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Lithium-ion battery (LIB) terminal voltage models are investigated using two modelling approaches. The first model is a third-order Thevenin equivalent circuit model (ECM), which consists of an open-circuit voltage in series with a nonlinear resistance and three parallel RC pairs. The parameters of the ECM are obtained by fitting the model to hybrid pulse power characterization (HPPC) test data. The parametrization of the ECM is performed through quadratic-based programming. The second is a novel modelling approach based on long short-term memory (LSTM) recurrent neural networks to estimate the battery terminal voltage. The LSTM is trained on multiple vehicle drive cycles at six different temperatures, including −20°C, without the necessity of battery characterization tests. The performance of both models is evaluated with four automotive drive cycles at each temperature. The results show that both models achieve acceptable performance at all temperatures.

With the demand of growing cooling requirements of fast charging and new thermal architecture design in battery electric vehicles, the automotive industry is exploring electric compressors of large displacement. Compared with small and mid-size (displacement less than 33 cc) compressor, large (34-44 cc) and extra-large (45 cc and above) compressor products are used. This paper investigates the compressor sizing effect for heat pump (HP) system of A-Segment and D-Segment battery electric vehicles. The system performance is evaluated with large (34 cc) and extra-large (57 cc) compressors by considering energy efficiency, cabin thermal management and battery fast charging use cases.

1. The present work introduces an approach for the analysis of the noise propagation behavior of mechatronic brake systems in modern passenger vehicles. While on the one hand, the number of features realized through the mechatronic brake system is strongly increasing; on the other hand, a continuous reduction of the overall vehicle interior noise level can be observed. This leads to an increase of interfering noise phenomena in the vehicle interior that customers might perceive as insufficient product quality. Therefore, noise elimination always plays an important role in vehicle development. The mechatronic brake system induces interfering noise that is transferred into the vehicle interior, differing from vehicle to vehicle and maneuver to maneuver. Supposedly, a wide frequency range, numerous components, and various branched transfer paths in the physical domains of airborne, structure-borne, and fluid-borne sound are involved in the noise propagation.

Several powertrain noise phenomena have been studied over the years. Sound quality metrics, like loudness, sharpness, modulation, and tonality, among others, have been developed to characterize powertrain noises. While these readily available metrics work well on steady state and some transient noises, they do not correlate directly with subjective impressions. Moreover, it is difficult to assign a meaningful single rating for time varying noises that may also be associated with simultaneous variations in frequency content. This paper summarizes the process of creating a vehicle level objective metric and its application to blind noise samples to verify correlation with subjective impressions, particularly in association with clatter noise at moderate engine speeds (2000-3500 rpm) with light to moderate throttle tip-ins.

Due to the recent progress in electrification, lithium-ion batteries have been widely used for electric and hybrid vehicles. Lithium-ion batteries exhibit high energy density and high-power density which are critical for vehicle development with high driving range enhanced performance. However, high battery temperature can negatively impact the battery life, performance, and energy delivery. In this paper, we developed and applied an analytical algorithm to estimate battery life-based vehicle level testing. A set of vehicle level tests were selected to represent customer duty cycles. Thermal degradation models are applied to estimate battery capacity loss during driving and park conditions. Due to the sensitivity of Lithium-Ion batteries to heat, the effect of high ambient temperatures throughout the year is considered as well. The analysis provides an estimate of the capacity loss due to calendar and cyclic effects throughout the battery life.

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.

The automotive Air Induction System (AIS) is an important part of the engine systems which delivers the air to the engine. A well-designed AIS should have low flow restriction and radiates a good quality sound at the snorkel. The GT-Power simulation tool has been widely utilized to evaluate the snorkel noise in industry. In Fiat Chrysler Automobiles, the simulation method enhanced with Design For Six Sigma (DFSS) approach has been developed and implemented in AIS development to meet the functional requirements. The development work included different types of DFSS projects such as identifying new concept, robust optimization and robust assessment etc. In this paper, the work of a robust optimization project is presented on developing an AIS parametric model to achieve optimized snorkel noise performance for a V8 engine. First, the theory of AIS acoustic modeling using GT-power and DFSS robust optimization using Taguchi’s parameter design method are described.

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.

This paper describes a reliable CAE methodology to model the linear vibratory behavior of windshields. The windshield is an important component in vehicle NVH performance. It plays an integral role in interior cabin noise. The windshield acts as a large panel typically oriented near vertical at the front of vehicle’s acoustic cavity, hence modeling it accurately is essential to have a reliable prediction of cabin interior noise. The challenge to model the windshield accurately rises from the structural composition of different types of windshields. For automotive applications, windshields come in several structural compositions today. In this paper, we will discuss two types of windshield glass used primarily by automotive manufacturers. First type is the typical laminated glass with polyvinyl butyral (PVB) layer and second type is the acoustic glass with PVB and vinyl layers. Acoustic glass improves acoustic characteristics of the glass in a frequency range of ~ 1200 Hz to ~4000 Hz.

In Automotive and Aerospace industries, sound package has an important role to control vehicle noise in order to improve passenger comfort and reduce environmental noise pollution. The most known approaches used to model the sound package are the Transfer Matrix Method (TMM) combined with Statistical Energy Analysis (SEA). The Transfer Matrix Method based approach is extensively used and well-validated for predicting the transmission loss and other vibro-acoustic indicators of multi-layer structures. However, to the best of our knowledge, the equivalent damping due to the multilayer has not been addressed yet in the literature, and it's a novel approach. In this paper, simplified formulations using TMM to compute the equivalent damping will be recalled, and an experimental study will be conducted to assess the add-on damping by sound package for different configurations.

Electrification is a key enabler to reduce emissions levels and noise in commercial vehicles. With electrification, Batteries are being used in commercial hybrid vehicles like city buses and trucks for kinetic energy recovery, boosting and electric driving. A battery management system monitors and controls multiple components of a battery system like cells, relays, sensors, actuators and high voltage loads to optimize the performance of a battery system. This paper deals with the development of modular control architecture for battery management systems in commercial vehicles. The key technical challenges for software development in commercial vehicles are growing complexity, rising number of functional requirements, safety, variant diversity, software quality requirements and reduced development costs. Software architecture is critical to handle some of these challenges early in the development process.

Automotive Heating Ventilation and Air Conditioning (HVAC) system is essential in providing the thermal comfort to the cabin occupants. The HVAC noise which is typically not the main noise source in IC engine vehicles, is considered to be one of the dominant sources inside the electric vehicle cabin. As air is delivered through ducts and registers into the cabin, it will create an air-rush/broadband noise and in addition to that, any sharp edges or gaps in flow path can generate monotone/tonal noise. Noise emanating from the HVAC system can be reduced by optimizing the airflow path using virtual tools during the development stage. This paper mainly focuses on predicting the noise from the HVAC ducts and registers. In this study, noise simulations were carried-out with ducts and registers. A Finite Volume Method (FVM) based 3-dimensional (3D) Computational Fluid Dynamics (CFD) solver was used for flow as well as acoustic simulations.

In order to reduce development cost and time, frontloading is an established methodology for automotive development programs. With this approach, particular development tasks are shifted to earlier program phases. One prerequisite for this approach is the application of Hardware-in-the-Loop test setups. Hardware-in-the-Loop methodologies have already successfully been applied to conventional as well as electrified powertrains considering various driving scenarios. Regarding driving performance and energy demand, electrified powertrains are highly dependent on the dc-link voltage. However, there is a particular shortage of studies focusing on the verification of variable dc-link voltage controls by Hardware-in-the-Loop setups. This article is intended to be a first step towards closing this gap. Thereto, a Hardware-in-the-Loop setup of a battery electric vehicle is developed.

Noise, vibration and harshness (NVH) is one of the most important performance evaluation aspect of electric motors. Among the different causes of the NVH issues of electrical drives, the high-frequency spatial and temporal harmonics of the electrical drive system is of great importance. To reduce the tonal noise of the electric motors, harmonic injection methods can be applied. However, a lot of the existing related work focuses more on improving the optimization process of the parameter settings of the injected current/flux/voltage, which are usually limited to some specific working conditions. The applicability and effectivity of the algorithm to the whole frequency/speed range are not investigated. In this paper, a multi-domain pipeline of harmonic injection controller design for a permanent magnet synchronous motor (PMSM) is proposed.

The automotive industry continues to develop new technologies aimed at reducing overall vehicle level fuel consumption. Powertrain and driveline related technologies will play a key role in helping OEM’s meet fleet CO2 reduction targets for 2025 and beyond. Specifically, use of technologies such as downsized engines, idle start-stop systems, aggressive torque converter lock-up schedules, wide-ratio spread transmissions, and electrified propulsion systems are vital towards meeting aggressive fuel economy targets. Judicious combinations of such powertrain and driveline technology packages in conjunction with measures such as the use of low rolling resistance tires and vehicle lightweighting will be required to meet future OEM fleet CO2 targets. Many of the technologies needed for meeting the fuel economy and CO2 targets come with unique NVH challenges. In order to ensure customer acceptance of new vehicles, it is imperative that these NVH challenges be understood and solved.

In this paper we present an integrated approach which combines analysis of the effect of simultaneous variations in model input parameters on component or system temperatures. The sensitivity analysis can be conducted by varying model input parameters using specific values that may be of interest to the user. The alternative approach is to use a structured set of parameters generated in the form of a DFSS DOE matrix. The matrix represents a combination of simulation conditions which combine the control factors (CF) and noise factors. CF’s are the design parameters that the engineer can modify to achieve a robust design. Noise factors include parameters that are outside the control of the design engineer. In automotive thermal management, noise factors include changes in ambient temperature, exhaust gas temperatures or aging of exhaust system or heat shields for example.

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.

With the increasing application of the lithium ion battery technology in automotive industry, development processes and validation methods for the battery management system (BMS) have drawn more and more attentions. One fundamental function of the BMS is to continuously estimate the battery’s state-of-charge (SOC) and state-of-health (SOH) to guarantee a safe and efficient operation of the battery system. For SOC as well as SOH estimations of a BMS, there are certain non-ideal situations in a real vehicle environment such as measurement inaccuracies, variation of cell characteristics over time, etc. which will influence the outcome of battery state estimation in a negative way. Quantifying such influence factors demands extensive measurements. Therefore, we have developed a model-in-the-loop (MIL) environment which is able to simulate the operating conditions that a BMS will encounter in a vehicle.

The automotive industry continues to develop new powertrain and vehicle technologies aimed at reducing overall vehicle-level fuel consumption. Specifically, the use of electrified propulsion systems is expected to play an increasingly important role in helping OEM’s meet fleet CO2 reduction targets for 2025 and beyond. This will also include a strong growth in the global demand for electric drive units (EDUs). The change from conventional vehicles to vehicles propelled by EDUs leads to a reduction in overall vehicle exterior and interior noise levels, especially during low-speed vehicle operation. Despite the overall noise levels being low, the NVH behavior of such vehicles can be objectionable due to the presence of tonal noise coming from electric machines and geartrain components as well as relatively high shares of road/wind noise. In order to ensure customer acceptance of electrically propelled vehicles, it is imperative that these NVH challenges are understood and solved.

The innovation and application of new technologies in battery electric vehicle (BEV) development continues to be a key objective of the automotive industry. One such area of development is glazing designs that reduce transmission of noise into vehicle interiors. Highly asymmetric laminated front side lites that consist of thick soda lime glass exterior plies laminated with thinner ion exchanged interior plies with acoustic polyvinyl butyral interlayers offer substantially reduced noise transmission compared to industry standard monolithic front side lites. These asymmetric laminate designs also provide additional benefits of improved toughness and penetration resistance. This paper documents a study that uses a systematic test-based approach to understand the sensitivity of interior vehicle noise behavior to changes in acoustic attenuation driven by installation of asymmetric laminated glass front side lites.