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.

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.

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.

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.

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.

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.

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.

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.

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.

Communication-less Management Strategy for Electric Vehicle Charging in Droop-controlled Islanded Microgrids

Adopting high penetration levels of electric vehicles(EVs)necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on pow-er distribution networks.Currently,most of the proposed Ev charging management techniques rely on the availability of high-bandwidth communication links.Such techniques are far from realization due to①the lack of utility-grade communica-tion systems in many cases such as secondary(low-voltage)pow-er distribution systems to which EVs are connected,rural ar-eas,remote communities,and islands,and②existing fears and concerns about the data privacy of EV users and cyber-physical security.For these cases,appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements.Accordingly,this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded mi-crogrids.The proposed strategy is autonomous,as it is based on the measurement of system frequency and local bus voltages.The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed genera-tors(DGs)are in short supply by allocating more system capaci-ty to the EVs with less charging in the past.Furthermore,a novel communication-less EV load shedding scheme is incorpo-rated into the management strategy to provide relief to the mi-crogrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages.Numerical simulations demonstrate the superiority of the pro-posed strategy over the state-of-the-art controllers in modulat-ing the EV charging demand to counteract microgrid instability.

Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production

Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.

Critical review and functional safety of a battery management system for large‑scale lithium‑ion battery pack technologies

The battery management system(BMS)is the main safeguard of a battery system for electric propulsion and machine electrifcation.It is tasked to ensure reliable and safe operation of battery cells connected to provide high currents at high voltage levels.In addition to efectively monitoring all the electrical parameters of a battery pack system,such as the voltage,current,and temperature,the BMS is also used to improve the battery performance with proper safety measures within the system.With growing acceptance of lithium-ion batteries,major industry sectors such as the automotive,renewable energy,manufacturing,construction,and even some in the mining industry have brought forward the mass transition from fossil fuel dependency to electric powered machinery and redefned the world of energy storage.Hence,the functional safety considerations,which are those relating to automatic protection,in battery management for battery pack technologies are particularly important to ensure that the overall electrical system,regardless of whether it is for electric transportation or stationary energy storage,is in accordance with high standards of safety,reliability,and quality.If the system or product fails to meet functional and other safety requirements on account of faulty design or a sequence of failure events,then the environment,people,and property could be endangered.This paper analyzed the details of BMS for electric transportation and large-scale energy storage systems,particularly in areas concerned with hazardous environment.The analysis covers the aspect of functional safety that applies to BMS and is in accordance with the relevant industrial standards.A comprehensive evaluation of the components,architecture,risk reduction techniques,and failure mode analysis applicable to BMS operation was also presented.The article further provided recommendations on safety design and performance optimization in relation to the overall BMS integration.

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.

An Overview to the Concept of Smart Coupling and Battery Management for Grid Connected Photovoltaic Battery System

The paper gives an overview on the need for smart coupling for battery management in grid integrated renewable energy system （RES）. Grid integrated photovoltaic （PV） battery system, as being popular and extensively used has been discussed in the paper. Smart coupling refers to intelligent grid integration such that it can foresee local network conditions and issue battery power flow management strategy accordingly to shave the peak PV and peak load. Therefore, a need for predictive energy management arises for smart integration to the grid and supervision of the power flow in accordance to the grid conditions. This is also a running project at the Institute of Energy Systems （INES）, Offenburg University of Applied Science, Germany since January, 2015. The paper should provide insights to the motivation, need and gives an outlook to the features of desired predictive energy management system （PEMS）.

Battery Management for the Electric Hydraulic Pump System of a Lifting Trolley

This study proposed a battery management approach for the electric hydraulic pump system of a lifting trolley.The pump system was powered by two 12-V lead-acid batteries in series.Because direct measurement of the actual battery state of charge is unlikely,it has mostly been determined through estimation based on the measured open-circuit voltage.A discharge current will result in a voltage drop and hence a lower voltage during discharge;however,the battery voltage will return to the original open-circuit voltage once the discharge stops.The operating current of the electric hydraulic pump system employed in this study was associated with three factors:the lifting height,lifting load,and battery state of charge.The operating current remained constant during the first half of the lifting phase and increased gradually with the lifting height in the second half.The operating current peaked when the lifting height reached the maximum.The power management approach for the electric hydraulic pump system featured the following basic functions:overcharge protection,overdischarge protection,short-circuit protection,overload protection,and an operating timer established in accordance with the system’s operating current variation.According to the manufacturer-defined maximum lifting load and lifting height of the lifting trolley,this study conducted experiments to obtain the maximum required operating time.An operating time greater than the maximum required operating time indicates the occurrence of an unexpected event,discharge should be stopped until the fault is resolved.

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.

A Numerical Investigation of the Thermal Performances of an Array of Heat Pipes for Battery Thermal Management

A comparative numerical study has been conducted on the thermal performance of a heat pipe cooling system considering several influential factors such as the coolant flow rate,the coolant inlet temperature,and the input power.A comparison between numerical data and results available in the literature has demonstrated that our numerical procedure could successfully predict the heat transfer performance of the considered heat pipe cooling system for a battery.Specific indicators such as temperature,heat flux,and pressure loss were extracted to describe the characteristics of such a system.On the basis of the distributions of the temperature ratio of the battery surface,together with the heat flux and the streamlines around the heat pipe condenser,we conclude that the low disturbance of the coolant is the cause of the temperature gradient along the fluid flow direction.

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.