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Due to stringent environmental requirements, the vehicle under-hood and underbody temperatures have been steadily increasing. The increased temperatures affect components life and therefore, more thermal protection measures may be necessary. In this paper, we present an algorithm for estimation of automotive component life due to thermal effects through the vehicle life. Traditional approaches consider only the maximum temperature that a component will experience during severe driving maneuvers. However, that approach does not consider the time duration or frequency of exposure to temperature. We have envisioned a more realistic and science based approach to estimate component life based on vehicle duty cycles, component temperature profile, frequency and characteristics of material thermal degradation. In the proposed algorithm, a transient thermal analysis model provides the exhaust gas and exhaust surface temperatures for all exhaust system segments, and for any driving scenario.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

The application of Computational Fluid Dynamics (CFD) for three-dimensional (3D) combustion analysis coupled with detailed chemistry in engine development is hindered by its expensive computational cost. Chemistry computation may occupy as much as 90% of the total computational cost. In the present paper, a new two-step iso-octane combustion model was developed for spark-ignited (SI) engine to maximize computational efficiency while maintaining acceptable accuracy. Starting from the model constants of an existing global combustion model, the new model was developed using an approach based on sensitivity analysis to approximate the results of a reference skeletal mechanism. The present model involves only five species and two reactions and utilizes only one uniform set of model constants. The validation of the new model was performed using shock tube and real SI engine cases.

Maintaining the fuel temperature and fuel system components below certain values is an important design objective. Predicting these temperatures is therefore one of the key parts of the vehicle’s thermal management process. One of the physical processes affecting fuel tank temperature is fuel vaporization, which is controlled by the vapor pressure in the tank, fuel composition and fuel temperature. Models are developed to enable the computation of the fuel temperature, fuel vaporization rate in the tank, fuel temperatures along the fuel supply lines, and follow its path to the charcoal canister and into the engine intake. For diesel fuel systems where a fuel return line is used to return excess fluid back to the fuel tank, an energy balance will be considered to calculate the heat added from the high-pressure pump and vehicle under-hood and underbody.

This paper focuses on an inherent problems of active damping control prevalent in contemporary hybrid torque controls. Oftentimes, a supervisory torque controller utilizes simplified system models with minimal system states representation within the optimization problem, often not accounting for nonlinearities and stiffness. This is motivated by enabling the generation of the optimum torque commands with minimum computational burden. When inherent lash and stiffness of the driveline are not considered, the resulting command can lead to vibrations and oscillations in the powertrain, reducing performance and comfort. The paper proposes a Linear Quadratic Integral (LQI)-based compensator to be integrated downstream the torque supervisory algorithm, which role is to shape transient electric machine torques, compensating for the stiffness and backlash present in the vehicle while delivering the driver-requested wheel torque.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

Thermal management of lithium-Ion battery (LIB) has become very critical issue in recent years. One of the challenges for the design and packaging of the battery is to maintain the battery temperature within acceptable ranges and also reduce temperature gradients within the battery cells. Controlling the battery temperature is essential for the battery performance and the long-term battery life. Increased difference between battery cell temperatures can lead to non-uniform charging and non-uniform ageing of battery cells. The purpose of this paper is to investigate available technologies using heat pipes as a means of improving battery thermal management. Several studies have been conducted regarding the effect of heat pipes on battery temperature. However, in this paper we present a comprehensive study of heat pipes effects through transient analysis of a complete vehicle thermal model.

The Hotelling multivariate control chart and the sample generalized variance |S| are used to monitor the mean and dispersion of vehicle build vision data including the pallet information to identify the non-conforming pallets that are used in body shops of FCA US LLC assembly plants. An iterative procedure and the Gaussian mixture model (GMM) are used to rank the non-conforming or bad pallets in the order of severity. The Hotelling multivariate T2 test statistic along with Mason-Tracy-Young (MYT) signal decomposition method is used to identify the features that are affected by the bad pallets. These algorithms were implemented in the Advanced Pallet Analysis module of the FCA US software Body Shop Analysis Toolbox (BSAT). The identified bad pallets are visualized in a scatter plot with a different color for each of the top bad pallets. The run chart of an affected feature confirms the bad pallet by highlighting data points from the bad pallet.

Two methods of assessing the similarity of a set of impact test signals have been proposed and used in the literature, which are cumulative variance-based and cross correlation-based. In this study, a normalized formulation unites these two approaches by establishing a relationship between the normalized cumulative variance metric (v), an overall similarity metric, and the normalized magnitude similarity metric (m) and shape similarity metric (s): v=1 − m · s. Each of these ranges between 0 and 1 (for the practical case of signals acquired with the same polarity), and they are independent of the physical unit of measurement. Under generally satisfied conditions, the magnitude similarity m is independent of the relative time shifts among the signals in the set; while the shape similarity s is a function of these.

Computational cost plays a major role in the performance of scientific and engineering simulation. This in turn makes the virtual validation process complex and time consuming. In the simulation process, achievement of appropriate level of accurate models as close as physical testing is the root for increase in the computational cost. During preliminary phase of product development, it is difficult to identify the appropriate size, shape and other parameters of the component and they will undergo several modifications in concept and other stages. An approximation model called metamodel or surrogate model has developed for reducing these effects and minimizing the computational cost. Metamodel can be used in the place of actual simulation models. Metamodel can be an algorithm or a mathematical relation representing the relations between input and output parameters.

This paper focuses on the conjugate heat transfer analysis of an I4 engine, and discusses optimization of the coolant passages in engine coolant jackets. Direct Optimization approach integrates an optimizer with the numerical solver. This method of optimization is compared with a metamodel-based optimization in which a metamodel is generated to aid in finding an optimal design. The direct optimization and metamodel approaches are compared in terms of their accuracy, and execution time.

The performance and durability of the lead acid battery is highly dependent on the internal battery temperature. The changes in internal battery temperatures are caused by several factors including internal heat generation and external heat transfer from the vehicle under-hood environment. Internal heat generation depends on the battery charging strategy and electric loading. External heat transfer effects are caused by customer duty cycle, vehicle under-hood components and under-hood ambient air. During soak conditions, the ambient temperature can have significant effect on battery temperature after a long drive for example. Therefore, the temperature rise in a lead-acid battery must be controlled to improve its performance and durability. In this paper a thermal model for lead-acid battery is developed which integrates both internal and external factors along with customer duty cycle to predict battery temperature at various driving conditions.

Lead-acid batteries have been widely used in automotive applications. Extending battery life and reducing battery warranty requires reducing any deteriorating to battery internals and battery electrolyte. At the end of battery life, it is required to maintain at least 50% of its initial capacity [1,2]. The rate of battery degradation increases at high battery temperatures due to increased rate of electrochemical reactions and potential loss of battery electrolyte. For Lead-Acid batteries, an electrolyte solution consists of diluted sulfuric acid. Battery electrolyte/water loss affects battery performance. Water loss is caused by high internal battery temperature and gassing off due to battery electrochemistry. High temperatures, high charging rates, and over charging can cause a loss of electrolyte in non-sealed batteries. In sealed batteries, the same factors will cause an increase in temperature and pressure which can eventually result in the release of hydrogen and oxygen gases.

In this paper we investigate the application of time constant to estimate the external heat transfer coefficient (h) around specific vehicle components. Using this approach, a test sample in the form of a steel plate is placed around the component of interest. A step change is applied to air temperature surrounding the sample. The response of the sample temperature can be analyzed and the heat transfer coefficient can therefore be calculated. Several test samples were installed at several locations in the vehicle under-hood and underbody. A series of vehicle tests were designed to measure the time constant around each component at various vehicle speeds. A correlation between estimated heat transfer coefficients and vehicle speed was generated. The developed correlations and the measured component ambient temperatures can be readily used as input for thermal simulation tools. This approach can be very helpful whenever CFD resources may not be available.

The mathematical models that predict friction losses for an internal combustion (IC) engine are described in this paper. These models are based on a combination of fundamental physics and empirical results. These include predictions of losses arising from friction and viscous fluid motion associated with the relative movement of solid surfaces within a piston assembly, the cranktrain, and valvetrain components. The engine friction losses are defined in the context of the geometries of the particular components within an IC engine. Details of these formulations are given, including novel geometry-related coefficients. Different regimes of lubricated friction are considered. In order to establish the model fidelity and robust solution methodology, the mathematical models are validated against engine friction tests. Utilization of these models enables practical solutions to the development of new low friction IC engines that leads to improved engine mechanical efficiency and fuel economy.

Internal combustion engine gets heated up due to continuous combustion of fuel. To keep engine working efficiently and prevent components damage due to very high temperature, the engine needs to be cooled down. Based on power output requirement and provision for cooling system, every engine has it’s unique cooling system. Liquid based cooling systems are majorly implemented in automobile. It’s important to keep in mind that during design phase that, cooling the engine will lower the power to fuel consumption ratio. Therefore, during lower ambient conditions, the cooling system should be able to uniformly increase the temperature of the engine components, engine oil and transmission oil. This is achieved by circulating the coolant through cooling jacket, engine oil heater and transmission oil heater, which will be heated by the combustion heat.

During input tracking, closed-loop performance is strongly influenced by the dynamic of the system under control. Internal and external delays, such as actuation and measurement delays, have a detrimental effect on the bandwidth and stability. Additionally, production controllers are discrete in nature and the sampling time selection is another critical factor to be considered. In this paper we analyze the impact of both transported delay and controller sampling time on tracking performance using an electric machine speed-control problem as an example. A simple linear PI controller is used for this exercise. Furthermore, we show how the PI parameters can be adjusted to maintain a certain level of performance as the delays and sampling times are modified. This is achieved through an optimization algorithm that minimizes a specifically designed cost function.

The Brake judder is a low-level vibration caused due to Disc Thickness Variation (DTV), Temperature, Brake Torque Variation (BTV), thermal degradation, hotspot etc. which is a major concern for the past decades in automobile manufacturers. To predict the judder performance, the modelling methods are proposed in terms of frequency and BTV respectively. In this study, a mathematical model is constructed by considering full brake assembly, tie rod, coupling rod, steering column, and steering wheel as a spring mass system for identifying judder frequency. Simulation is also performed to predict the occurrence of brake judder and those results are validated with theoretical results. Similarly, for calculating BTV a separate methodology is proposed in CAE and validated with experimental and theoretical results.

Accurate battery state of charge (SOC) estimation is essential for safe and reliable performance of electric vehicles (EVs). Lithium-ion batteries, commonly used for EV applications, have strong time-varying and non-linear behaviour, making SOC estimation challenging. In this paper, a processor in the loop (PIL) platform is used to assess the execution time and memory use of different SOC estimation algorithms. Four different SOC estimation algorithms are presented and benchmarked, including an extended Kalman filter (EKF), EKF with recursive least squares filter (EKF-RLS) feedforward neural network (FNN), and a recurrent neural network with long short-term memory (LSTM). The algorithms are deployed to two different NXP S32Kx microprocessors and executed in real-time to assess the algorithms' computational load. The algorithms are benchmarked in terms of accuracy, execution time, flash memory, and random access memory (RAM) use.