Siemens Digital Industries Software has sponsored this post.
Cars don’t look like they used to, and not just because there are no chrome grilles or tail fins anymore, either. The world is in the midst of a full-scale mobility revolution, fueled both by electrification and artificial intelligence (AI), where battery-powered cars are learning to drive themselves and getting scarily good at it. It’s an exciting time to have wheels.
But it’s an equally exciting time to be an engineer, because the future of mobility is full of unique challenges that require innovative solutions. These challenges are emblematic of the engineering problems permeating nearly every product, process and system made for the 21st century and beyond.
In a word, that challenge is complexity. Everything is becoming more interconnected, more data-driven, more multi-disciplinary. Supply chains are in turmoil. Customer expectations are changing. Timelines are accelerating and competition is increasing. Complexity abounds.
But engineers shouldn’t think of that complexity as a problem so much as an opportunity.
“Turn that complexity into a competitive advantage,” says Nand Kochhar, Vice President of Automotive and Transportation Industry at Siemens Digital Industries Software. He argues that the best way to manage complexity is to embrace it through a holistic and digital-first approach to product development.
How to Harness Complexity
One of the keys to managing complexity, Kochhar says, is model-based systems engineering (MBSE). This methodology is based on the notion that every part of a system should be created in the context of the whole. The links between the system level, subsystem levels and all individual components must be kept up-to-date, and all designers must engage in continuous collaboration to ensure they’re creating a congruous whole.
One of the specific tools that MBSE allows engineers to create is a digital twin, a virtual model of their design that is built in silico instead of in a factory. The digital twin can be used as a virtual prototype for simulation, a testbed for embedded software, a subject for machine learning training, a guide for factory planning and a tool to optimize the performance of a product or process all the way to the end of its lifecycle.
Useful as a digital twin may be, it also adds a layer of complexity to the design and manufacturing process. It must be in sync with the MBSE models and with every component and subsystem it contains. And once the system is manufactured and in the real world, the digital twin should receive real-time sensor data from the actual system and analyze that data to optimize performance in the real world. It sounds like a tall order, and it is. But for modern products, it’s absolutely essential. An example will reveal why.
From Chip to City
Consider once more the future of mobility. The top-level system of a modern vehicle must account for a dizzying range of design disciplines, subsystems, suppliers and other stakeholders, from the chips at the heart of the car to the smart, sensor-studded city it will eventually drive in.
Chip designers create the brains of the vehicle, which are making up an increasingly large percentage of a car’s bill of materials (BOM). It’s far from a one-person job. Many engineers and software developers must work together to design, verify and prove the functional safety of many discrete chips controlling a vehicle’s driving systems, infotainment systems, communications systems and others. The embedded software must also be proven safe and effective, and if machine learning is involved, it may require massive datasets and driving simulations.
Battery designers develop the powerhouse of electric vehicles. EV batteries are not one-size-fits-all, and battery designers must work together with structural engineers, mechanical designers and software developers to ensure the battery packs provide the right power at the right time, without compromising safety. And the process of building battery packs brings its own challenges. Machine builders must develop tools to manufacture thousands of cells with specific safety parameters and tolerances, with the ability to adapt to changes in battery chemistry and other design revisions.
Electronic control unit (ECU) developers design the components that control the most safety-critical functions of the vehicle—all of which must be carefully tested for compliance with industry regulations.
Electric motor design brings together electrical engineering, software development, mechanical analysis and noise, vibration and harshness (NVH) testing. Every designer needs insight into the larger workflow to understand how their decisions affect size, weight, power, noise and other trade-offs.
Once the car is designed and built, it becomes part of a network of vehicles that populate a smart city. Data from all vehicles should be shared with city planners to optimize traffic, charging and energy capacity to manage and balance grid loads. Operational data is also linked to the digital twin of every vehicle, providing automotive manufacturers with indispensable insight into how to optimize their next generation of vehicles.
From chip to city, the common thread between every person and part of the design process is data.
Domesticating Data
The concept of data continuity is so crucial that it has its own name: the digital thread. The metaphor is apt, as every stage of a product’s lifecycle should be tied together with a single source of truth that evolves from concept to design to manufacturing, operation and end-of-life.
Managing all this data is the true key to harnessing complexity, and it’s the real underlying goal of MBSE and the tools that accompany it.
“That's where our digital twin, artificial intelligence and machine learning technologies come into play,” Kochhar says. “The information you’re getting from there is complex, but the insights will help you avoid any failures or quality issues down the road. It’s worth the journey to leverage and harness this complexity and convert that into a competitive advantage.”
Kochhar advises engineering companies to look at their entire end-to-end processes to identify the biggest opportunities, issues or waste. That will guide them towards a use case for implementing digital tools and methods that, Kochhar maintains, will ultimately allow them to leverage complexity and create better products.
For more information, visit Siemens.com.