density of 100 mA cm^(-2)during the charge process.At higher charge current density,more aggressive measures must be taken to counter back-diffusion driven by the acid concentration gradient between the iron and hydrogen electrodes.Our water-management approach is based on water vapor feeding in the hydrogen electrode and water evaporation in the iron electrode,thus enabling high current density operation of 300 mA cm^(-2).

Reinforcement Learning-Based Electric Vehicles Energy Management Strategy with Battery Thermal Model

The promotion of electric vehicles(EVs)is restricted due to their short cruising range.It is desirable to design an effective energy management strategy to improve their energy efficiency.Most existing work concerning energy management strategies focused on hybrids rather than the EVs.The work focusing on the energy management strategy for EVs mainly uses the traditional optimization strategies,thereby limiting the advantages of energy economy.To this end,a novel energy management strategy that considered the impact of battery thermal effects was proposed with the help of reinforcement learning.The main idea was to first analyze the energy flow path of EVs,further formulize the energy management as an optimization problem,and finally propose an online strategy based on reinforcement learning to obtain the optimal strategy.Additionally,extensive simulation results have demonstrated that our strategy reduces energy consumption by at least 27.4%compared to the existing methods.

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.

基于BMS与云平台的动力电池健康管理体系

为解决电动汽车现有BMS系统对锂离子动力电池SOH评估与预测难以满足多种工况条件、各种类动力电池,且难同时兼顾预测精度与反馈速度等应用缺陷,提出了一套全新的EVs电池健康管理系统设计思路,采用了结合云计算与存储平台,融入BMS评估体系等关键方法;通过BMS增加5G通讯模块,利用5G/4G信号实时上传电芯数据,经过云平台搭载的多种SOH评估模型与算法,多线程在线计算得到预测结果,及时反馈至用户端和BMS,实现电池健康管理;该体系的设计案例展示出较好的未来应用价值,为电动汽车电池管理设计提供了新方向。

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems.The main idea is to use the adaptive dynamic programming(ADP) technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery.Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally,simulation and comparison results are given to illustrate the performance of the presented method.

Experimental Investigation on Cooling/Heating Characteristics of Ultra-Thin Micro Heat Pipe for Electric Vehicle Battery Thermal Management

Due to the heat pipes’ transient conduction,phase change and fluid dynamics during cooling/heating with high frequency charging/discharging of batteries,it is crucial to investigate in depth the experimental dynamic thermal characteristics in such complex heat transfer processes for more accurate thermal analysis and design of a BTMS. In this paper,the use of ultra?thin micro heat pipe(UMHP) for thermal management of a lithium?ion battery pack in EVs is explored by experiments to reveal the cooling/heating characteristics of the UMHP pack. The cooling performance is evaluated under di erent constant discharging and transient heat inputs conditions. And the heating e ciency is assessed under several sub?zero temperatures through heating films with/without UMHPs. Results show that the pro?posed UMHP BTMS with forced convection can keep the maximum temperature of the pack below 40 °C under 1 ~ 3 C discharging,and e ectively reduced the instant temperature increases and minimize the temperature fluctuation of the pack during transient federal urban driving schedule(FUDS) road conditions. Experimental data also indicate that heating films stuck on the fins of UMHPs brought about adequate high heating e ciency comparing with that stuck on the surface of cells under the same heating power,but has more convenient maintenance and less cost for the BTMS. The experimental dynamic temperature characteristics of UMHP which is found to be a high?e cient and low?energy consumption cooling/heating method for BTMSs,can be performed to guide thermal analysis and optimiza?tion of heat pipe BTMSs.

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.

Design and management of lithium-ion batteries:A perspective from modeling,simulation,and optimization

Although the lithium-ion batteries(LIBs) have been increasingly applied in consumer electronics, electric vehicles,and smart grid, they still face great challenges from the continuously improving requirements of energy density, power density, service life, and safety. To solve these issues, various studies have been conducted surrounding the battery design and management methods in recent decades. In the hope of providing some inspirations to the research in this field, the state of the art of design and management methods for LIBs are reviewed here from the perspective of process systems engineering. First, different types of battery models are summarized extensively, including electrical model and multi-physics coupled model, and the parameter identification methods are introduced correspondingly. Next, the model based battery design methods are reviewed briefly on three different scales, namely, electrode scale, cell scale, and pack scale. Then, the battery model based battery management methods, especially the state estimation methods with different model types are thoroughly compared. The key science and technology challenges for the development of battery systems engineering are clarified finally.

Application of Digital Twin in Smart Battery Management Systems

Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery research.Firstly,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and control.Furthermore,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management system.We also give development opportunities for digital twin in the battery field.Finally we summarize the development trends and challenges of smart battery management.

Autonomous Multi-Factor Energy Flows Controller (AmEFC): Enhancing Renewable Energy Management with Intelligent Control Systems Integration

The transition to sustainable energy systems is one of the defining challenges of our time, necessitating innovations in how we generate, distribute, and manage electrical power. Micro-grids, as localized energy hubs, have emerged as a promising solution to integrate renewable energy sources, ensure energy security, and improve system resilience. The Autonomous multi-factor Energy Flow Controller (AmEFC) introduced in this paper addresses this need by offering a scalable, adaptable, and resilient framework for energy management within an on-grid micro-grid context. The urgency for such a system is predicated on the increasing volatility and unpredictability in energy landscapes, including fluctuating renewable outputs and changing load demands. To tackle these challenges, the AmEFC prototype incorporates a novel hierarchical control structure that leverages Renewable Energy Sources (RES), such as photovoltaic systems, wind turbines, and hydro pumps, alongside a sophisticated Battery Management System (BMS). Its prime objective is to maintain an uninterrupted power supply to critical loads, efficiently balance energy surplus through hydraulic storage, and ensure robust interaction with the main grid. A comprehensive Simulink model is developed to validate the functionality of the AmEFC, simulating real-world conditions and dynamic interactions among the components. The model assesses the system’s reliability in consistently powering critical loads and its efficacy in managing surplus energy. The inclusion of advanced predictive algorithms enables the AmEFC to anticipate energy production and consumption trends, integrating weather forecasting and inter-controller communication to optimize energy flow within and across micro-grids. This study’s significance lies in its potential to facilitate the seamless incorporation of RES into existing power systems, thus propelling the energy sector towards a more sustainable, autonomous, and resilient future. The results underscore the potential of such a system to revolutionize energy management practices and highlight the importance of smart controller systems in the era of smart grids.

Design of Real Time Battery Management Unit for PV-Hybrid System by Application of Coulomb Counting Method

This paper presents a real-time battery management unit designed by applying the Coulomb counting method and intended for use in an integrated renewable energy system for PV-Hybrid power supply. Battery management is required to stabilize hybrid systems and extend battery lifetimes. The battery management unit is divided into three main stages. Firstly, analysis of the basic components of the battery type used in the system is considered. Secondly, the state of charge (SOC) estimation method and the deterioration factor of the battery are analyzed. Finally, the overall battery management system, including a computer-based measurement and control unit, is constructed. The control system displays real-time information through LabVIEW 8.5 by estimating the state of charge through various measurements. The system will issue alerts when malfunctions are detected, and the operator can analyze and react to the system in real time to stabilize the system and extend the battery lifetime.

Thermal Management of Air-Cooling Lithium-Ion Battery Pack

Lithium-ion battery packs are made by many batteries, and the difficulty in heat transfer can cause many safety issues. It is important to evaluate thermal performance of a battery pack in designing process. Here, a multiscale method combining a pseudo-two-dimensional model of individual battery and three-dimensional computational fluid dynamics is employed to describe heat generation and transfer in a battery pack. The effect of battery arrangement on the thermal performance of battery packs is investigated. We discuss the air-cooling effect of the pack with four battery arrangements which include one square arrangement, one stagger arrangement and two trapezoid arrangements. In addition, the air-cooling strategy is studied by observing temperature distribution of the battery pack. It is found that the square arrangement is the structure with the best air-cooling effect, and the cooling effect is best when the cold air inlet is at the top of the battery pack. We hope that this work can provide theoretical guidance for thermal management of lithium-ion battery packs.

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.

A new state of charge determination method for battery management system

State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.

Physics-based battery SOC estimation methods:Recent advances and future perspectives

The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.