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.

Adaptive battery thermal management systems in unsteady thermal application contexts

With the increasing attention paid to battery technology,the microscopic reaction mechanism and macroscopic heat transfer process of lithium-ion batteries have been further studied and understood from both academic and industrial perspectives.Temperature,as one of the key parameters in the physical fra mework of batteries,affects the performa nce of the multi-physical fields within the battery,a nd its effective control is crucial.Since the heat generation in the battery is determined by the real-time operating conditions,the battery temperature is essentially controlled by the real-time heat dissipation conditions provided by the battery thermal management system.Conventional battery thermal management systems have basic temperature control capabilities for most conventional application scenarios.However,with the current development of la rge-scale,integrated,and intelligent battery technology,the adva ncement of battery thermal management technology will pay more attention to the effective control of battery temperature under sophisticated situations,such as high power and widely varied operating conditions.In this context,this paper presents the latest advances and representative research related to battery thermal management system.Firstly,starting from battery thermal profile,the mechanism of battery heat generation is discussed in detail.Secondly,the static characteristics of the traditional battery thermal management system are summarized.Then,considering the dynamic requirements of battery heat dissipation under complex operating conditions,the concept of adaptive battery thermal management system is proposed based on specific research cases.Finally,the main challenges for battery thermal management system in practice are identified,and potential future developments to overcome these challenges are presented and discussed.

Functional thermal fluids and their applications in battery thermal management:A comprehensive review

With the increasing requirements for fast charging and discharging,higher requirements have been put forward for the thermal management of power batteries.Therefore,there is an urgent need to develop efficient heat transfer fluids.As a new type of heat transfer fluids,functional thermal fluids mainly includ-ing nanofluids(NFs)and phase change fluids(PCFs),have the advantages of high heat carrying density,high heat transfer rate,and broad operational temperature range.However,challenges that hinder their practical applications remain.In this paper,we firstly overview the classification,thermophysical prop-erties,drawbacks,and corresponding modifications of functional thermal fluids.For NFs,the high ther-mal conductivity and high convective heat transfer performance were mainly elaborated,while the stability and viscosity issues were also analyzed.And then for PCFs,the high heat carrying density was mainly elaborated,while the problems of supercooling,stability,and viscosity were also analyzed.On this basis,the composite fluids combined NFs and PCFs technology,has been summarized.Furthermore,the thermal properties of traditional fluids,NFs,PCFs,and composite fluids are compared,which proves that functional thermal fluids are a good choice to replace traditional fluids as coolants.Then,battery thermal management system(BTMS)based on functional thermal fluids is summarized in detail,and the thermal management effects and pump consumption are compared with that of water-based BTMS.Finally,the current technical challenges that parameters optimization of functional thermal fluids and structures optimization of BTMS systematically are presented.In the future,it is necessary to pay more attention to using machine learning to predict thermophysical properties of functional thermal fluids and their applications for BTMS under actual vehicle conditions.

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.

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%.

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.

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 paper,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 the high current density operation of 300 mA cm^(-2).

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).

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.

Battery Management System with State ofCharge Indicator for Electric Vehicles

Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.

New battery management system for multi-cell li-ion battery packs

This paper proposes a new battery management system （BMS） based on a master-slave control mode for multi-cell li-ion battery packs. The proposed BMS can be applied in li-ion battery packs with any cell number. The whole system is composed of a master processor and a string of slave manager cells （SMCs）. Each battery cell corresponds to an SMC. Unlike the conventional BMS, the proposed one has a novel method for communication, and it collects the battery status information in a direct and simple way. An SMC communicates with its adjacent counterparts to transfer the battery information as well as the commands from the master processor. The nethermost SMC communicates with the master processor directly. This method allows the battery management chips to be implemented in a standard CMOS （ complementary metal-oxide-semiconductor transistor） process. A testing chip is fabricated in the CSMC 0.5 μm 5 V N-well CMOS process. The testing results verify that the proposed method for data communication and the battery management system can protect and manage multi-cell li-ion battery packs.

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 U+0028 ADP U+0029 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. © 2017 Chinese Association of Automation.

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.

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.

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.

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.

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.

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.

Design and parametric optimization of thermal management of lithium-ion battery module with reciprocating air-flow

Single cell temperature difference of lithium-ion battery(LIB) module will significantly affect the safety and cycle life of the battery. The reciprocating air-flow module created by a periodic reversal of the air flow was investigated in an effort to mitigate the inherent temperature gradient problem of the conventional battery system with a unidirectional coolant flow with computational fluid dynamics(CFD). Orthogonal experiment and optimization design method based on computational fluid dynamics virtual experiments were developed. A set of optimized design factors for the cooling of reciprocating air flow of LIB thermal management was determined. The simulation experiments show that the reciprocating flow can achieve good heat dissipation, reduce the temperature difference, improve the temperature homogeneity and effectively lower the maximal temperature of the modular battery. The reciprocating flow improves the safety, long-term performance and life span of LIB.