In the coming year, FEV plans to open two new battery test centers – one in Germany and the other one in France. Additionally, new e-motor and e-axle test benches have been integrated into FEV’s test centers and on customer sites. Based upon long-term planning and construction experience with FEV’s own test cells and test centers, as well as in numerous customer projects, FEV provides an effective methodology for specification development, concept layout and planning for e-mobility test benches, test cells and test centers, this methodology covers hardware (test equipment, technical infrastructure, building), software (data management), logistic and operation aspects.
Based upon FEV’s long-term experience, the sustainable success for the construction of new test cells and test centers is highly influenced from quality and completeness of the specification and planning phases. Precise requirement analysis, complete specification development and well-designed concept development are the key factors which deliver the solid foundation for a successful realization of these projects. Due to the extensive experience acquired by FEV, the described project phases can be actively organized and guided in close collaboration with future users/ customers in order to ensure the development of sustainable and cost-effective solutions which cover future requirements to the highest possible degree.
The final goal is to develop a technical solution covering building construction aspects, concepts for the test cells and test benches, laboratories, workshops, the technical infrastructure including supply media and energy supply, furthermore operational and logistical issues. Due to long-term, global experience, FEV’s experts provide the right solutions. They have the in-depth knowledge and experience gained in the construction of their own test centers for the mobility of the future to support customers. They use specific calculation and simulation tools to simulate the different scenarios.
Boosting the test center performance
In state-of-the-art test centers, the visible parts, such as the buildings, the building infrastructure and the test benches can no longer be separated from the invisible parts – the comprehensive information system with a high automation degree.
Let’s evaluate how this information system controls the workflow and use cases in a battery test center. When the battery pack, module or cells and (sub-) components are received, a bar code is created that follows the Unit Under Test (UUT) throughout the entire workflow. The UUT is taken from a safe storage room and subsequently equipped with sensors and measuring devices in a preparation area. The availability and maintenance status of resources (equipment, test benches, employees) is documented in a database, thereby supporting an efficient and effective planning and assignment of UUT and resources. After the installation of the UUT at the test bench, the test programme is executed, followed by the post processing of the measurement data being acquired via the automation system and further measuring devices. The measurement data is checked regarding plausibility and finally documented in standardized test reports. The information system allows data on the UUT, the assigned resources, the test program and test results to be logically linked throughout the workflow. The above information system is based on the FEVFLEX™ software suite.
This modular, layer-based suite features dedicated modules for managing the main workflow of a test center, starting from the test demands up to the final test reports:
- Enterprise functionality at the layer of the overall test center:
FEVFLEX™ facilitates experiments in the field of simulation, benchmarking, and component and system test benches up to vehicle fleet tests, as well as combinations of those. At this layer, work orders are created by combining data from ERP and MES systems (e. g. customer and project data, cost centers) with information on the UUT, the test program and the availability and status of resources (equipment, test benches, employees). Tasks are planned and subsequently assigned to test benches and resources. Moreover, FEVFLEX™ allows the UUT and its (sub-) components to be defined in a Build of Material (BOM) list – well known from benchmarking contexts – thereby supporting UUT life cycle control. In the final stage of the workflow, FEVFLEX™ handles test results from any source (benchmark or simulation data and measurement data obtained from the automation system and measuring devices), which are subsequently time-synchronized and pushed to data evaluation tools.
- Host system functionality as binding factor between test center and test benches:
FLEX Lab™ takes care of the overall data handling and parametrization of MORPHEE® automation systems at component and system test benches. At this layer, the FEVFLEX™ work orders are translated into the preparation of the automation system resulting in a base parametrization (including e.g. a measuring plan, channel limits, log lists, integration of measuring devices, test program).
Furthermore FLEX Lab™ supports the management of MORPHEE® configurations, including back-up and versioning. Launching the execution of test programs at the test bench is secured via communication between the FLEX Lab™ host system and the MORPHEE® automation system. Finally, FLEX Lab™ pushes the measurement data, which was acquired via the automation system to data evaluation tools, such as UniPlot.
As a final conclusion, the workflow in FEVFLEX™ is supported by SCADA remote monitoring and run-time statistics:
- Remote monitoring supports immediate alerts and interventions in case of incidents
- Run-time statistics support facility managers to repair weaknesses in their workflow sustainably
With the help of this comprehensive information system based on the FEVFLEX™, an effective test bench usage of 95 percent was reached in FEV’s battery durability test center.
Legislative requirements for emissions and fuel consumption reduction are driving OEMs to develop innovative powertrain and vehicle technologies. In addition to continued development of new technologies with conventional internal combustion engines (ICE), there is an increasing trend toward electrification. These trends make it essential to develop relevant means of assessing the NVH performance of electric drive tunits (EDUs). These components do not generate the amplitudes of noise and vibration observed from internal combustion engines (ICE). As such, the methods used for NVH assessment and target development of IC engines are not sufficient for electric machines: While the objectives of ICE-based NVH development are reduction and refinement of source excitations, EDU-based NVH development focuses on the elimination of potential objectionable noise behavior in the context of ever-changing or missing masking noise content. For example, there is a reduced background noise for masking tonal noise in the absence of a running internal combustion engine.
The expectation for interior noise content from ICE-based vehicles (i.e., “powertrain presence”) depends highly on the vehicle class and target demographic; while luxury cars target low interior noise content, performance vehicles demand some level of powertrain noise feedback (with an emphasis on development of the desired “brand character”). Conversely, the tonal noise typically associated with EDUs is universally considered annoying; hence the goal is to minimize perception of this content in the vehicle. This becomes challenging, given that the reduced overall noise content available to hide (mask) this tonal noise content is lower on electric vehicles than ICE-based vehicles. Figure 1 below illustrates the difference in typical noise levels observed in ICE-powered vs. electric vehicles (EV) in the form of FEV scatterbands. Clearly, significant reduction in overall noise levels on EV are evident, especially at low-to-mid vehicle speeds.
To predict the perceptibility of tonal noise content in-vehicle, masking band analysis can be used. As shown in the figure below, the order content can be compared to surrounding 3rd octave levels to determine how much noise is available in adjacent frequencies to mask the tonal noise. If the order level (of whine noise) is high relative to the corresponding 3rd octave band noise levels, this is an indication that there is insufficient background noise to mask the order, resulting in a perceptible, and hence, objectionable whine noise. Also shown below is a masking surface which illustrates the masking content for various orders over the operating range of an example vehicle. At higher vehicle speeds, wind noise is more prominent; this results in more masking content and an associated reduction in perception of whine noise.
NVH issue root-cause analysis & mitigation
Increased trends in electrification and associated technologies have posed new challenges in NVH development. In addition to minimizing tonal noise content in the vehicle’s interior, there are multiple potential NVH issues related to transient instabilities (e.g., gear rattle or other driveline issues). FEV utilizes a structured approach, with extensive experience in 8D analysis and Design-of-Experiments (DoE) to address such problems. As part of this root-cause analysis, FEV utilizes a combination of industry-standard methods (e.g., Ishikawa diagrams), as well as FEV developed tools and processes. FEV’s Vehicle Interior Noise Simulation (VINS) is an example of a unique methodology that can be effectively utilized in the support of root cause analysis with complex noise issues. The VINS process is a unique time-domain transfer path analysis which provides insights into noise sources and transfer paths which contribute to sound quality issues under steady-state or transient conditions. Any noise issues identified at the vehicle’s interior can be broken down to identify contributions of various structureborne and airborne noise paths. The critical noise paths can be further decomposed to identify any potential opportunities for improvement (mount isolation, attachment point stiffness, vibroacoustic sensitivity, acoustic attenuation, etc.). Because the results generated are in the time-domain, advanced analysis methods or subjective evaluations (listening studies) can be used for assessment of the overall simulated noise or individual path contributions. Figure 2 schematically shows the integration of the VINS methodology in a structured 8D root-cause analysis process.
Component-level EDU NVH assessments
FEV has established standard testing procedures for quantifying radiated noise, sound power, and vibration at the component-level to facilitate assessment of source-level inputs to support electric vehicle NVH development. Analogous to ICE-based powertrain NVH testing, overall EDU radiated noise levels are typically assessed based on average radiated noise, measured at a distance of 1 m from the EDU (e.g., using SAE J074 standard). Additionally, it is common practice for electric machines and EDUs to augment these assessments with measurement of sound power, utilizing a hemispherical or parallelepiped array (e.g., IS0 3744 or 3745 standards). Structureborne excitations can be assessed by measurement of vibration at the EDU mounting locations (i.e., interface points between the EDU and vehicle).
Comparison of average overall sound pressure levels between ICE-based powertrains and EDUs in the figure below illustrates that noise levels radiated from EDUs are significantly lower than those observed from ICE powertrains. As such, assessment of individual orders excited by the electric machines and/or gear meshing frequencies is more relevant (than overall noise levels) for quantification of EDU NVH performance. An example of order content relative to overall radiated noise levels is illustrated below. This comparison provides information regarding the contribution of orders to overall noise levels. Additional investigation of the frequency content of the component noise levels can provide insights into perceptibility of this noise in a test cell environment. However, component level data analysis alone does little to predict the perceptibility of these orders by the customer in-vehicle. For this, a vehicle-centric data analysis approach is required, as described below.
Vehicle-centric EDU NVH target development
Derived from the VINS methodology, FEV has developed an additional process for interior noise prediction, called dBVINS. Unlike VINS (which utilizes vehicle-specific noise transfer functions), the dBVINS process predicts interior noise based on a combination of source data (noise and vibration, as measured in the test cell) and standardized vehicle noise transfer functions. These “standardized” noise transfer functions are based on median vehicle noise sensitivity performance, derived from the extensive database of vehicles assessed by FEV. By standardizing the transfer functions, the interior noise relevant NVH performance of a given component (e.g., EDU) can be judged based on component-level tests from a NVH test bench. This allows for direct comparison of the expected interior noise performance of different EDUs or design variants of a development EDU. Specific to EDU development, this process allows for prediction of relevant order content at the vehicle interior. Comparison of this order content to the masking noise levels discussed above provides insights into the potential perceptibility of tonal noise issues by the customer. Appropriate design changes using a combination of CAE (e.g., MBS/FEA) and test-based approaches (e.g., calibration changes, NVH countermeasure development) can be employed to improve the component-level NVH performance of the EDU, utilizing such a vehicle-centric approach.