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The safety, durability and power density of lithium‐ion batteries (LIBs) are currently inadequate to satisfy the continuously growing demand of the emerging battery markets. Rapid progress has been made from material engineering to system design, combining experimental results and simulations to enhance LIB performance. Limited by spatial and temporal resolution, state‐of‐the‐art advanced characterization techniques fail to fully reveal the complex multi‐scale degradation mechanism in LIBs. Strengthening interaction and iteration between characterization and modeling improves the understanding of reaction mechanisms as well as design and management of LIBs. Herein, a seed cyber hierarchy and interactional network framework is demonstrated to evaluate the overall lifecycle of LIBs. The typical examples of bridging the characterization techniques and modeling are discussed. The critical parameters extracted from multi‐scale characterization can serve as digital inputs for modeling. Furthermore, advanced computational techniques including cloud computing, big data, machine learning, and artificial intelligence can also promote the comprehensive understanding and precise control of the whole battery lifecycle. Digital twins techniques will be introduced enabling the real‐time monitoring and control of LIBs, autonomous computer‐assisted characterizations and intelligent manufacturing. It is anticipated that this work will provide a roadmap for further intensive research on developing high‐performance LIBs and intelligent battery management. [ABSTRACT FROM AUTHOR]

• Validated single-particle model used to understand cell variations in battery packs. • Higher resistance cells in high stress locations reduces imbalance. • Impact of thermal gradients in battery packs simulated. • Increasing the temperature of poorly performing cells can homogenize pack currents. • Direction and magnitude of thermal gradient affects degradation rate. The performance of lithium-ion battery packs are often extrapolated from single cell performance however uneven currents in parallel strings due to cell-to-cell variations, thermal gradients and/or cell interconnects can reduce the overall performance of a large scale lithium-ion battery pack. In this work, we investigate the performance implications caused by these factors by simulating six parallel connected batteries based on a thermally coupled single particle model with the solid electrolyte interphase growth degradation mechanism modelled. Experimentally validated simulations show that cells closest to the load points of a pack experience higher currents than cells further away due to uneven overpotentials caused by the interconnects. When a cell with a four times greater internal impedance was placed in the location with the higher currents this actually helped to equalise the cell-to-cell current distribution, however if this was placed at a location furthest from the load point this would cause a ∼6% reduction in accessible energy at 1.5 C. The influence of thermal gradients can further affect this current heterogeneity leading to accelerated aging. Simulations show that in all cases, cells degrade at different rates in a pack due to the uneven currents, with this being amplified by thermal gradients. In the presented work a 5.2% increase in degradation rate, from −7.71 mWh/cycle (isothermal) to −8.11 mWh/cycle (non-isothermal) can be observed. Therefore, the insights from this paper highlight the highly coupled nature of battery pack performance and can inform designs for higher performance and longer lasting battery packs. [ABSTRACT FROM AUTHOR]

The precise and robust parameterization of the battery models are of crucial important to improve safety and efficiency of electric vehicles and other applications. However, the traditional parameter identification (PI) methods usually suffer from the inaccuracy and poor robustness due to their limited searching solution. In this article, a coupled hybrid adaptive particle swarm optimization-hybrid simulated annealing (HA-PSO) algorithm along with diverse improvements is promoted for precise and robust PI process. Three categories of equivalent circuit models are performed to validate the precision and adaptability for PI on three different types of batteries, and the simulation results confirm an excellent consistency with experimental data which can satisfy the requirement of battery management system (BMS). Additionally, the numerical analysis demonstrates that the method has a satisfactory convergence speed and reasonable distribution based on Monte Carlo method. These results confirm that the presented method can be used as an effective tool for parameterizing the battery model, delivering great potential to predict battery states and other related functions based on digital technologies and cloud-control platform. Image 1 • Validated method for developing adaptive parameterization on cloud-controlling. • Automagical adaptivity with algorithm from factors on swarm characteristics. • Comprehensive performances assessed by stable and dynamic operational conditions. • Broad future applications for cloud-battery management system. [ABSTRACT FROM AUTHOR]

State-of-Health (SOH) estimation of lithium-ion batteries is critical for reliable and efficient energy storage and usage. Traditional SOH estimation approaches either compromise on real-time performance or on estimation accuracy. This paper introduces an end-cloud collaboration approach that synergistically combines a cloud-based deep learning model with an end-based empirical model to achieve both high-accuracy and real-time SOH estimation. Utilizing the Transformer architecture, the cloud-side model employs multiple features extracted through signal analysis technology. On the other hand, the end-side employs a fast, iterative double exponential empirical model that provides real-time estimates. Two methods of end-cloud collaborative updating are presented, both leveraging Kalman Filter and Unscented Kalman Filter (UKF) to integrate and iteratively update the models. Evaluation shows that the cloud-side model achieves a Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of around 0.8 %, while the end-cloud collaboration system maintains these metrics at 1 %, successfully capturing nonlinear capacity rebound phenomena. The work contributes to the domain by offering an approach that balances computational load and latency, making it suitable for online applications. Under the Cyber Hierarchy and Interactional Network (CHAIN) framework, this end-cloud collaboration architecture holds promise for high-accuracy, real-time health management of lithium-ion batteries across their full lifecycle. The entire end-cloud collaboration system includes a cloud based deep learning model, an end-side fast empirical model and an end-cloud collaboration algorithm. On the cloud-side, firstly, data preprocessing such as data cleaning and filtering is performed on the data. Then, multiple features are extracted based on various signal analysis methods, including differential thermal voltammetry analysis, incremental capacity analysis and equal voltage discharge interval discharge time analysis. The deep learning model based on Transformer is constructed. On the end-side, the fast model is constructed based on a dual exponential empirical model. And the end-cloud collaboration algorithm is arranged on the end-side. Two end-cloud collaboration methods are used simultaneously, including result fusion and iterative update based on Kalman filter, and online parameter identification based on UKF by sequentially linking end and cloud models. Finally, under the cyber hierarchy and interactional network framework, the end-cloud collaboration approach proposed in this paper has the potential to realize high-accuracy and high-real-time health management of lithium batteries throughout the full life cycle. [Display omitted] • A high accuracy and real-time SOH estimation method is presented. • Deep learning model on cloud-side is constructed with Transformer model as the core. • Feature extraction is based on multi feature collaborative analysis. • Two end-cloud collaboration methods are proposed to update the end-side. [ABSTRACT FROM AUTHOR]

Lithium-ion battery is a kind of energy storage devices with complex internal reaction and many factors affecting its performance. Accurate prediction of its SOH (State of Health) is of great significance to prolong its service life and improve safety performance. However, the current prediction for SOH has the difficulties of selecting health factors and using data-driven methods with opaque mechanisms. In this paper, a data-driven model will be established with the capacity change during aging of lithium-ion batteries as a health indicator to realize precise prediction of capacity degradation. Firstly, the feature parameters were extracted and analyzed from the battery cyclic aging test dataset. Two in-situ nondestructive characterization methods, incremental capacity analysis curve and differential thermal voltammetry curve, were utilized to resolve the evolution paths of the feature parameters. After that, a data-driven battery capacity degradation estimation is realized based on the GEP (Gene Expression Programming) algorithm, the performance is compared with the existing vehicle-end and cloud-end models, and a higher-accuracy SOH stacking model is developed with an increase of less than 1 ms in computational time. The results indicated that the GEP model proposed in this paper has obvious advantages in terms of physical explain-ability, computational efficiency and robustness. • Establishing a high-precision battery SOH estimation model based on the GEP algorithm. • The relationship between ICA and DTV curves with aging evolution laws is revealed. • Fusion of GEP model with existing vehicle-end and cloud-end models using stacking strategy. [ABSTRACT FROM AUTHOR]

Polyethylene oxide (PEO) based polymer electrolytes have been widely used in solid-state lithium batteries (SSBs) owing to the high solubility of lithium salt, favourable ionic conductivity, flexibility for improved interfacial contact and scalable processing. In this work, we summarize the main limitations remaining to be solved before the large-scale commercialization of PEO-based SSBs, including (1) improving ionic conductivity toward high-rate performance and lower operating temperature, (2) enhancing mechanical strength for improved lithium dendrite resistance and large-scale processing, (3) strengthening electrochemical stability to match high energy density electrodes with high voltage, and (4) achieving high thermal stability toward safe operation. Meanwhile, the characterization methods to investigate the ion transportation mechanism, lithium dendrite growth and decomposition reaction are also discussed. [ABSTRACT FROM AUTHOR]

Summary: The temperature has a significant influence on the performance of lithium‐ion batteries (LiBs). Meanwhile, the heat‐generated accumulation in the battery can trigger the battery's thermal runaway. Hence, the battery thermal management system (BTMS) is essential to ensure the safe and reliable operation of the battery. This paper comprehensively reviewed key technologies of BTMS and proposed a novel digital solution to improve the battery system performance. Firstly, the heat generation mechanisms and the thermal models were reviewed. Then, this paper focuses on a review of liquid cooling thermal management methods. Simultaneously, the solid‐liquid phase change gas‐liquid phase change is also summarized in the phase change material cooling system, and a summary is presented for the existing novel phase change cooling systems. Additionally, the preheating methods to heat the LiBs at low temperatures and the emergency battery thermal barriers upon thermal runaway were discussed. Eventually, a new approach for the BTMS leveraging from the Cyber Hierarchy and Interactional Network framework is indicated and constructed the digital twin reflecting physical battery to improve the LiBs temperature control strategies. Besides, this paper provides a new direction for the design of BTMS through vehicle‐side sensing, edge computing, and cloud‐based digital twin three levels. Novelty Statement: A state‐of‐the‐art digital solution for battery thermal management system.A comprehensive review of battery thermal management methods.A novel guidance scheme for the design of a battery thermal management system. [ABSTRACT FROM AUTHOR]

Accurate early prediction of the degradation trajectory of lithium-ion batteries (LIBs) can accelerate battery development, production, and design optimization. However, existing early-stage prediction methods for degradation trajectory prediction face challenges in dealing with insufficient data and the generalization under different operational conditions. To address this issue, a novel transfer learning based data-driven method is proposed, which integrates convolutional neural network (CNN) and long short-term memory (LSTM) neural networks. Additionally, we incorporate a temporal attention (TA) mechanism to selectively focus on informative capacity fade patterns and leverage Bayesian Optimization (BO) for hyper-parameters optimization. The proposed method is validated using experimental degradation data from six 5Ah low-temperature batteries (−20 °C, −10 °C) and public datasets. Results demonstrate high accuracy leveraging merely 10% of initial battery data. For the low-temperature aging experimental data, the best prediction result proposed is 0.025 Ah root mean square error (RMSE) and 0.019 Ah mean absolute error (MAE). Compared to baseline CNN-LSTM models, our framework achieved an average reduction of 62.6% in RMSE for early battery capacity trajectory prediction, while maintaining high accuracy across diverse operational conditions. The proposed framework puts forward a promising solution toward enabling adaptive data-driven capacity monitoring under different practical application conditions. • Early degradation trajectory prediction method is proposed. • Bayesian optimization strategy efficiently optimizes the hyper-parameters. • Temporal attention mechanism is used to focus on informative fading patterns. • Validation is conducted on low-temperature experimental and public data. • Realizes high prediction accuracy across diverse operating conditions. [ABSTRACT FROM AUTHOR]

In order to prepare electrodes with high mass loading and areal capacities, the key issue is to achieve depolarization for both ion and electron transfer on the electrode material surface. In this work, through copolymerization of xanthan gum (XG) and amorphophallus konjac gum (KG) followed by an ice‐templating method, aligned electrodes with high areal mass loading of active materials are prepared. In addition to firmly holding active materials together, the prepared KG–XG copolymer also facilitates improved effective porosity as well as homogeneous dispersion of conductive agents (i.e., CNTs). Consequently, with minimum inactive components (i.e., binder and conductive agents), the proposed electrode structure delivers good cycling stability and rate capability under high areal loading (as high as 200 mg cm−2). The excellent electrochemical performance can be attributed to the unique aligned structure where the robust conductive network provides an efficient electron and lithium‐ion pathway, and the homogenous porosity is beneficial for the electrolyte percolation, hence the reduced polarization during charge transfer. In addition, this electrode preparation method is found to be universal as it is suitable for various types of anode and cathode materials. [ABSTRACT FROM AUTHOR]

An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions. [ABSTRACT FROM AUTHOR]

Summary: The rapid growth of transportation demand has been enlarged strongly which has promoted electric vehicles powered by lithium‐ion batteries. However, the inconsistencies within the battery pack will deteriorate over the lifecycle and affect the performance of electric vehicles. Therefore, various thermal management systems and equalization systems have been applied in battery management system to deal with the inconsistencies, extend battery service life, and improve safety performance. This review summarizes the origination of inconsistency within lithium‐ion batteries from production to usage process, and then introduces the classification methods and application scenarios of the balance management system in detail. Based on the circuit topology, equalization systems can be classified into passive and active topologies. Active topologies are widely researched due to the advantages of high equalization efficiency and high speed, and the state‐of‐art innovations are presented and compared from the prospective of circuit, energy flow, efficiency and system complexity. In addition, this review focuses on the mainstream equalization strategies based on the analysis of balancing variables and control algorithms in terms of efficiency, complexity and stability, especially in the areas of variables optimal selection and advanced control algorithms. It is expected that innovations such as cloud control methods and hybrid balancing systems equipped with thermal management will become the future direction of lithium‐ion equalization technologies. [ABSTRACT FROM AUTHOR]

Summary: The rapid growth of transportation demand has been enlarged strongly which has promoted electric vehicles powered by lithium‐ion batteries. However, the inconsistencies within the battery pack will deteriorate over the lifecycle and affect the performance of electric vehicles. Therefore, various thermal management systems and equalization systems have been applied in battery management system to deal with the inconsistencies, extend battery service life, and improve safety performance. This review summarizes the origination of inconsistency within lithium‐ion batteries from production to usage process, and then introduces the classification methods and application scenarios of the balance management system in detail. Based on the circuit topology, equalization systems can be classified into passive and active topologies. Active topologies are widely researched due to the advantages of high equalization efficiency and high speed, and the state‐of‐art innovations are presented and compared from the prospective of circuit, energy flow, efficiency and system complexity. In addition, this review focuses on the mainstream equalization strategies based on the analysis of balancing variables and control algorithms in terms of efficiency, complexity and stability, especially in the areas of variables optimal selection and advanced control algorithms. It is expected that innovations such as cloud control methods and hybrid balancing systems equipped with thermal management will become the future direction of lithium‐ion equalization technologies. [ABSTRACT FROM AUTHOR]

To ensure the secure and healthy usage of lithium-ion batteries, it is necessary to accurately estimate the state of charge (SOC) in battery management systems. The development of deep learning (DL) provides a new solution for battery SOC estimation. However, the directly measured physical quantities contain less useful information and have low estimation accuracy. This paper proposes a method of integrating the mechanism knowledge of the battery domain into the DL framework. Firstly, the simplified electrochemical model is utilized to obtain the mechanism-related physical variables to expand the input of the DL model. Secondly, the long short-term memory (LSTM) network is used with the Bayesian optimization, and the variables with high correlation are identified. The best SOC estimation performance can be obtained by adding all the selected highly-correlated variables to the input for training together. The results show that the proposed method can improve the SOC estimation performance with only a slight increase in computation cost. Finally, other DL models are utilized to further validate the effectiveness, to reveal the universality. These results show that the performance of the DL model can be effectively improved by using the knowledge of the battery domain. • Electrochemical mechanism provides useful information for deep learning. • Variables highly correlated with SOC are selected to be added into input. • All statistical errors are reduced by more than half relatively to each other. • Multiple deep learning models are used to verify universality of the method. [ABSTRACT FROM AUTHOR]

• Stacked layer model for a pouch cell is established and well verified. • Detailed temperature distribution pattern across electrode is revealed and analysed. • Electrode current density distribution results in thermal inhomogeneity. • High temperature facilitates fast charging by increasing anode potential. • Enhanced convection reduces thermal inhomogeneity during fast charging. With the emerging demands for precise control in next-generation battery managements systems (BMSs), more fundamental understanding of external characteristics for lithium-ion batteries (LIBs) is urgently required. The electrical-thermal distribution across electrode is closely coupled with cell configuration and operating conditions. Especially during fast charging, substantial temperature rise can lead to inhomogeneous in-plane thermal distribution with large thermal gradients, causing uneven local lithium (Li) plating on the anode surface, which highly undermines the safety of LIBs. In this study, aiming at probing the electrical-thermal inhomogeneity on electrode of pouch cells, distributed temperature measurements are conducted under various charge rates and ambient temperatures. A stacked layer model is established and further verified with measured anode potentials. Detailed temperature distribution inside the pouch cell and the temperature field evolution at different charge stages are thoroughly elucidated. Electrode current density distribution that results in thermal inhomogeneity is revealed, and three thermal distribution patterns coupled with various operating conditions are summarized. Then, the effects of different convection methods on electrical-thermal distribution are analysed, and enhanced convection is found to effectively reduce the thermal inhomogeneity but increase the risk for Li plating. Under uneven convection scenarios, Li plating preferably occurs on the colder region under fast charging. This study provides novel insights in electrical-thermal inhomogeneity inside LIBs, highly underpinning the smart control strategy development of next-generation BMSs. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been widely used in electric vehicles and play an important role in the energy storage system. However, lithium plating is one of the major degradation processes in lithium-ion batteries, leading to severe capacity fading and other latent safety problems. Lithium plating should be prevented or detected effectively to ensure the operation of LIBs safe and reliable. In this paper, an electrochemical model of lithium plating combined with Monte Carlo method is established to investigate the lithium plating process at different operating conditions, considering the influence of electric field and concentration field. Particle movement and system evolution in atomic scale is simulated at two dimensions with the mobility of each Li+ computed. In-situ optical detection experiments of lithium plating at different charging rates are designed and carried out with a charge coupled device (CCD) digital camera and an optical microscope to validate the viability of model. Finally, other usages of this model are discussed. This model will contribute to the novel microscopic insight for mechanism of lithium plating, with the combination of modeling analysis and experiments developing detection techniques for lithium plating. • An electrochemical and graphics model for Li dendrites. • In-situ optical detection experiments of lithium plating are designed and developed. • Constant voltage edge conditions and charging rates should be strictly controlled. • The simulation result demonstrates the behavior of Li dendrites formation and growth. [ABSTRACT FROM AUTHOR]

Cycle aging of commercial 2.5 Ah 18650 cylindrical lithium-ion batteries with LiNi0.8Co0.1Mn0.1O2(NCM)/graphite chemistry is investigated at different charging rates. The cells charged at 1C-3C follow a similar aging path, and the degradation mechanisms under 2C and 3C charging are characterized by non-destructive electrochemical techniques and post-mortem analyses. Electrochemical impedance spectroscopy measurements indicate that the impedance rise of cells is primarily attributed to the increase of charge transfer resistance. Post mortem analyses reveal that the side reactions on NCM cathode are secondary particle cracking and transition metal dissolution, and anode degradation is caused by the growth of solid-electrolyte interface layer and the lithium plating. Voltage fitting analyses of 3C charging demonstrate that the main degradation mode is the insufficient active lithium that is available for intercalation/deintercalation in highly-lithiated anodes. Quantitative analyses of the individual electrodes based on differential voltage curves identify that loss of lithium inventory (LLI) contributes the dominant aging mode to full cell, followed by loss of active material of delithiated negative electrode (LAM deNE) and LAM of lithiated negative electrode (LAM liNE), while LAM of positive electrode (LAM PE) exerts a minor effect. Accurate identification of the battery degradation mechanisms cycling under fast charging conditions helps to provide guidance for charging optimization. [ABSTRACT FROM AUTHOR]

As demands for massive energy consumption inevitably grow, lithium-ion batteries have been widely recognized as promising energy devices and fundamental components for electric vehicles or grid application. However, certain circuit configuration especially parallel connection exhibits highly heterogeneous current distributions in different branches, some of which undertakes considerable current fluctuation during charge/discharge. Therefore, a charging strategy for integrated energy storage facilities is highly desirable to avoid the side reactions like lithium plating that can cause severe degradation or even risks of thermal runaway. In this study, a parallel-module model is established based on a modified single particle model with side reactions, and experimentally validated under various C-rates. Then a minimum Li plating overpotential control (MLPOC) based charging strategy is proposed for this parallel-module model and effectiveness for reducing capacity loss is verified by module simulations under high C-rate charging. Furthermore, parameter sensitivity of resistance ratio between branch and interconnect resistances on module performance with or without MLPOC are investigated. Results show that a medium resistance ratio of 20–30 is recommended for this parallel module. The insights from this paper not only provide charging strategy with minor degradation but also guide the design of battery module for better pack charging performance. [Display omitted] • Parallel battery module exhibits inhomogeneous current distribution. • Experiments and simulations confirm the validity of parallel battery module model. • Side reactions like lithium plating cause capacity loss during cycling. • Charging strategy for parallel battery module is proposed, preventing side reactions. • Excellent reduction of capacity loss for parallel battery module is achieved. [ABSTRACT FROM AUTHOR]

• A freestanding PDCNT film is fabricated by an easy "peel off" method. • The PDCNT film shows low density, good flexibility and electrochemical stability. • Hollow silicon composited HSSi-PDCNT anode delivered high specific capacity. • This fabricating method is well applicable to construct flexible electrodes. A freestanding polydopamine-normal dodecanethiol coated MWCNT film (PDCNT) was fabricated by an easy and scalable slurry coating - peeling off method. When applied as the current collector, the PDCNT film demonstrated low mass density, excellent flexibility and high electrochemical stability. Composite with hollow structure silicon (HSSi), HSSi-PDCNT anode was able to deliver a specific capacity of 750 mAh g−1 after 200 cycles, reaching equivalent performance of Cu foiled current collectors. This innovative and simple fabricating method proposed in the present study can be widely used to develop high performance flexible current collectors, especially in application of lithium ion batteries (LIBs). [ABSTRACT FROM AUTHOR]

• Cathode electrolyte interphase plays important role in determining the life of lithium-ion batteries. • The formation of cathode electrolyte interphase is discussed. • The modification strategies of cathode electrolyte interphase are also presented. Cathode electrolyte interphase (CEI) has obtained increasing attention due to the importance in sustaining full cell life. However, it has rarely been overviewed. Therefore, it is timely and necessary to write this mini-review on CEI. This mini-review will involve the formation mechanism and modification methods of CEI. The perspective on CEI is also given out. We expect that this mini-review will provide rational guidelines for the CEI design of highly-performance lithium-ion batteries (LIBs). [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) have been widely used in electric vehicles due to the advantages of high energy/power densities, high reliability and long service life. However, considering that a massive number of LIBs will likely retire and enter the waste stream in the near future, the handling of end-of-life LIBs must be taken carefully. The effective utilization of retired LIBs, which still remain about 70–80% of the initial capacity, can extend battery life, conserve natural resources and protect the environment. Herein, this review provides a systematic discussion on the circular value chain (CVC) of spent LIBs, and proposes a 5R principle entailing reduce, redesign, remanufacturing, repurpose and recycling in the CVC process. Then the state-of-the-art technologies for remanufacturing, and a thorough summary of key issues and applications of repurpose process, are presented in detail. Subsequently, this article presents a comprehensive discussion on the recycling process, including pre-treatments and mainstream recycling technologies, from the prospects of technical, economic and regulation perspectives. Advanced technologies such as big data, block chain and cloud-based services, as well as the improvement of regulation and standardization processes, are required to solve the issues. Finally, the future challenges and prospects for sustainable CVC are highlighted. Image 1 • Systematic summary of the sustainable value chain of lithium-ion batteries. • A 5R principle in the circular value chain process is demonstrated. • Comprehensive analysis of reuse and recycling from different perspectives. • Remaining challenges and perspectives of circular value chain are discussed. [ABSTRACT FROM AUTHOR]

During operation of large prismatic lithium-ion batteries, temperature heterogeneities are aggravated which affect the performance, lifetime and safety of the cells and packs. Therefore, an accurate model to predict the evolution of temperature profiles in a cell is essential for effective thermal management. In this paper, a pseudo 3D coupled multi-node electro-thermal model is presented for real-time prediction of the heterogeneous temperature field evolution on the surface and inside the battery. The model consists of two parts: a heat generation model based on a second-order equivalent-circuit model and a multi-node heat transfer model based on the battery geometry. Three types of nodes are adopted to describe the thermal characteristics of various components of the cell. Simulation results show that the proposed model has a great consistency with finite element method, and its computational cost is reduced by 90%. The validity of the coupled electrical and thermal model is also demonstrated experimentally for a 105 Ah prismatic cell applying wide ranges of temperature and SOC. The maximum error is less than 2 K throughout the cycles. The proposed model holds a great potential for online temperature estimation in advanced lithium-ion battery thermal management system design. Image 1 • A pseudo 3D electro-thermal model for online temperature estimation is presented. • Detailed structure of the cell is considered without increasing computing power. • High accuracy online temperature estimation is achieved under a wide operation range. [ABSTRACT FROM AUTHOR]