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基于“無依賴（system-agnostic）”概念打造的這一系統(tǒng)可以整合來自其他供應(yīng)商的組件和系統(tǒng)。

未來底盤會是什么樣的？這是采埃孚在最近于其英國研發(fā)中心舉辦的活動上提出的問題。該中心專注于控制技術(shù)、材料、機電一體化、軟件、系統(tǒng)集成、嵌入式電子和電力電子系統(tǒng)的研發(fā)。

和其他汽車領(lǐng)域一樣，底盤也將不可避免地適應(yīng)新興趨勢，如電氣化、軟件定義汽車、自動駕駛和新型電氣架構(gòu)。不過，未來的底盤動力學(xué)仍然需要處理車輛的側(cè)傾、偏轉(zhuǎn)和俯仰等問題。為了整合需求，如L5級自動駕駛以及包括駕駛員參與的所有階段，采埃孚設(shè)想了一個同時適用于內(nèi)燃機和電驅(qū)系統(tǒng)的系統(tǒng)，該系統(tǒng)基于一個底盤控制器打造，比如在2022年末在Lotus Eletre上首次亮相的cubiX系統(tǒng)。cubiX的設(shè)計采用了“system agnostic”概念，因此可以整合來自其他供應(yīng)商的組件和系統(tǒng)。

cubiX系統(tǒng)并沒有對轉(zhuǎn)向、制動、側(cè)傾控制或扭矩矢量等方面進行單獨優(yōu)化，而是將所有功能集成至一個中央系統(tǒng)，以實現(xiàn)各車載系統(tǒng)的交互，并使其能夠利用來自云端的外部輸入信號，從而實現(xiàn)提高乘客舒適度和底盤性能、以及優(yōu)化運行狀況、減少維修成本等多種效益。不僅如此，它還可以用來控制主動阻尼系統(tǒng)、主動穩(wěn)定桿和后輪轉(zhuǎn)向等多個系統(tǒng)。

What can we expect from the chassis of the future? That was the question posed by ZF at a recent event staged at its UK Hub, a center for R&D into control, materials, mechatronics, software, system integration, embedded electronics and power electronics.

Inevitably, chassis will adapt to emerging trends such as electrification, software defined vehicles, autonomous driving, and new electrical architectures. Chassis dynamics will still need to deal with vehicle roll, yaw and pitch. To integrate future requirements such as autonomy at Level 5, and all stages including with a driver, using either an internal combustion engine or electric drive, ZF envisages a system based around a chassis controller such as its cubiX system, first seen on the Lotus Eletre in late 2022. CubiX is designed to be system agnostic, so can integrate components and systems from other suppliers.

Instead of further optimizing the individual dimensions of steering, braking, roll control or torque vectoring, CubiX integrates all features in a central system, enabling interaction between various on-board systems, while also factoring in external inputs from the cloud. The results from this could be wide ranging, from improved passenger comfort and chassis performance to optimization of operational and warranty costs. Systems such as active damping, active stabilizer bars and rear-wheel steering could all be handled by such a system.

ZF provided a demonstration of steer-by-wire systems on a short maneuvering course, using a modified Volkswagen ID.3. In place of a steering wheel, column, and rack, the ID.3 was equipped with just a steering, or hand, wheel on the front axle. “Potentially, you’re now having fewer rotations of the steering wheel, compared to the movement of the steered wheels”, said Jake Morris, ZF’s portfolio director for steer-by-wire products. “Then in higher levels of autonomous driving, that also allows you to change or move the steering wheel, or retract it away from the driver potentially in autonomous levels 4 and above. For larger vehicles, you may need two different power units driving it in combination with rear steer.”

The demonstration ID.3 retained its steering wheel and ZF had set it up with three steering modes, including a simulation of standard mechanical steering and one with an adaptive ratio that changed with vehicle speed. The third used a “steering yoke” mode offering just 180 degrees of rotation to left or right.

The adaptive ratio provided greater front wheel movements from relatively small steering wheel inputs, making parking, and reversing simpler as they required less wheel movement. At higher speeds, steering wheel movement and steered wheel movement was closer to what one might expect from a conventional, mechanical system. The yoke mode was a natural progression from this, offering easy maneuvering at low speeds and more conventional movements at higher speeds. Both modes were easy to adapt to, which ZF says has been the case in tests carried out so far.

AI in Design

Harvey Smith is ZF’s team leader in electro-magnetic design and has wide-ranging responsibilities for magnetic materials and components from motors to sensors to solenoid actuators. While simulation has been part of the design process used for many years, there’s more AI can offer, he said. “As electromagnetic design engineers, we live and breathe the simulations because it really tells us something. As we’re able to advance our simulation tools, we can couple this into more and more things,” Smith said. “Can we literally ask an AI bot to assign regions of magnet steel and copper and then manipulate those regions in infinite combinations until they come up with a topology that gives us what we want? How would that work versus the more traditional approach where you take traditional topologies, parameterize all the dimensions and ask your AI bot to learn which combination of parameters give you the result that is most likely what you want?

“I’m thinking, in my own mind that AI could perhaps do a really good job of getting 90% of the way there to do this job, for these performance requirements. The AI bot can have learned through experience and pattern matching, it can look at its large bank of different machine topologies and dimensions and say, ‘What you probably want is this many poles, this many slots, these kinds of windings. These sorts of things to get 80% towards selecting good options for machines that match these requirements.’ The jury is out, but it might be that you then do the last bits of refinement using the more traditional techniques.”