Modeling the temperature dependence of the discharge behavior of a lithium-ion battery in low environmental temperature

Yi, Jaeshin; Kim, Ui Seong; Shin, Chee Burm; Han, Taeyoung; Park, Seongyong

doi:10.1016/j.jpowsour.2013.02.085

Cited by 95 publications

(56 citation statements)

References 22 publications

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“…Electro-chemical modelling using partial differential equations can predict battery characteristics accurately, but with a high computational cost. This poses a main challenge for accurate battery modelling and emphasises the need to focus more on electro-chemistry as well as numerical schemes for an 20 accurate and computationally fast monitoring system. This paper analyses the dependency of main factors such as temperature, depth of discharge (DoD) and the charging current rate on the SEI layer development.…”

Section: Introductionmentioning

confidence: 99%

Capacity fade modelling of lithium-ion battery under cyclic loading conditions

Ashwin

Chung

Wang

2016

Journal of Power Sources

122

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Section: Introductionmentioning

confidence: 99%

Capacity fade modelling of lithium-ion battery under cyclic loading conditions

Ashwin

Chung

Wang

2016

Journal of Power Sources

122

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“…In order to save computation time, a simplified co-simulated semi-empirical electrical and thermal model was proposed by Newman and Tiedemann [193,194], which assumed that the inner electrolyte cannot move [195]. After tests of battery discharging at various rates, the U-I polarization characteristic curve could be obtained and transformed into a specific linear expression with slope and intercept, then it was fed to the 3D CFD computation as a boundary condition [196,197]. Gu [197] and Kim [198] then adopted this co-simulation approached to study the electro-thermal reaction mechanism of heat generation, and what's more, they conducted a modification to the slope and intercept of linear expression at room temperature of 25 • C and low environmental temperature of 0 • C.…”

Section: Co-simulation For Electric Vehiclementioning

confidence: 99%

Advances in Integrated Vehicle Thermal Management and Numerical Simulation

et al. 2017

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Abstract:With the increasing demands for vehicle dynamic performance, economy, safety and comfort, and with ever stricter laws concerning energy conservation and emissions, vehicle power systems are becoming much more complex. To pursue high efficiency and light weight in automobile design, the power system and its vehicle integrated thermal management (VITM) system have attracted widespread attention as the major components of modern vehicle technology. Regarding the internal combustion engine vehicle (ICEV), its integrated thermal management (ITM) mainly contains internal combustion engine (ICE) cooling, turbo-charged cooling, exhaust gas recirculation (EGR) cooling, lubrication cooling and air conditioning (AC) or heat pump (HP). As for electric vehicles (EVs), the ITM mainly includes battery cooling/preheating, electric machines (EM) cooling and AC or HP. With the rational effective and comprehensive control over the mentioned dynamic devices and thermal components, the modern VITM can realize collaborative optimization of multiple thermodynamic processes from the aspect of system integration. Furthermore, the computer-aided calculation and numerical simulation have been the significant design methods, especially for complex VITM. The 1D programming can correlate multi-thermal components and the 3D simulating can develop structuralized and modularized design. Additionally, co-simulations can virtualize simulation of various thermo-hydraulic behaviors under the vehicle transient operational conditions. This article reviews relevant researching work and current advances in the ever broadening field of modern vehicle thermal management (VTM). Based on the systematic summaries of the design methods and applications of ITM, future tasks and proposals are presented. This article aims to promote innovation of ITM, strengthen the precise control and the performance predictable ability, furthermore, to enhance the level of research and development (R&D).

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“…The "art" of trial and error [29] was adopted to determine the value of x . However, it requires exhaustive enumeration to search the optimal value of x .…”

Section: Battery Electric Model and Problem Formulationmentioning

confidence: 99%

“…The proper value of parameter vector x in the seven-dimensional hyperspace is usually difficult to obtain in the simulation [28]. The "art" of trial and error [29] was adopted to determine the value of x. However, it requires exhaustive enumeration to search the optimal value of x.…”

Section: Battery Electric Model and Problem Formulationmentioning

confidence: 99%

Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm

Shen

2017

Energies

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This paper proposes a multi-scale parameter identification algorithm for the lithium-ion battery (LIB) electric model by using a combination of particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithms. Two-dimensional Poisson equations with unknown parameters are used to describe the potential and current density distribution (PDD) of the positive and negative electrodes in the LIB electric model. The model parameters are difficult to determine in the simulation due to the nonlinear complexity of the model. In the proposed identification algorithm, PSO is used for the coarse-scale parameter identification and the LM algorithm is applied for the fine-scale parameter identification. The experiment results show that the multi-scale identification not only improves the convergence rate and effectively escapes from the stagnation of PSO, but also overcomes the local minimum entrapment drawback of the LM algorithm. The terminal voltage curves from the PDD model with the identified parameter values are in good agreement with those from the experiments at different discharge/charge rates.

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Modeling the temperature dependence of the discharge behavior of a lithium-ion battery in low environmental temperature

Cited by 95 publications

References 22 publications

Capacity fade modelling of lithium-ion battery under cyclic loading conditions

Capacity fade modelling of lithium-ion battery under cyclic loading conditions

Advances in Integrated Vehicle Thermal Management and Numerical Simulation

Multi-Scale Parameter Identification of Lithium-Ion Battery Electric Models Using a PSO-LM Algorithm

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