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Saber Accelerates Robust Design

Successful automotive designs require systematic methods to meet aggressive performance goals and compressed schedules.

Functional Safety/ISO 26262

Saber¡¯s Functional Safety solution enables engineers to investigate thousands of fault-effects which can¡¯t be handled using traditional methods such as prototyping. The high-level fault-effect coverage and efficiency in fault-effect simulation are achieved by automating the analysis and result validation in the Saber-products.

ISO 26262 strongly recommends simulation for system-level and hardware-level verification. SaberRD and SaberES Designer are ISO 26262 certified and support Functional Safety by Fault Effect Analysis. 

Saber Advantages:

• Investigation of fault effects and fault mitigation in a virtual way
• Automation of simulation and validation of fault-behavior
• Boost simulation throughput by Multicore- and Grid-computing support
• Enables the development/verification of safety mechanism
• Proving robustness of safety mechanism by taking into account of component tolerances, temperature dependencies, aging-effects, parameter-drifts, stress-levels

Powernet

As electronic content in vehicles increases, so do the challenges in meeting this rising energy demand on the power network. Designers must create efficient power generation and distribution systems that perform with flawless reliability.

Design teams use Robust Design methodologies to manage complex energy generation and distribution problems, such as designing an alternator charging system or taking into account system and environmental variations that affect performance. Saber's comprehensive simulation, modeling resources, and analysis capabilities enable design teams to deploy Robust Design methods to meet the challenges of powernet design.

SAE Technical Papers:

Software Test and Calibration Using Virtual Manufacturing (General Motors)

Model Based Design of Robust Vehicle Power Networks (Jaguar Land Rover)

Saber Advantages:

• Accurately size system components to match energy generation with corresponding consumption
• Eliminate surprises in power network loading and distribution through early verification
• Test the complete system earlier with hardware/software co-simulation
• Save time and eliminate errors with industry-proven power model libraries
• Enable design portability using industry standard VHDL-AMS and MAST languages
• Maximize reliability with advanced sensitivity, statistical, and fault analyses
• Increase analysis throughput with distributed simulations across multiple CPUs

In-Vehicle Networking

Advances in automotive design have been propelled by dramatic increases in electronic systems content. Numerous electronic systems must communicate with each other over a complex network which often contains multiple communication protocols, such as CAN, LIN, and FlexRay standards. These complexities add significant challenges to verifying the physical network layer.

With a traditional prototyping approach it is impossible to build enough prototypes to adequately test even the most important variations of a network design. Virtual prototyping through simulation has become the proven solution for verifying data network reliability. Saber provides the comprehensive simulation and analyses capabilities needed to apply Robust Design methodologies to in-vehicle networks and ensure reliability.

SAE Technical Papers:

An Automated Model Based Design Flow for the Design of Robust FlexRay? Networks (C&S)
Development of the Physical Layer and Signal Integrity Analysis of FlexRay? Design Systems(U. of Applied Sciences)

Saber Advantages:

• Verify network concepts and topologies early in the development cycle
• Analyze specific network variants (min vs. max number of ECUs)
• Save time and eliminate errors with industry proven IVN libraries
• Include wire characteristics to analyze possible topology extensions
• Enable design portability with VHDL-AMS & MAST language standards
• Maximize reliability with advanced sensitivity, statistical, and fault analyses
• Increase analysis throughput with distributed simulations across multiple CPUs (grid computing)