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Homologation is an important process in vehicle development and aerodynamics a main data contributor. The process is heavily interconnected: Production planning defines the available assemblies. Construction defines their parts and features. Sales defines the assemblies offered in different markets, where Legislation defines the rules applicable to homologation. Control engineers define the behavior of active, aerodynamically relevant components. Wind tunnels are the main test tool for the homologation, accompanied by surface-area measurement systems. Mechanics support these test operations. The prototype management provides test vehicles, while parts come from various production and prototyping sources and are stored and commissioned by logistics. Several phases of this complex process share the same context: Production timelines for assemblies and parts for each chassis-engine package define which drag coefficients or drag coefficient contributions shall be determined.

Reducing CO2 emissions in on-the-road transport is important to limit global warming and follow a green transition towards net zero Carbon by 2050. In a long-term scenario, electrification will be the future of transportation. However, in the mid-term, the priority should be given more strongly to other technological alternatives (e.g., decarbonization of the electrical energy and battery recharging time). In the short- to mid-term, the technological and environmental reinforcement of ICEs could participate in the effort of decarbonization, also matching the need to reduce harmful pollutant emissions, mainly during traveling in urban areas. Engine thermal management represents a viable solution considering its potential benefits and limited implementation costs compared to other technologies. A variable flow coolant pump actuated independently from the crankshaft represents the critical component of a thermal management system.

When travelling in an open-jet wind tunnel, the path of an acoustic wave is affected by the flow causing a shift of source positions in acoustical maps of phased arrays outside the flow. The well-known approach of Amiet attempts to correct for this effect by computing travel times between microphones and map points based on the assumption that the boundary layer of the flow, the so-called shear-layer, is infinitely thin and refracts the acoustical ray in a conceptually analogy to optics. However, in reality, the turbulent nature of both the not-so thin shear-layer and the acoustic emission process itself causes an additional smearing of sources in acoustic maps, which in turn causes deconvolution methods based on these maps - the most prominent example being CLEAN-SC - to produce certain ring effects, so-called halos, around sources.

For battery-electric vehicles (BEVs), the climate control and the driving range are crucial criteria in the ongoing electrification of automobiles in Europe towards the targeted carbon neutrality of the automotive industry. The thermal management system makes an important contribution to the energy efficiency and the cabin comfort of the vehicle. In addition to the system architecture, the refrigerant is crucial to achieve high cooling and heating performance while maintaining high efficiency and thus low energy consumption. Due to the high efficiency requirements for the vehicle, future system architectures will largely be heat pump systems. The alternative refrigerant R-474A based on the molecule R-1132(E) achieved top performance for both parameters in various system and vehicle tests.

In contrast to refrigeration circuits in internal combustion engine vehicles (ICEVs) mainly used for cabin cooling, in electric vehicles (EVs) additional functions need to be taken into consideration, e.g., cabin heating, which in ICEVs is realized by the combustion engine’s waste heat, conditioning of the electric battery and drive train components. Additionally, each of these functions demands a different temperature level. Therefore, requirements towards the thermal management in EVs are more challenging. In modern EVs most of these functions are realized by direct refrigerant circuits, which are optimal in terms of efficiency and response time, however, result in greater complexity and different architectures for almost every vehicle model. In addition, the vast majority of EVs worldwide use chemical refrigerants that contain PFAS, e.g. R1234yf, which are known to be persistent and harmful for human health and environment.

A major issue of battery electric vehicles (BEV) is optimizing driving range and energy consumption. Under actual driving, transient thermal and electrical performance changes could deteriorate the battery cells and pack. These performances can be investigated and controlled efficiently with a thermal management system (TMS) via model-based development. A complete battery pack contains multiple cells, bricks, and modules with numerous coolant pipes and flow channels. However, such an early modeling stage requires detailed cell geometry and specifications to estimate the thermal and electrochemical energies of the cell, module, and pack. To capture the dynamic performance changes of the LIB pack under real driving cycles, the thermal energy flow between the pack and its TMS must be well predicted. This study presents a BTMS model development and validation method for a 75-kWh battery pack used in mass-production, mid-size battery SUV under WLTC.

The powertrain electrification is currently not only taking place in public road mobility vehicles, but is also making its way to the racetrack, where it’s driving innovation for developments that will later be used in series production vehicles. The current development focus for electric vehicles is the balance between driving power, range and weight, which is given even greater weighting in racing. To redefine the current limits, IAV developed a complete e-powertrain for a racing MX motorcycle and integrated it into a real drivable demonstrator bike. The unique selling point is the innovative direct phase-change cooling (PCC) of the three-phase e-motor and its power electronics, which enables significantly increased continuous power (Pe = 40 kW from 7,000 rpm to 9,000 rpm) without thermal power reduction. The drive unit is powered by a replaceable Lithium-Ion round cell battery (Ubat,max = 370V) with an energy storage capacity of Ebat = 5 kWh.

A 300 mile-range automotive battery pack is comprised of many individual cells connected in series/parallel to make up the required voltage, energy, and power. The cell groupings can take the form of parallel strings of series cell groups (S-P), series string of parallel cell groups (P-S), or a hybrid of the two. Though the different battery configurations deliver identical output voltage and energy, they exhibit varying cell level behaviors due to differing electrical structure, particularly when cell imbalance occurs. In this work, we explore the relative merits of various cell grouping configurations using a model-based approach. The emphasis of the study is to evaluate the impact of electrical variation between cell-to-cell, originating from cell manufacturing process variation, battery assembly (laser tab bonding) process variation or from normal operation, on the performance of the battery pack. A first-order equivalent circuit model is used to represent a lithium-ion cell.

The growing global adoption of electric vehicles (EVs) emphasizes the pressing need for a comprehensive understanding of thermal runaway in lithium-ion batteries. Prevention of the onset of thermal runaway and its subsequent propagation throughout the entire battery pack is one of the pressing challenges of lithium-ion batteries. In addition to generating excess heat, thermal runaway of batteries also releases hazardous flammable gases, posing risks of external combustion and fires. Most existing thermal runaway models in literature primarily focus on predicting heat release or the total amount of vent gas. In this study, we present a model capable of predicting both heat release and the transient composition of emitted gases, including CO, H2, CO2, and hydrocarbons, during thermal runaway events. We calibrated the model using experimental data obtained from an 18650 cell from the literature, ensuring the accuracy of reaction parameters.

The significance of thermal management performance in electric vehicles (EVs) has grown considerably, leading to increased complexity in thermal systems and a rapid rise in safety and quality-related concerns. The present real-vehicle-based development methods encounter several constraints in their approach when dealing with highly complex systems. Huge number of verification and validation work To overcome these limitations and enhance the thermal system development process, a novel virtual development environment established using the XiLS (X in the Loop Simulation) methodology. This XiLS methodology basically based on real-time coupling between physical thermal system hardware and analytical models for the other systems of vehicle. To control vehicle model and thermal system, various options were realized through hardware, software and model for VCU (Vehicle control unit) and TMS (Thermal management system) control unit.

Energy management of battery electric vehicle (BEV) is a very important and complex multi-system optimisation problem. The thermal energy management of a BEV plays a crucial role in consistent efficiency and performance of vehicle in all weather conditions. But in order to manage the thermal management, it requires a significant number of temperature sensors throughout the car including high voltage batteries, thus increasing the cost, complexity and weight of the car. Virtual sensors can replace physical sensors with a data-driven, physical relation-driven or machine learning-based prediction approach. This paper presents a framework for the development of a neural network virtual sensor using a thermal system hardware-in-the-loop test rig as the target system. The various neural network topologies, including RNN, LSTM, GRU, and CNN, are evaluated to determine the most effective approach.

Platooning is a coordinated driving strategy by which following trucks are placed into the wake of leading vehicles. Doing this leads to two primary benefits. First, the vehicles following are shielded from aerodynamic drag by a “pulling” effect. Secondly, by placing vehicles behind the leading truck, the leading vehicles experience a “pushing” effect. The reduction in aerodynamic drag leads to reduced fuel usage and, consequently, reduced greenhouse gas emissions. To maximize these effects, the inter-vehicle distance, or headway, needs to be minimized. In current platooning strategy iterations, Coordinated Adaptive Cruise Control (CACC) is used to maintain close following distances. Many of these strategies utilize the fuel rate signal as a controller cost function parameter. By using fuel rate, current control strategies have limited applicability to non-conventional powertrains.

As part of Nissan’s strategy of electrification and the shift to smart technologies, our powertrain department has two main pillars: zero emissions and ICE Evolution. As a core unit of ICE Evolution, we have developed a brand new 3.5L V6 Twin turbocharged gasoline engine for Nissan’s next generation full-size flagship SUV to deliver luxury and toughness at the highest level. This brand-new engine will be applied to vehicles in all corners of the world and must have strong performance in every corner. More specifically, it has to meet the latest emissions and fuel efficiency regulations, have strong power performance beyond expectation, and provide reliable drivability on rough roads and deserts. To achieve these requirements, the new engine is incorporating many cutting-edge technologies.

In the process of designing the aerodynamic kit for Formula SAE racing cars, there is a lot of repetitive work and low efficiency in optimizing parameters such as wing angle of attack and chord length. Moreover, the optimization of these parameters in past designs heavily relied on design experience and it's difficult to achieve the optimal solution through theoretical calculations. By establishing a parametric model in CAD software and integrating it with CFD software, we can automatically modify model parameters, run a large number of simulations, and analyze the simulation results using statistical methods. After multiple iterations, we achieve fully automatic parameter optimization and obtain higher negative lift. At the same time, the simulation process is optimized, and simulations are run based on GPUs, resulting in a significant increase in simulation speed compared to the original.

Model-based Development (MBD) has been employed for engine development to reconcile the contradictory relationship between numerous functions and systems at a high level and in a short span of time. However, in actuality, as engines have become more advanced, it has become challenging to even satisfy the requirements of individual components. Moreover, reconciling multiple contradictory functions like engine power and strength and durability performance, as well as coordinating many related systems, requires an even higher level of skill. Such harmonization techniques require total optimization studies that cover a wide range of designs, and which requires several years of examination with current development processes. Multi-objective Design Exploration (MODE) methods [1] using parametric models [2] and surrogate models [3] are being used to shorten the development period and achieve more balanced designs.