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Polypropylene is typically reinforced with commodities that are non renewable and require a great deal of energy to produce. The marketplace needs a reinforcement that can offer beneficial physical properties, such as impact, while being attained from a renewable green source. Compounding flax fiber, which is traditionally an agricultural waste product burned by farmers, with polypropylene yields physical properties similar to traditional glass filled polypropylene. This combination should lead to cost saving opportunities while not sacrificing part performance. Presenter James Preston, Rhetech Inc.

Software usability is a quality attribute defined as ?the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specific context of use? (ISO 9241, 1998), usability is also referred to as ?quality in use? (ISO 14598, 1999). Presenter Anabell Beltran, Stoneridge Electronics North America

Abstract In this article, a unique design for Hydro-Pneumatic Energy Harvesting Suspension HPEHS system is introduced. The design includes a hydraulic rectifier to maintain one-way flow direction in order to obtain maximum power generation from the vertical oscillation of the suspension system and achieve handling and comfort car drive. A mathematical model is presented to study the system dynamics and non-linear effects for HPEHS system. A simulation model is created by using Advanced Modeling Environment Simulations software (AMEsim) to analyze system performance. Furthermore, a co-simulation platform model is developed using Matlab-Simulink and AMEsim to optimize the PID controller parameters of the external variable load resistor applied on the generator by using Particle Swarm Optimization (PSO).

Abstract Recently, electric heavy-duty tractor-trailers (EHDTTs) have assumed significance as they present an immediate solution to decarbonize the transportation sector. Hence, to illustrate the economic viability of electrifying the freight industry, a detailed numerical model to estimate the battery capacity for an EHDTT is proposed for a route between Washington, DC, to Knoxville, TN. This model incorporates the effects of the terrain, climate, vehicular forces, auxiliary loads, and payload in order to select the appropriate motor and optimize the battery capacity. Additionally, current and near-future battery chemistries are simulated in the model. Along with equations describing vehicular forces based on Newton’s second law of motion, the model utilizes the Hausmann and Depcik correlation to estimate the losses caused by the capacity offset of the batteries. Here, a Newton-Raphson iterative scheme determines the minimum battery capacity for the required state of charge.

Abstract This article investigates the fuel savings potential of a series hybrid military truck using a simultaneous battery pack design and powertrain supervisory control optimization algorithm. The design optimization refers to the sizing of the lithium-ion battery pack in the hybrid configuration. The powertrain supervisory control optimization determines the most efficient way to split the power demand between the battery pack and the engine. Despite the available design and control optimization techniques, a generalized mathematical formulation and solution approach for combined design and control optimization is still missing in the literature. This article intends to fill that void by proposing a unified framework to simultaneously optimize both the battery pack size and power split control sequence. This is achieved through a combination of genetic algorithm (GA) and Pontryagin’s minimum principle (PMP) where the design parameters are integrated into the Hamiltonian function.

Fuel consumption reduction on medium-duty tactical truck has and continues to be a significant initiative for the U.S. Army. The Crankshaft-Integrated-Starter-Generator (C-ISG) is one of the parallel hybrid propulsions to improve the fuel economy. The C-ISG configuration is attractive because one electric machine can be used to propel the vehicle, to start the engine, and to be function as a generator. The C-ISG has been implemented in one M1083A1 5-ton tactical cargo truck. This paper presents the experimental assessments of the C-ISG hybrid truck characteristics. The experimental assessments include all electric range for on- and off-road mission cycles and fuel consumption for the high voltage battery charging. Stationary tests related to the charging profile of the battery pack and the silent watch time duration is also conducted.

Fuel cells are a promising energy source on account of their high efficiency and low emissions. Proton exchange membrane fuel cells (PEMFC) are clean and environmental-friendly power sources, which can become future energy solutions especially for transport vehicles. They exhibit good energy efficiency and high power density per volume. Working at low temperatures (<90°C), hydrogen fuelled proton exchange membrane fuel cells (PEMFCs) are identified as promising alternatives for powering autos, houses and electronics. At the middle of the proton exchange membrane (PEM) fuel cell is the membrane electrode assembly (MEA). The MEA consists of a proton exchange membrane, catalyst layers, and gas diffusion layers (GDL). However, most of the researchers have already mentioned that PEMFC are not competitive enough to rechargeable lithium ion battery with respect to price because of the rare metal used such as platinum in it.

Battery electric vehicles possess great potential for decreasing lifecycle costs in medium-duty applications, a market segment currently dominated by internal combustion technology. Characterized by frequent repetition of similar routes and daily return to a central depot, medium-duty vocations are well positioned to leverage the low operating costs of battery electric vehicles. Unfortunately, the range limitation of commercially available battery electric vehicles acts as a barrier to widespread adoption. This paper describes the National Renewable Energy Laboratory's collaboration with the U.S. Department of Energy and industry partners to analyze the use of small hydrogen fuel-cell stacks to extend the range of battery electric vehicles as a means of improving utility, and presumably, increasing market adoption.

This paper presents 3D finite element analysis performed for a composite cylindrical tank made of 6061-aluminum liner overwrapped with carbon fibers subjected to a burst internal pressure of 1610 bars. As the service pressure expected in these tanks is 700 bars, a factor of safety of 2.3 is kept the same for all designs. The optimal design configuration of such high pressure storage tanks includes an inner liner used as a gas permeation barrier, geometrically optimized domes, inlet/outlet valves with minimum stress concentrations, and directionally tailored exterior reinforcement for high strength and stiffness. Filament winding of pressure vessels made of fiber composite materials is the most efficient manufacturing method for such high pressure hydrogen storage tanks. The complexity of the filament winding process in the dome region is characterized by continually changing the fiber orientation angle and the local thickness of the wall.

This paper describes the system architecture together with control and diagnostics features of an indigenously developed electric vehicle controller for Light Commercial Vehicle. The key functions of vehicle controller include power management, driveline controls, regeneration and vehicle mode controls. In particular this paper presents vehicle's operational strategy in economy, normal and performance modes based on the vehicle speed and SOC. It also has feature to enable vehicle operation in reduced performance mode at low battery voltages. The battery fault predictor algorithm is also described in detail that is used to control discharge current to prevent sudden dip in SOC and to increase battery life. The vehicle control strategy is modeled & simulated using MATLAB™ environment and results for a specific test case are validated with embedded controllers-in-the-loop in a test-bench environment.

Many aspects of battery electric vehicles are very challenging from the engineering point of view in terms of safety, weight, range, and drivability. Commercial vehicle engines are often subjected to high loads even at low speeds and this can lead to an intense increment of the battery pack temperature and stress of the cooling system. For these reasons the optimal design of the battery pack and the relative cooling system is essential. The present study deals with the challenge of designing a battery pack that satisfies both the conditions of lowest weight and efficient temperature control. The trade-off between the battery pack size and the electrical stress on the cells is considered. The electric system has the aim to substitute a 3.0 liters compression ignition engine mainly for commercial vehicles.

Electric vehicles have a limitation of limited range and long charging time. Energy optimization plays a very crucial role in determining the range of an electric vehicle. The innovative system proposed here gives the opportunity to reduce energy wastage and efficiently direct the electrical energy to improve the driving range of a 9 meter AC electric bus. The high voltage air conditioner unit alone consumes more than 40% of the electrical energy stored in the traction battery which reduces the driving range of the electric bus drastically. The proposed system optimizes the air conditioner utilization to direct cool air only in areas where passengers are present. Buses do not always run on full capacity, when there are less number of people in the bus the system detects the locations of the passengers using sensors and occupant detection algorithm, this enables the controller to identify the areas where cooling has to be focused and where cooling can be reduced or stopped.

The public transport in India is gradually shifting towards electric mobility. Long range in electric mobility can be served with High Voltage Battery (HVB), but HVB can sustain for its designed life if it’s maintained within a specific operating temperature range. Appropriate battery thermal management through Battery Cooling System (BCS) is critical for vehicle range and battery durability This work focus on two aspects, BCS sizing and its coolant flow optimization in Electric bus. BCS modelling was done in 1D CAE software. The objective is to develop a model of BCS in virtual environment to replicate the physical testing. Electric bus contain numerous battery packs and a complex piping in its cooling system. BCS sizing simulation was performed to keep the battery packs in operating temperature range.

Electric Vehicles (EVs), with its inherent advantage of zero tailpipe emissions, are gaining importance because of aggressive push from government not only to reduce air pollution but also to reduce dependency of fossil fuel. EVs and necessary charging infrastructure along with ‘connected’ technology is redefining mobility. Considering the fast growing EV market, it becomes important for an EV Powertrain Architect to design and develop a powertrain solution having low engineering efforts and satisfying business, market and regulatory requirements at a competitive price. This paper presents a compact, flexible, convenient and smart featured simulation tool for an EV Powertrain Architect for estimating the specifications of key powertrain components such as traction battery and electric motor. The proposed tool takes into consideration the end-user as well as the regulatory requirements of range, maximum speed, acceleration and gradeability.

This paper addresses scheduling of quantized power levels (including part load operation and startup/shutdown periods) for a propane powered solid oxide fuel cell (SOFC) hybridized with a lithium-ion battery for a tracked mobile robot. The military requires silent operation and long duration missions, which cannot be met by batteries alone due to low energy density or with combustion engines due to noise. To meet this need we consider an SOFC operated at a few discrete power levels where maximum system efficiency can be achieved. The fuel efficiency decreases during transients and resulting thermal gradients lead to stress and degradation of the stack; therefore switching power levels should be minimized. Excess generated energy is used to charge the battery, but when it’s fully charged the SOFC should be turned off to conserve fuel.

This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc. This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices. After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results.

The temperature distribution is studied theoretically in a battery module stacked with 12 high-power Li-ion pouch cells. The module is cooled indirectly with ambient air through aluminum heat-sink plates or cooling plates sandwiched between each pair of cells in the module. Each of the cooling plates has an extended cooling fin exposed in the cooling air channel. The cell temperatures can be controlled by changing the air temperature and/or the heat transfer coefficient on the cooling fin surfaces by regulating the air flow rate. It is found that due to the high thermal conductivity and thermal diffusivity of the cooling plates, heat transfer of the cooling plate governs the cell temperature distribution by spreading the cell heat over the entire cell surface. Influence of thermal from the cooling fins is also simulated.

Electrification and hybridization show great potential for improving fuel economy and reducing emission in heavy-duty vehicles. However, high battery cost is unavoidable due to the requirement for large batteries capable of providing high electric power for propulsion. The battery size and cost can be reduced with advanced battery control strategies ensuring safe and robust operation covering infrequent extreme conditions. In this paper, the impact of such a battery control strategy on battery sizing and fuel economy is investigated under various military and heavy-duty driving cycles. The control strategy uses estimated Li-ion concentration information in the electrodes to prevent battery over-charging and over-discharging under aggressive driving conditions. Excessive battery operation is moderated by adjusting allowable battery power limits through the feedback of electrode-averaged Li-ion concentration estimated by an extended Kalman filter (EKF).

Ideal operation temperatures for Li-ion batteries fall in a narrow range from 20°C to 40°C. If the cell operation temperatures are too high, active materials in the cells may become thermally unstable. If the temperatures are too low, the resistance to lithium-ion transport in the cells may become very high, limiting the electrochemical reactions. Good battery thermal management is crucial to both the battery performance and life. Characteristics of various battery thermal management systems are reviewed. Analyses show that the advantages of direct and indirect air cooling systems are their simplicity and capability of cooling the cells in a battery pack at ambient temperatures up to 40°C. However, the disadvantages are their poor control of the cell-to-cell differential temperatures in the pack and their capability to dissipate high cell generations.

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