State of Health Estimation for Smart Batteries using Transfer Learning with Data Cleaning

IFAC-PapersOnLine

Published On 2023/1/1

Smart battery with optimized pulsed current produced by the bypass is one prospective technology to prolong the service life of the batteries in electric vehicles. The accurate and reliable state of health (SOH) estimation is one significant process before the decision of the control. This paper proposes a proper solution for battery SOH estimation that can be applied to both constant and pulsed current charging scenarios. Specifically, a data cleaning process is proposed for preprocessing the fluctuated measurement, while retaining the main aging information. From the pre-processed data under different charging profiles, four SOH features are extracted, and the correlation coefficients prove their effectiveness with both constant current and pulsed currents. Later, a transfer learning-based model is developed which shows improved accuracy of the SOH estimations under pulsed current scenarios. Finally, experiments …

Journal

IFAC-PapersOnLine

Published On

2023/1/1

Volume

56

Issue

2

Page

3782-3787

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

Other Articles from authors

Yunhong Che

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

arXiv preprint arXiv:2402.07777

Novel Low-Complexity Model Development for Li-ion Cells Using Online Impedance Measurement

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Remus Teodorescu

Remus Teodorescu

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Remus Teodorescu

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Remus Teodorescu

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Aalborg Universitet

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Remus Teodorescu

Remus Teodorescu

Aalborg Universitet

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Remus Teodorescu

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Aalborg Universitet

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IFAC-PapersOnLine

Minimizing the operation costs of a smart home using a HEMS with a MILP-based model predictive control approach

The energy management of a home nowadays is a challenging task, since it is necessary to take into account not only technical aspects, but also information regarding the purchase and sale prices of energy, in order to obtain an operation with minimum cost. Thus, it is necessary to develop advanced energy management systems, the so-called home energy management systems (HEMS). This paper presents a new HEMS with a mixed-integer linear programming (MILP)-based model predictive control (MPC) approach. This approach allows to obtain better results than an approach purely using MILP, by having access to updated information at every moment. The results of a real case study in Algarve, Portugal, show the superiority of MILP-based MPC over MILP and over experimental results.

Daniele Casagrande

Daniele Casagrande

Università degli Studi di Udine

IFAC-PapersOnLine

A Note on the Realization of Nonlinear ODEs in Optimal Control Problems

When in the differential equation describing the behaviour of a dynamical system the time derivative of the input is involved, a naive realization may mislead the application of the Pontryagin Maximum Principle for the solution of optimal control problems. We show that a suitable procedure to eliminate the time derivative of the input leads to a realization that provides more information on the existence and on the form of the solution.

Alejandro Astudillo

Alejandro Astudillo

Katholieke Universiteit Leuven

IFAC-PapersOnLine

Anderson Accelerated Feasible Sequential Linear Programming

This paper proposes an accelerated version of Feasible Sequential Linear Programming (FSLP): the AA(d)-FSLP algorithm. FSLP preserves feasibility in all intermediate iterates by means of an iterative update strategy based on repeated evaluation of zero-order information. This technique was successfully applied to techniques such as Model Predictive Control and Moving Horizon Estimation, but it can exhibit slow convergence. Moreover, keeping all iterates feasible in FSLP entails a large number of additional constraint evaluations. In this paper, Anderson Acceleration (AA(d)) is applied to the zero-order update strategy improving the convergence rate and therefore decreasing the number of constraint evaluations in the inner iterative procedure of the FSLP algorithm. AA(d) achieves an improved contraction rate in the inner iterations, with proven local linear convergence. In addition, it is observed that due to the …

Alejandro Astudillo

Alejandro Astudillo

Katholieke Universiteit Leuven

IFAC-PapersOnLine

IMPACT: A Toolchain for Nonlinear Model Predictive Control Specification, Prototyping, and Deployment

We present IMPACT, a flexible toolchain for nonlinear model predictive control (NMPC) specification with automatic code generation capabilities. The toolchain reduces the engineering complexity of NMPC implementations by providing the user with an easy-to-use application programming interface, and with the flexibility of using multiple state-of-the-art tools and numerical optimization solvers for rapid prototyping of NMPC solutions. IMPACT is written in Python, users can call it from Python and MATLAB, and the generated NMPC solvers can be directly executed from C, Python, MATLAB and Simulink. An application example is presented involving problem specification and deployment on embedded hardware using Simulink, showing the effectiveness and applicability of IMPACT for NMPC-based solutions.

Mohammad Javad Mirzaei

Mohammad Javad Mirzaei

University of Tabriz

IFAC-PapersOnLine

Neural network-based supertwisting control for floating wind turbine in region III

In this paper, a hybrid control strategy based on the super-twisting sliding mode approach and artificial neural network method has been proposed for collective blade pitch (CBP) control of floating wind turbines (FWT) above the rated wind speed. Besides the presence of uncertainties and external disturbances due to the complexity of the model of wind turbines, this paper uses the radial basis function (RBF) neural network to approximate model uncertainties and unmodeled dynamics, reducing the controller dependency on the exact model of the system. The implemented neural network adaptive law has been achieved based on the Lyapunov stability, and the convergence of the closed-loop system is guaranteed by adjusting the learning rate. As the floating wind turbine is a highly nonlinear system, the main objectives are limitation of platform pitch motion and related fatigues, blade fatigue load reduction, and …