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Insulation coordination requirements for electrical equipment applications are defined in various standards. The standards are defined for application to stationary mains connected equipment, like IT, power supply or industrial equipment. Protection from an electric shock is considered the primary hazard in these standards. These standards have also been used in the design of various automotive components. IEC 60664-1 is an example of the standard. Automobiles are used across the world, in various environments and in varied usage by the customers. Automobiles need to consider possible additional hazards including electric shock. This paper will provide an overview of how to adapt these standards for automotive application in the design of High Voltage (HV) automotive components, including High Voltage batteries and other HV components connected to the battery. The basic definitions from the standards and the principles are applied for usage in automotive applications.

Reducing vehicle CO2 emissions is an important measure to help address global warming. To reduce CO2 emissions on a global basis, Toyota Motor Corporation is taking a multi-pathway approach that involves the introduction of the optimal powertrains according to the circumstances of each region, including hybrid electric (HEVs) and plug-in hybrid electric vehicles (PHEVs), as well as battery electric vehicles (BEVs). This report describes the development of a new PHEV system for the Toyota Prius. This system features a traction battery pack structure, transaxle, and power control unit (PCU) with boost converter, which were newly developed based on the 2.0-liter HEV system. As a result, the battery capacity was increased by 1.5 times compared to the previous model with almost the same battery pack size. Transmission efficiency was also improved, extending the distance that the Prius can be driven as an EV by 70%.

Due to the expense and time commitment associated with extensive product testing, vehicle manufacturers are developing new simulation techniques to verify vehicle component performance with less testing and more confidence in the final product. Battery lifetime is of particular difficulty to predict, since each battery is different and there are many different control scenarios that could be implemented based on the specific requirements of each battery type. In order to solve this problem for a 12V auxiliary lead-acid battery, a battery durability analysis model has been previously adapted from lithium-ion applications, which is capable of verifying the impact of lead-acid battery durability in a short period of time. In this study, calibration tools for this model were developed and are presented here, and durability analysis and verification are performed for the application of new electric vehicles.

This paper discusses the dependency between powertrain design and automated driving. The research questions are to what extent automated driving influences the powertrain design and how energy and fuel consumption is affected in comparison to customer driving. For this investigation a concept study is carried out for a D-segment vehicle and multiple powertrain topologies, ranging from non-electrified to plug-in hybrids and battery electric vehicles. In order to answer the research questions, the used development process and the methods for optimizing the drive system are presented accordingly, taking into account all vehicle requirements, the drive system and the components and their interactions with each other. This work focuses on two automated driving functions developed at the Institute of Automotive Engineering of the Technische Universität Braunschweig. The functions are an “automated valet parking” and a “highway pilot”.

Nissan have developed a new powertrain for the electric vehicle, and have installed it in the Nissan LEAF. In order to achieve an improved driving range, power performance and dynamic performance, Nissan have adapted a high efficiency synchronous motor, a water-cooled inverter, and passive-cooled laminated Li-ion battery. Especially Nissan has been emphasizing electric powered technology with a focus on advanced lithium ion battery from 1992. This presentation will introduce the features of Nissan LEAF and its battery technologies.

The Toyota Prius battery pack consists of 38 individual battery modules, each module contains 6 NiMH cells in series. This means that each pack contains 228 NiMH cells. Each cell has the potential to fail. This report investigates the mode of failure of Prius battery packs by first analysing a number of packs in the lab, and then road testing them in a Toyota Prius. The analysis of the battery packs show that some packs had aged “linearly”, that is in a balanced manner, such that the state of health of all modules remained similar. However, in other packs discrete modules had significantly different states of health. A pack that consists of cells that are matched in both state of health and state of charge delivers the best performance. The research also showed that the worst cell in the pack determines the overall pack performance. This was demonstrated by substituting reduced capacity or short-circuited modules into a functioning battery pack.

As part of a U.S. Department of Energy supported study, the National Renewable Energy Laboratory has benchmarked a Toyota Prius hybrid electric vehicle from three aspects: system analysis, auxiliary loads, and battery pack thermal performance. This paper focuses on the testing of the battery back out of the vehicle. More recent in-vehicle dynamometer tests have confirmed these out-of-vehicle tests. Our purpose was to understand how the batteries were packaged and performed from a thermal perspective. The Prius NiMH battery pack was tested at various temperatures (0°C, 25°C, and 40°C) and under driving cycles (HWFET, FTP, and US06). The airflow through the pack was also analyzed. Overall, we found that the U.S. Prius battery pack thermal management system incorporates interesting features and performs well under tested conditions.

Collision reconstruction often involves calculations and computer simulations, which require an estimation of the weights of the involved vehicles. Although weight data is readily available for automobiles and light trucks, there is limited data for heavy vehicles, such as tractor-semitrailers, straight trucks, and the wide variety of trailers and combinations that may be encountered on North American roads. Although manufacturers always provide the gross vehicle weight ratings (GVWR) for these vehicles, tare weights are often more difficult to find, and in-service loading levels are often unknown. The resulting large uncertainty in the weight of a given truck can often affect reconstruction results. In Canada, the Ministry of Transportation of Ontario conducted a Commercial Vehicle Survey in 2012 that consisted of weight sampling over 45,000 heavy vehicles of various configurations.

Electric vehicles (EVs) and the development around them has been rapid in recent years. As the battery is the most essential component of an electric vehicle, a lot of research and analysis has been focused on ensuring safe and reliable performance of batteries. Considering the location, size, and operating conditions for EV batteries, they must be designed with an in-built safety infrastructure keeping in mind certain realistic scenarios such as fire exposure, mechanical vibration, collisions, over-charging, single cell failures, and others. In this paper, we discuss an overview of various EV battery failure mechanisms, present current safety and abuse testing methods and standards associated with such mechanisms and discuss the need for the development and implementation of additional testing standards to better characterize the safety performance of EV battery packs.

In this paper, a control strategy for state of charge (SOC) allocation using navigation data for Hybrid Electric Vehicle (HEV) propulsion systems is proposed. This algorithm dynamically defines and adjusts a SOC target as a function of distance travelled on-line, thereby enabling proactive management of the energy store in the battery. The proposed approach incorporates variances in road resistance and adheres to geolocation constraints, including ultra-low emission zones (uLEZ). The anticipated advantages are particularly pronounced during scenarios involving extensive medium-to-long journeys characterized by abrupt topological changes or the necessity for exclusive electric vehicle (EV) mode operation. This novel solution stands to significantly enhance both drivability and fuel economy outcomes.

Increasing awareness of the harmful effects on the environment of traditional Internal Combustion Engines (ICE) drives the industry toward cleaner powertrain technologies such as battery-driven Electric Vehicles (EV). Nonetheless, the high energy density of Li-Ion batteries can cause strong exothermic reactions under certain conditions that can lead to catastrophic results, called Thermal Runaway (TR). Hence, a strong effort is being made to understand this phenomenon and increase battery safety. Specifically, the vented gases and their ignition can cause the propagation of this phenomenon to adjacent batteries in a pack. In this work, Computational Fluid Dynamics (CFD) is employed to predict this venting process in an LG18650 cylindrical battery. The shape of the venting cap deformation obtained from experimental results was introduced in the computational model.

Thermal runaway is a critical safety concern in lithium-ion battery systems, emphasising the necessity to comprehend its behaviour in various modular setups. This research compares thermal runaway propagation in different modular configurations of lithium-ion batteries by analysing parameters such as cell spacing and applying phase change materials (PCMs) and Silica Aerogel. The study at the module level includes experimental validation and employs a comprehensive model considering heat transfer due to thermal runaway phenomena. It aims to identify the most effective modular configuration for mitigating thermal runaway risks and enhancing battery safety. The findings provide valuable insights into the design and operation of modular lithium-ion battery systems, guiding engineers and researchers in implementing best practices to improve safety and performance across various applications.

In the dynamic landscape of battery development, the quest for improved energy storage and efficiency has become paramount. The contemporary energy transition, coupled with growing demands for electric vehicles, renewable energy sources, and portable electronic devices, has underscored the critical role that batteries play in our modern world. To navigate this challenging terrain and harness the full potential of battery technology, a well-defined and comprehensive data strategy resp. knowledge management strategy are indispensable. Conversely, the imminent and rapid progression of artificial intelligence (AI) is poised to have a substantial impact on the forthcoming landscape of work and the methodologies organizations employ for the management of their knowledge management (KM) procedures. Conventional KM endeavors encompass a spectrum of activities such as the creation, transmission, retention, and evaluation of an enterprise’s knowledge over the entire knowledge lifecycle.

The extension of traction batteries from electric vehicles with supercapacitors is regularly discussed as a possibility to increase the lifetime of lithium-ion batteries as well as the performance of the vehicle drive. The objective of this work was to validate these assumptions by developing a simulation model. In addition, an economic analysis is performed to qualitatively classify the simulation results. Initially, a hybrid energy storage system consisting of battery and supercapacitor was developed. A semi-active hybrid energy storage topology was selected. Subsequently, the selection of use cases as well as the application-specific definition of load cycles took place. In addition, the control strategy was further developed so that a simulation on lifetime was made possible. The end-of-life of the battery cells was defined, according to the USABC guideline values.

This paper has been withdrawn by the publisher because of non-attendance and not presenting at WCX 2024.

Trailing axles, otherwise known as tag axles, are utilized in many states to allow heavy duty dump trucks and cement trucks to maximize their capacity. The trailing axle is an additional axle mounted on an arm on the rear of the truck that can be raised and lowered. When lowered, the axle extends the overall wheelbase of the vehicle and increases the total number of axles, thereby allowing for additional load to be carried without exceeding load-restriction regulations. There are multiple manufactures of trailing axles that utilize different suspension designs. One design uses an articulating axle that is mounted to the framework that lowers it. In this study, the sensitivity of this design to tire blowout on one of the trailing axle tires is studied. Testing was conducted that involved initiating a sudden air-loss event by creating a hole in the sidewall of the tire. The handling response of the vehicle was documented with on-board instrumentation and on-board and off-board video.

The transition towards battery electric vehicles (BEVs) has increased the focus of vehicle manufacturers on energy efficiency. Ensuring adequate airflow through the heat exchanger is necessary to climatize the vehicle, at the cost of an increase in the aerodynamic drag. With lower cooling airflow requirements in BEVs during driving, the front air intakes could be made smaller and thus be placed with greater freedom. This paper explores the effects on exterior aerodynamics caused by securing a constant cooling airflow through intakes at various positions across the front of the vehicle. High-fidelity simulations were performed on a variation of the open-source AeroSUV model that is more representative of a BEV configuration. To focus on the exterior aerodynamic changes, and under the assumption that the cooling requirements would remain the same for a given driving condition, a constant mass flow boundary condition was defined at the cooling airflow inlets and outlets.

This article proposes a novel methodology for the definition of an optimized immersion cooling fluid for lithium-ion battery applications aimed to minimize maximum temperature and temperature gradient during most critical battery operations. The battery electric behavior is predicted by a first order equivalent circuit model, whose parameters are experimentally determined. Thermal behavior is described by a nodal network, assigning to each node thermal characteristics. Hence, the electro-thermal model of a battery is coupled with a thermal management model of an immersion cooling circuit developed in MATLAB/Simulink. A first characterization of the physical properties of an optimal dielectric liquid is obtained by means of a design of experiment. The optimal values of density, thermal conductivity, kinematic viscosity, and specific heat are defined to minimize the maximum temperature and temperature gradient during a complete discharge of the battery at 2.5C.