Fully electric vehicles are rapidly gaining user and market interest worldwide, due to their zero direct emissions, appealing driving experience and fashionable perception. Unfortunately, cost, range and reliability have not reached the desired targets yet. Since consumers are prone to spend money to have a more reliable system, Design-for-Reliability will be a useful tool for the Design of tomorrow’s EVs, justifying part of the increased cost for these products. In this work, a vertical model-based approach to design-for-Reliability of power converters for EVs is presented, paying special attention to thermally-induced aging. The design starts from various driving cycles, properly assembled to describe the vehicle mission, then load profiles for the converters are found and the resulting thermal stress is quantified. The converter life-time can be estimated, taking into account also parameter dispersion, and requirements for the active thermal control of the parts modeled achieved, thus giving practical information to the system designers.
Electric, hybrid and fuel cell vehicles have attracted significant attention in the past decades due to their distinguish advantages to reduce fossil fuel consumption and improve the environment. Electric vehicles use electronic subsystems – in comparison to conventional vehicles, which include electric machines, power electronics, electronic continuously variable transmissions(CVT), on-board chargers, and embedded powertrain controllers. Advanced energy storage systems, such as Li-ion batteries, ultracapacitors, and fuel cells, together with intelligent energy management algorithms, are introduced in the next generation powertrains. In addition to these electrification components or subsystems, conventional internal combustion engines(ICE), mechanical and hydraulic systems may still be present. As a result, the complexity of new powertrain designs and dependence on embedded software is a cause of concern to automotive research and development efforts. This leads to an increasing difficulty in predicting interactions among various vehicle components and systems.