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Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are getting more attention in the automotive industry with the technology improvement and increasing focus on fuel economy. For EVs and HEVs, especially all-wheel drive (AWD) EVs with two electric motors powering front and rear axles separately, an accurate motor speed measurement through resolver is significant for vehicle performance and drivability requirement, subject to resolver faults including amplitude imbalance, quadrature imperfection and reference phase shift. This paper proposes a diagnostic scheme for the specific type of resolver fault, amplitude imbalance, in AWD EVs. Based on structural analysis, the vehicle structure is analyzed considering the vehicle architecture and the sensor setup. Different vehicle drive scenarios are studied for designing diagnostic decision logic. The residuals are designed in accordance with the results of structural analysis and the diagnostic decision logic.

A variety of vehicle controls, from active safety systems to power management algorithms, can greatly benefit from accurate, reliable, and robust real-time estimates of vehicle mass and road grade. This paper develops a parallel mass and grade (PMG) estimation scheme and presents the results of a study investigating its accuracy and robustness in the presence of various noise factors. An estimate of road grade is calculated by comparing the acceleration as measured by an on-board longitudinal accelerometer with that obtained by differentiation of the undriven wheel speeds. Mass is independently estimated by means of a longitudinal dynamics model and a recursive least squares (RLS) algorithm using the longitudinal accelerometer to isolate grade effects. To account for the influences of acceleration-induced vehicle pitching on PMG estimation accuracy, a correction factor is developed from controlled tests under a wide range of throttle levels.

Developing requirements for automotive electric/electronic systems is challenging, as those systems become increasingly software-intensive. Designs must account for unintended interactions among software features, combined with unforeseen environmental factors. In addition, engineers have to iteratively make architectural tradeoffs and assign responsibilities to each component in the system to accommodate new safety requirements as they are revealed. ISO 26262 is an industry standard for the functional safety of automotive electric/electronic systems. It specifies various processes and procedures for ensuring functional safety, but does not limit the methods that can be used for hazard and safety analysis. System Theoretic Process Analysis (STPA) is a new technique for hazard analysis, in the sense that hazards are caused by unsafe interactions between components (including humans) as well as component failures and faults.

The safety monitor is a high integrity control that monitors the health and performance of safety related computer controlled functions in vehicles. The integrity of the safety monitor code is critical to the overall performance of the control software. Traditionally, once monitor requirements are understood, then the safety monitor is hand coded or created in a modeling environment. New practices such as ISO 26262 prescribe formal or semiformal methods are used against certain classes of foreseeable faults. Recently, a new tool, which is capable of auto-generating C-code based on safety monitor formal functional requirements is available from BTC Company. Ford Motor Company investigated the tool using an application example from a powertrain control feature safety monitor.