Imperial College London

Dr Haijun Ruan (阮海军)

Faculty of EngineeringDyson School of Design Engineering

 
 
 
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h.ruan

 
 
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Location

 

Dyson BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

25 results found

Ruan H, Kirkaldy N, Offer G, Wu Bet al., 2024, Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data, Energy and AI, Vol: 16, ISSN: 2666-5468

Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.

Journal article

Ma S, Sun B, Su X, Zhang W, Ruan Het al., 2023, Sensitivity analysis of electrochemical model parameters for lithium-ion batteries on terminal voltages and anode lithium plating criterion, JOURNAL OF ENERGY STORAGE, Vol: 71, ISSN: 2352-152X

Journal article

Sun B, Qi X, Song D, Ruan Het al., 2023, Review of Low-Temperature Performance, Modeling and Heating for Lithium-Ion Batteries, Energies, Vol: 16

Lithium-ion batteries (LIBs) have the advantages of high energy/power densities, low self-discharge rate, and long cycle life, and thus are widely used in electric vehicles (EVs). However, at low temperatures, the peak power and available energy of LIBs drop sharply, with a high risk of lithium plating during charging. This poor performance significantly impacts the application of EVs in cold weather and dramatically limits the promotion of EVs in high-latitude regions. This challenge recently attracted much attention, especially investigating the performance decrease for LIBs at low temperatures, and exploring the solutions; however, limited reviews exist on this topic. Here, we thoroughly review the state-of-the-arts about battery performance decrease, modeling, and preheating, aiming to drive effective solutions for addressing the low-temperature challenge of LIBs. We outline the performance limitations of LIBs at low temperatures and quantify the significant changes in (dis)charging performance and resistance of LIBs at low temperatures. The various models considering low-temperature influencing factors are also tabulated and summarized, with the modeling improvement for describing low-temperature performance highlighted. Furthermore, we categorize the existing heating methods, and the metrics such as heating rate, energy consumption, and lifetime impact are highlighted to provide fundamental insights into the heating methods. Finally, the limits of current research on low-temperature LIBs are outlined, and an outlook on future research direction is provided.

Journal article

Ruan H, Barreras JV, Steinhardt M, Jossen A, Offer GJ, Wu Bet al., 2023, The heating triangle: A quantitative review of self-heating methods for lithium-ion batteries at low temperatures, Journal of Power Sources, Vol: 581, Pages: 1-16, ISSN: 0378-7753

Lithium-ion batteries at low temperatures have slow recharge times alongside reduced available power and energy. Battery heating is a viable way to address this issue, and self-heating techniques are appealing due to acceptable efficiency and speed. However, there are a lack of studies quantitatively comparing self-heating methods rather than qualitatively, because of the existence of many different batteries with varied heating parameters. In this work, we review the current state-of-the-art self-heating methods and propose the heating triangle as a new quantitative indicator for comparing self-heating methods, towards identifying/developing effective heating approaches. We define the heating triangle which considers three fundamental metrics: the specific heating rate (°C·g·J−1), coefficient of performance (COP) (−), and specific temperature difference (°C·hr), enabling a quantitative assessment of self-heating methods using data reported in the literature. Our analysis demonstrates that very similar metrics are observed for the same type of self-heating method, irrespective of the study case, supporting the universality of the proposed indicator. With the comparison insights, we identify research gaps and new avenues for developing advanced self-heating methods. This work demonstrates the value of the proposed heating triangle as a standardised approach to compare heating methods and drive innovation.

Journal article

Su X, Sun B, Wang J, Ruan H, Zhang W, Bao Yet al., 2023, Experimental study on charging energy efficiency of lithium-ion battery under different charging stress, JOURNAL OF ENERGY STORAGE, Vol: 68, ISSN: 2352-152X

Journal article

He X, Sun B, Zhang W, Su X, Ma S, Li H, Ruan Het al., 2023, Inconsistency modeling of lithium-ion battery pack based on variational auto-encoder considering multi-parameter correlation, ENERGY, Vol: 277, ISSN: 0360-5442

Journal article

Ruan H, Barreras JV, Engstrom T, Merla Y, Millar R, Wu Bet al., 2023, Lithium-ion battery lifetime extension: A review of derating methods, Journal of Power Sources, Vol: 563, Pages: 1-17, ISSN: 0378-7753

Extending lithium-ion battery lifetime is essential for mainstream uptake of electric vehicles. However, battery degradation is complex and involves coupling of underpinning electrochemical, thermal and mechanical processes, with behaviours varying based on chemistry, operating conditions and design. Derating is an attractive approach for extending lifetime due to ease of implementation, however, uncertainties remain around the optimal approach and their impacts. In this paper, we present a critical review of derating methods; dividing approaches into dynamic or static approaches based on whether the derated parameters changed with battery aging or not. Furthermore, we analyse and comment on approaches which are classified as being either heuristic or model-based. Analysis, comparison, and discussion around the derating sub-categories are presented towards highlighting underpinning insights of derating. Benefits and impacts of derating are quantified, and challenges with implementation are identified along with identification of research gaps, practical considerations and perspectives for future directions.

Journal article

Ruan H, Sun B, Jiang J, Su X, He X, Ma S, Gao Wet al., 2023, Optimal switching temperature for multi-objective heated-charging of lithium-ion batteries at subzero temperatures, JOURNAL OF POWER SOURCES, Vol: 562, ISSN: 0378-7753

Journal article

Sun B, Zhao X, He X, Ruan H, Zhu Z, Zhou Xet al., 2023, Virtual Battery Pack-Based Battery Management System Testing Framework, ENERGIES, Vol: 16

Journal article

Su X, Sun B, Wang J, Zhang W, Ma S, He X, Ruan Het al., 2022, Fast capacity estimation for lithium-ion battery based on online identification of low-frequency electrochemical impedance spectroscopy and Gaussian process regression, APPLIED ENERGY, Vol: 322, ISSN: 0306-2619

Journal article

Ruan H, Chen J, Ai W, Wu Bet al., 2022, Generalised diagnostic framework for rapid battery degradation quantification with deep learning, Energy and AI, Vol: 9, Pages: 1-13, ISSN: 2666-5468

Diagnosing lithium-ion battery degradation is challenging due to the complex, nonlinear, and path-dependent nature of the problem. Here, we develop a generalised and rapid degradation diagnostic method with a deep learning-convolutional neural network that quantifies degradation modes of batteries aged under various conditions in 0.012 s without feature engineering. Rather than performing extensive aging experiments, synthetic aging datasets for network training are generated. This dramatically lowers training cost/time, with these datasets covering almost all the aging paths, enabling a generalised degradation diagnostic framework. We show that the five thermodynamic degradation modes are correlated, and systematically elucidate their correlations. We thus propose a non-invasive comprehensive evaluation method and find the degradation diagnostic errors to be less than 1.22% for three leading commercial battery chemistries. The comparison with the traditional diagnostic methods confirms the high accuracy and fast nature of the proposed approach. Quantification of degradation modes with the partial discharge/charge data using the proposed diagnostic framework validates the real-world feasibility of this approach. This work, therefore, enables the promise of online identification of battery degradation and efficient analysis of large-data sets, unlocking potential for long lifetime energy storage systems.

Journal article

He X, Sun B, Zhang W, Fan X, Su X, Ruan Het al., 2022, Multi-time scale variable-order equivalent circuit model for virtual battery considering initial polarization condition of lithium-ion battery, ENERGY, Vol: 244, ISSN: 0360-5442

Journal article

Steinhardt M, Barreras JV, Ruan H, Wu B, Offer GJ, Jossen Aet al., 2022, Meta-analysis of experimental results for heat capacity and thermal conductivity in lithium-ion batteries: A critical review, Journal of Power Sources, Vol: 522, Pages: 1-25, ISSN: 0378-7753

Scenarios with rapid energy conversion for lithium-ion batteries are increasingly relevant, due to the desire for more powerful electric tools or faster charging electric vehicles. However, higher power means higher cooling requirements, affecting the battery temperature and its thermal gradients. In turn, temperature is a key quantity influencing battery performance, safety and lifetime. Therefore, thermal models are increasingly important for the design and operation of battery systems. Key parameters are specific heat capacity and thermal conductivity. For these parameters, this paper presents a comprehensive review of the experimental results in the literature, where the median values and corresponding uncertainties are summarized. Whenever available, data is analyzed from component to cell level with the discussion of dependencies on temperature, state of charge (SOC) and state of health (SOH). This meta-analysis reveals gaps in knowledge and research needs. For instance, we uncover inconsistencies between the specific heat capacity of electrode-separator stacks and full-cells. For the thermal conductivity, we found that thermal contact resistance and dependencies on battery states have been poorly studied. There is also a lack of measurements at high temperatures, which are required for safety studies. Overall, this study serves as a valuable reference material for both modellers and experimenters.

Journal article

Ruan H, Sun B, Cruden A, Zhu T, Jiang J, He X, Su X, Ghoniem Eet al., 2022, Optimal external heating resistance enabling rapid compound self-heating for lithium-ion batteries at low temperatures, APPLIED THERMAL ENGINEERING, Vol: 200, ISSN: 1359-4311

Journal article

Ruan H, Sun B, Jiang J, Zhang W, He X, Su X, Bian J, Gao Wet al., 2021, A modified-electrochemical impedance spectroscopy-based multi-time-scale fractional-order model for lithium-ion batteries, ELECTROCHIMICA ACTA, Vol: 394, ISSN: 0013-4686

Journal article

Zhu T, Wills RGA, Lot R, Ruan H, Jiang Zet al., 2021, Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting, APPLIED ENERGY, Vol: 292, ISSN: 0306-2619

Journal article

Ruan H, Sun B, Zhu T, He X, Su X, Cruden A, Gao Wet al., 2021, Compound self-heating strategies and multi-objective optimization for lithium-ion batteries at low temperature, Applied Thermal Engineering, Vol: 186, Pages: 1-14, ISSN: 1359-4311

Rapid and effective battery preheating for thermal management is particularly significant to overcome the performance limitation of batteries and guarantee the efficient operation of electric vehicles in cold environments. A low-temperature compound self-heating (CSH) strategy integrating the inner-battery direct-current heating and outer-battery electric heating is proposed to enhance heating efficiency and shorten heating duration without the requirement of extra power supplies. Computationally efficient distributed thermal equivalent circuit models, to capture the temperature distribution within batteries, are developed and experimentally validated with good accuracy. Four typical CSH methods are systematically discussed and compared in terms of the heating rate, temperature uniformity, energy consumption, capacity fade, and fabrication and safety challenge. The CSH method with electric heaters installed on the largest battery surfaces is found preferable due to its relatively easy implementation and low safety risk, and slightly small temperature gradient within the battery. Three crucial yet competing objectives, the heating time, temperature gradient, and capacity fade, are formulated for the favorable CSH method, and the Pareto front is obtained using the multi-objective optimization algorithm. An optimal low-temperature CSH method is thus proposed, where the battery is heated from −30 °C to 2 °C within 62.1 s. Compared with the direct-current heating method, the proposed optimal CSH method strengthens the heating rate by 60.8%, reduces energy consumption by 54.8%, and relieves battery degradation by 45.2%.

Journal article

Sun B, He X, Zhang W, Ruan H, Su X, Jiang Jet al., 2021, Study of Parameters Identification Method of Li-Ion Battery Model for EV Power Profile Based on Transient Characteristics Data, IEEE Transactions on Intelligent Transportation Systems, Vol: 22, Pages: 661-672, ISSN: 1524-9050

Power simulation of lithium ion battery through battery model is of great significance for dynamic response simulation, heat generation calculation and charge-discharge strategy development. The accuracy and applicability of the model become crucial. In order to demonstrate the battery transient characteristics more effectively, a novel identification method for parameters of the 2nd order RC equivalent circuit model was proposed. Based on the derived evolution law of battery transient characteristics under the continuous pulse excitation, four feature points are extracted for parameter identification in each cycle. The proposed method reduced the time cost of identification from 11796.88s to 0.06s while ensuring that the error of voltage doesn't exceed 2.2mV. In order to verify the power profiles applicability of the proposed method, applicability analysis of power profile for different identification methods was carried out including the methods using different amount of data (4N points, 200 points, 6000 points) under unidirectional current pulse excitation (UCPE), bidirectional current pulse excitation (BCPE) and unidirectional voltage pulse excitation (UVPE). It was illustrated that the identification process using data of multiple cycles could significantly reduce errors, including maximum error and average error. What's more, the proposed method under UCPE had the lowest maximum error of 0.420% in voltage simulation and -0.421% in the current simulation of power profiles. Compared with the conventional method (using 200 points of single pulse data for parameter identification), the proposed method can reduce the average voltage error and the maximum error by 62.5% and 11.8% respectively under the DST power profile.

Journal article

Ruan H, Sun B, Zhang W, Su X, He Xet al., 2021, Quantitative analysis of performance decrease and fast-charging limitation for lithium-ion batteries at low temperature based on the electrochemical model, IEEE Transactions on Intelligent Transportation Systems, Vol: 22, Pages: 640-650, ISSN: 1524-9050

The mechanism revelation of performance decrease and fast-charging limitation of lithium-ion batteries at low temperatures is indispensable to optimize battery design and develop fast-charging methods. In this article, an electrochemical model-based quantitative analysis method is proposed to uncover the dominant reason for performance decrease and fast-charging limitation of batteries at low temperatures. The highly important dynamic parameters are carefully determined by the experimental data from the checked three-electrode battery and optimized by the genetic algorithm, rather than directly taken from the references. Validation results confirm that the electrochemical model can well reproduce battery behaviors under different conditions and that identified parameters are accurate. The quantitative analysis indicates that the sluggish diffusion in cathode and anode electrodes is the principal reason for battery available capacity loss. Battery available power attenuation is primarily attributed to the increased film resistance of anode and the reduced exchange current density of cathode, and it is substantially independent of the reduced diffusivity. The comparison result from the lithium-plating-prevention charging current reveals that the increased film resistance of the anode is responsible for the predominant limitation of low-temperature fast-charging, despite the most change in the exchange current density of the anode. This quantitative revelation breaks through the traditional understanding from the qualitative analysis that performance decrease and fast-charging limitation of batteries at low temperatures are highly associated with the degree of the change of characteristic parameters.

Journal article

Ruan H, Jiang J, Sun B, Su X, He X, Zhao Ket al., 2019, An optimal internal-heating strategy for lithium-ion batteries at low temperature considering both heating time and lifetime reduction, Applied Energy, Vol: 256, Pages: 113797-113797, ISSN: 0306-2619

Journal article

Jiang J, Ruan H, Sun B, Wang L, Gao W, Zhang Wet al., 2018, A low-temperature internal heating strategy without lifetime reduction for large-size automotive lithium-ion battery pack, Applied Energy, Vol: 230, Pages: 257-266, ISSN: 0306-2619

Journal article

Ruan H, Jiang J, Sun B, Gao W, Wang L, Zhang Wet al., 2018, Online estimation of thermal parameters based on a reduced wide-temperature-range electro-thermal coupled model for lithium-ion batteries, Journal of Power Sources, Vol: 396, Pages: 715-724, ISSN: 0378-7753

To improve the accuracy of thermal model, optimize the design of heat dissipation system and evaluate online the thermal management system, an online estimation method of key thermal parameters is developed from carefully designed experiments, rather than being taken from the literature or the empirical value. The accurate prediction of heat generation, which is based on a reduced wide-temperature-range electro-thermal coupled model, is presented under easily obtainable alternating current excitation at different temperatures. To circumvent some inherent errors, a combined experimental/computational approach to simultaneously estimate the specific heat capacity and thermal resistance is proposed using quasi step power, with which the identification time is significantly reduced. The identified values of specific heat capacity and thermal resistance are validated with high accuracy. The adaptability validation is carried out under different temperatures and cooling conditions, as well as using different battery chemistries, indicating that the proposed method is generic. The in-situ methodology, thanks to good robustness on the colored noise, is capable of providing a promising candidate for accurate thermal modeling, on-board evaluation of battery thermal safety, and advanced design of thermal management system for electric vehicles.

Journal article

Ruan H, Jiang J, Ju Q, Sun B, Cheng Get al., 2017, A Reduced Wide-temperature-range Electro-thermal Model and Thermal Parameters Determination for Lithium-ion Batteries, Energy Procedia, Vol: 105, Pages: 805-810, ISSN: 1876-6102

Journal article

Ruan H, Jiang J, Sun B, Zhang W, Gao W, Wang LY, Ma Zet al., 2016, A rapid low-temperature internal heating strategy with optimal frequency based on constant polarization voltage for lithium-ion batteries, Applied Energy, Vol: 177, Pages: 771-782, ISSN: 0306-2619

Journal article

Jiang J, Ruan H, Sun B, Zhang W, Gao W, Wang LY, Zhang Let al., 2016, A reduced low-temperature electro-thermal coupled model for lithium-ion batteries, Applied Energy, Vol: 177, Pages: 804-816, ISSN: 0306-2619

Journal article

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