Collaborative Artificial Intelligence in Actuator Development
参考中译:协作式人工智能在执行器开发中的应用


          

刊名：ATZ Electronics Worldwide
作者：Guillaume Callerant（Business Development Manager Electronics at the Sonceboz SA in Sonceboz (Switzerland)）
Mathieu Gerber（Leader Development & Innovation Electronics at the Sonceboz SA in Sonceboz (Switzerland)）
Jacques Wicht（R&D Electronics Manager at the Sonceboz SA in Sonceboz (Switzerland)）
Christophe Audouy（R&D Electronics Manager at the Sonceboz SA in Sonceboz (Switzerland)）
刊号：873E0046-1/I
出版年：2022
年卷期：2022, vol.17, no.12
页码：40-45
总页数：6
分类号：U4
语种：eng
文摘：Future demands on actuator systems for use in battery electric vehicles require new approaches to development and application. Functions must be optimized in the system network so that the potential of each component is used in the best possible way. In the following, Sonceboz outlines how collaborative artificial intelligence in smart actuators can help improve the functionality of individual domains in electric vehicles. New Battery Electric Vehicle (BEV) platforms and associated applications bring new challenges to the core embedded mechatronic domains, such as vehicle thermal systems. One of the major challenges for domain control and diagnostics and its peripherals is dealing with a wide variety of environmental influences and a multitude of past and new use cases. Analyzing the different elements and domains individually without considering the global integration of the domains would be pointless. There is a multitude of direct and indirect causal relationships between the domains themselves (e. g. heat and heating, ventilation, and air conditioning (HVAC) systems), the domain and its subcomponents such as water pumps and valves, as well as its environment (e. g. air temperature, heat demand of battery packs) [1]. Obviously, the Domain Control Unit (DCU) supports the essential tasks for control, security and diagnostics of the associ-ated domain, but is this architecture truly optimal? Today, development teams try to analyze a system step by step. They draw up lists of all potential use and test cases, taking into account the combination of loads and stresses, determining the most important limiting conditions.
参考中译：未来对用于电池电动汽车的致动器系统的需求需要新的开发和应用方法。必须对系统网络中的功能进行优化，以便以尽可能最佳的方式利用每个组件的潜力。在下文中，Sonceboz概述了智能执行器中的协作人工智能如何帮助提高电动汽车各个领域的功能。新的电池电动汽车(BEV)平台及其相关应用给汽车热系统等核心嵌入式机电一体化领域带来了新的挑战。域控制和诊断及其外围设备面临的主要挑战之一是处理各种环境影响以及大量过去和新的用例。单独分析不同的要素和领域，而不考虑领域的全球整合将是毫无意义的。在域本身(例如，热和供暖、通风和空调(HVAC)系统)、域及其子组件(例如水泵和阀门)以及其环境(例如，空气温度、电池组的热需求)之间存在多种直接和间接的因果关系[1]。显然，域控制单元(DCU)支持相关域的控制、安全和诊断的基本任务，但这种架构真的是最优的吗？如今，开发团队试图一步一步地分析系统。他们制定了所有潜在使用和测试用例的列表，考虑了载荷和应力的组合，确定了最重要的限制条件。



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