Thermal Model For Lithium-ion Batteries Research Articles

Influence of uncertainty of thermal conductivity on prediction accuracy of thermal model of lithium-ion battery

Influence of uncertainty of thermal conductivity on prediction accuracy of thermal model of lithium-ion battery

Learning-based sparse spatiotemporal modeling for distributed thermal processes of Lithium-ion batteries

Distributed thermal modeling of Lithium-ion batteries (LIBs) is critical for the safety of electric vehicles. Due to the installation and cost constraints, only limited sensors are allowed for practical applications. In this paper, a learning-based framework is proposed for online spatiotemporal modeling of distributed thermal processes in pouch-type LIBs under sparse sensing. It consists of two stages. In the offline learning stage under full sensing, the Karhunen–Loève (KL) decomposition is used to extract the full spatial basis functions (BFs). In the subsequent online modeling stage under sparse sensing, a spatial mapping filter is first designed to recover the missing spatial information using the initial full BFs, which are then dynamically updated by the incremental KL technique as the thermal process evolves. By iteratively repeating these two steps, the streaming sparse spatiotemporal output can be accurately completed. Finally, the typical KL-based time-space separation method can be used for online temperature prediction. The simulation results of the distributed thermal processes on a pouch-type cell and a LIB pack demonstrate the effectiveness of the proposed method.

A novel methodology to determine the specific heat capacity of lithium-ion batteries

The specific heat capacity of a battery is an essential parameter for the thermal modeling of lithium-ion batteries, but it is not generally provided by the manufacturers. To determine the specific heat capacity, equipment such as calorimeters can be utilized which is costly, whereas this paper proposed a novel method to determine the specific heat capacity that only requires conventional equipment, which is easy to find. The specific heat capacity is determined under a forced convection environment by introducing a heat loss compensate model. With this thermal compensation model, the test results wave near a constant (1044 J kg −1 ° C −1 ) regardless of the time change. To validate the method, an aluminum alloy block with the same shape as the cell is used. Results showed a specific heat capacity of 867 J kg −1 ° C −1 with a 3.3% error compared to the reference value of 897 J kg −1 ° C −1 . In addition, to verify the stability, repeated independent experiments were carried out and the results showed that this method has a good consistency. This novel methodology presents an effective way to determine the specific heat capacity at a lower cost and can be achieved quickly. • A new method to determine the heat capacity of lithium-ion batteries. • The method performs without any expensive equipment and sophisticated setups. • The method is verified by measuring the aluminum specific heat capacity. • Independent experiments were carried out with the same and different batteries.

State of charge estimation at different temperatures based on dynamic thermal model for lithium-ion batteries

In the existing modeling and state of charge (SOC) estimation of lithium batteries, the effect of ambient temperature is often ignored, which brings large errors to the model parameter identification, so the battery modeling and SOC estimation under temperature variation are performed. In this paper, an improved dual-polarized dynamic thermal model is proposed, firstly, the thermal effect of lithium battery is analyzed, and the model parameters adopt the dynamic values of coupling temperature and SOC, and the model is improved in terms of both heat generation mechanism and heat transfer characteristics; then the model parameters are identified based on the least-squares method with variable forgetting factor (VFF-RLS). Finally, the SOC at different temperatures is evaluated based on the dynamic thermal model combined with the extended Kalman filtering (EKF) with nonlinear filtering capability. The results show that the temperature factor is an important and non-negligible influence factor in modeling, the proposed battery model in this paper has high computational accuracy, and the proposed SOC estimation method has better SOC estimation capability for the temperature variation problems.

Fast identification method for thermal model parameters of Lithium-ion battery based on discharge temperature rise

An accurate thermal model of lithium-ion battery is extremely important for the safe operation of electric vehicles. The entropy coefficient is a key thermal characteristic of the battery, which is usually measured in advance. However, traditional measurement methods require a long test time or expensive equipment. In this paper, a novel thermal model parameter identification method with fast and continuous entropy coefficient identification is proposed, in which thermal parameters are obtained with lower time and equipment costs. First, a lumped thermal equivalent circuit model is established to describe the dynamic behaviors of battery temperature. Second, the thermal model parameters are identified based on experiments and calculations. The entropy coefficient is calculated based on temperature rise responses with different discharge currents. Finally, the identification results of the entropy coefficient are compared with traditional methods, which are found to be in good agreement. Furthermore, the thermal models are thoroughly verified under both galvanostatic discharge tests and dynamic profiles over the temperature range from 0 °C to 40 °C. Three kinds of thermal models with different entropy coefficient treatments are compared in terms of temperature errors, which indicates the superiority of the model with dynamic entropy coefficient, especially at high temperatures.

Peltier effects in lithium-ion battery modeling

A high battery temperature has been shown to be critical for lithium-ion batteries in terms of performance, degradation, and safety. Therefore, a precise knowledge of heat sources and sinks in the battery is essential. We have developed a thermal model for lithium-ion batteries, a model that includes terms not included before, namely, Peltier and Dufour heat effects. The model is derived using non-equilibrium thermodynamics for heterogeneous systems, the only theory which is able to describe in a systematic manner the coupling of heat, mass, and charge transport. The idea of this theory is to deal with surfaces as two-dimensional layers. All electrochemical processes in these layers are defined using excess variables, implying, for instance, that the surface has its own temperature. We show how the Peltier and Dufour heats affect a single cell and may produce an internal temperature rise of 8.5 K in a battery stack with 80 modules. The heat fluxes leaving the cell are also functions of these reversible heat effects. Most of the energy that is dissipated as heat occurs in the electrode surfaces and the electrolyte-filled separator. The analysis shows that better knowledge of experimental data on surface resistances, transport coefficients, and Dufour and Peltier heats is essential for further progress in thermal modeling of this important class of systems.

Extreme Learning Machine-Based Thermal Model for Lithium-Ion Batteries of Electric Vehicles under External Short Circuit

Abstract External short circuit (ESC) of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles. In this study, a novel thermal model is developed to capture the temperature behavior of batteries under ESC conditions. Experiments were systematically performed under different battery initial state of charge and ambient temperatures. Based on the experimental results, we employed an extreme learning machine (ELM)-based thermal (ELMT) model to depict battery temperature behavior under ESC, where a lumped-state thermal model was used to replace the activation function of conventional ELMs. To demonstrate the effectiveness of the proposed model, we compared the ELMT model with a multi-lumped-state thermal (MLT) model parameterized by the genetic algorithm using the experimental data from various sets of battery cells. It is shown that the ELMT model can achieve higher computational efficiency than the MLT model and better fitting and prediction accuracy, where the average root mean squared error (RMSE) of the fitting is 0.65 °C for the ELMT model and 3.95 °C for the MLT model, and the RMES of the prediction under new data set is 3.97 °C for the ELMT model and 6.11 °C for the MLT model.

Cooling optimization strategy for lithium-ion batteries based on triple-step nonlinear method

In the battery cooling system, the actual heat dissipation demand of the battery varies with the external environment and load current. If the battery does not dissipate heat in time under high current load and excessive increase of the temperature will threat to the safety of battery. In this paper, a cooling optimization strategy for lithium-ion batteries based on triple-step nonlinear method is proposed. Firstly, a lumped thermal model for lithium-ion batteries under liquid cooling considering the change of heat transfer coefficient with coolant flow rate was established. Then the accuracy of the lumped thermal model was verified by comparing with the battery model in AMESim. Based on the nonlinear and time-varying characteristics of lumped thermal model, a triple-step nonlinear cooling optimization algorithm is presented and its stability and robustness are proved. The triple-step nonlinear method and PID method are compared under different operating conditions, the simulation results show that the triple-step nonlinear method ensures that the operating temperature of the battery is lower than 305 K and the deviation from the target temperature is lower than 2.0 K, it also improves the speed and stability of the cooling process of lithium-ion batteries.

Core temperature estimation based on electro‐thermal model of lithium‐ion batteries

The performance of battery management systems relies on the core temperature estimation, which is one of the major technical bottlenecks for electric vehicles. Aiming to tackle this problem, a lumped-parameter two-state thermal model for lithium-ion batteries is established in this paper. Then, this thermal model is coupled with a temperature-dependent second-order RC equivalent circuit model to form an electro-thermal model for lithium-ion batteries. Using the proposed electro-thermal model, an adaptive estimation algorithm based on joint Kalman filtering is proposed for battery core temperature estimation, considering the heat transfer condition variations between the battery surface and ambient media. The verification results show that the proposed algorithm enhances the temperature estimation accuracy, compared to the results obtained directly from the electro-thermal model. Besides, the verification results demonstrate the high adaptability of the proposed algorithm, that is, it is robust to variations in ambient temperature, as well as variations in thermal resistance between the battery surface and ambient media. Considering the influences of temperature and SOC on the thermal generation rate of the battery, an electro-thermal model for lithium-ion batteries is established. This model is composed of a lumped-parameter two-state thermal model and a temperature-dependent second-order RC equivalent circuit model. An adaptive core temperature estimation algorithm based on joint Kalman filtering is proposed, considering the heat transfer condition variations between the battery surface and the ambient media.

A hybrid observer for SOC estimation of lithium-ion battery based on a coupled electrochemical-thermal model

ABSTRACTAn accurate prediction of the state of charge (SOC) of lithium-ion battery provides crucial information for the Battery Management System (BMS). In this paper, a simplified uneven electrochemical model coupled to a bulk thermal model of Lithium-ion battery is built up. The parameters of battery model identified in previous research can provide a more accurate description of the battery dynamic characteristics. This implementation of the coupled model is then applied to amalgamation with the extend Kalman filter combined with smoothing variable structure filter (EK-SVSF) to obtain an accurate and robust SOC result. MATLAB/Simulink and experiments, including 1C pulse discharge and NEDC cycle tests are adopted to evaluate the performance of the proposed hybrid algorithm. The simulation results indicate that the SOC error is less than 2%, therefore the algorithm is suitable for the new energy vehicle power BMS.

Multi-Scale and Multi-Dimensional Thermal Modeling of Lithium-Ion Batteries

In this study, we present a three-dimensional (3-D), multi-scale, multi-physics lithium-ion battery (LIB) model wherein a microscale spherical particle model is applied to an electrode particle domain and a comprehensive 3-D continuum model is applied to a single cell domain consisting of current collectors, porous electrodes, and a separator. Particular emphasis is placed on capturing the phase transition process inside the lithium iron phosphate (LFP) particles that significantly influences the LIB charge and discharge behaviors. The model is first validated against the experimental data measured at various discharge rates. In general, the model predictions compare well with the experimental data and further highlight key electrochemical and transport phenomena occurring in LIBs. Besides elucidating the phase transition evolution inside LFP particles and location-specific heat generation mechanism, multi-dimensional contours of species concentration, temperature, and current density are analyzed under a 3-D cell configuration to provide valuable insight into the charge and discharge characteristics of LIBs. The present multi-scale LIB model can be applied to a realistic LIB geometry to search for the optimal design and operating conditions.

A novel resistance-based thermal model for lithium-ion batteries

A novel resistance-based thermal model for lithium-ion batteries

Two-Dimensional Thermal Modeling of Lithium-Ion Battery Cell Based on Electrothermal Impedance Spectroscopy

Thermal modeling of lithium-ion batteries is gaining its importance together with increasing power density and compact design of the modern battery systems in order to assure battery safety and long lifetime. Thermal models of lithium-ion batteries are usually either expensive to develop and accurate or equivalent thermal circuit based with moderate accuracy and without spatial temperature distribution. This work presents initial results that can be used as a fundament for the cost-efficient development of the two-dimensional thermal model of lithium-ion battery based on multipoint electrothermal impedance spectroscopy.

Electrochemical-Thermal Modeling to Evaluate Battery Thermal Management Strategies: I. Side Cooling

Thermal modeling of lithium-ion batteries is important for assessing the impact of different thermal management strategies on electrochemical performance. Existing models either do not correctly couple the electrochemical and thermal fields, or require significant computational resources that preclude rapid investigation of various cooling methods in hybrid electric vehicle (HEV) simulations. In this study, a fully coupled electrochemical-thermal model that utilizes temperature dependent experimental data is described for a single spirally wound lithium-ion cell within a larger battery pack. In the model, temperature, current, heat generation, and depth of discharge (DOD) are allowed to vary locally and with time. In Part I of this study, a two dimensional cross-section for two different cell designs (8 Ah and 20 Ah) is simulated with both air and liquid surface cooling using a dynamic power profile. The results show that although liquid cooling reduces peak temperature significantly, it imposes a temperature difference across the cell. This causes the current and, subsequently, DOD to vary locally, which may adversely impact the life of the cell. Furthermore, as the cell increases in size and power level, the imposed temperature and DOD differences grow. Neither air nor liquid cooling allowed the pack size to be reduced beyond factors of 2 or 4, respectively.

Development of a Thermal Model for Lithium-Ion Batteries for Plug-In Hybrid Electric Vehicles

This paper describes a novel thermal model for lithium-ion battery technology. The model has been developed in Matlab/Simulink, which is based mainly on the electrical parameters such as thermal resistance, thermal capacitance, and thermal convection. In this study, a new methodology is presented for extraction of the model parameters based on the Matlab/Simulink parameter optimization tool. The results exhibit that the error percentage between the simulated and experimental results is less than 5%. According to this model, the battery cell temperature can be kept in a safe operating region by coupling the surface temperature of the battery with the heating and cooling systems. This will allows to enhance the battery performances on one hand and to avoid thermal runway effect on the other hand. The proposed battery model is not only useful for pouch battery cells but can be employed for any battery design concept.

Reformulated Thermal Model for Lithium-Ion Batteries

not Available.