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As mechanical damage induced thermal runaway of lithium-ion batteries has become one of the research hotspots, it is quite crucial to understand the mechanical behavior of component materials of lithium battery. This study focuses on the mechanical performance of separators and electrodes under different loading conditions and the error sources analysis for test results. Uniaxial tensile tests were conducted under both quasi-static and dynamic loading conditions. The strain was acquired through the combination of high speed camera and digital image correlation (DIC) method while the force was obtained with a customized load cell. Noticeable anisotropy and strain rate effect were observed for separators. The fracture mode of separators is highly correlated to the microscopic fiber orientation. To demonstrate the correlation microscopic images of separator material were obtained through SEM to match the facture edges of tensile tests at different loading directions.

Metal foil is a widely used material in the automobile industry, which not only is the honeycomb barrier material but is also used as current collectors in Li-ion batteries. Plenty of studies proved that the mechanical property of the metal foil is quite different from that of the metal sheet because of the size effect on microscopic scale, as the metal foil shows a larger fracture stress and a lower ductility than the metal sheet. Meanwhile, the fracture behavior and accurate constitutive model of the metal foil with the consideration of the strain rate effect are widely concerned in further studies of battery safety and the honeycomb. This article conducted experiments on 8011H18 aluminum foil, aiming to explore the quasi-static and dynamic tension testing method and the anisotropic mechanical behavior of the very thin foil. Two metal foil dog-bone specimens and three types of notched specimens were tested with a strain rate ranging from 2 × 10−4/s to 40/s and various stress states.

Fuel cell vehicles are entering the automotive market with significant potential benefits to reduce harmful greenhouse emissions, facilitate energy security, and increase vehicle efficiency while providing customer expected driving range and fill times when compared to conventional vehicles. One of the challenges for successful commercialization of fuel cell vehicles is transitioning the on-board fuel system from liquid gasoline to compressed hydrogen gas. Storing high pressurized hydrogen requires a specialized structural pressure vessel, significantly different in function, size, and construction from a gasoline container. In comparison to a gasoline tank at near ambient pressures, OEMs have aligned to a nominal working pressure of 700 bar for hydrogen tanks in order to achieve the customer expected driving range of 300 miles.

The EcoCAR 2: Plugging into the Future team at the Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 50 miles of all-electric range. The vehicle features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 75 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report details the rigorous design process followed by the Ohio State team during Year 1 of the competition. The design process includes identifying the team customer's needs and wants, selecting an overall vehicle architecture and completing detailed design work on the mechanical, electrical and control systems. This effort was made possible through support from the U.S.

Although energy harvesting systems are extensively used in different fields, studies on the application of energy harvesters embedded in tires for vehicle control are rare and mostly focus on solving power supply problems of tire pressure sensors. Sensors are traditionally powered by an embedded battery, which must be replaced periodically because of its limited energy storage. Heightened interest in vehicle safety is expected to drive increased design and manufacture of in-tire sensors, which in turn, translates to rising demand for power generation in tires. These challenges emphasize the need to investigate the substitution of batteries and in-tire energy harvesting systems. Current in-tire energy harvesting methods involve piezoelectric, electromagnetic, and electrostatic power generation, whose energy sources include tire vibrations, deformations, and rotations. Piezoelectric harvesters are generally compact but operate for short durations.

Due to its high hydrogen storage capacity (up to 16% by weight for the release of 2.5 molar equivalents of hydrogen gas) and its stability under typical ambient conditions, ammonia borane (AB) is a promising material for chemical hydrogen storage for fuel cell applications in transportation sector. Several systems models for chemical hydride materials such as solid AB, solvated AB and alane were developed and evaluated at Pacific Northwest National Laboratory (PNNL) to determine an optimal configuration that would meet the 2010 and future DOE targets for hydrogen storage. This paper presents an overview of those systems models and discusses the simulation results for various transient drive cycle scenarios.

Electric vehicles maintain the fastest development in China and undertake the responsibility of optimizing energy consumption and carbon emission in the transportation field. However, from the entire life cycle point of view, although electric vehicles have a certain degree of energy consumption and carbon emission reduction in the use phase, they cause extra energy consumption and carbon emission in the manufacturing phase, which weakens the due environmental benefits to some extent. The recycling of electric vehicles can effectively address the issue and indirectly reduce the energy consumption and carbon emission in the manufacturing phase. China is setting up the recycling system and strengthening regulation force to achieve proper energy consumption and carbon emission reduction benefits of electric vehicles. Under the current electric vehicle recycling technologies, China can reduce about 34% of carbon emission in electric vehicle manufacturing phase.

In recent years, many motorsports have been developing competitions based on electric vehicles. The demanding performance requires the battery pack to have the perfect balance between energy, power, and weight. This research paper presents a systematic methodology for the initial design of the battery pack (size and cell chemistry) by statistically analyzing the characteristics of the mission profile. The power profile for the battery pack of a motorsport vehicle can be estimated by considering the duty cycle of a racing car using the technical and sporting regulations and vehicle parameters. In this paper, many statistical metrics correlated to this power profile have been defined and analyzed (such as the max, mean, and standard deviation of the power profile, the total energy consumed, and the expected heat generation). These metrics have been used to estimate the cell energy and power density requirement and the pack sizing considering the weight constraints.

Physics-based electrochemical models and empirical Equivalent Circuit Models (ECMs) are well-established and widely used modeling techniques to predict the voltage behavior of lithium-ion cells. Electrochemical models are typically very accurate and require relatively little experimental data to calibrate, but present high mathematical and computational complexity. Conversely, ECMs are more computationally efficient and mathematically simpler, making them well-suited for applications in controls, diagnosis, and state estimation of lithium-ion battery packs. However, the calibration process requires extensive testing to calibrate the parameters of the model over a wide range of operating conditions. This paper bridges the gap between these two classes of models by developing a method to analytically define the ECM parameters starting from an already-calibrated Extended Single-Particle Model (ESPM).

This paper benchmarks three different lithium-ion (Li-ion) battery voltage modelling approaches, a physics-based approach using an Extended Single Particle Model (ESPM), an equivalent circuit model, and a recurrent neural network. The ESPM is the selected physics-based approach because it offers similar complexity and computational load to the other two benchmarked models. In the ESPM, the anode and cathode are simplified to single particles, and the partial differential equations are simplified to ordinary differential equations via model order reduction. Hence, the required state variables are reduced, and the simulation speed is improved. The second approach is a third-order equivalent circuit model (ECM), and the third approach uses a model based on a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)). A Li-ion pouch cell with 47 Ah nominal capacity is used to parameterize all the models.

Batteries are useful in Fuel Cell Hybrid Electric Vehicles (FCHEV) to fulfill transient demands and for regenerative braking. Efficient energy management strategies paired with optimal powertrain design further improves the efficiency. In this paper, a new methodology to simultaneously size the propulsive elements and optimize the power-split strategy of a Range Extended Battery Electric Vehicle (REBEV), using a Polymer Electron Membrane Fuel Cell (PEMFC), is proposed and preliminary studies on the effects of the driving mission profile and the auxiliary power loads on the sizing and optimal performance of the powertrain design are carried out. Dynamic Programming is used to compute the optimal energy management strategy for a given driving mission profile, providing a global optimal solution.

Battery packs used in electrified automotive powertrains support heavy electrical loads resulting in significant heat generation within them. Cooling systems are used to regulate the battery pack temperatures, helping to slow down battery aging. Vehicle-level energy consumption simulations serve as a first step for determining the specifications of a battery cooling system based on the duty cycle and interactions with the rest of the powertrain. This paper presents the development of a battery model that takes into account the energy impact of heating in the battery and demonstrates its use in a vehicle-level energy consumption simulator to set the specifications of a suitable cooling system for a vehicle application. The vehicle application used in this paper is a Class 6 Pickup and Delivery commercial vehicle with a Range-Extended Electric Vehicle (REEV) powertrain configuration.

The EcoCAR 2: Plugging into the Future team at The Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 44 miles of all-electric range. The vehicle features an 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes. This is made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 50 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This paper details three years of modeling and simulation development for the OSU EcoCAR 2 vehicle. Included in this paper are the processes for developing simulation platform and model requirements, plant model and soft ECU development, test development and validation, automated regression testing, and controls and calibration optimization.

Electric vehicle (EV) is a worldwide researching focus due to its environmental friendliness, but the inaccurate Remaining Driving Range (RDR) estimation hinders the EVs' popularity, and an accurate determination of the battery Residual Usable Energy (RUE) is the key factor to obtain a precise RDR value. A common RUE estimation method is based on State-of-Charge (SOC) estimation, in which the RUE is proportionally related to the current SOC. However, the battery voltage varies significantly under real-world conditions, and the traditional method results in certain estimation errors. An adaptive RUE prediction method (AEP) is introduced in this paper, in which the dynamic voltage is predicted based on the future discharge profile and a battery model, while the RUE is then calculated by the predicted voltage and current sequences.

In order to reduce driver's anxiety about range and energy, a direct and effective approach is to offer the remaining driving range based on the vehicle's states. Consequently, the estimation accuracy of the battery's remaining energy is very important. This paper introduces a experiment-based model for predicting the remaining energy, which considers many factors, such as current, temperature, difference between battery cells, and so on. This approach ensures the accuracy of the remaining driving range. Finally the method is validated through the environment space test. Validation results show that this method can offer exact remaining energy, which reduces the estimation error of the remaining range greatly.

Fuel cell vehicle and battery electric vehicle are two environmentally benign vehicle technology types possibly meeting the zero-emission regulations in the future. The premise is they can achieve parity with conventional vehicle both environmentally and economically. Besides, it is necessary to distinguish which technology is more suitable in China's current and future context. This paper compares their cost-effectiveness for reducing greenhouse gas emissions, examining the life-cycle greenhouse gas emissions of conventional gasoline vehicle, battery electric vehicle and fuel cell vehicle in China's energy context under three different scenarios. The results indicate that under the 500km drive range, fuel cell vehicles are less competitive than battery electric vehicles currently. Fuel cell vehicles generate much more greenhouse gas emissions than battery vehicles and conventional gasoline vehicles.

As the only energy storage component in the internal combustion engine vehicles (ICEVs), the battery is lack of comprehensive supervision and effective protection. Excessive discharge or aging cannot be detected and dealt with, which may lead to damage of the battery, even startup failure of the vehicle. In this paper, a full protection and optimization management scheme of the battery is proposed, to achieve comprehensive protection of the battery and energy optimization. Firstly, power partitioning model of the battery is established to reveal the battery characteristics in different states, which divides the battery into several function zones. Then, based on the power partitioning model, over discharge protection and graded overcurrent protection method are proposed, to achieve full protection of the battery. Thirdly, energy optimization management strategy based on generator’s multimode operation is introduced.

DC-fast charge (DCFC) may be amenable for widespread EV adoption. However, there are potential challenges associated with implementation and operation of the DCFC infrastructures. The integration of energy storage systems can limit the scale of grid installation required for DCFC and enable more efficient grid energy usage. In addition, second-life batteries (SLBs) can find application in DCFC, significantly reducing installation cost when compared to solutions based on new battery packs. However, both system architecture and control strategy require optimization to ensure an optimal use of SLBs, including degradation and thermal aspects. This study proposes an application of automotive SLBs for DCFC stations where high power grid connection is not available or feasible. Several SLBs are connected to the grid by means of low power chargers (e.g. L2 charging station), and a DC/DC converter controls the power to the EV power dispenser.

Small lightweight electric vehicle (SLEV) is an approach for compensating low energy density of the current battery. However, small lightweight vehicle presents technical challenges to crash safety design. One issue is that mass of battery pack and occupants is a significant portion of vehicle's total weight, and therefore, the mass distribution has great influence on crash response. This paper presents a parametric analysis using finite element modeling. We first build LS-DYNA model of a two-seater SLEV with curb weight of 600 kg. The model has no complex components and can provide reasonable crash pulses under full frontal rigid barrier crash loading and offset deformable barrier (ODB) crash loading. For given mass of battery pack and one occupant (the driver), different battery layouts, representing different combinations of center of gravity and moment of inertia of the whole vehicle, are analyzed for their influences on the crash responses under the two frontal crash loadings.

Hybrid-electric gas turbine generators are considered a promising technology for more efficient and sustainable air transportation. The Ohio State University is leading the NASA University Leadership Initiative (ULI) Electric Propulsion: Challenges and Opportunities, focused on the design and demonstration of advanced components and systems to enable high-efficiency hybrid turboelectric powertrains in regional aircraft to be deployed in 2030. Within this large effort, the team is optimizing the design of the battery energy storage system (ESS) and, concurrently, developing a supervisory energy management strategy for the hybrid system to reduce fuel burn while mitigating the impact on the ESS life. In this paper, an energy-based model was developed to predict the performance of a battery-hybrid turboelectric distributed-propulsion (BHTeDP) regional jet.