Improving Hybrid Vehicle Reliability through Simulation-Based Design Integration

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Due to the integration requirements, hybrid vehicles become one of the most complex systems to design, manufacture and maintain. Robust design methods provide a framework for designing reliable hybrid vehicle systems.

Electric vehicles were developed in the past to address high fuel costs and increasing tailpipe emissions, however, their development has suffered from limited driving range and a lack of supporting infrastructure (i.e. charging stations). The advancement of hybrid vehicles lies in the conversion bridge between the internal combustion engine and the electric vehicle. The high fuel efficiency of hybrid vehicles allows electric vehicles to reduce emissions, thus enabling longer driving range and the convenience of using the infrastructure of internal combustion engines for energy support.

The main transmission assembly system of a hybrid passenger vehicle includes: motor/generator (front), controller (middle) and battery pack (rear).

In a hybrid vehicle, the powertrain includes components from both the internal combustion engine and the electric vehicle. The list of system components includes: a battery pack, an electric motor/generator, and an internal combustion engine. The internal combustion engine provides both electrical and mechanical power to the system. The powertrain of a hybrid vehicle is configured in three configurations: series, parallel, and a combination of series and parallel. Regardless of the configuration, reliable operation of the vehicle depends on the successful integration of the powertrain components.

Electromechanical systems

Both standard and hybrid vehicles rely on the integration of electrical, mechanical and software technologies, with automotive electronics and software increasingly being used to control or replace mechanical work. The intersection of these three design disciplines is mechatronics. Hybrid vehicles are at the heart of mechatronics design.

Hybrid vehicles rely on the effective integration of mechanical, electrical and software technologies.

Incorporating these technologies into a standard vehicle presents complex design challenges where electronic and software controls are used for non-power source applications. Hybrid vehicle design faces the same challenges in integrating non-power vehicle electronic source systems, with the added complexity of electronic and software controls for the vehicle powertrain. Because of this integration requirement, hybrid vehicles are among the most complex systems to design, manufacture, and maintain.

As vehicle complexity increases, reliability concerns arise. Therefore, designing a hybrid vehicle system requires a systematic, organized development approach. This organizational approach needs to consider reliability issues as an integral part of the design process from the beginning to ensure system reliability. Robust design methods provide the organizational framework needed to design a reliable hybrid vehicle system.

The Robust Design methodology is an organized and proven development philosophy designed to provide system reliability. Robust design principles enable design teams to handle complex system integration issues in a repeatable process. As shown in the figure below, the system concept based on robust design inputs a signal and processes an appropriate response. However, in a typical environment, design changes may affect the performance of the system. The design team must implement control techniques to compensate for design changes.

The general robust design system block diagram is based on the Taguchi method.

The focus of the robust design process is to reduce the impact of design changes on system performance and reliability. These changes may come from sources inside or outside the design, including changes caused by factors such as component tolerances, manufacturing processes, user patterns, environment, and system aging. Although these changes are of various types, each factor may have a large impact on the reliability of the system. The main goal of the robust design process is to optimize the system design in terms of performance, reliability, and cost while solving the problems caused by these changes.

In a typical design process, solving problems caused by multiple variations requires extensive testing. This means that once the system is designed, it must be prototyped and tested. A robust design process requires testing multiple variations, which means building new prototypes and testing each variation. Obviously, this design-prototype-test process is too time-consuming and practically expensive to achieve robust design.

The solution is to move the design-prototype-test operations into the virtual world for simulation and analysis. This is often referred to as virtual prototyping. Using modern design tools like Saber , design teams can design and build virtual prototypes of their systems and run multiple tests within the time and budget allocated to the traditional design-prototype-test process. Therefore, simulation and modeling are key requirements for achieving a robust design process.

Design Process

A robust design process based on modeling and simulation must follow the system process. The key to this process is to determine:

-Key performance metrics of the system;

- Model the system in a way that highlights these metrics;

- Validate metrics at every stage of the system development process;

A robust design flow has a fundamental development process that requires the use of simulation capabilities as shown here.

An effective robust design process depends on the system development process and requires advanced simulation capabilities.

This robust design process can be conveniently described using the hybrid vehicle system development process.

Performance measures are derived from the design specifications. A typical hybrid vehicle design specification will contain several performance requirements. As an example, a vehicle will typically meet emissions, performance, and fuel economy requirements. Each of these requirements becomes a performance measure that must be analyzed during the design process. For the present discussion, fuel economy will be used as the key performance measure.

Using the chosen fuel economy metric, the design team must select or develop a simulation model to highlight the design variables that affect that metric. Because the robust design process can be simulation intensive, the model is chosen to optimize simulation accuracy and simulation performance.

When developing models for a robust design flow, design teams should create models in hardware description languages (HDLs). Using HDLs gives design teams greater control over model accuracy and performance, including the ability to create models at different levels of design abstraction. Synopsys' MAST language is the de facto standard for hybrid powertrain modeling in the automotive industry; VHDL-AMS is another optional modeling language that has recently been standardized by the IEEE. Both languages are supported by the Saber simulator.

Verify rated system operation

Once the system is modeled, the focus can turn to analyzing fuel economy. The next step is to verify the rated fuel economy performance of the hybrid vehicle. The rated analysis shows the best-case fuel economy of the design under ideal conditions. The design is analyzed using standard operating point, time domain, and frequency domain analysis. The fuel economy results from the rated analysis become the performance baseline for the other steps in the robust design process.

Identify parameters that affect performance

The hybrid vehicle model should contain the key variables that affect fuel economy. These variables are selected by the design team based on their knowledge of the vehicle systems. Once the variables are selected, the design team needs to identify those variables that have the greatest impact on the vehicle's fuel economy.

Sensitivity analysis is the most effective way to analyze the parameters that have the greatest impact on the system. Using sensitivity analysis, you can analyze how the vehicle's fuel economy changes with changes in various system parameters. These parameters and their impact on the performance of the hybrid vehicle become the focus of other design processes.

Analyze system performance based on variables

After identifying the key parameters, the next step is to examine the impact of changes in these parameters on the fuel economy of the hybrid vehicle. Based on the understanding of the system, the design team established a range of values for the key parameters and set simulation instructions to sweep the entire range of values.

Sweeping the range of parameter variations is an important capability. The design team must set up the simulator to automatically execute in a series of loops to sweep the range of design parameters. This allows the simulator to cover every possible parameter combination, giving the design team a complete understanding of how fuel economy is affected. The goal of parametric analysis is to establish a range of values for each key system parameter. This range of values is then converted to a nominal value plus tolerance for statistical analysis.

Optimize system performance

At this stage, the design team needs to have a good understanding of the impact of system parameter changes on fuel economy, implement compensation technology to improve fuel economy, and determine which group to choose to obtain the best system performance. The next step is to verify the fuel economy of the car based on all possible combinations of design parameter values.

This is accomplished using statistical analysis. Based on the results of the parametric analysis, the design team assigns tolerances to the identified critical parameters in the system. The list of critical parameters should include those found in the sensitivity analysis. The goal is to verify fuel economy as the design parameters are randomly varied within the tolerance range and combined with other parameters in the design. The end result should be a hybrid vehicle with optimized fuel economy over a wide range of system variables.

Assess system stress and failure modes

The final step in ensuring system reliability is to analyze the stresses on system components and then examine what happens if a critical component in the system fails.

Stress analysis is used to analyze the effects of stress on hybrid vehicle components, and the design team applies the maximum stress ratings to these components and derates where necessary. The simulator uses this information to determine how far from "maximum" or "derated" the component is operating? The design team can then take corrective action where necessary.

The role of failure mode analysis is to verify the performance of the system as key components are initially set to failure modes. The design team must first select an acceptable range for fuel economy measurements. Then, select key components to fail during the analysis and define the failure mechanisms of the components.

Failure mode analysis then runs a series of analyses that cause the selected component to fail. Fuel economy is monitored during the analysis to see if the system—even after the failure—continues to perform within specifications. The end result is an analysis report detailing the component failure and whether the fuel economy metric was passed.