Yang Li

Yang Li

Chalmers tekniska högskola

H-index: 22

Europe-Sweden

About Yang Li

Yang Li, With an exceptional h-index of 22 and a recent h-index of 21 (since 2020), a distinguished researcher at Chalmers tekniska högskola, specializes in the field of sustainable energy, energy storage systems, transportation electrification.

His recent articles reflect a diverse array of research interests and contributions to the field:

Online adaptive model identification and state of charge estimation for vehicle-level battery packs

Multi-time scale scheduling optimization of integrated energy systems considering seasonal hydrogen utilization and multiple demand responses

Model optimization of a high-power commercial PEMFC system via an improved grey wolf optimization method

Electrical load forecasting model using hybrid LSTM neural networks with online correction

Taguchi optimization and thermoelectrical analysis of a pin fin annular thermoelectric generator for automotive waste heat recovery

A unified model for active battery equalization systems

Energy management and performance analysis of an off-grid integrated hydrogen energy utilization system

Innovative design for thermoelectric power generation: Two-stage thermoelectric generator with variable twist ratio twisted tapes optimizing maximum output

Yang Li Information

University

Chalmers tekniska högskola

Position

___

Citations(all)

1752

Citations(since 2020)

1682

Cited By

297

hIndex(all)

22

hIndex(since 2020)

21

i10Index(all)

46

i10Index(since 2020)

44

Email

University Profile Page

Chalmers tekniska högskola

Yang Li Skills & Research Interests

sustainable energy

energy storage systems

transportation electrification

Top articles of Yang Li

Online adaptive model identification and state of charge estimation for vehicle-level battery packs

Authors

Xiaohua Wu,Junhao Shu,Zhanfeng Fan,Jianbo Xie,Yang Li,Jibin Yang,Zhongwei Deng

Journal

IEEE Transactions on Transportation Electrification

Published Date

2024/3

Accurate state of charge (SOC) estimation of traction batteries plays a crucial role in energy and safety management for electric vehicles. Existing studies focus primarily on cell battery SOC estimation. However, numerical instability and divergence problems might occur for a large-size lithium-ion battery pack consisting of many cells. This paper proposes a high-performance online model identification and SOC estimation method based on an adaptive square root unscented Kalman filter (ASRUKF) and an improved forgetting factor recursive least squares (IFFRLS) for vehicle-level traction battery packs. The model parameters are identified online through the IFFRLS, where the conventional method might encounter numerical stability problems. By updating the square root of the covariance matrix, the divergence problem in the traditional unscented Kalman filter is solved in the ASRUKF algorithm, where the positive …

Multi-time scale scheduling optimization of integrated energy systems considering seasonal hydrogen utilization and multiple demand responses

Authors

Zhewei Wang,Banghua Du,Yang Li,Changjun Xie,Han Wang,Yunhui Huang,Peipei Meng

Journal

International Journal of Hydrogen Energy

Published Date

2024/5

Hydrogen energy is recognized as a crucial solution for addressing energy crises and advancing energy conservation and emissions reduction. It will play a significant role in the future integrated energy systems (IESs). However, the influence of seasonal variations in scheduling optimization of hydrogen-integrated energy system has rarely been investigated. A low-carbon scheduling model for IES, adopting multiple demand responses and a ladder-type carbon trading mechanism, has been established. Additionally, a multi-time scale dispatch optimization strategy considering seasonal hydrogen utilization is thus proposed in this paper. Specifically, day-ahead scheduling optimizes the system taking into account the seasonal variations of renewable energy and load. In the intraday stage, rolling optimization is adopted to address the forecasting errors introduced by wind and photovoltaic fluctuations. In the real …

Model optimization of a high-power commercial PEMFC system via an improved grey wolf optimization method

Authors

Hongxu Zhou,Xiaohua Wu,Yang Li,Zhanfeng Fan,Weishan Chen,Jianwei Mao,Pengyi Deng,Torsten Wik

Journal

Fuel

Published Date

2024/2

Proton exchange membrane fuel cell (PEMFC) models are conventionally established with a set of parameters identified under steady-state operating conditions. However, such an approach is insufficient to accurately capture the dynamic characteristics of multi-parameter changes in real-world scenarios. This paper develops a semi-empirical model for a 110-kW commercial PEMFC system based on its dynamic operation data to remedy the defects. To improve the fitting accuracy of the semi-empirical PEMFC model, an improved grey wolf optimization (IGWO) algorithm is proposed for model parameter identification. The IGWO algorithm adopts chaotic mapping to optimize the initial population distribution, and a random walk strategy is incorporated to boost the local search ability of the traditional grey wolf optimization (GWO) algorithm. The effectiveness of this IGWO algorithm in optimizing the semi-empirical …

Electrical load forecasting model using hybrid LSTM neural networks with online correction

Authors

Nan Lu,Quan Ouyang,Yang Li,Changfu Zou

Journal

arXiv preprint arXiv:2403.03898

Published Date

2024/3/6

Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems. In this work, a hybrid long short-term memory (LSTM)-based model with online correction is developed for day-ahead electrical load forecasting. Firstly, four types of features are extracted from the original electrical load dataset, including the historical time series, time index features, historical statistical features, and similarity features. Then, a hybrid LSTM-based electrical load forecasting model is designed, where an LSTM neural network block and a fully-connected neural network block are integrated that can model both temporal features (historical time series) and non-temporal features (the rest features). A gradient regularization-based offline training algorithm and an output layer parameter fine-tuning-based online model correction method are developed to enhance the model's capabilities to defend against disturbance and adapt to the latest load data distribution, thus improving the forecasting accuracy. At last, extensive experiments are carried out to validate the effectiveness of the proposed electrical load forecasting strategy with superior accuracy compared with commonly used forecasting models.

Taguchi optimization and thermoelectrical analysis of a pin fin annular thermoelectric generator for automotive waste heat recovery

Authors

Wenlong Yang,Chenchen Jin,Wenchao Zhu,Yang Li,Rui Zhang,Liang Huang,Changjun Xie,Ying Shi

Journal

Renewable Energy

Published Date

2024/1

Enhancing thermoelectric performance while minimizing exhaust back pressure is a crucial step in advancing the commercial viability of automotive thermoelectric generators. To achieve high overall performance in a thermoelectric generator, an annular thermoelectric generator equipped with circular pin fins is proposed. A comprehensive three-dimensional numerical model is established to accurately predict thermoelectric performance and thermomechanical behavior. Detailed multi-physics field distribution characteristics are analyzed. Using an L25 orthogonal array, we examine five influencing factors and their five levels: exhaust temperature, exhaust mass flow rate, fin height, fin diameter, and the number of fins. The Taguchi analysis suggests that exhaust temperature is the most influential factor in determining thermoelectric performance, followed by mass flow rate, fin height, fin diameter, and fin number …

A unified model for active battery equalization systems

Authors

Quan Ouyang,Nourallah Ghaeminezhad,Yang Li,Torsten Wik,Changfu Zou

Journal

arXiv preprint arXiv:2403.03910

Published Date

2024/3/6

Lithium-ion battery packs demand effective active equalization systems to enhance their usable capacity and lifetime. Despite numerous topologies and control schemes proposed in the literature, conducting quantitative analyses, comprehensive comparisons, and systematic optimization of their performance remains challenging due to the absence of a unified mathematical model at the pack level. To address this gap, we introduce a novel, hypergraph-based approach to establish the first unified model for various active battery equalization systems. This model reveals the intrinsic relationship between battery cells and equalizers by representing them as the vertices and hyperedges of hypergraphs, respectively. With the developed model, we identify the necessary condition for all equalization systems to achieve balance through controllability analysis, offering valuable insights for selecting the number of equalizers. Moreover, we prove that the battery equalization time is inversely correlated with the second smallest eigenvalue of the hypergraph's Laplacian matrix of each equalization system. This significantly simplifies the selection and optimized design of equalization systems, obviating the need for extensive experiments or simulations to derive the equalization time. Illustrative results demonstrate the efficiency of the proposed model and validate our findings.

Energy management and performance analysis of an off-grid integrated hydrogen energy utilization system

Authors

Banghua Du,Shihao Zhu,Wenchao Zhu,Xinyu Lu,Yang Li,Changjun Xie,Bo Zhao,Leiqi Zhang,Guizhi Xu,Jie Song

Journal

Energy Conversion and Management

Published Date

2024/1/1

In integrated hydrogen energy utilization systems, due to the low efficiency of hydrogen/electricity conversion, coordination of energy management and efficient waste heat recovery is required to optimize performance. To address this challenge, this paper presents a comprehensive and sophisticated modeling and energy management strategy to enhance the off-grid energy utilization rate while prolonging the main components’ lifetime. The developed model incorporates multiphase flow and heat transport balance for electricity and heat production, enabling a highly accurate representation of real-world behaviors of the system. The proposed off-grid operation strategy is complemented by a designed heat recovery scheme, ensuring the use of energy resources and waste heat. In addition, the proposed energy management strategy monitors the real-time status of each subsystem, actively reducing the number of …

Innovative design for thermoelectric power generation: Two-stage thermoelectric generator with variable twist ratio twisted tapes optimizing maximum output

Authors

Wenlong Yang,Chenchen Jin,Wenchao Zhu,Changjun Xie,Liang Huang,Yang Li,Binyu Xiong

Journal

Applied Energy

Published Date

2024/6/1

In recent years, considerable effort has been dedicated to the development of highly efficient thermoelectric generators for waste heat recovery and thermoelectric power generation. In this study, we present employing twisted tapes with variable twist ratio to enhance thermal energy extraction efficiency, coupled with two-stage thermoelectric modules for heat-to-electricity conversion, resulting in a substantial increase in the power output of the thermoelectric generator. We established an experimental system that validated the superior power generation and heat recovery characteristics of the two-stage thermoelectric generator. Building upon these findings, we propose further optimizing the variable twist ratio twisted tapes to enhance the power output. We investigated the impact of tape pitch ratio, twist ratio, and twist ratio variation range on thermoelectric performance. Experimental results indicate that the influence …

Performance improvement and thermomechanical analysis of a novel asymmetrical annular thermoelectric generator

Authors

Wenlong Yang,Aoqi Xu,Wenchao Zhu,Yang Li,Ying Shi,Liang Huang,Hao Li,Wei Lin,Changjun Xie

Journal

Applied Thermal Engineering

Published Date

2024/1

Enhancing thermoelectric performance hinges on optimizing the geometry of thermoelectric legs. In this study, we present a novel asymmetrical annular thermoelectric generator (ATEG) in which the proportions of P-type and N-type legs are meticulously balanced. We construct a one-dimensional analytical model tailored to this ATEG. Utilizing this model, we derive the relationship governing thermal-electrical impedance matching in an asymmetrical ATEG and formulate a general expression for optimizing the asymmetry coefficient. We explore the influence of various thermal boundary conditions on optimal impedance matching, ideal annular leg parameters, and the optimal asymmetry coefficient. Our findings reveal that thermal boundary conditions significantly affect the optimal load ratio. Furthermore, in comparison to traditional ATEGs, our proposed asymmetrical ATEG with the optimized structure exhibits a …

Hybrid physics-based and data-driven prognostic for PEM fuel cells considering voltage recovery

Authors

Hangyu Wu,Wei Wang,Yang Li,Wenchao Zhu,Changjun Xie,Hoay Beng Gooi

Journal

IEEE Transactions on Energy Conversion

Published Date

2024/3

Predicting the degradation behaviors is challenging and essential for prognostics and health management for proton exchange membrane fuel cells (PEMFCs). However, existing methods based on data-driven or model-based methods can face the problem of significant performance inconsistencies in different prediction stages. We investigate the cause and attribute it to the ignorance of the voltage recovery phenomena of PEMFCs observed during the frequent start-stop processes during practical applications. A novel prognostic method is proposed to provide a more comprehensive analysis of PEMFC aging that integrates data-driven and model-based methods. Specifically, a physics-based aging model considering voltage recovery (PA-VR) is first reported as a model-based method to enhance the prediction effect at voltage mutation points. Then, the moving window method with iterative function is used to …

Optimization-free fast charging for lithium-ion batteries using model inversion techniques

Authors

Yang Li,Torsten Wik,Yicun Huang,Changfu Zou

Published Date

2023/7

We propose a novel fast-charging control framework for lithium-ion (Li-ion) batteries that can leverage a class of models including the high-dimensional, electrochemical-thermal pseudo-two-dimensional model. The control objective is to find the highest battery current while fulfilling various operating constraints. Conventionally, computationally demanding optimization is needed to solve such a constrained optimal control problem when an electrochemical-thermal model is used, leading to practical difficulties in achieving low-cost implementation. Instead, this paper provides an optimization-free solution to Li-ion battery fast charging by converting the constrained optimal control problem into an output tracking problem with multiple tracking references. The required control input, i.e., the charging current, is derived by inverting the battery model. As a result, a nonlinear inversion-based control algorithm is obtained for …

An improved metaheuristic-based MPPT for centralized thermoelectric generation systems under dynamic temperature conditions

Authors

Yifeng Chen,Changjun Xie,Yang Li,WenChao Zhu,Lamei Xu,Hoay Beng Gooi

Journal

Energy

Published Date

2023/8/15

This paper proposes a multi-peak maximum power point tracking (MPPT) method based on the Global Flying Squirrel Search-Particle Swarm Optimization (GFSS-PSO) for centralized thermoelectric generator (TEG) systems operating under uneven temperature distribution conditions. Conventionally, metaheuristic-based MPPT methods mainly focused on indicators such as tracking speed, oscillation amplitude, and system efficiency. However, the real-time global search ability of conventional metaheuristic-based MPPT methods designed for photovoltaic systems may not be suitable for the gradual temperature change in the thermoelectric scene. A strong global search capability also can add to the computational burden and increase the power loss in the search process. To solve these problems, the GFSS-PSO algorithm introduces improved position updating method and multi-threshold restart mechanisms to …

Energy storage optimization

Published Date

2023/12/7

The disclosure relates to a device configured to optimize an energy storage strategy of a community comprising a plurality of households. Further, the disclosure relates to a cloud computing device configured to optimize an energy storage strategy of a community comprising a plurality of households. Further, the disclosure relates to an electric vehicle associated with a household in a community comprising a plurality of households, the electric vehicle comprising a battery aging model indicative of a battery aging status of a battery pack of said electric vehicle. Further, the disclosure relates to methods directed to optimize energy storage of households and communities.

Global structural optimization of annular thermoelectric generators based on a dual-finite-element multiphysical model

Authors

Wenlong Yang,WenChao Zhu,Yang Li,Changjun Xie,Binyu Xiong,Ying Shi,Wei Lin

Journal

Applied Thermal Engineering

Published Date

2023/2/5

The conventional methods to improve the performance of annular thermoelectric generators (ATEGs) heavily rely on optimizing the thermal design of individual annular thermoelectric couples (ATECs). However, since a practical ATEG consists of many ATECs, the optimal structure of the ATEG can differ from the ATEC-based design. On the other hand, optimization by simply considering all ATECs can lead to a heavy computation burden. This work first proposes a high-fidelity, fluid-thermal-electric multiphysical ATEG model, solved by a computationally-efficient dual-finite-element method to cope with the challenge. This model explores the effects of ATEC microstructure and heat exchanger structure on ATEG performance under various operating conditions. Comparative multi-objective optimization studies were performed at three levels, i.e., for a single ATEC, a single ring of ATEG, and the entire ATEG. The results …

Numerical analysis of vanadium redox flow batteries considering electrode deformation under various flow fields

Authors

Binyu Xiong,Yang Li,Yuming Ding,Jinsong Wang,Zhongbao Wei,Jiyun Zhao,Xiaomeng Ai,Jiakun Fang

Journal

Journal of Power Sources

Published Date

2023/4/30

The porous electrode of vanadium redox flow batteries (VRBs) is subject to deformation due to mechanical stress during stack assembling. The forces compress the electrode fiber into the flow channel and thus alter the electrode porosity ratio. Due to the complex mechanisms, the effects of resulting electrode morphological changes on VRB performance were usually ignored in existing studies. This paper proposes a three-dimensional VRB model considering the uneven electrode deformation to investigate the cell performance under different electrode compression ratios with three flow-field designs. Compression ratio (CR) and the intrusive part of the electrode are obtained under various mechanical stress by adjusting gasket thickness in the experiment. The proposed electrochemical model is established based on the comprehensive description of conservation laws and analyzed using the COMSOL platform …

Multi-level data-driven battery management: From internal sensing to big data utilization

Authors

Zhongbao Wei,Kailong Liu,Xinghua Liu,Yang Li,Liang Du,Fei Gao

Published Date

2023/8/7

A battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence, and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multilevel perspective. The widely explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multidimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for …

A flow-rate-aware data-driven model of vanadium redox flow battery based on gated recurrent unit neural network

Authors

Binyu Xiong,Yang Li,Jinrui Tang,Peng Zhou,Shaofeng Zhang,Xinan Zhang,Chaoyu Dong,Hoay Beng Gooi

Journal

Journal of Energy Storage

Published Date

2023/12

The vanadium redox flow battery (VRB) system involves complex multi-physical and multi-timescale interactions, where the electrolyte flow rate plays a pivotal role in both static and dynamic performance. Traditionally, fixed flow rates have been employed for operational convenience. However, in today’s highly dynamic energy market environment, adjusting flow rates based on operating conditions can provide significant advantages for improving VRB energy conversion efficiency and cost-effectiveness. Unfortunately, incorporating the electrolyte flow rate into conventional multi-physical models is overly complex for VRB management and control systems, as real-time operations demand low-computational and low-complexity models for onboard functionalities. This paper introduces a novel data-driven approach that integrates flow rates into VRB modeling, enhancing data processing capabilities and prediction …

Physics-based model predictive control for power capability estimation of lithium-ion batteries

Authors

Yang Li,Zhongbao Wei,Changjun Xie,D Mahinda Vilathgamuwa

Journal

IEEE Transactions on Industrial Informatics

Published Date

2023/2/2

The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for today's high-energy type of batteries with thicker electrodes. In this work, we propose a novel physics-based battery power capability estimation method to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power but also the internal constraints on lithium plating and thermal runaway, can be readily taken into account. The online …

MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling

Authors

Yicun Huang,Changfu Zou,Yang Li,Torsten Wik

Journal

arXiv preprint arXiv:2304.14422

Published Date

2023/4/27

The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based models. These models often trade accuracy for speed but lack the generalisability, adaptability, and interpretability inherent in physics-based models, which are often indispensable in the modeling of real-world dynamic systems for optimization and control purposes. In this work, we propose a novel architecture for generating model-integrated neural networks (MINN) to allow integration on the level of learning physics-based dynamics of the system. The obtained hybrid model solves an unsettled research problem in control-oriented modeling, i.e., how to obtain an optimally simplified model that is physically insightful, numerically accurate, and computationally tractable simultaneously. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries and show that MINN is extremely data-efficient to train while being sufficiently generalizable to previously unseen input data, owing to its underlying physical invariants. The MINN battery model has an accuracy comparable to the first principle-based model in predicting both the system outputs and any locally distributed electrochemical behaviors but achieves two orders of magnitude reduction in the solution time.

Operating conditions combination analysis method of optimal water management state for PEM fuel cell

Authors

Wenxin Wan,Yang Yang,Yang Li,Changjun Xie,Jie Song,Zhanfeng Deng,Jinting Tan,Ruiming Zhang

Journal

Green Energy and Intelligent Transportation

Published Date

2023/8

The water content of proton exchange membrane fuel cells (PEMFCs) affects the transport of reactants and the conductivity of the membrane. Effective water management measures can improve the performance and extend the lifespan of the fuel cell. The water management state of the stack is influenced by various external operating conditions, and optimizing the combination of these conditions can improve the water management state within the stack. Considering that the stack's internal resistance can reflect its water management state, this study first establishes an internal resistance-operating condition model that considers the coupling effect of temperature and humidity to determine the variation trend of total resistance and stack humidity with single-factor operating conditions. Subsequently, the water management state optimization method based on the ANN-HGPSO algorithm is proposed, which not only …

See List of Professors in Yang Li University(Chalmers tekniska högskola)

Yang Li FAQs

What is Yang Li's h-index at Chalmers tekniska högskola?

The h-index of Yang Li has been 21 since 2020 and 22 in total.

What are Yang Li's top articles?

The articles with the titles of

Online adaptive model identification and state of charge estimation for vehicle-level battery packs

Multi-time scale scheduling optimization of integrated energy systems considering seasonal hydrogen utilization and multiple demand responses

Model optimization of a high-power commercial PEMFC system via an improved grey wolf optimization method

Electrical load forecasting model using hybrid LSTM neural networks with online correction

Taguchi optimization and thermoelectrical analysis of a pin fin annular thermoelectric generator for automotive waste heat recovery

A unified model for active battery equalization systems

Energy management and performance analysis of an off-grid integrated hydrogen energy utilization system

Innovative design for thermoelectric power generation: Two-stage thermoelectric generator with variable twist ratio twisted tapes optimizing maximum output

...

are the top articles of Yang Li at Chalmers tekniska högskola.

What are Yang Li's research interests?

The research interests of Yang Li are: sustainable energy, energy storage systems, transportation electrification

What is Yang Li's total number of citations?

Yang Li has 1,752 citations in total.

What are the co-authors of Yang Li?

The co-authors of Yang Li are Josep Pou, D Mahinda Vilathgamuwa, Chun Yang, Daniel E. Quevedo, King Jet Tseng, Torbjörn Thiringer.

    Co-Authors

    H-index: 67
    Josep Pou

    Josep Pou

    Nanyang Technological University

    H-index: 63
    D Mahinda Vilathgamuwa

    D Mahinda Vilathgamuwa

    Queensland University of Technology

    H-index: 61
    Chun Yang

    Chun Yang

    Nanyang Technological University

    H-index: 54
    Daniel E. Quevedo

    Daniel E. Quevedo

    Queensland University of Technology

    H-index: 54
    King Jet Tseng

    King Jet Tseng

    Singapore Institute of Technology

    H-index: 47
    Torbjörn Thiringer

    Torbjörn Thiringer

    Chalmers tekniska högskola

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