A control-oriented lithium-ion battery pack model for plug-in hybrid electric vehicle cycle-life studies and system design with consideration of health management

Cordoba-Arenas, Andrea; Onori, Simona; Rizzoni, Giorgio

doi:10.1016/j.jpowsour.2014.12.048

Cited by 112 publications

(47 citation statements)

References 37 publications

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“…The cell-to-cell variation will become even larger as the cells age, which makes the unit cell model assumption unsuitable to represent battery pack. In addition to this, in a battery pack the SOC as well as the ability to store energy and deliver power, and therefore the system level SOC/SOH, highly depends on individual cell's performance, SOH, electrical topology and balance control [32]. For example, in a battery pack composed of cells in series with a passive balance control, the 'weakest cell' will firstly reach fully discharged state during the discharge operation, limiting the discharge capacity of the entire pack.…”

Section: Introductionmentioning

confidence: 99%

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

Hua

Cordoba-Arenas

Warner

et al. 2015

Journal of Power Sources

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154

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Section: Introductionmentioning

confidence: 99%

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

Hua

Cordoba-Arenas

Warner

et al. 2015

Journal of Power Sources

Self Cite

154

View full text Add to dashboard Cite

“…Many factors are recognized to damage the battery health, such as high or low SOC, large C-rate and high temperature. In order to serve the control problem in this paper, the schematic of a control-oriented battery model is given in Figure 3 [25]. The model is composed of the electrical model, the thermal model and the aging model.…”

Section: Battery Modelmentioning

confidence: 99%

“…With the assumption that each cell inside the battery pack is equivalent, the dynamics of battery temperature T batt can be expressed as the following equations [25]:…”

Section: Battery Modelmentioning

confidence: 99%

“…Thus, in this paper, semi-empirical models are employed considering the good balance between accuracy and simplicity. In fact, the empirical and semi-empirical models that can be applied easily to solve the control problem have been proposed in [25]. Generally, the empirical and semi-empirical models are curve fitted by the battery aging experimental data with the consideration of a simplified physical principle.…”

Section: Battery Modelmentioning

confidence: 99%

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Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

et al. 2017

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This paper presents an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that not only tries to minimize the energy consumption, but also considers the battery health. First, a battery model that can be applied to energy management optimization is given. In this model, battery health damage can be estimated in the different states of charge (SOC) and temperature of the battery pack. Then, because of the inevitability that limiting the battery health degradation will increase energy consumption, a Pareto energy management optimization problem is formed. This multi-objective optimal control problem is solved numerically by using stochastic dynamic programming (SDP) and particle swarm optimization (PSO) for satisfying the vehicle power demand and considering the tradeoff between energy consumption and battery health at the same time. The optimization solution is obtained offline by utilizing real historical traffic data and formed as mappings on the system operating states so as to implement online in the actual driving conditions. Finally, the simulation results carried out on the GT-SUITE-based PHEV test platform are illustrated to demonstrate that the proposed multi-objective optimal control strategy would effectively yield benefits.Keywords: plug-in hybrid electric vehicle (PHEV); battery health; energy management strategy; stochastic dynamic programming (SDP); particle swarm optimization (PSO)

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“…8,9 Furthermore, cell-to-cell variations in large battery packs (due to packaging constraints) may lead to non-uniform heat transfer in packs and induce further thermal gradients among the various cells. 10,11 This motivates the development of procedures and tools that allow for characterization and prediction of thermal gradients in large-format Lithium ion batteries, and understanding their impact on the performance and durability.Multi-Scale, Multi-Dimensional (MSMD) modeling approaches have been proposed to simulate the distribution of surface temperature and Lithium concentration in large format, prismatic cells. As a first approximation, the rate of heat generation within the cell can be approximated as uniform across the surface.…”

mentioning

confidence: 99%

A Reduced-Order Multi-Scale, Multi-Dimensional Model for Performance Prediction of Large-Format Li-Ion Cells

Fan

Pan

Storti

et al. 2016

J. Electrochem. Soc.

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In this paper, a reduced-order, Multi-Scale, Multi-Dimensional (MSMD) model is developed to achieve accurate and fast simulation of the 2D distribution of thermal and electrochemical properties across the surface of a large-format Li-ion battery cell. Since the proposed model aims at supporting long-term simulation, virtual design and optimization studies, minimization of the computational complexity is achieved through analytical Model Order Reduction (MOR) based on a Galerkin projection method. The model is then verified against experimental data and a high-fidelity numerical model at various input current conditions. Results show that the computational complexity of the MSMD model is significantly reduced without sacrificing the accuracy in characterizing the distribution of the electrochemical and thermal properties. Finally, the impact of the applied current and thermal boundary conditions on the distribution of transverse current density is also evaluated. Temperature and temperature gradients are some of the key factors that influence the performance and degradation of Li-ion batteries. Despite in Hybrid Electric Vehicles (HEVs) or Electric Vehicles (EVs) the average pack temperature is regulated by the thermal management systems, thermal gradients may still occur within the pack or across individual cells.1-3 For instance, when cooling plates are in contact with only one side of the cells, the heat generated inside each cell will be dissipated to the cooling plates along a preferential direction. Therefore, thermal gradients in the order of ±5• C are common, and particularly in high-power packs for HEVs or high-performance EVs. 4,5 Thermal gradients on the cell surface are particularly critical in large-format batteries because hot spots may induce localized changes in the physical and chemical properties of the electrodes and electrolyte solution, which in turn can increase the local transverse current density and ultimately cause non-uniform degradation of the electrodes.6,7 Multiscale characterization methods have in fact evidenced that the non-uniform utilization of electrode surface leads to highly uneven degradation. 8,9 Furthermore, cell-to-cell variations in large battery packs (due to packaging constraints) may lead to non-uniform heat transfer in packs and induce further thermal gradients among the various cells. 10,11 This motivates the development of procedures and tools that allow for characterization and prediction of thermal gradients in large-format Lithium ion batteries, and understanding their impact on the performance and durability.Multi-Scale, Multi-Dimensional (MSMD) modeling approaches have been proposed to simulate the distribution of surface temperature and Lithium concentration in large format, prismatic cells. As a first approximation, the rate of heat generation within the cell can be approximated as uniform across the surface.12-16 More recently, this simplification has been abandoned in favor of more rigorous approaches that accurately compute the local heat generation rate ...

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A control-oriented lithium-ion battery pack model for plug-in hybrid electric vehicle cycle-life studies and system design with consideration of health management

Cited by 112 publications

References 37 publications

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm

A Reduced-Order Multi-Scale, Multi-Dimensional Model for Performance Prediction of Large-Format Li-Ion Cells

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