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Maitane Berecibar
Research Group MOBI—Mobility, Logistics, and Automotive Technology Research Centre, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium

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Journal article
Published: 28 June 2021 in Energies
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Lithium-ion (Li-ion) batteries have emerged as a promising energy source for electric vehicle (EV) applications owing to the solution offered by their high power, high specific energy, no memory effect, and their excellent durability. However, they generate a large amount of heat, particularly during the fast discharge process. Therefore, a suitable thermal management system (TMS) is necessary to guarantee their performance, efficiency, capacity, safety, and lifetime. This study investigates the thermal performance of different passive cooling systems for the LTO Li-ion battery cell/module with the application of natural convection, aluminum (Al) mesh, copper (Cu) mesh, phase change material (PCM), and PCM-graphite. Experimental results show the average temperature of the cell, due to natural convection, Al mesh, Cu mesh, PCM, and PCM-graphite compared with the lack of natural convection decrease by 6.4%, 7.4%, 8.8%, 30%, and 39.3%, respectively. In addition, some numerical simulations and investigations are solved by COMSOL Multiphysics®, for the battery module consisting of 30 cells, which is cooled by PCM and PCM-graphite. The maximum temperature of the battery module compared with the natural convection case study is reduced by 15.1% and 17.3%, respectively. Moreover, increasing the cell spacing in the battery module has a direct effect on temperature reduction.

ACS Style

Hamidreza Behi; Danial Karimi; Rekabra Youssef; Mahesh Suresh Patil; Joeri Van Mierlo; Maitane Berecibar. Comprehensive Passive Thermal Management Systems for Electric Vehicles. Energies 2021, 14, 3881 .

AMA Style

Hamidreza Behi, Danial Karimi, Rekabra Youssef, Mahesh Suresh Patil, Joeri Van Mierlo, Maitane Berecibar. Comprehensive Passive Thermal Management Systems for Electric Vehicles. Energies. 2021; 14 (13):3881.

Chicago/Turabian Style

Hamidreza Behi; Danial Karimi; Rekabra Youssef; Mahesh Suresh Patil; Joeri Van Mierlo; Maitane Berecibar. 2021. "Comprehensive Passive Thermal Management Systems for Electric Vehicles." Energies 14, no. 13: 3881.

Journal article
Published: 18 May 2021 in Energies
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A lithium-ion capacitor (LiC) is one of the most promising technologies for grid applications, which combines the energy storage mechanism of an electric double-layer capacitor (EDLC) and a lithium-ion battery (LiB). This article presents an optimal thermal management system (TMS) to extend the end of life (EoL) of LiC technology considering different active and passive cooling methods. The impact of different operating conditions and stress factors such as high temperature on the LiC capacity degradation is investigated. Later, optimal passive TMS employing a heat pipe cooling system (HPCS) is developed to control the LiC cell temperature. Finally, the effect of the proposed TMS on the lifetime extension of the LiC is explained. Moreover, this trend is compared to the active cooling system using liquid-cooled TMS (LCTMS). The results demonstrate that the LiC cell temperature can be controlled by employing a proper TMS during the cycle aging test under 150 A current rate. The cell’s top surface temperature is reduced by 11.7% using the HPCS. Moreover, by controlling the temperature of the cell at around 32.5 and 48.8 °C, the lifetime of the LiC would be extended by 51.7% and 16.5%, respectively, compared to the cycling of the LiC under natural convection (NC). In addition, the capacity degradation for the NC, HPCS, and LCTMS case studies are 90.4%, 92.5%, and 94.2%, respectively.

ACS Style

Danial Karimi; Sahar Khaleghi; Hamidreza Behi; Hamidreza Beheshti; Sazzad Hosen; Mohsen Akbarzadeh; Joeri Van Mierlo; Maitane Berecibar. Lithium-Ion Capacitor Lifetime Extension through an Optimal Thermal Management System for Smart Grid Applications. Energies 2021, 14, 2907 .

AMA Style

Danial Karimi, Sahar Khaleghi, Hamidreza Behi, Hamidreza Beheshti, Sazzad Hosen, Mohsen Akbarzadeh, Joeri Van Mierlo, Maitane Berecibar. Lithium-Ion Capacitor Lifetime Extension through an Optimal Thermal Management System for Smart Grid Applications. Energies. 2021; 14 (10):2907.

Chicago/Turabian Style

Danial Karimi; Sahar Khaleghi; Hamidreza Behi; Hamidreza Beheshti; Sazzad Hosen; Mohsen Akbarzadeh; Joeri Van Mierlo; Maitane Berecibar. 2021. "Lithium-Ion Capacitor Lifetime Extension through an Optimal Thermal Management System for Smart Grid Applications." Energies 14, no. 10: 2907.

Journal article
Published: 17 May 2021 in World Electric Vehicle Journal
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In electrified vehicle applications, understanding the battery characteristics is of great importance as it is the state-of-art principal energy source. The key battery parameters can be identified by one of the robust and nondestructive characterization techniques, such as electrochemical impedance spectroscopy (EIS). However, relaxing the battery cell before performing the EIS method is crucial for the characterization results to be standardized. In this study, the three most common and commercially available lithium-ion technologies (NMC/graphite, LFP/graphite, NCA/LTO) are investigated at 15–45 °C temperature, in the range of 20–80% state of charge (SoC) and in fresh and aged state of health (SoH) conditions. The analysis shows that the duration of the relaxation time before impedance measurement has an impact on the battery’s nonlinear behavior. A rest time of 2 h can be proposed, irrespective of battery health condition, considering neutral technology-based impedance measurement. An impedance growth in ohmic and charge transfer characteristics was found, due to aging, and the effect of rest periods was also analyzed from an electrochemical standpoint. This experimental data was fitted to develop an empirical model, which can predict the nonlinear dynamics of lithium technologies with a 4–8% relative error for longer rest time.

ACS Style

Sazzad Hosen; Rahul Gopalakrishnan; Theodoros Kalogiannis; Joris Jaguemont; Joeri Van Mierlo; Maitane Berecibar. Impact of Relaxation Time on Electrochemical Impedance Spectroscopy Characterization of the Most Common Lithium Battery Technologies—Experimental Study and Chemistry-Neutral Modeling. World Electric Vehicle Journal 2021, 12, 77 .

AMA Style

Sazzad Hosen, Rahul Gopalakrishnan, Theodoros Kalogiannis, Joris Jaguemont, Joeri Van Mierlo, Maitane Berecibar. Impact of Relaxation Time on Electrochemical Impedance Spectroscopy Characterization of the Most Common Lithium Battery Technologies—Experimental Study and Chemistry-Neutral Modeling. World Electric Vehicle Journal. 2021; 12 (2):77.

Chicago/Turabian Style

Sazzad Hosen; Rahul Gopalakrishnan; Theodoros Kalogiannis; Joris Jaguemont; Joeri Van Mierlo; Maitane Berecibar. 2021. "Impact of Relaxation Time on Electrochemical Impedance Spectroscopy Characterization of the Most Common Lithium Battery Technologies—Experimental Study and Chemistry-Neutral Modeling." World Electric Vehicle Journal 12, no. 2: 77.

Journal article
Published: 23 February 2021 in Energy
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Over the last few decades investigating the performance of thermal management in the high charge/discharge current has been taken into consideration in many studies. In this study, a mature heat pipe-based air cooling system is built to control the temperature of the lithium-ion (Li-ion) cell/module in the high current (184 A) discharging rate. The temperature of the cell/module experimentally and numerically is considered by the lack of natural convection, natural convection, forced convection, and evaporative cooling. According to the experimental results, the natural and forced convection decrease the average temperature of the cell by 6.2% and 33.7% respectively. Moreover, several numerical simulations are solved by COMSOL Multiphysics®, the commercial computational fluid dynamics (CFD) software. The simulation results are validated against experimental results at the cell level for natural and forced convection. It indicates that the evaporative cooling method is robust to enhance the current cooling system method for further optimization. The results show that there is a 35.8% and 23.8% reduction in the maximum temperature of the cell and module due to the effect of the evaporative cooling method respectively.

ACS Style

Hamidreza Behi; Danial Karimi; Joris Jaguemont; Foad Heidari Gandoman; Theodoros Kalogiannis; Maitane Berecibar; Joeri Van Mierlo. Novel thermal management methods to improve the performance of the Li-ion batteries in high discharge current applications. Energy 2021, 224, 120165 .

AMA Style

Hamidreza Behi, Danial Karimi, Joris Jaguemont, Foad Heidari Gandoman, Theodoros Kalogiannis, Maitane Berecibar, Joeri Van Mierlo. Novel thermal management methods to improve the performance of the Li-ion batteries in high discharge current applications. Energy. 2021; 224 ():120165.

Chicago/Turabian Style

Hamidreza Behi; Danial Karimi; Joris Jaguemont; Foad Heidari Gandoman; Theodoros Kalogiannis; Maitane Berecibar; Joeri Van Mierlo. 2021. "Novel thermal management methods to improve the performance of the Li-ion batteries in high discharge current applications." Energy 224, no. : 120165.

Review
Published: 03 February 2021 in World Electric Vehicle Journal
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Today, there are many recent developments that focus on improving the electric vehicles and their components, particularly regarding advances in batteries, energy management systems, autonomous features and charging infrastructure. This plays an important role in developing next electric vehicle generations, and encourages more efficient and sustainable eco-system. This paper not only provides insights in the latest knowledge and developments of electric vehicles (EVs), but also the new promising and novel EV technologies based on scientific facts and figures—which could be from a technological point of view feasible by 2030. In this paper, potential design and modelling tools, such as digital twin with connected Internet-of-Things (IoT), are addressed. Furthermore, the potential technological challenges and research gaps in all EV aspects from hard-core battery material sciences, power electronics and powertrain engineering up to environmental assessments and market considerations are addressed. The paper is based on the knowledge of the 140+ FTE counting multidisciplinary research centre MOBI-VUB, that has a 40-year track record in the field of electric vehicles and e-mobility.

ACS Style

Joeri Van Mierlo; Maitane Berecibar; Mohamed El Baghdadi; Cedric De Cauwer; Maarten Messagie; Thierry Coosemans; Valéry Jacobs; Omar Hegazy. Beyond the State of the Art of Electric Vehicles: A Fact-Based Paper of the Current and Prospective Electric Vehicle Technologies. World Electric Vehicle Journal 2021, 12, 20 .

AMA Style

Joeri Van Mierlo, Maitane Berecibar, Mohamed El Baghdadi, Cedric De Cauwer, Maarten Messagie, Thierry Coosemans, Valéry Jacobs, Omar Hegazy. Beyond the State of the Art of Electric Vehicles: A Fact-Based Paper of the Current and Prospective Electric Vehicle Technologies. World Electric Vehicle Journal. 2021; 12 (1):20.

Chicago/Turabian Style

Joeri Van Mierlo; Maitane Berecibar; Mohamed El Baghdadi; Cedric De Cauwer; Maarten Messagie; Thierry Coosemans; Valéry Jacobs; Omar Hegazy. 2021. "Beyond the State of the Art of Electric Vehicles: A Fact-Based Paper of the Current and Prospective Electric Vehicle Technologies." World Electric Vehicle Journal 12, no. 1: 20.

Review
Published: 15 December 2020 in Sustainability
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Renewable energy sources (RESs) such as wind and solar are frequently hit by fluctuations due to, for example, insufficient wind or sunshine. Energy storage technologies (ESTs) mitigate the problem by storing excess energy generated and then making it accessible on demand. While there are various EST studies, the literature remains isolated and dated. The comparison of the characteristics of ESTs and their potential applications is also short. This paper fills this gap. Using selected criteria, it identifies key ESTs and provides an updated review of the literature on ESTs and their application potential to the renewable energy sector. The critical review shows a high potential application for Li-ion batteries and most fit to mitigate the fluctuation of RESs in utility grid integration sector. However, for Li-ion batteries to be fully adopted in the RESs utility grid integration, their cost needs to be reduced.

ACS Style

Henok Behabtu; Maarten Messagie; Thierry Coosemans; Maitane Berecibar; Kinde Anlay Fante; Abraham Kebede; Joeri Mierlo. A Review of Energy Storage Technologies’ Application Potentials in Renewable Energy Sources Grid Integration. Sustainability 2020, 12, 10511 .

AMA Style

Henok Behabtu, Maarten Messagie, Thierry Coosemans, Maitane Berecibar, Kinde Anlay Fante, Abraham Kebede, Joeri Mierlo. A Review of Energy Storage Technologies’ Application Potentials in Renewable Energy Sources Grid Integration. Sustainability. 2020; 12 (24):10511.

Chicago/Turabian Style

Henok Behabtu; Maarten Messagie; Thierry Coosemans; Maitane Berecibar; Kinde Anlay Fante; Abraham Kebede; Joeri Mierlo. 2020. "A Review of Energy Storage Technologies’ Application Potentials in Renewable Energy Sources Grid Integration." Sustainability 12, no. 24: 10511.

Journal article
Published: 16 September 2020 in Sustainability
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The system under consideration in this paper consists of a photovoltaic (PV) array, described as having a 10 kWp capacity, battery storage, and connection to the grid via a university grid network. It is stated that the system meets a local load of 4–5 kVA. The system is in Ethiopia, and the authors give details of the location and solar resource to provide information to assess its performance. However, the performance assessment will be specific to the details of the installation and the operational rules, including the variable nature of the load profile, charging and discharging the battery storage, and importing from and exporting to the university grid. The nearby load is mostly supplied from PV and grid sources, and hence the battery installed is found to be idle, showing that the PV together with storage battery system was not utilized in an efficient and optimized way. This in turn resulted in inefficient utilization of sources, increased dependency of the load on the grid, and hence unnecessary operational expenses. Therefore, to alleviate these problems, this paper proposes a means for techno-economic optimization and performance analysis of an existing photovoltaic grid-connected system (PVGCS) by using collected data from a plant data logger for one year (2018) with a model-based Matlab/Simulink simulation and a hybrid optimization model for electric renewables (HOMER) software. According to the simulation result, the PVGCS with 5 kWp PV array optimized system was recommended, which provides a net present cost (NPC) of 5770 (€/kWh), and a cost of energy (COE) of 0.087 (€/kWh) compared to an existing 10 kWp PV system, which results in a NPC value of 6047 (€/kWh) and COE of 0.098 (€/kWh). Therefore, the resulting 5 kWp PV system connected with a storage battery was found to be more efficient and techno-economically viable as compared to the existing 10 kWp PVGCS plant.

ACS Style

Abraham Kebede; Maitane Berecibar; Thierry Coosemans; Maarten Messagie; Towfik Jemal; Henok Behabtu; Joeri Van Mierlo. A Techno-Economic Optimization and Performance Assessment of a 10 kWP Photovoltaic Grid-Connected System. Sustainability 2020, 12, 7648 .

AMA Style

Abraham Kebede, Maitane Berecibar, Thierry Coosemans, Maarten Messagie, Towfik Jemal, Henok Behabtu, Joeri Van Mierlo. A Techno-Economic Optimization and Performance Assessment of a 10 kWP Photovoltaic Grid-Connected System. Sustainability. 2020; 12 (18):7648.

Chicago/Turabian Style

Abraham Kebede; Maitane Berecibar; Thierry Coosemans; Maarten Messagie; Towfik Jemal; Henok Behabtu; Joeri Van Mierlo. 2020. "A Techno-Economic Optimization and Performance Assessment of a 10 kWP Photovoltaic Grid-Connected System." Sustainability 12, no. 18: 7648.

Book chapter
Published: 01 April 2020 in Advances in Modelling and Control of Wind and Hydrogenerators
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Wind energy is one of the most important sources of energy in the world. In recent decades, wind as one of the massive marine energy resources in the ocean to produce electricity has been used. This chapter introduces a comprehensive overview of the efficient ocean wind energy technologies, and the global wind energies in both offshore and onshore sides are discussed. Also, the classification of global ocean wind energy resources is presented. Moreover, different components of a wind farm offshore as well as the technologies used in them are investigated. Possible layouts regarding the foundation of an offshore wind turbine, floating offshore, as well as the operation of wind farms in the shallow and deep location of the ocean are studied. Finally, the offshore wind power plant challenges are described.

ACS Style

Foad H. Gandoman; Abdollah Ahmadi; Shady H.E. Abdel Aleem; Masoud Ardeshiri; Ali Esmaeel Nezhad; Joeri Van Mierlo; Maitane Berecibar. Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges. Advances in Modelling and Control of Wind and Hydrogenerators 2020, 1 .

AMA Style

Foad H. Gandoman, Abdollah Ahmadi, Shady H.E. Abdel Aleem, Masoud Ardeshiri, Ali Esmaeel Nezhad, Joeri Van Mierlo, Maitane Berecibar. Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges. Advances in Modelling and Control of Wind and Hydrogenerators. 2020; ():1.

Chicago/Turabian Style

Foad H. Gandoman; Abdollah Ahmadi; Shady H.E. Abdel Aleem; Masoud Ardeshiri; Ali Esmaeel Nezhad; Joeri Van Mierlo; Maitane Berecibar. 2020. "Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges." Advances in Modelling and Control of Wind and Hydrogenerators , no. : 1.

Journal article
Published: 09 March 2020 in Energies
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The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

ACS Style

Sahar Khaleghi; Yousef Firouz; Maitane Berecibar; Joeri Van Mierlo; Peter Van Den Bossche. Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features. Energies 2020, 13, 1262 .

AMA Style

Sahar Khaleghi, Yousef Firouz, Maitane Berecibar, Joeri Van Mierlo, Peter Van Den Bossche. Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features. Energies. 2020; 13 (5):1262.

Chicago/Turabian Style

Sahar Khaleghi; Yousef Firouz; Maitane Berecibar; Joeri Van Mierlo; Peter Van Den Bossche. 2020. "Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features." Energies 13, no. 5: 1262.

Journal article
Published: 23 October 2019 in Energies
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A lithium-ion battery cell’s electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light- and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs’ parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

ACS Style

Theodoros Kalogiannis; Sazzad Hosen; Mohsen Akbarzadeh Sokkeh; Shovon Goutam; Joris Jaguemont; Lu Jin; Geng Qiao; Maitane Berecibar; Joeri Van Mierlo. Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model. Energies 2019, 12, 4031 .

AMA Style

Theodoros Kalogiannis, Sazzad Hosen, Mohsen Akbarzadeh Sokkeh, Shovon Goutam, Joris Jaguemont, Lu Jin, Geng Qiao, Maitane Berecibar, Joeri Van Mierlo. Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model. Energies. 2019; 12 (21):4031.

Chicago/Turabian Style

Theodoros Kalogiannis; Sazzad Hosen; Mohsen Akbarzadeh Sokkeh; Shovon Goutam; Joris Jaguemont; Lu Jin; Geng Qiao; Maitane Berecibar; Joeri Van Mierlo. 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model." Energies 12, no. 21: 4031.

Journal article
Published: 23 April 2019 in Energies
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Electric vehicles (EVs) are recognized as promising options, not only for the decarbonization of urban areas and greening of the transportation sector, but also for increasing power system flexibility through demand-side management. Large-scale uncoordinated charging of EVs can impose negative impacts on the existing power system infrastructure regarding stability and security of power system operation. One solution to the severe grid overload issues derived from high penetration of EVs is to integrate local renewable power generation units as distributed generation units to the power system or to the charging infrastructure. To reduce the uncertainties associated with renewable power generation and load as well as to improve the process of tracking Pareto front in each time sequence, a predictive double-layer optimal power flow based on support vector regression and one-step prediction is presented in this study. The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid via EVs. Moreover, vehicle to grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation.

ACS Style

Omid Rahbari; Noshin Omar; Joeri Van Mierlo; Marc A. Rosen; Thierry Coosemans; Maitane Berecibar. Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme. Energies 2019, 12, 1525 .

AMA Style

Omid Rahbari, Noshin Omar, Joeri Van Mierlo, Marc A. Rosen, Thierry Coosemans, Maitane Berecibar. Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme. Energies. 2019; 12 (8):1525.

Chicago/Turabian Style

Omid Rahbari; Noshin Omar; Joeri Van Mierlo; Marc A. Rosen; Thierry Coosemans; Maitane Berecibar. 2019. "Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme." Energies 12, no. 8: 1525.

News and views
Published: 09 April 2019 in Nature
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ACS Style

Maitane Berecibar. Machine-learning techniques used to accurately predict battery life. Nature 2019, 568, 325 -326.

AMA Style

Maitane Berecibar. Machine-learning techniques used to accurately predict battery life. Nature. 2019; 568 (7752):325-326.

Chicago/Turabian Style

Maitane Berecibar. 2019. "Machine-learning techniques used to accurately predict battery life." Nature 568, no. 7752: 325-326.

Journal article
Published: 03 October 2018 in Applied Energy
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Machine-learning based methods have been widely used for battery health state monitoring. However, the existing studies require sophisticated data processing for feature extraction, thereby complicating the implementation in battery management systems. This paper proposes a machine-learning technique, random forest regression, for battery capacity estimation. The proposed technique is able to learn the dependency of the battery capacity on the features that are extracted from the charging voltage and capacity measurements. The random forest regression is solely based on signals, such as the measured current, voltage and time, that are available onboard during typical battery operation. The collected raw data can be directly fed into the trained model without any pre-processing, leading to a low computational cost. The incremental capacity analysis is employed for the feature selection. The developed method is applied and validated on lithium nickel manganese cobalt oxide batteries with different ageing patterns. Experimental results show that the proposed technique is able to evaluate the health states of different batteries under varied cycling conditions with a root-mean-square error of less than 1.3% and a low computational requirement. Therefore, the proposed method is promising for online battery capacity estimation.

ACS Style

Yi Li; Changfu Zou; Maitane Berecibar; Elise Nanini-Maury; Jonathan C.-W. Chan; Peter Van Den Bossche; Joeri Van Mierlo; Noshin Omar. Random forest regression for online capacity estimation of lithium-ion batteries. Applied Energy 2018, 232, 197 -210.

AMA Style

Yi Li, Changfu Zou, Maitane Berecibar, Elise Nanini-Maury, Jonathan C.-W. Chan, Peter Van Den Bossche, Joeri Van Mierlo, Noshin Omar. Random forest regression for online capacity estimation of lithium-ion batteries. Applied Energy. 2018; 232 ():197-210.

Chicago/Turabian Style

Yi Li; Changfu Zou; Maitane Berecibar; Elise Nanini-Maury; Jonathan C.-W. Chan; Peter Van Den Bossche; Joeri Van Mierlo; Noshin Omar. 2018. "Random forest regression for online capacity estimation of lithium-ion batteries." Applied Energy 232, no. : 197-210.

Journal article
Published: 01 July 2018 in Journal of Power Sources
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ACS Style

Yi Li; Mohamed Abdel-Monema; Rahul Gopalakrishnan; Maitane Berecibar; Elise Nanini-Maury; Noshin Omar; Peter Van Den Bossche; Joeri Van Mierlo. Erratum to ‘A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter’ [J. Power Sources 373 (2018) 40–53]. Journal of Power Sources 2018, 393, 230 .

AMA Style

Yi Li, Mohamed Abdel-Monema, Rahul Gopalakrishnan, Maitane Berecibar, Elise Nanini-Maury, Noshin Omar, Peter Van Den Bossche, Joeri Van Mierlo. Erratum to ‘A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter’ [J. Power Sources 373 (2018) 40–53]. Journal of Power Sources. 2018; 393 ():230.

Chicago/Turabian Style

Yi Li; Mohamed Abdel-Monema; Rahul Gopalakrishnan; Maitane Berecibar; Elise Nanini-Maury; Noshin Omar; Peter Van Den Bossche; Joeri Van Mierlo. 2018. "Erratum to ‘A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter’ [J. Power Sources 373 (2018) 40–53]." Journal of Power Sources 393, no. : 230.

Journal article
Published: 01 January 2018 in Journal of Power Sources
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ACS Style

Yi Li; Mohamed Abdel-Monem; Rahul Gopalakrishnan; Maitane Berecibar; Elise Nanini-Maury; Noshin Omar; Peter Van Den Bossche; Joeri Van Mierlo. A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter. Journal of Power Sources 2018, 373, 40 -53.

AMA Style

Yi Li, Mohamed Abdel-Monem, Rahul Gopalakrishnan, Maitane Berecibar, Elise Nanini-Maury, Noshin Omar, Peter Van Den Bossche, Joeri Van Mierlo. A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter. Journal of Power Sources. 2018; 373 ():40-53.

Chicago/Turabian Style

Yi Li; Mohamed Abdel-Monem; Rahul Gopalakrishnan; Maitane Berecibar; Elise Nanini-Maury; Noshin Omar; Peter Van Den Bossche; Joeri Van Mierlo. 2018. "A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter." Journal of Power Sources 373, no. : 40-53.

Journal article
Published: 01 August 2017 in Journal of Power Sources
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ACS Style

Matthieu Dubarry; Maitane Berecibar; A. Devie; David Anseán; N. Omar; I. Villarreal. State of health battery estimator enabling degradation diagnosis: Model and algorithm description. Journal of Power Sources 2017, 360, 59 -69.

AMA Style

Matthieu Dubarry, Maitane Berecibar, A. Devie, David Anseán, N. Omar, I. Villarreal. State of health battery estimator enabling degradation diagnosis: Model and algorithm description. Journal of Power Sources. 2017; 360 ():59-69.

Chicago/Turabian Style

Matthieu Dubarry; Maitane Berecibar; A. Devie; David Anseán; N. Omar; I. Villarreal. 2017. "State of health battery estimator enabling degradation diagnosis: Model and algorithm description." Journal of Power Sources 360, no. : 59-69.

Conference paper
Published: 22 December 2016 in 2016 IEEE Vehicle Power and Propulsion Conference (VPPC)
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This work presents a study of the degradation mechanism of NMC/Graphite high power and high energy cells cycled under different depth of discharges. Analysis will be based on the study of incremental capacity curves together with the usage of the 'alawa toolbox. The goal is to investigate the main differences in degradation mechanism between the HE and HP cells.

ACS Style

M. Berecibar; M. Dubarry; I. Villarreal; N. Omar; J. Van Mierlo. Degradation Mechanisms Detection for HP and HE NMC Cells Based on Incremental Capacity Curves. 2016 IEEE Vehicle Power and Propulsion Conference (VPPC) 2016, 1 -5.

AMA Style

M. Berecibar, M. Dubarry, I. Villarreal, N. Omar, J. Van Mierlo. Degradation Mechanisms Detection for HP and HE NMC Cells Based on Incremental Capacity Curves. 2016 IEEE Vehicle Power and Propulsion Conference (VPPC). 2016; ():1-5.

Chicago/Turabian Style

M. Berecibar; M. Dubarry; I. Villarreal; N. Omar; J. Van Mierlo. 2016. "Degradation Mechanisms Detection for HP and HE NMC Cells Based on Incremental Capacity Curves." 2016 IEEE Vehicle Power and Propulsion Conference (VPPC) , no. : 1-5.

Journal article
Published: 01 July 2016 in Journal of Power Sources
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ACS Style

Maitane Berecibar; Floris Devriendt; Matthieu Dubarry; Igor Villarreal; Noshin Omar; Wouter Verbeke; Joeri Van Mierlo. Online state of health estimation on NMC cells based on predictive analytics. Journal of Power Sources 2016, 320, 239 -250.

AMA Style

Maitane Berecibar, Floris Devriendt, Matthieu Dubarry, Igor Villarreal, Noshin Omar, Wouter Verbeke, Joeri Van Mierlo. Online state of health estimation on NMC cells based on predictive analytics. Journal of Power Sources. 2016; 320 ():239-250.

Chicago/Turabian Style

Maitane Berecibar; Floris Devriendt; Matthieu Dubarry; Igor Villarreal; Noshin Omar; Wouter Verbeke; Joeri Van Mierlo. 2016. "Online state of health estimation on NMC cells based on predictive analytics." Journal of Power Sources 320, no. : 239-250.

Journal article
Published: 24 June 2016 in World Electric Vehicle Journal
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Path dependence degradation detection is getting lot of attention due to high demand of long lasting batteries for applications with sporadic usage such as electric vehicles. In this regard, this work presents a study of the degradation mechanism of NMC/Graphite cells cycled under different depth of discharges. Analysis will be based on the study of incremental capacity curves. The trends observed on experimental curves will be compared to simulations in order to exemplify the differences and highlight the path dependence of the degradation.

ACS Style

M. Berecibar; M. Dubarry; N. Omar; I. Villarreal; J. Van Mierlo. Degradation Mechanism Detection for NMC Batteries based on Incremental Capacity Curves. World Electric Vehicle Journal 2016, 8, 350 -361.

AMA Style

M. Berecibar, M. Dubarry, N. Omar, I. Villarreal, J. Van Mierlo. Degradation Mechanism Detection for NMC Batteries based on Incremental Capacity Curves. World Electric Vehicle Journal. 2016; 8 (2):350-361.

Chicago/Turabian Style

M. Berecibar; M. Dubarry; N. Omar; I. Villarreal; J. Van Mierlo. 2016. "Degradation Mechanism Detection for NMC Batteries based on Incremental Capacity Curves." World Electric Vehicle Journal 8, no. 2: 350-361.

Journal article
Published: 01 May 2016 in Energy
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This paper discusses a novel differential voltage curve capacity estimation to determine the state of health of LiFePO4 cells. Differential voltage curves are used because of their ability to detect and quantify degradation mechanisms. The estimation is carried out through partial charging or discharging tests, and is specifically designed for battery management systems, due to the trade off between accuracy and low computational effort. This means the method can be effectively executed online, in a real application. The technique is also able to accurately detect the end of life of the cells. Aging datasets of 18 cells with identical chemistry were used for both parametrization and validation. The cells were subjected to a wide range of cycling and storage conditions, including temperature, state of charge, charging and discharging rate, depth of discharge and state of health. The performance and robustness of the estimation are validated by means of the degradation datasets from more than 25 different scenarios at the cell and battery pack level. The related results indicate that the proposed health management strategy has an average relative error of 1.5% at the battery pack level.

ACS Style

Maitane Berecibar; Maitane Garmendia; Iñigo Gandiaga; Jon Crego; Igor Villarreal. State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application. Energy 2016, 103, 784 -796.

AMA Style

Maitane Berecibar, Maitane Garmendia, Iñigo Gandiaga, Jon Crego, Igor Villarreal. State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application. Energy. 2016; 103 ():784-796.

Chicago/Turabian Style

Maitane Berecibar; Maitane Garmendia; Iñigo Gandiaga; Jon Crego; Igor Villarreal. 2016. "State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application." Energy 103, no. : 784-796.