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Conventional cooling approaches that target either a singular tab or outer surface of common format cylindrical lithium-ion battery cells suffer from a high cell thermal resistance. Under an aggressive duty cycle, this resistance can result in the formation of large in-cell temperature gradients and high hot spot temperatures, which are known to accelerate ageing and further reduce performance. In this paper, a novel approach to internal thermal management of cylindrical battery cells to lower the thermal resistance for heat transport through the inside of the cell is investigated. The effectiveness of the proposed method is analysed for two common cylindrical formats when subject to highly aggressive electrical loading conditions representative of a high performance electric vehicle (EV) and hybrid electric vehicle (HEV). A mathematical model that captures the dominant thermal properties of the cylindrical cell is created and validated using experimental data. Results from the extensive simulation study indicate that the internal cooling strategy can reduce the cell thermal resistance by up to 67.8 ± 1.4% relative to single tab cooling, and can emulate the performance of a more complex pack-level double tab cooling approach whilst targeting cooling at a single tab. [ABSTRACT FROM AUTHOR]

The exposure of electric vehicle batteries to rapid charging and discharging profiles, particularly under uncertainty in the drive schedules, requires effective strategies to forecast and avoid thermal excursions during operation. In this work, we present a light gradient boosting-based machine learning model to create probabilistic forecasts of the discharge current over a forecast horizon in real time. A surrogate of a physics-based computational model provides the functional relationship for the battery temperature, using which a stochastic model-predictive control strategy is developed to derive the optimal cooling schedule based on the probabilistic forecast. The effectiveness of the stochastic model-predictive control approach is assessed on the US06 driving cycle, in comparison to a constant coolant flow and persistence forecast-based control. It is shown that the total number of temperature excursion instances using the stochastic model-predictive control is reduced by about 69% compared to the constant coolant flow case and by over 51% compared to persistence-forecast-based control. Further, the total coolant usage is reduced by about 73% compared to persistence forecast-based control. The stochastic model-predictive control approach provides more flexibility by allowing a wider range of control and battery pack design parameters to obtain optimal performance with minimum coolant use. • Presents model-predictive thermal control of LIBs under drive cycle uncertainty. • Probabilistic forecast of discharge current obtained using machine learning model. • Uses physics-based computational model to derive optimal cooling schedule. • Stochastic model-predictive control approach is assessed on US06 drive cycle. • Reduces temperature excursion instances by ∼ 69% and coolant usage by ∼ 73%. [ABSTRACT FROM AUTHOR]

This paper provides a methodology to assess the average surface temperature of battery cells under realistic transient scenarios. Computational fluid dynamics modelling of battery cooling is conducted for the cases exposed to the ramps of internal heat generation inferred from the standard driving cycles. The results are then post-processed to determine the effectiveness of air and water as the coolant fluids. A quantitative measure of the maximum overshoot, dimensionless settling (DST), heating (DHT), and cooling (DCT) time is subsequently presented. It is shown that, compared to water, air produces a considerably delayed response to temporal changes in the internal heat generation and is slower at reaching the new steady state condition. Cooling battery cells by using water almost always ensures remaining within the safe operating range. Nonetheless, regardless of the coolant type, the long period ramps tend to produce smaller values of DST. The primary origin of the delay is the slow heat conduction within the battery cells. In addition, it is shown that water responds to changes in the internal heat generation far quicker during the heating and the cooling phases. The study highlights the importance of transient analyses for characterising the thermal behaviour of battery packs. • A novel measure of battery heat release due to internal heat generation is presented. • Transient cases with sharp gradients cause large delays in system response. • Air cases always produce an overshoot and are far slower at reaching steady state. • Battery cells remain within the safe operating range in almost all water cases. • Water cases react far quicker to changes in the internal heat generation. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) battery cooling using a phase change material (PCM)/compressed expanded natural graphite (CENG) composite is investigated, for a cylindrical battery cell and for a battery module scale. An electrochemistry model (average model) is coupled to the thermal model, with the addition of a one-dimensional model for the solution and solid diffusion using the nodal network method. The analysis of the temperature distribution of the battery module scale has shown that a two-dimensional model is sufficient to describe the transient temperature rise. In consequence, a two-dimensional cell-centred finite volume code for unstructured meshes is developed with additions of the electrochemistry and phase change. This two-dimensional thermal model is used to investigate a new and usual battery module configurations cooled by PCM/CENG at different discharge rates. The comparison of both configurations with a constant source term and heat generation based on the electrochemistry model showed the superiority of the new design. In this study, comparisons between the predictions from different analytical and computational tools as well as open-source packages were carried out, and close agreements have been observed. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) are extremely sensitive to their temperature and therefore, require a battery thermal management system (BTMS). BTMS is commonly employed to prevent thermal runaway of the LIB pack by controlling their operating temperature. This review paper provides various cooling strategies, including active and passive approaches, utilized in BTMSs. Air-cooled, liquid-based, and PCM-cooled techniques are first reviewed. This article presents an assessment of the application of nanofluids and nano-phase change materials (nano-PCMs) in BTMSs to improve heat dissipation from the LIB pack. This work emphasizes the utilization of these materials to intensify their thermal conductivity and suggests further investigations on their application in BTMSs. • Lithium-ion batteries are extremely sensitive to their temperature. • BTMS is employed to prevent battery thermal runaway by controlling its temperature. • Providing various cooling strategies for BTMS: active and passive approaches. • Presenting an assessment of the application of nanofluids and nano-PCMs) in BTMSs. [ABSTRACT FROM AUTHOR]

The thermal management of a cylindrical battery cell by a phase change material (PCM)/compressed expanded natural graphite (CENG) is investigated in this study. The transient thermal behaviour of both the battery and the PCM/CENG is described with a simplified one-dimensional model taking into account the physical and phase change properties of the PCM/CENG composite. The 1D analytical/computational model yielded nearly identical results to the three-dimensional simulation results for various cooling strategies. Therefore, the 1D model is sufficient to describe the transient behaviour of the battery cooled by a PCM/CENG composite. Moreover, the maximum temperature reached by the PCM/CENG cooling strategy is much lower than that by the forced convection in the same configuration. In the test case studied, the PCM showed superior transient characteristics to forced convection cooling. The PCM cooling is able to maintain a lower maximum temperature during the melting process and to extend the transient time for temperature rise. Furthermore, the graphite-matrix bulk density is identified as an important parameter for optimising the PCM/CENG cooling strategy. [ABSTRACT FROM AUTHOR]

A simplified one-dimensional transient computational model of a prismatic lithium-ion battery cell is developed using thermal circuit approach in conjunction with the thermal model of the heat pipe. The proposed model is compared to an analytical solution based on variable separation as well as three-dimensional (3D) computational fluid dynamics (CFD) simulations. The three approaches, i.e. the 1D computational model, analytical solution, and 3D CFD simulations, yielded nearly identical results for the thermal behaviours. Therefore the 1D model is considered to be sufficient to predict the temperature distribution of lithium-ion battery thermal management using heat pipes. Moreover, a maximum temperature of 27.6 °C was predicted for the design of the heat pipe setup in a distributed configuration, while a maximum temperature of 51.5 °C was predicted when forced convection was applied to the same configuration. The higher surface contact of the heat pipes allows a better cooling management compared to forced convection cooling. Accordingly, heat pipes can be used to achieve effective thermal management of a battery pack with confined surface areas. [ABSTRACT FROM AUTHOR]

Abstract: Many portable devices such as soldier carrying devices are powered by low-weight but high-capacity lithium ion (Li-ion) batteries. An effective battery thermal management (BTM) system is required to keep the batteries operating within a desirable temperature range with minimal variations, and thus to guarantee their high efficiency, long lifetime and great safety. However, the rigorous constraints imposed by the budgets in weight and volume for this specific application eliminate the possible consideration of many existing classical cooling approaches and make the development of BTM system very challenging in this field. In this paper, a flexible hydrogel-based BTM system is developed to address this challenge. The proposed BTM system is based on cost-effective sodium polyacrylate and can be arbitrarily shaped and conveniently packed to accommodate any Li-ion stacks. This BTM system is tested through a series of high-intensity discharge and abnormal heat release processes, and its performance is compared with three classical BTM systems. The test results demonstrate that the proposed low-cost, space-saving, and contour-adaptable BTM system is a very economic and efficient approach in handling the thermal surge of Li-ion batteries. [Copyright &y& Elsevier]

Abstract: This work studied the thermal management of lithium ion batteries both numerically and experimentally. Numerically, a two dimensional CFD (computational fluid dynamics) model has been developed to perform detailed simulations of the thermal management issues within a battery pack cooled by air. Experimentally, systematic tests were performed to provide datasets to validate the CFD model. The main components in the experimental facility included a multi-cell battery pack and a wind tunnel. The wind tunnel facility generated well-controlled cooling air flow with velocity up to 30 m s−1 (∼67 miles per hour). So that the study can be performed under flow conditions directly relevant to practice. The major contributions from this combined numerical-experimental study are threefold. First, the CFD model has been shown to capture the dynamics of the cooling of battery modules consisting of multiple battery cells, including temperature non-uniformity among cells. Second, the CFD simulations have been compared directly against experimental data to quantify the accuracy and validity of the CFD models. Third, based on the validated CFD models, a reduced-order model is developed to predict the maximum cell temperature in the battery module. The accuracy and simplicity of the reduced-order model makes it promising for in situ monitoring and control purposes. [Copyright &y& Elsevier]

Abstract: Time-dependent, thermal behaviour of a lithium-ion (Li-ion) polymer cell has been modelled for electric vehicle (EV) drive cycles with a view to developing an effective battery thermal management system. The fully coupled, three-dimensional transient electro-thermal model has been implemented based on a finite volume method. To support the numerical study, a high energy density Li-ion polymer pouch cell was tested in a climatic chamber for electric load cycles consisting of various charge and discharge rates, and a good agreement was found between the model predictions and the experimental data. The cell-level thermal behaviour under stressful conditions such as high power draw and high ambient temperature was predicted with the model. A significant temperature increase was observed in the stressful condition, corresponding to a repeated acceleration and deceleration, indicating that an effective battery thermal management system would be required to maintain the optimal cell performance and also to achieve a full battery lifesapn. [Copyright &y& Elsevier]

The optimal performance of a Li-ion battery is directly impacted by temperature. In order to control the temperature rise and provide even temperature distributions in the battery pack, a thermal management scheme comprises thin heat sinks with multiple phase change materials (PCMs) and air channels is investigated in this paper. The cooling performance and temperature homogenization of the battery thermal management (BTM) system are carefully studied under various configurations of PCMs. The results show that increasing the air inlet velocity has less effect in suppressing the temperature rise at early discharge stage, but ameliorates as the discharge prolonged to 3600 s. The standard deviation of the temperature (STDV) and maximum temperature of the batteries can be decreased by arranging PCMs with a lower melting temperature at the midsection and a higher melting temperature at the air outlet region of the heat sinks. In addition, for volume fraction of PCMs, Case IV, having a PCM with a higher melting point adjacent to the air outlet region and occupying one-half the height of the heat sink, illustrates a lower temperature rise and decreases the maximum temperature in the battery module by 1.024 K, 2.186 K, and 2.553 K, compared to Case I, II, and III, respectively. • Multiple PCMs incorporated thin heat sinks for battery thermal management is proposed. • The thermal performance of the battery based on maximum temperature upsurge is investigated. • The distributions of PCMs based on melting point and latent heat were carefully explored. • The optimal design for the distribution of PCMs is obtained for the proposed battery. [ABSTRACT FROM AUTHOR]

Battery modeling and state estimation are critical to the battery safety and vehicle driving range. Currently, electrochemical mechanism based models require huge computation effort for solving partial differential equations, equivalent circuit models do not consider actual battery mechanism, while machine learning models are lack of generalization ability due to their data-driven nature. All these problems bring the challenges to guarantee the model effectiveness in wide temperature and large current conditions. To estimate the battery state of charge (SOC) and state of temperature (SOT) under these conditions, an electrochemical-thermal-neural-network (ETNN) model is formulated in this paper. Specifically, a simplified single particle model and a lumped thermal model are served as the sub-models of ETNN to predict core temperature and provide approximate terminal voltage. Then a neural network is incorporated to enhance the performance of sub-models. According to the extensive experiments, ETNN model is able to accurately estimate battery voltage and core temperature under the ambient temperatures of -10–40 °C and the discharge rate of 10-C. After that, an unscented Kalman filter (UKF) is integrated with ETNN to achieve reliable co-estimation of SOC-SOT. Experimental results illustrate that proposed ETNN-UKF can rapidly eliminate initial errors and provide satisfactory co-estimation performance. • Formulation of a hybrid battery electrochemical-thermal-neural-network model. • High model accuracy under large currents and wide range of ambient temperatures. • Accurate co-estimation of SOC and SOT by integrating the unscented Kalman filter. [ABSTRACT FROM AUTHOR]