Search

High current rate charging causes inevitable severe heat generation, thermal inconsistency, and even thermal runaway of lithium-ion batteries. Concerning this, a liquid cooling plate comprising a multi-stage Tesla valve (MSTV) configuration with high recognition in microfluidic applications was proposed to provide a safer temperature range for a prismatic-type lithium-ion battery. Meanwhile, a surrogate model with the objectives of the cooling performance and energy cost was constructed, and the impact of some influential design parameters was explored through the robustness analysis of the model. On this basis, the multi-objective optimization design of the neighborhood cultivation genetic algorithm (NCGA) was carried out. The obtained results demonstrated that if the MSTV channel was four channels, the valve-to-valve distance was 14.79 mm, and the thickness was 0.94 mm, the cold plate had the most effective cooling performance and a lower pumping power consumption. Finally, the optimization results were verified by a numerical simulation and an experiment, and the performance evaluation was compared with the traditional serpentine channel. The results reported that the optimized design reduced the maximum temperature and standard surface standard deviation of the cold plate by 26% and 35%, respectively. The additional pump power consumption was 17.3%. This research guides the design of battery thermal management systems to improve efficiency and energy costs, especially under the high current rate charging conditions of lithium-ion batteries.

The optimization of lithium-ion (Li-ion) battery pack usage has become essential due to the increasing demand for Li-ion batteries. Since degradation in Li-ion batteries is inevitable, there has been some effort recently on research to maximize the utilization of Li-ion battery cells in the pack. Some promising concepts include reconfigurable battery packs and cell replacement to limit the negative impact of early-degraded cells on the entire pack. This paper used a simulation framework, based on a cell voltage model and a degradation model, to study the feasibility and benefits of the cell replacement concept. The simulation conducted in MATLAB involves generating and varying Li-ion cells in the packs stochastically and simulating the life of the cells as well as the packs until they reach their end-of-life stage. It was found that the cell replacement method can increase the total number of cycles of the battery packs, effectively prolonging the lifespan of the packs. It is also determined that this approach can be more economically beneficial than the current approach of simple pack replacement. For the cell replacement concept to be practical, two main design criteria should be satisfied including individual cell monitoring and easy accessibility to cells at failure stage.

The proliferation of electric vehicle (EV) technology is an important step towards a more sustainable future. In the current work, two-layer feed-forward artificial neural-network-based machine learning is applied to design soft sensors to estimate the state of charge (SOC), state of energy (SOE), and power loss (PL) of a formula student electric vehicle (FSEV) battery-pack system. The proposed soft sensors were designed to predict the SOC, SOE, and PL of the EV battery pack on the basis of the input current profile. The input current profile was derived on the basis of the designed vehicle parameters, and formula Bharat track features and guidelines. All developed soft sensors were tested for mean squared error (MSE) and R-squared metrics of the dataset partitions; equations relating the derived and predicted outputs; error histograms of the training, validation, and testing datasets; training state indicators such as gradient, mu, and validation fails; validation performance over successive epochs; and predicted versus derived plots over one lap time. Moreover, the prediction accuracy of the proposed soft sensors was compared against linear or nonlinear regression models and parametric structure models used for system identification such as autoregressive with exogenous variables (ARX), autoregressive moving average with exogenous variables (ARMAX), output error (OE) and Box Jenkins (BJ). The testing dataset accuracy of the proposed FSEV SOC, SOE, PL soft sensors was 99.96%, 99.96%, and 99.99%, respectively. The proposed soft sensors attained higher prediction accuracy than that of the modelling structures mentioned above. FSEV results also indicated that the SOC and SOE dropped from 97% to 93.5% and 93.8%, respectively, during the running time of 118 s (one lap time). Thus, two-layer feed-forward neural-network-based soft sensors can be applied for the effective monitoring and prediction of SOC, SOE, and PL during the operation of EVs.

To ensure optimum working conditions for lithium-ion batteries, a numerical study is carried out for three-dimensional temperature distribution of a battery liquid cooling system in this work. The effect of channel size and inlet boundary conditions are evaluated on the temperature field of the battery modules. Based on the thermal behavior of discharging battery obtained experimental measurements, two temperature control strategies are proposed and studied. The results show that the channel width of the cooling plates has a great influence on the maximum temperature in the battery module. It is also revealed that increasing inlet water flow rate can significantly improve the heat transfer capacity of the battery thermal management system, while the relationship between them is not proportional. Lowering the inlet temperature can reduce the maximum temperature predicted in the battery module significantly. However, this will also lead to additional energy consumed by the cooling system. It is also found that the Scheme 5 among various temperature control strategies can ensure the battery pack working in the best temperature range in different depths of discharge. Compared with the traditional one with a given flow rate, the parasitic energy consumption in Scheme 5 can be reduced by around 80%.

The first year course, “ME 100: Introduction to Mechanical Engineering Practice, 1”, was redesignedfor the Fall 2017-2019 offerings. The goals of the redesign were to include: a major design project, opportunities for individual communication assessments, and opportunities for development of professional skills. A toy design project was piloted in Fall 2017 as a unifying course theme. In thisproject, industrial partners come to discuss the engineering and design that happens in the toy industry. They also help critique student work as they design a toy of their choosing. With the impacts of COVID 19 the decision was made to pivot to a challenge to design new classroomphysics demonstrations. The course redesign has generally been successful. Both projects have been well received by students, faculty, and industry partners, with students reporting on an end-of-term survey that it was engaging and doable, and that it helped develop their confidence andunderstanding of design, and mechanical engineering. The demo project was generally slightly better received, with 2-8% more students agreeing to statements about the usefulness and appeal of the project. Both projects, the toy project especially, serve as a vehicle to discuss differentaspects of design and professionalism. Challenges exist with giving students guidance at the start and throughout the project to ensure that all student teams have suitably scoped projects. There is also the challenge of helping students develop a design mindset, as several groups struggle with performing the justified decision making necessary to actual design a toy.

Compressed Air Energy Storage (CAES) systems, if designed right, can provide a range of high-value grid services that are required for stable operation of the electrical grid. In this paper, a new design approach for customized CAES based on user-centered design (UCD) methodology is developed. To this end, a study of the electrical grid infrastructure and challenges, with a focus on Ontario (Canada) is presented. Afterward, a system approach methodology called CAES-by-Design is developed that incorporates the impact of grid profiles. Hourly load profiles of the Ontario grid are analyzed. Using a thermodynamic model, it is shown how the performance requirements of an adiabatic CAES (A-CAES) system, in terms of capacity, charge and discharge rates and duration are affected by the grid demand and supply profile. For example, it is observed that a compressor capacity of 13% of maximum excess energy and a turbine capacity of 10% of maximum required energy would be sufficient to capture more than 50% of the charging and discharging opportunities. Furthermore, the impact of charging and discharging cycle dynamics on the sizing and operating characteristics of thermal energy storage (TES) system is discussed. Results of this analysis suggest that a comprehensive assessment tool can be developed based on the presented methodology.

As more renewable energy is developed, energy storage is increasingly important and attractive, especially grid-scale electrical energy storage; hence, finding and implementing cost-effective and sustainable energy storage and conversion systems is vital. Batteries of various types and sizes are considered one of the most suitable approaches to store energy and extensive research exists for different technologies and applications of batteries; however, environmental impacts of large-scale battery use remain a major challenge that requires further study. In this paper, batteries from various aspects including design features, advantages, disadvantages, and environmental impacts are assessed. This review reaffirms that batteries are efficient, convenient, reliable and easy-to-use energy storage systems (ESSs). It also confirms that battery shelf life and use life are limited; a large amount and wide range of raw materials, including metals and non-metals, are used to produce batteries; and, the battery industry can generate considerable amounts of environmental pollutants (e.g., hazardous waste, greenhouse gas emissions and toxic gases) during different processes such as mining, manufacturing, use, transportation, collection, storage, treatment, disposal and recycling. Battery use at a large scale or grid-scale (>50 MW), which is widely anticipated, will have significant social and environmental impacts; hence, it must be compared carefully with alternatives in terms of sustainability, while focusing on research to quantify externalities and reduce risk. Alternatives such as pumped hydro and compressed air energy storage must be encouraged because of their low environmental impact compared to different types of batteries.

Nitrogen stress plays a critical role in corn yield reduction. Thermal remote sensing has many applications: as an assessment tool for urban heat island, as an ecological indicator of ecosystem development, and as a water-stress-detection tool. In this study, it was hypothesized that corn crops supplied with optimum or high rates of nitrogen would have lower surface temperatures compared to corn grown under nitrogen-stressed conditions. Two experiments were conducted in the greenhouse at the University of Guelph, Canada, from the period between 2015 and 2016, involving three rates of nitrogen (high, medium, and low rates) supplied to corn plants after seed emergence. Leaf and whorl temperatures were collected by using a high-resolution thermal camera, an infrared handheld point measurements gun, and a type T thermocouple, respectively. An approximate difference of 2 &deg, C was observed in temperatures between plants receiving high and low rates of nitrogen. These results supported the hypothesis that nitrogen-stressed plants have higher temperatures compared to less stressed plants, at a 0.05 significance level. This study investigated the application of the exergy destruction principle through thermal remote sensing, to detect crop stress at early growth stages under greenhouse conditions, to increase the production and reduce the harmful environmental impact.

Emissions from the transportation sector due to the consumption of fossil fuels by conventional vehicles have been a major cause of climate change. Hybrid electric vehicles (HEVs) are a cleaner solution to reduce the emissions caused by transportation, and well-designed HEVs can also outperform conventional vehicles. This study examines various powertrain configurations and components to design a hybrid powertrain that can satisfy the performance criteria given by the EcoCAR Mobility Challenge competition. These criteria include acceleration, braking, driving range, fuel economy, and emissions. A total of five different designs were investigated using MATLAB/Simulink simulations to obtain the necessary performance metrics. Only one powertrain design was found to satisfy all the performance targets. This design is a P4 hybrid powertrain consisting of a 2.5 L engine from General Motors, a 150 kW electric motor with an electronic drive unit (EDU) from American Axle Manufacturing, and a 133 kW battery pack from Hybrid Design Services.

The investigation and improvement of the cooling process of lithium-ion batteries (LIBs) used in battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs) are required in order to achieve better performance and longer lifespan. In this manuscript, the temperature and velocity profiles of cooling plates used to cool down the large prismatic Graphite/LiFePO4 battery are presented using both laboratory testing and modeling techniques. Computed tomography (CT) scanning was utilized for the cooling plate, Detroit Engineering Products (DEP) MeshWorks 8.0 was used for meshing of the cooling plate, and STAR CCM+ was used for simulation. The numerical investigation was conducted for higher C-rates of 3C and 4C with different ambient temperatures. For the experimental work, three heat flux sensors were attached to the battery surface. Water was used as a coolant inside the cooling plate to cool down the battery. The mass flow rate at each channel was 0.000277677 kg/s. The k-ε model was then utilized to simulate the turbulent behaviour of the fluid in the cooling plate, and the thermal behaviour under constant current (CC) discharge was studied and validated with the experimental data. This study provides insight into thermal and flow characteristics of the coolant inside a cooing plate, which can be used for designing more efficient cooling plates.

In this paper, both thermal and electrical performance evaluations of a lithium-ion battery pack using real world drive cycles from an electric vehicle (EV) are presented. For the experimental measurements, a data logger is installed in the EV, and the real world drive cycles are collected. The EV has three lithium-ion battery packs consisting of a total of 20 battery modules in series. Each module contains six series × 49 parallel IFR 18650 cylindrical valence cells. The reported drive cycles consist of different modes: acceleration, constant speed, and deceleration in both highway and city driving at 2 °C, 10 °C and 17 °C ambient temperatures with all accessories on. Later, the same drive cycles are conducted in an experimental facility where four cylindrical lithium-ion cells are connected in series, and both electrical and thermal performances are evaluated. In addition, the battery model is developed using artificial neural network, which is validated with the real world drive cycles. The validation is carried out in terms of voltage, state of charge (SOC), and temperature profiles for all the collected drive cycles. The present model closely estimates the profiles observed in the experimental data. Moreover, with this study, the mathematical function for the average temperature, SOC, and voltage prediction are developed with weights and bias values.

Large-sized lithium-ion battery (LIB) mathematical models are critical in design of cells, packs, and their associated thermal management systems. A high-fidelity fully coupled electrochemical-thermal model for a commercial 20 Ah LIB is developed to simulate the distribution of electrochemical and thermal variables three-dimensionally (3D) through 48 electrode layers in the pouch cell. As a new feature, the details of heat generation and voltage drop due to the tab, current collectors, and associated contact resistances are included in the developed model. A series of galvanostatic charge/discharge tests at operating rates ranging from 1C (20 A) to 5C (100 A) and at room temperature is conducted on the pouch cell and the output voltage and temperature distribution are recorded. The developed model voltage and temperature distribution predictions are successfully compared against the experimental data, demonstrating the accuracy of the model. As a key finding in this work, it is found that self-heating is the main contributor for electrochemical performance improvement in large-sized LIBs over small cells. This is due to the improved kinetic and transport properties of LIB electrodes at elevated temperatures. It is also demonstrated that the temperature variation between the surface and center layers of a pouch cell is not significant at a maximum difference of about 1.7 °C for charge/discharge rates up to 5C (100 A). As a result of this finding, it is suggested that a single temperature measurement close to the tabs at the battery surface would be sufficient for thermal management development and control purposes.

Study of thermal performance in lithium-ion battery cell is crucial which directly affects the safety. Even though the operation of a lithium-ion battery cell is transient phenomena in most cases, most available thermal models for lithium-ion battery cell predicts only steady-state temperature fields. This paper presents a mathematical model to predict the transient temperature and voltage distributions of 18650 cylindrical lithium-ion battery at different discharge rates. For this, the 18650 cylindrical lithium-ion battery cell is tested inside the lab with an air-cooling method by four thermocouples mounted on the battery surface under four constant current discharge rates of 1 C, 2 C, 3 C, and 4 C in order to provide quantitative data regarding thermal behavior of lithium-ion batteries. Later, the numerical model is developed using ANSYS CFD software and it is found that the model predictions are in good agreement with experimental data for temperature and voltage profiles. The highest temperature is 46.86 °C at 4 C discharge rate as obtained from simulation. The results also show that the increased C-rates results in increased temperature on the principle surface of the battery.

In this paper, the heat flux distributions on a prismatic lithium-ion battery at 1C, 2C, 3C and 4C discharge rates under various operating temperatures and boundary conditions (BCs) of 22 °C for air cooling and 5 °C, 15 °C, and 25 °C for water cooling are presented. The goal is to provide significant quantitative data on the thermal behaviour of lithium-ion batteries. In this regard, a battery thermal management system with water cooling is designed and developed for a 20 Ah capacity pouch type lithium-ion battery using dual cold plates. Three heat flux sensors are placed at different locations on the principle surface of the battery: the first near the anode, the second near the cathode, and the third at the mid surface of the body. From these the average and peak heat flux values are obtained and presented in this study. In addition to this, the heat flux and voltage distributions are simulated using the neural network approach with the above mentioned discharge rates and BCs. The present results show that increased discharge rates and decreased operating temperature result in increased heat fluxes at the three locations as experimentally measured. Furthermore, the sensors nearest the electrodes (anode and cathode) measured the heat fluxes (and hence temperatures) higher than the sensors located at the center of the battery surface.

Although Compressed Air Energy Storage (CAES) is not a new technology, it has not yet been widely adopted due to location restrictions and inefficiencies. Thermal energy storage improves the round-trip efficiency of CAES systems. This study sets out to investigate the cyclic thermal storage behavior of a small-scale, site-flexible, scalable, cased-wellbore compressed air energy storage (CW-CAES) system in which both heat and mechanical energy can be stored in an array of wellbores. The concept of storing high-temperature compressed air (around 200 ° C) inside cased wells is a promising approach to expanding the utility of CAES systems through site flexibility, partial adiabatic efficiency improvements over conventional non-adiabatic CAES, no need for a separate heat storage system as found in Adiabatic CAES (A-CAES) systems, and small (order 1 MW; 10 MWh) to large (order 150 MW; 600 MWh) capacity that can be achieved through the modular nature of multiple wellbores. This paper provides a detailed numerical model coupled with a semi-analytical model to assess the first year operation of PA-CW-CAES. The results reveal that the semi-analytical model yields results in excellent agreement with the numerical model. To improve thermal storage an array of boreholes is considered. Several charge/discharge cycles are used to appraise the system behavior and determine system efficiency. The simulations confirm that the modeled partial adiabatic CW-CAES has a round-trip efficiency of around 40% which is higher than the corresponding diabatic CW-CAES operating at the same conditions, and that this efficiency increases with time the more charge/discharge cycles CW-CAES experiences.

Lithium-ion battery, the indispensable part of electric vehicles or hybrid electric vehicles because of their high energy capacity and power density but usually suffer from a high temperature rise due to heat generation within a battery. This heat generation is mainly a function of the state of charge and charge/discharge rate. A passive technique like phase change material cooling has receiving a wide recognition due to its high latent heat, compact nature, and lightweight without consuming any external power. In this article, the thermal performance of the battery module containing 5 × 5 lithium-ion battery arranged in series and parallel is evaluated using phase change material. Initially, the performance of a battery module is examined with and without PCM at different discharge rate. It was found that more heat is accumulated at the interior portion of the battery pack due to mutual heating and low heat dissipation ability of PCM at a higher discharge rate. To improve such interior heat dissipation, different fin structure layout like Type I, Type II, Type III and Type IV are proposed and analysed using maximum temperature and average temperature distribution in a PCM based battery pack. It reveals that fin structure layout of Type III minimizes heat accumulation at the interior with adequate melting time among all. Furthermore, charge and discharge characteristics are investigated at different rate using rest time, convection effect and fin structure. The results indicated that use of rest time and increasing convection effect not only reduces maximum temperature but also recover melting fraction of PCM. Results also illustrate that the thermal performance of PCM based battery pack slightly get affected with the use of fin structure at lower convection, but decreases the maximum temperature by 8.17% at higher convection. Heat source a function of the state of charge and charge/discharge rate are given using Ansys-Fluent code and results are reported in the form of maximum temperature, average temperature and melting fraction.

The equivalent circuit model (ECM) is a battery model often used in the battery management system (BMS) to monitor and control lithium-ion batteries (LIBs). The accuracy and complexity of the ECM, hence, are very important. State of charge (SOC) and temperature are known to affect the parameters of the ECM and have been integrated into the model effectively. However, the effect of the state of health (SOH) on these parameters has not been widely investigated. Without a good understanding of the effect of SOH on ECM parameters, parameter identification would have to be done manually through calibration, which is inefficient. In this work, experiments were performed to investigate the effect of SOH on Thevenin ECM parameters, in addition to the effect of SOC and temperature. The results indicated that with decreasing SOH, the ohmic resistance and the polarization resistance increase while the polarization capacitance decreases. An empirical model was also proposed to represent the effect of SOH, SOC, and temperature on the ECM parameters. The model was then validated experimentally, yielding good results, and found to improve the accuracy of the Thevenin model significantly. With low complexity and high accuracy, this model can be easily integrated into real-world BMS applications.

In this paper, we present a detailed comparison study on the prediction accuracy by two different and simplified battery models, namely Lumped and Li-ion (1D electrochemical) model, for a prismatic 20Ah LiFePO4 battery sandwiched between minichannel cold-plates with distributed flow. A two-way coupling between the battery model (Li-ion/Lumped) and 3D conjugate heat transfer model is considered for heat generation and dissipation rates at different discharge rates ( 1 - 4 C ) and coolant inlet temperatures ( 15 - 35 ° C ). The predicted battery voltage response at room temperature ( 22 ° C ) and the performance of Battery Thermal Management System (BTMS) in terms of the battery surface temperatures (maximum temperature, T max and temperature difference, Δ T ) are compared between the models. Further, to get the insights on the battery heating rate by these models, ten different locations are selected on the battery surface and temperature variation during the discharge process are compared. It is found that Lumped model predicted temperatures are in close agreement with that of Li-ion model with a difference of 2.1% at 1C discharge rate. However, the difference has increased to 16.4% at 4C discharge rate, and is attributed to the non-accountability of chemical reactions by the Lumped model in contrast to the Li-ion model (as a benchmark). Therefore, it is suggested to use Lumped model for preliminary studies on designing the BTMS because it requires only three measurable electrical parameters.

This article includes a sensitivity analysis of a rolling stock hydrogen hybrid powertrain using different power splitting scenarios based on the load’s frequency contents. The model used for this purpose mimics the dynamics of a hydrogen hybrid powertrain from the component scale. The simulated powertrain consists of five different subsystems including the lithium-ion battery, hydrogen fuel cell, vehicle dynamics, power split, and high-level controller. As a novel case study, utilizing a customised frequency based power splitting scenario, a real-world load profile is observed throughout an implemented observer subsystem. The duty cycle is extracted based on the real world drive cycles of the current Diesel Multiple Units (DMUs) which are currently running in the Greater Toronto Area (GTA), Ontario, Canada. The model and its submodules are implemented in the MATLAB Simulink. As a result, the sensitivity of the system to the frequency content of the drive cycle were assessed in terms of different parameters such as simulated power, voltage, temperature, and state of charge (SOC). As a conclusion, different scenarios are compared based on the level of their fidelity to the other related researches.

Summary This paper presents a degradation testing of a lithium-ion battery developed using real world drive cycles obtained from an electric vehicle (EV). For this, a data logger was installed in the EV, and real world drive cycle data were collected. The EV battery system consists of 3 lithium-ion battery packs with a total of 20 battery modules in series. Each module contains 6 series by 49 parallel lithium-ion cells. The vehicle was driven in the province of Ontario, Canada, and several drive cycles were recorded over a 3-month period. However, only 4 drive cycles with statistical analysis are reported in this paper. The reported drive cycles consist of different modes: acceleration, constant speed, and deceleration in both highway and city driving at −6°C, 2°C, 10°C, and 23°C ambient temperatures with all accessories on. Additionally, individual cell characterization was conducted using a C/25 (0.8A) charge-discharge cycle and hybrid pulse power characterization (HPPC). The Thevenin battery model was constructed in MATLAB along with an empirical degradation model and validated in terms of voltage and SOC for all drive cycles reported. The presented model closely estimated the profiles observed in the experimental data. Data collected from the drive cycles showed that a 4.6% capacity fade occurred over the 3 months of driving. The empirical degradation model was fitted to these data, and an extrapolation estimated that 20% capacity fade would occur after 900 daily drive cycles.

This paper has presented a comparative study of the temperature and velocity distributions within the mini-channel cold plates placed on a prismatic lithium-ion battery cell using experimental and numerical techniques. The study was conducted for water cooling methods at 1C and 2C discharge rates and different operating temperatures of 5 °C, 15 °C, and 25 °C. A total of nineteen thermocouples were used for this experimental work, and were purposefully placed at different locations. Out of nineteen, ten T-type thermocouples were placed on the principal surface of the battery, and four K-type thermocouples were used to measure water inlet and outlet temperature. Computationally, the k-e model in ANSYS Fluent was used to simulate the flow in a mini-channel cold plate, and the data was validated with the experimental data for temperature profiles. The present results show that increased discharge rates and increased operating temperature results in increased temperature of the cold plates. Furthermore, the thermocouple sensors nearest the electrodes (anode and cathode) measured the higher temperatures than the sensors located at the center of the battery surface.

Both measurement and modeling of thermal performance in lithium-ion battery cell are considered crucial as they directly affect the safety. Even though the operation of a lithium-ion battery cell is transient phenomena in most cases, most available thermal models for lithium-ion battery cell predicts only steady-state temperature fields. This paper presents a mathematical model to predict the transient temperature distributions of a large sized 20Ah-LiFePO4 prismatic battery at different C-rates. In this regard, the lithium-ion battery is placed in a vertical position on a stand inside the lab with an ambient air cooling and the battery is discharged under constant current rate of 1C, 2C, 3C, and 4C in order to provide quantitative data regarding thermal behavior of lithium-ion batteries. Additionally, IR images are taken for the same battery cell during discharging. The present model predictions are in very good agreement with the experimental data and also with an IR imaging for temperature profiles. The present results show that the increased C-rates result in increased temperatures on the principle surface of the battery.

In this paper, a numerical model using ANSYS Fluent for a minichannel cold plate is developed for water-cooled LiFePO4 battery. The temperature and velocity distributions are investigated using exp...