Model-based systems engineering (MBSE) is comprehensive. The International Council on Systems Engineering (INCOSE) defines MBSE as the “formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases.” It’s especially applicable to complex systems of systems like EVs and stationary battery energy storage systems (BESS) that require a wide range of engineering disciplines.
This FAQ starts with an overview of general systems engineering concepts and standards, reviews the numerous complex systems that make up a completed EV battery pack or BESS installation, looks at MBSE and how it fits with EV and BESS development activities, considers how MBSE is used in the automotive industry with a deeper dive into the benefits of using BMSE for energy storage systems and closes by looking at an advanced MBSE implementation for EV fleet analysis and presents a view of the return on investment (ROI) from MBSE.
Systems engineering is a well-established discipline that describes methodologies for designing, implementing, using, and disposing of systems. It’s described by ISO 15288 on system life cycle processes and encompasses EIA 632 which includes processes for systems engineering and IEEE 1220 guide for practical systems engineering (Figure 1).
Traditional systems engineering is implemented using extensive paper and digital documentation-centric approaches. That’s where MBSE departs. Like the earlier approaches, MBSE is a formalized methodology. But instead of being document-centric, MBSE employs domain models to develop digital twins of complex systems. A digital twin is a fully digital representation of the physical system that duplicates all the functionality of the physical system and is usually maintained on the cloud.
Digital twins are supported by digital threads that include the complete history of development activities and changes to the digital twin from initial system conceptualization to design development and simulation through production, use, and retirement of the system. Domain models provide digital representations of real-world objects or systems using the natural language of the specific domain. In MBSE, domain models are the primary means of information exchange and replace document-based exchanges.
MBSE in the automotive industry
MBSE is already widely used in the automotive industry to analyze, develop, design, verify, and validate technologies, from the initial concept phase to vehicle production and after-sale servicing. Today’s automobiles, and especially EVs, are cyber-physical systems of systems and are exactly the type of applications MBSE is optimized for. Verification (simulation) and validation (testing) are important aspects of MBSE in the automobile industry. The industry uses model in the loop (MIL), software in the loop (SIL), processor in the loop (PIL), hardware in the loop (HIL) component in the loop (CIL), and vehicle in the loop (VIL) simulation and testing. Some elements of MBSE include:
- Behavioral analysis
- Systems architecture development
- Requirements traceability support
- Design change impact analysis and tracking
- Performance Analysis
- Simulation and testing
The design of EV batteries and BESS installations involves electrochemical, electronics, thermal, mechanical, and other engineering disciplines ultimately leading to integration into a larger system like an EV or utility grid including. Aspects of EV battery design include (Figure 2):
- Battery material design
- Battery system engineering
- Battery management system design
- Battery cell engineering
- Battery module and pack engineering
Stepping through the quadrants
Another way to visualize MBSE is to look at it as a series of four quadrants with the lower right quadrant being the destination of the process where the physical system is realized. The quadrants represent combinations of the logical description of the system and the actual physical system. It starts in the upper left quadrant with a detailed logical description of system operation and drives toward the lower right quadrant where the physical system is realized (Figure 3).
The process begins with the conceptual problem description that includes a series of performance goals or specifications for the system, like “we need an EV battery pack that supports a range of 300 km, it needs to recharge in 30 minutes, etc.” The two operational quadrants represent the needs of various stakeholders including, in the case of EVs, drivers, various businesses, engineering, manufacturing, maintenance, and other groups. Once adequately defined, the information in the operational and system logical quadrants remains relatively stable throughout the development process.
The physical quadrants are where the system architecture and implementation are defined and developed. These quadrants are also where simulation and testing of the digital twin are extensively used until the design is completed and the physical system is produced. During the operational life of the system, changes to the physical design can result from a range of needs like adding more advanced technologies, implementing government-initiated safety recalls, and so on.
Energy storage and MBSE
MBSE platforms are available that have been optimized for battery system development. designing energy storage systems is a complex process that includes system geometry and size, cell chemistry, cooling BMS, and other factors.
Design reuse is a key benefit of MBSE. Common features exist between battery packs in different EV platforms in various BESS installations. MBSE can be used to quickly understand what can be reused in specific applications by connecting the electrical, electronic, mechanical, network, and software engineering teams in a collaborative virtual environment to understand the opportunities to reuse as many elements as possible from current and previous platforms.
System performance can be optimized using MBSE to balance potentially conflicting attributes like thermal control and charging/discharging rates. Considerations can include complex interactions between cell chemistries, cell sizes and geometries, thermal management technologies, BMS functionality, and charger and powertrain designs. And functional safety considerations can be constantly monitored and updated during the design process and throughout the operational life of the system.
Manufacturing process optimization is another important benefit of MBSE. As the system is being developed, new materials or structures may be considered. MBSE enables the design team to work collaboratively with the manufacturing engineering team to make sure the design can be successfully produced and launched into the market. On-going maintenance needs can also be simulated, and the design validated. Finally, the digital twin and digital thread support a feedback loop from the field to the assembly line for continuous improvement in production processes and system performance.
Next-gen MBSE for EVs
Researchers at the Argonne National Laboratory Vehicle & Systems Mobility Group (VMS) have developed an advanced MBSE resource called AMBER to address the unique challenges related to developing advanced mobility systems like EVs. AMBER makes extensive use of metadata information to support the development of individual EVs or fleets of millions of EVs. AMBER includes an end-to-end MBSE workflow manager, including MIL, SIL, HIL, CIL, and VIL simulations and it supports numerous MBSE activities specifically related to EVs including:
- Individual vehicle energy consumption and cost analysis.
- Energy-efficient controls are enabled by connectivity and automation systems.
- Measuring energy impact of new technologies at the vehicle level.
- Measuring the impact of new technologies at the transportation system level.
EV battery systems and BESS installations share the characteristics of high system complexities, high environmental impact complexities, and long lifespans that make them well suited for using MBSE and can experience positive returns on investment (ROIs). In addition, industries like aerospace, energy, and automotive already have extensive experience with traditional systems engineering practices simplifying and speeding the adoption of MBSE.
The initial costs of adopting MBSE can be significant and include new digital infrastructure and possible migration of legacy models and data into the MBSE environment. Next, MBSE requires a much larger investment in model development and validation. Once the design and implementation process has begun, MBSE can reduce costs through early defect identification using the digital twin, design reuse, improvements in traceability and standards conformance, etc. The investment in MBSE takes place over a limited time period, while the gains are enjoyed for much longer. As a result, MBSE can provide a significant ROI in systems like EV battery packs and BESS (Figure 4).
MBSE is well suited for use with EV battery packs and BESS design. It supports the systems engineering process requirements of ISO 15288, EIA 632, and IEEE 1220 and is already widely used in the automotive and energy industries. The use of digital twins and digital threads enables cross-functional collaboration among diverse engineering disciplines and supports ongoing design improvements throughout the system life cycle. And for complex systems of systems like EV battery packs and BESS, the use of MBSE provides a positive financial ROI.
AMBER, Vehicle and Mobility Systems Argonne National Laboratory
Battery Modeling and Simulation, Siemens
Design of a Customizable Test Bench of an Electric Vehicle Powertrain for Learning Purposes Using
Model-Based System Engineering, MDPI sustainability
Economic Analysis of Model-Based Systems Engineering, MDPI systems
MBSE Initiative, INCOS
Model-Based Systems Engineering Approach to Battery Design, Dassault Systems
Model-Based Design and Integration of Large Li-ion Battery Systems, National Renewable Energy Laboratory