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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State key laboratory for Manufacturing systems Engineering Xi'an Jiaotong University Shaanxi key laboratory of intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical Engineering and Automation Luoyang Institute of Science and Technology School of Mechanical Engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

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High-energy-density lithium manganese iron phosphate for lithium-ion batteries: Progresses, challenges, and prospects

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Journal of Energy Chemistry 2025年第1期100卷 1-17页

作者： Bokun Zhang Xiaoyun Wang Shuai Wang Yan Li Libo Chen Handong Jiao Zhijing Yu Jiguo Tu State key laboratory of Advanced Metallurgy University of Science and Technology Beijing Research Institute for intelligent Wearable systems The Hong Kong Polytechnic University Institute of Advanced Structure Technology Beijing Institute of Technology

The soaring demand for smart portable electronics and electric vehicles is propelling the advancements in high-energy–density lithium-ion *** manganese iron phosphate (LiMnxFe1-xPO4) has garnered significant attention as a promising positive electrode material for lithium-ion batteries due to its advantages of low cost,high safety,long cycle life,high voltage,good high-temperature performance,and high energy *** LiMnxFe1-xPO4has made significant breakthroughs in the past few decades,there are still facing great challenges in poor electronic conductivity and Li-ion diffusion,manganese dissolution affecting battery cycling performance,as well as low tap *** review systematically summarizes the reaction mechanisms,various synthesis methods,and electrochemical properties of LiMnxFe1-xPO4to analyze reaction processes accurately and guide material ***,the main challenges currently faced are concluded,and the corresponding various modification strategies are discussed to enhance the reaction kinetics and electrochemical performance of LiMnxFe1-xPO4,including multi-scale particle regulation,heteroatom doping,surface coating,as well as microscopic morphology ***,in view of the current research challenges faced by intrinsic reaction processes,kinetics,and energy storage applications,the promising research directions are *** importantly,it is expected to provide key insights into the development of high-performance and stable LiMnxFe1-xPO4materials,to achieve practical energy storage requirements.

关键词： Lithium manganese iron phosphate High energy density Lithium-ion batteries Reaction mechanism Tap density

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Finite-time sliding mode attitude control for a reentry vehicle with blended aerodynamic surfaces and a reaction control system

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Chinese Journal of Aeronautics 2014年第4期27卷 964-976页

作者： Geng Jie Sheng Yongzhi Liu Xiangdong key laboratory for intelligent control&decision of complex systems Beijing Institute of Technology School of Automation Beijing Institute of Technology

This paper proposes a finite-time robust flight controller, targeting for a reentry vehicle with blended aerodynamic surfaces and a reaction control system（RCS）. Firstly, a novel finite-time attitude controller is pointed out with the introduction of a nonsingular finite-time sliding mode manifold. The attitude tracking errors are mathematically proved to converge to zero within finite time which can be estimated. In order to improve the performance, a second-order finite-time sliding mode controller is further developed to effectively alleviate chattering without any deterioration of robustness and accuracy. Moreover, an optimization control allocation algorithm, using linear programming and a pulse-width pulse-frequency（PWPF） modulator, is designed to allocate torque commands for all the aerodynamic surface deflections and on–off switching-states of RCS *** are provided for the reentry vehicle considering uncertain parameters and external disturbances for practical purposes, and the results demonstrate the effectiveness and robustness of the attitude control system.

关键词： Chattering alleviation control allocation Finite-time convergence Flight control systems Second-order sliding mode Singularity elimination Sliding mode control

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Effects of aggregate size distribution and carbon nanotubes on the mechanical properties of cemented gangue backfill samples under true triaxial compression

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 311-324页

作者： Qian Yin Fan Wen Zhigang Tao Hai Pu Tianci Deng Yaoyao Meng Qingbin Meng Hongwen Jing Bo Meng Jiangyu Wu State key laboratory of intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering China University of Mining and TechnologyXuzhou 221116China State key laboratory of Mining Response and Disaster Prevention and control in Deep Coal Mines Anhui University of Science and TechnologyHuainan 232001China State key laboratory for Geomechanics&Deep Underground Engineering China University of Mining and Technology(Beijing)Beijing 100083China key laboratory of Xinjiang Coal Resources Green Mining Ministry of EducationXinjiang Institute of EngineeringUrumqi 830023China

The mechanical behavior of cemented gangue backfill materials(CGBMs)is closely related to particle size distribution(PSD)of aggregates and properties of cementitious ***,the true triaxial compression tests,CT scanning,SEM,and EDS tests were conducted on cemented gangue backfill samples(CGBSs)with various carbon nanotube concentrations(P_(CNT))that satisfied fractal theory for the PSD of *** mechanical properties,energy dissipations,and failure mechanisms of the CGBSs under true triaxial compression were systematically *** results indicate that appropriate carbon nanotubes(CNTs)effectively enhance the mechanical properties and energy dissipations of CGBSs through micropore filling and microcrack bridging,and the optimal effect appears at P_(CNT)of 0.08wt%.Taking PSD fractal dimension(D)of 2.500 as an example,compared to that of CGBS without CNT,the peak strength(σ_(p)),axial peak strain(ε_(1,p)),elastic strain energy(Ue),and dissipated energy(U_(d))increased by 12.76%,29.60%,19.05%,and90.39%,***,excessive CNTs can reduce the mechanical properties of CGBSs due to CNT agglomeration,manifesting a decrease inρ_(p),ε_(1,p),and the volumetric strain increment(Δε_(v))when P_(CNT)increases from 0.08wt%to 0.12wt%.Moreover,the addition of CNTs improved the integrity of CGBS after macroscopic failure,and crack extension in CGBSs appeared in two modes:detour and pass through the ***σ_(p)and U_(d)firstly increase and then decrease with increasing D,and porosity shows the opposite ***ε_(1,p)andΔε_(v)are negatively correlated with D,and CGBS with D=2.150 has the maximum deformation parameters(ε_(1,p)=0.05079,Δε_(v)=0.01990)due to the frictional slip effect caused by coarse *** increasing D,the failure modes of CGBSs are sequentially manifested as oblique shear failure,"Y-shaped"shear failure,and conjugate shear failure.

关键词： cemented gangue backfill materials particle size distribution true triaxial compression test carbon nanotubes mechanical properties failure modes

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Optimal fusion estimation for stochastic systems with cross-correlated sensor noises

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Science China(Information Sciences) 2017年第12期60卷 57-70页

作者： Liping YAN Yuanqing XIA Mengyin FU School of Automation Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Institute of Technology

This paper is concerned with the optimal fusion of sensors with cross-correlated sensor *** taking linear transformations to the measurements and the related parameters, new measurement models are established, where the sensor noises are decoupled. The centralized fusion with raw data, the centralized fusion with transformed data, and a distributed fusion estimation algorithm are introduced, which are shown to be equivalent to each other in estimation precision, and therefore are globally optimal in the sense of linear minimum mean square error(LMMSE). It is shown that the centralized fusion with transformed data needs lower communication requirements compared to the centralized fusion using raw data directly, and the distributed fusion algorithm has the best flexibility and robustness and proper communication requirements and computation complexity among the three algorithms(less communication and computation complexity compared to the existed distributed Kalman filtering fusion algorithms). An example is shown to illustrate the effectiveness of the proposed algorithms.

关键词： optimal estimation distributed fusion Kalman filter cross-correlated noises linear transformation

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Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

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Journal of systems Engineering and Electronics 2013年第4期24卷 655-665页

作者： Fang Deng Jie Chen Chen Chen School of Automation Beijing Institute of Technology key laboratory of intelligent control and decision of complex systems

An adaptive unscented Kalman filter （AUKF） and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter （UKF） when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

关键词： parameter estimation state estimation unscented Kalman filter (UKF) strong tracking filter wavelet transform.

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Adaptive Robust Dead-Zone Compensation control of Electro-Hydraulic Servo systems with Load Disturbance Rejection

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Journal of systems Science & complexity 2015年第2期28卷 341-359页

作者： HE Yudong WANG Junzheng HAO Renjian key laboratory of intelligent control and decision of complex systems School of AutomationBeijing Institute of Technology

A backstepping method based adaptive robust dead-zone compensation controller is pro- posed for the electro-hydraulic servo systems （EHSSs） with unknown dead-zone and uncertain system parameters. Variable load is seen as a sum of a constant and a variable part. The constant part is regarded as a parameter of the system to be estimated real time. The variable part together with the friction are seen as disturbance so that a robust term in the controller can be adopted to reject them. Compared with the traditional dead-zone compensation method, a dead-zone compensator is incor- porated in the EH$S without constructing a dead-zone inverse. Combining backstepping method, an adaptive robust controller （ARC） with dead-zone compensation is formed. An easy-to-use ARC tuning method is also proposed after a further analysis of the ARC structure. Simulations show that the proposed method has a splendid tracking performance, all the uncertain parameters can be estimated, and the disturbance has been rejected while the dead-zone term is well estimated and compensated.

关键词： Adaptive robust control dead-zone servo systems tuning method. compensation disturbance rejection electro-hydraulic

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Online adaptive Q-learning method for fully cooperative linear quadratic dynamic games

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Science China(Information Sciences) 2019年第12期62卷 164-177页

作者： Xinxing LI Zhihong PENG Lei JIAO Lele XI Junqi CAI School of Automation Beijing Institute of Technology State key laboratory of intelligent control and decision of complex systems

A model-based offline policy iteration(PI) algorithm and a model-free online Q-learning algorithm are proposed for solving fully cooperative linear quadratic dynamic games. The PI-based adaptive Q-learning method can learn the feedback Nash equilibrium online using the state samples generated by behavior policies, without sending inquiries to the system model. Unlike the existing Q-learning methods, this novel Q-learning algorithm executes both policy evaluation and policy improvement in an adaptive *** prove the convergence of the offline PI algorithm by proving its equivalence to Newton’s method while solving the game algebraic Riccati equation(GARE). Furthermore, we prove that the proposed Q-learning method will converge to the Nash equilibrium under a small learning rate if the method satisfies certain persistence of excitation conditions, which can be easily met by suitable behavior policies. Our simulation results demonstrate the good performance of the proposed online adaptive Q-learning algorithm.

关键词： adaptive dynamic programming reinforcement learning Q-learning fully cooperative linear quadratic dynamic games policy iteration off-policy

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Interactive multiobjective evolutionary algorithm based on decomposition and compression

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Science China(Information Sciences) 2021年第10期64卷 166-181页

作者： Lu CHEN Bin XIN Jie CHEN School of Automation Beijing Institute of Technology State key laboratory of intelligent control and decision of complex systems

Many real-world optimization problems involve multiple conflicting objectives. Such problems are called multiobjective optimization problems(MOPs). Typically, MOPs have a set of so-called Pareto optimal solutions rather than one unique optimal solution. To assist the decision maker(DM) in finding his/her most preferred solution, we propose an interactive multiobjective evolutionary algorithm(MOEA)called iDMOEA-εC, which utilizes the DM’s preferences to compress the objective space directly and progressively for identifying the DM’s preferred region. The proposed algorithm employs a state-of-the-art decomposition-based MOEA called DMOEA-εC as the search engine to search for solutions. DMOEA-εC decomposes an MOP into a series of scalar constrained subproblems using a set of evenly distributed upper bound vectors to approximate the entire Pareto front. To guide the population toward only the DM’s preferred part on the Pareto front, an adaptive adjustment mechanism of the upper bound vectors and two-level feasibility rules are proposed and integrated into DMOEA-εC to control the spread of the population. To ease the DM’s burden, only a small set of representative solutions is presented in each interaction to the DM,who is expected to specify a preferred one from the set. Furthermore, the proposed algorithm includes a two-stage selection procedure, allowing to elicit the DM’s preferences as accurately as possible. To evaluate the performance of the proposed algorithm, it was compared with other interactive MOEAs in a series of experiments. The experimental results demonstrated the superiority of iDMOEA-εC over its competitors.

关键词： multiobjective optimization interactive decision making preference incorporation decomposition compression

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A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets

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Chinese Journal of Aeronautics 2014年第3期27卷 622-633页

作者： Zhang Xing Chen Jie Xin Bin Peng Zhihong School of Automation Beijing Institute of Technology State key laboratory of intelligent control and decision of complex systems

The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles （UAVs） performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so that the constraints of UAVs＇ minimal turning radius can be taken into account. In view of the effective surveillance range of the sensors equipped on UAVs, the problem is formulated as a Dubins traveling salesman problem with neighborhood （DTSPN）. Considering its prohibitively high computational complexity, the Dubins paths in the sense of terminal heading relaxation are introduced to simplify the calculation of the Dubins distance, and a boundary-based encoding scheme is proposed to determine the visiting point of every target neighborhood. Then, an evolutionary algorithm is used to derive the optimal Dubins tour. To further enhance the quality of the solutions, a local search strategy based on approximate gradient is employed to improve the visiting points of target neighborhoods. Finally, by a minor modification to the individual encoding, the algorithm is easily extended to deal with other two more sophisticated DTSPN variants （multi-UAV scenario and multiple groups of targets scenario）. The performance of the algorithm is demonstrated through comparative experiments with other two state-of-the-art DTSPN algorithms identified in literature. Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.

关键词： Approximate gradient Dubins traveling salesmanproblem with neighborhood Local search Memetic algorithm Unmanned aerial vehicles

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