Battery health prognostic with sensor-free differential temperature voltammetry reconstruction and capacity estimation based on multi-domain adaptation

Etransportation

Published On 2023/7/1

Battery health prognostic is a key part of battery management used to ensure safe and optimal usage. A novel method for end-to-end sensor-free differential temperature voltammetry reconstruction and state of health estimation based on the multi-domain adaptation is proposed in this paper. Firstly, the partial charging or discharging curve is used to reconstruct the differential temperature curve, removing the requirement for the temperature sensor measurement. The partial differential capacity curve and the reconstructed differential temperature curve are input and then used in an end-to-end state of health estimation. Finally, to reduce the domain discrepancy between the source and target domains, the maximum mean discrepancy is included as an additional loss to improve the accuracy of both differential temperature curve reconstruction and state of health estimation with unlabeled data from the testing battery …

Journal

Etransportation

Published On

2023/7/1

Volume

17

Page

100245

Authors

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Position

Professor at

H-Index(all)

104

H-Index(since 2020)

72

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Power Electronics

Smart Batteries

AI

University Profile Page

Yunhong Che

Yunhong Che

Chongqing University

Position

H-Index(all)

16

H-Index(since 2020)

16

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Energy Storage Systems

Transportation electrification

Prognostics and Health Management

Battery

University Profile Page

Xin Sui

Xin Sui

Aalborg Universitet

Position

H-Index(all)

14

H-Index(since 2020)

14

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Li-ion batteries

SOH estimation

RUL prediction

University Profile Page

Søren B. Vilsen

Søren B. Vilsen

Aalborg Universitet

Position

Post Doc

H-Index(all)

9

H-Index(since 2020)

9

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

Statistics

Forensic Genetics

Battery's

University Profile Page

Other Articles from authors

Yunhong Che

Yunhong Che

Chongqing University

IEEE Transactions on Industrial Electronics

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Søren B. Vilsen

Søren B. Vilsen

Aalborg Universitet

On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health

As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles, to more dynamic profiles constructed to mimic real operation, when ageing the Li-ion batteries. However, in most cases, when ageing the batteries, the same exact profile is just repeated until the battery reaches its end-of-life. Using data from batteries aged in this fashion to build a model, there is a very real possibility that the model will rely on the built-in repetitiveness of the profile. Therefore, this work will examine the dependence of the performance of a multiple linear regression on the number of charges used to train the model, and their location within the profile used to age the batteries. The investigation shows that it is possible to build models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only two randomly sampled partial charges are needed to achieve errors of less than 1%. Lastly, as the number of randomly sampled partial charges used to create the model increases, then the dependence on particular partial charges tends to decrease.

Yunhong Che

Yunhong Che

Chongqing University

IEEE/ASME Transactions on Mechatronics

Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling

Owing to the nonnegligible impacts of temperature on the safety, performance, and lifespan of lithium-ion batteries, it is essential to regulate battery temperature to an optimal range. Temperature monitoring plays a fundamental role in battery thermal management, yet it is still challenged by limited onboard temperature sensors, particularly in large-scale battery applications. As such, developing sensorless temperature estimation is of paramount importance to acquiring the temperature information of each cell in a battery system. This article proposes an estimation approach to obtain the cell temperature by taking advantage of the electrothermal coupling effect of batteries. An electrothermal coupled model, which captures the interactions between the electrical and the thermal dynamics, is established, parameterized, and experimentally validated. A closed-loop observer is then designed based on this coupled model …

Søren B. Vilsen

Søren B. Vilsen

Aalborg Universitet

Data in Brief

Dataset of lithium-ion battery degradation based on a forklift mission profile for state-of-health estimation and lifetime prediction

Lithium-ion (Li-ion) batteries are becoming an increasingly integral part of modern society, through consumer electronics, stabilisation of the electric grid, and electric vehicles. However, Lithium-ion batteries degrade in effectiveness over time; a degradation which is extremely dependent on the usage of the battery. Therefore, to study how a battery cell degrades under dynamic conditions, a realistic load profile was constructed based on the operation of forklifts. This profile was used to age three Lithium-ion battery cells at 45, 40, and 35°C and the response of the cells was measured on a second-by-second basis. Periodically the ageing was halted to perform a reference test of the cells allowing for the tracking of their degradation.

Xin Sui

Xin Sui

Aalborg Universitet

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Small-Sample-Learning-Based Lithium-Ion Batteries Health Assessment: An Optimized Ensemble Framework

Machine Learning is widely studied in battery state of health (SOH) estimation due to its advantage in establishing the non-linear mapping between measurements and SOH. However, the requirement of a big dataset and the lack of robustness limit the practical application, especially in small sample learning. To tackle these challenges, an optimal ensemble framework called BaggELM (bagging extreme learning machine) is proposed for battery SOH estimation. Specifically, the required dataset is reduced by optimizing the input voltage and the hyperparameters of the BaggELM algorithm. Moreover, a statistical post-processing method is used to aggregate multiple ELMs, and the final estimate is determined by the maximum probability density value. As a result, the effects of random parameterization of ELM and the training data size on SOH estimation are suppressed, thus improving the robustness and accuracy of …

Yunhong Che

Yunhong Che

Chongqing University

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Transportation electrification is a promising solution to meet the ever-rising energy demand and realize sustainable development. Lithium-ion batteries, being the most predominant energy storage devices, directly affect the safety, comfort, driving range, and reliability of many electric mobilities. Nevertheless, thermal-related issues of batteries such as potential thermal runaway, performance degradation at low temperatures, and accelerated aging still hinder the wider adoption of electric mobilities. To ensure safe, efficient, and reliable operations of lithium-ion batteries, monitoring their thermal states is critical to safety protection, performance optimization, as well as prognostics, and health management. Given insufficient onboard temperature sensors and their inability to measure battery internal temperature, accurate and timely temperature estimation is of particular importance to thermal state monitoring. Toward …

Yunhong Che

Yunhong Che

Chongqing University

Reliability Engineering & System Safety

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Predictive health assessment is of vital importance for smarter battery management to ensure optimal and safe operations and thus make the most use of battery life. This paper proposes a general framework for battery aging prognostics in order to provide the predictions of battery knee, lifetime, state of health degradation, and aging rate variations, as well as the assessment of battery health. Early information is used to predict knee slope and other life-related information via deep multi-task learning, where the convolutional-long-short-term memory-bayesian neural network is proposed. The structure is also used for online state of health and degradation rate predictions for the detection of accelerating aging. The two probabilistic predicted boundaries identify the accelerating aging regions for battery health assessment. To avoid wrong and premature alarms, the empirical model is used for data preprocessing and …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Electronics

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions using domain-adaptive multitask learning (MTL) with long-term regularization. First, features extracted from partial charging curves are utilized for short-term state of health predictions. Then, the long-term degradation trajectory is directly predicted by recursively using the predicted features within the multitask framework, enhancing the model integrity and lowering the complexity. Then, domain adaptation (DA) is adopted to reduce the discrepancies between different working conditions. Additionally, a long-term regularization is introduced to address the shortcoming that arises when the model is extrapolated recursively for future health predictions. Thus, the short …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE/ASME Transactions on Mechatronics

Online Sensorless Temperature Estimation of Lithium-Ion Batteries Through Electro-Thermal Coupling

Owing to the nonnegligible impacts of temperature on the safety, performance, and lifespan of lithium-ion batteries, it is essential to regulate battery temperature to an optimal range. Temperature monitoring plays a fundamental role in battery thermal management, yet it is still challenged by limited onboard temperature sensors, particularly in large-scale battery applications. As such, developing sensorless temperature estimation is of paramount importance to acquiring the temperature information of each cell in a battery system. This article proposes an estimation approach to obtain the cell temperature by taking advantage of the electrothermal coupling effect of batteries. An electrothermal coupled model, which captures the interactions between the electrical and the thermal dynamics, is established, parameterized, and experimentally validated. A closed-loop observer is then designed based on this coupled model …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

arXiv preprint arXiv:2402.07777

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industrial Informatics

A Battery Digital Twin From Laboratory Data Using Wavelet Analysis and Neural Networks

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

IEEE Transactions on Industry Applications

Small-Sample-Learning-Based Lithium-Ion Batteries Health Assessment: An Optimized Ensemble Framework

Machine Learning is widely studied in battery state of health (SOH) estimation due to its advantage in establishing the non-linear mapping between measurements and SOH. However, the requirement of a big dataset and the lack of robustness limit the practical application, especially in small sample learning. To tackle these challenges, an optimal ensemble framework called BaggELM (bagging extreme learning machine) is proposed for battery SOH estimation. Specifically, the required dataset is reduced by optimizing the input voltage and the hyperparameters of the BaggELM algorithm. Moreover, a statistical post-processing method is used to aggregate multiple ELMs, and the final estimate is determined by the maximum probability density value. As a result, the effects of random parameterization of ELM and the training data size on SOH estimation are suppressed, thus improving the robustness and accuracy of …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Intelligent Cell Balancing Control for Lithium-Ion Battery Packs

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Grid Impedance Shaping for Grid-Forming Inverters: A Soft Actor-Critic Deep Reinforcement Learning Algorithm

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Electric vehicle battery charging strategy

As a key enabler for transportation electrification and a contributor toward the net-zero carbon future, battery plays a pivotal role in determining the energy management performance of electric vehicles. Technical challenges facing the development of advanced automotive battery charging arise from various contradictory objectives, immeasurable internal states, and hard constraints. This chapter presents a critical introduction to the state-of-the-art charging strategies for the electric vehicle battery and their key enabling technologies. Specifically, battery charging solutions for electric vehicles are first classified and discussed. Then, the battery models on which these solutions rest are stated, the related charging frameworks are summarized, and the advantages and drawbacks of the adopted technologies are discussed. Suggestions for overcoming the limitations of the discussed charging strategies are proposed …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities

Transportation electrification is a promising solution to meet the ever-rising energy demand and realize sustainable development. Lithium-ion batteries, being the most predominant energy storage devices, directly affect the safety, comfort, driving range, and reliability of many electric mobilities. Nevertheless, thermal-related issues of batteries such as potential thermal runaway, performance degradation at low temperatures, and accelerated aging still hinder the wider adoption of electric mobilities. To ensure safe, efficient, and reliable operations of lithium-ion batteries, monitoring their thermal states is critical to safety protection, performance optimization, as well as prognostics, and health management. Given insufficient onboard temperature sensors and their inability to measure battery internal temperature, accurate and timely temperature estimation is of particular importance to thermal state monitoring. Toward …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Reliability Engineering & System Safety

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Predictive health assessment is of vital importance for smarter battery management to ensure optimal and safe operations and thus make the most use of battery life. This paper proposes a general framework for battery aging prognostics in order to provide the predictions of battery knee, lifetime, state of health degradation, and aging rate variations, as well as the assessment of battery health. Early information is used to predict knee slope and other life-related information via deep multi-task learning, where the convolutional-long-short-term memory-bayesian neural network is proposed. The structure is also used for online state of health and degradation rate predictions for the detection of accelerating aging. The two probabilistic predicted boundaries identify the accelerating aging regions for battery health assessment. To avoid wrong and premature alarms, the empirical model is used for data preprocessing and …

Søren B. Vilsen

Søren B. Vilsen

Aalborg Universitet

Journal of Energy Chemistry

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health (SOH) estimation of e-mobilities operated in real and dynamic conditions is essential and challenging. Most of existing estimations are based on a fixed constant current charging and discharging aging profiles, which overlooked the fact that the charging and discharging profiles are random and not complete in real application. This work investigates the influence of feature engineering on the accuracy of different machine learning (ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered. Fifteen features were extracted from the battery partial recharging profiles, considering different factors such as starting voltage values, charge amount, and charging sliding windows. Then, features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection. Multiple linear regression (MLR …

Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

Fractional-order control techniques for renewable energy and energy-storage-integrated power systems: A review

The worldwide energy revolution has accelerated the utilization of demand-side manageable energy systems such as wind turbines, photovoltaic panels, electric vehicles, and energy storage systems in order to deal with the growing energy crisis and greenhouse emissions. The control system of renewable energy units and energy storage systems has a high effect on their performance and absolutely on the efficiency of the total power network. Classical controllers are based on integer-order differentiation and integration, while the fractional-order controller has tremendous potential to change the order for better modeling and controlling the system. This paper presents a comprehensive review of the energy system of renewable energy units and energy storage devices. Various papers are evaluated, and their methods and results are presented. Moreover, the mathematical fundamentals of the fractional-order method are mentioned, and the various studies are categorized based on different parameters. Various definitions for fractional-order calculus are also explained using their mathematical formula. Different studies and numerical evaluations present appropriate efficiency and accuracy of the fractional-order techniques for estimating, controlling, and improving the performance of energy systems in various operational conditions so that the average error of the fractional-order methods is considerably lower than other ones.

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eTransportation

Lithium-ion battery sudden death: safety degradation and failure mechanism

Environmental pollution and energy scarcity have acted as catalysts for the energy revolution, particularly driving the rapid progression of vehicle electrification. Lithium-ion batteries play a fundamental role as the pivotal components in electric vehicles. Nevertheless, battery sudden death poses substantial challenges to battery design and management. This work comprehensively investigates the failure mechanism of cell sudden death under different degradation paths and its impact on cell performances. Multi-angle characterization analysis shows that lithium plating is the primary failure mechanism of battery sudden death under different degradation paths. However, the formation mechanisms of lithium plating differ in various degradation paths. In the path-L and path-F, the limited lithium intercalation rate in graphite leads to lithium plating, while localized anode drying and uneven potential distribution are the …

Chong Zhu

Chong Zhu

Shanghai Jiao Tong University

eTransportation

Modeling of an all-solid-state battery with a composite positive electrode

All solid-state batteries are considered as the most promising battery technology due to their safety and high energy density. This study presents an advanced mathematical model that accurately simulates the complex behavior of all-solid-state lithium-ion batteries with composite positive electrodes. The partial differential equations of ionic transport and potential dynamics in the electrode and electrolyte are solved and reduced to a low-order system with Padé approximation. Moreover, the imperfect contact and the electrical double layers at the solid-solid interface are also taken into consideration. Subsequent experiments are conducted for the blocked cell and half-cells to extract parameters. Next, the parameterized model is validated with extensive experimental data from NCM811/LPSC/Li4.4Si batteries, illustrating the superior capability of predicting cell voltage with an average RMSE of 19.5 mV for the …

Cesar Diaz-Londono

Cesar Diaz-Londono

Politecnico di Torino

eTransportation

Enhanced EV charging algorithm considering data-driven workplace chargers categorization with multiple vehicle types

The increasing penetration of Electric Vehicles (EVs) presents significant challenges in integrating EV chargers. To address this, precise smart EV charging strategies are imperative to prevent a surge in peak power demand and ensure seamless charger integration. In this article, a smart EV charging pool algorithm employing optimal control is proposed. The main objective is to minimize the charge point operator’s cost while maximizing its EV chargers’ flexibility. The algorithm adeptly manages the charger pilot signal standard and accommodates the non-ideal behavior of EV batteries across various vehicle types. It ensures the fulfillment of vehicle owners’ preferences regarding the departure state of charge. Additionally, we develop a data-driven characterization of EV workplace chargers, considering power levels and estimated battery capacities. A novel methodology for computing the EV battery’s arrival state of …

Chao Zhang

Chao Zhang

Northwestern Polytechnical University

eTransportation

Dynamic mechanical behaviors of load-bearing battery structure upon low-velocity impact loading in electric vehicles

As the electrification trend of vehicles continues, new energy vehicles such as electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) are being equipped with new functional energy storage devices demanding a trade-off between electrical and mechanical property. Accordingly, composite-battery integrated structures which simultaneously carry mechanical resistance and energy-storage capacity, are being explored to offer great potential for the next generation of EVs or PHEVs. Herein, the dynamic responses and failure mechanisms of the integrated structure under the commonly occurring low-velocity impact events are studied both experimentally and numerically. A macro-scale finite element (FE) model was developed by implementing constitutive models of component materials, including lithium‐ion polymer (LiPo) battery cells, polymer foams, and carbon fiber-reinforced polymers (CFRP). The …

Xin Lai (来鑫)

Xin Lai (来鑫)

University of Shanghai for Science and Technology

eTransportation

Lithium-ion battery sudden death: safety degradation and failure mechanism

Environmental pollution and energy scarcity have acted as catalysts for the energy revolution, particularly driving the rapid progression of vehicle electrification. Lithium-ion batteries play a fundamental role as the pivotal components in electric vehicles. Nevertheless, battery sudden death poses substantial challenges to battery design and management. This work comprehensively investigates the failure mechanism of cell sudden death under different degradation paths and its impact on cell performances. Multi-angle characterization analysis shows that lithium plating is the primary failure mechanism of battery sudden death under different degradation paths. However, the formation mechanisms of lithium plating differ in various degradation paths. In the path-L and path-F, the limited lithium intercalation rate in graphite leads to lithium plating, while localized anode drying and uneven potential distribution are the …

HAO, Han / 郝瀚

HAO, Han / 郝瀚

Tsinghua University

eTransportation

Assessment of vehicle-side costs and profits of providing vehicle-to-grid services

The rapid expansion of electric vehicle market brings a huge stock of batteries, which can potentially serve as distributed energy storage systems to provide grid services through Vehicle-to-Grid (V2G) technology. Existing research on V2G's economic viability often simplifies intricate technical details and neglects the influence of key parameters on the results. To address these gaps, a technology-rich model was developed to evaluate the vehicle-side costs and profits of V2G. Given the current state of V2G-related technologies and costs, V2G's levelized cost of storage ranges from $0.085/kWh to $0.243/kWh, and its net present value ranges from $-1,317 to $3,013, depending on the operational strategies implemented. The variations in assessment results due to changes in key parameters were further evaluated to analyze the impacts of technological advancements and user behavior. With advancements in battery …

Markus Lienkamp

Markus Lienkamp

Technische Universität München

eTransportation

Thermal runaway propagation in automotive lithium-ion batteries with NMC-811 and LFP cathodes: Safety requirements and impact on system integration

Thermal runaway propagation mitigation is a prerequisite in battery development for electric vehicles to meet legal requirements and ensure vehicle occupants’ safety. Thermal runaway propagation depends on many factors, eg, cell spacing, intermediate materials, and the entire cell stack setup. Furthermore, the choice of cell chemistry plays a decisive role in the safety design of a battery system. However, many studies considering cell chemistry only focus on the cell level or neglect the energetic impacts of safety measures on system integration. This leads to a neglect of the conflict of objectives between battery safety and energy density. In this article, a comprehensive analysis of the thermal runaway propagation in lithium-ion batteries with NMC-811 and LFP cathodes from a Mini Cooper SE and Tesla Model 3 SR+ is presented. The focus is set on the identification of differences in battery safety, the derivation of …

Joakim Widén

Joakim Widén

Uppsala Universitet

eTransportation

Urban-scale energy matching optimization with smart EV charging and V2G in a net-zero energy city powered by wind and solar energy

Renewable energy and electric vehicles (EVs) are crucial technologies for achieving sustainable cities. However, intermittent power generation from renewable energy sources and increased peak load due to EV charging can pose technical challenges for the power systems. Improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). A city on the west coast of Sweden is used as a case study. The smart charging and V2G schemes aim to minimize the mismatch between generation and load, and are formulated as quadratic programming problems. The simulation results show that the optimal load matching performance is achieved in a net …