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Powertrain Control

Our award-winning OnRAMPTM Design and Calibration Suites use linear and non-linear Model Predictive Control to reduce development and calibration time, improve vehicle fuel economy, reduce material costs and increase emissions robustness. We leverage our unique expertise in automotive, automation and aerospace to develop innovative solutions for our customers, Original Equipment Manufacturers (OEM).

Controls engineering is offered in the form of system models, software packages or services.

System Architecture

OnRAMP Design and Calibration Suites (OnRAMP) are an innovative Model Predictive Control toolsets for advanced powertrain control and virtual sensing development.

The advanced controls tools offer an intuitive and systematic methodology to:

  • Build and identify physics-based dynamic engine models
  • Design and tune an optimal control strategy for steady-state and transient operations
  • Deploy real-time control onto prototyping or production hardware

OnRAMP offerings available are:

Turnkey Solutions:

  • Diesel and Gasoline Airpath Solutions
  • Thermal Management
  • External Model Integration (AMESim or Simulink)
  • Virtual Sensing (Turbo Speed Sensor, NOx, EGR /MAF)

Engineering Services & Consultancy:

  • After-Treatment
  • Predictive Cruise Control
  • Battery Management
  • Hybrid Powertrain Solutions
  • Cycle Optimizer
  • Waste Heat Recovery
  • Transmission Control

OnRAMP leverages Honeywell’s expertise in advanced control to offer:

  • Plant modeling accomplished by specifying the basic engine layout from a component library then automatically fitting over the entire operating space of the engine
  • Multivariable Model Predictive Control respecting engine constraints while being robust to model uncertainty due to production variability and engine ageing
  • Controllers designed to fit into a low memory footprint
  • ECU code whose structure does not change when the control is reconfigured for different engines.

OnRAMP Design and Calibration Suites are divided into a series of phases:

1. Model Design and Model Configuration
The user creates a physics-based engine model in Simulink by selecting predefined components from the library.

2. Design of Experiment and Model Identification
The model parameters are identified from plant data. Design of experiment data is generated by the tool. It covers both steady-state and transient modes. A simple process supports model parameter identification in three stages:

  1. Components identification
  2. Steady-state identification
  3. Transient identification

3. Control Problem Specification
The user creates a physics-based engine model in Simulink by selecting predefined components from the library.

4. Controller Design and Simulation
The controller includes both feedforward and feedback parts. The feedforward term is derived based on an inversion of the nonlinear model. The local linear dynamic models are used to design the multivariable’s feedback controller. The controller is then simulated against the identified nonlinear model.

5. Controller Deployment
The controller can be deployed as either MISRA compliant C-code or as a Simulink S-function. The handwritten C-code remains the same for all controller configurations – all that changes is the parameterized data.

6. Controller Implementation (RPS or ECU)
Both rapid prototyping system and direct implementation onto the ECU are supported.

7. Controller Tuning
Controller tuning on the target platform is supported for on-engine or in-vehicle operation.

 

Model Predictive Control

Model Predictive Control (MPC) is an advanced optimization based control strategy applicable to a wide range of industrial applications such as chemical plants and internal combustion engines. It is inherently a multivariable strategy that encompasses constraints, handling of actuators, states, process outputs and other variables. For example, it provides a systematic method for the design and tuning of a controller for a diesel airpath system that simultaneously governs both Mass Air Flow (MAF) and Manifold Air Pressure (MAP) while operating below a maximum turbo speed limit.

MPC is truly a model based control strategy. The model is not used exclusively for tuning purposes but also acts as the corner stone of a decision to derive the correct control action to mitigate a hazardous situation in future (e.g. turbo over speeding, smoke forming etc). This is done through the prediction of engine states and outputs based on the engine plant model. Thus, the controller can change its control action to prevent a hazardous situation from occurring. This represents an impossible task for simple controller loops. The performance of the resulting controller is highly sensitive to the quality of the plant models and consequently these models need be as accurate as possible. Nevertheless, it is possible to tune an MPC controller to be robust to model uncertainty.

Principles of MPC

In a MPC framework the control goals, such as the tracking of a reference or the satisfaction of constraints, are formulated as a numerical optimization problem. In most cases this problem is represented as a Quadratic programming or QP problem. For such an optimization problem, the cost function is the additive sum of individual terms that express various control requirements. These terms are multiplied by weighting factors defining the relative importance of each individual control goal. More importantly, controller tuning is reduced to a more intuitive decision process. For example, emphasizing the tracking of MAF or MAP set points in order to eliminate NOx emissions while ensuring operation below the maximum turbo speed limit.

MPC falls into the class of receding horizon control algorithms. This means that the resulting optimization problem from the control design needs to be solved at each sampling interval based on actual measurements. In the past, the computationally demanding nature of algorithms solving receding horizon control problems prevented the application of MPC for embedded systems. The computation power of the CPUs of embedded systems has increased dramatically and substantial effort has been invested into the innovation of fast and reliable solvers. Consequently, this obstacle has been removed. The main contributor to this achievement was discovery that MPC problems can be reformulated to multi-parametric quadratic programming or mp-QP problems. A problem formulated in this manner can be solved off-line once only for a given range of parameters; then at each sampling time the MPC controller is effectively reduced to a look-up table process. This represents a very fast controller implementation solution. It should be noted that this approach is not an approximation but gives exactly the same results as if the optimization problem would have been solved at each sampling interval.

Main Features of MPC

  • Mathematical theory based
  • Model based
  • Multivariable
  • Enables systematic, intuitive controller tuning
  • Enables constraint handling of actuators, states and engine outputs
  • Guaranteed to be stable and robust to model uncertainty
  • Independent of engine layout
  • Scalable to a number of actuators and sensors
  • Optimization based
  • Applicable to real-time applications with sampling intervals in ms

Model Predictive Control in OnRAMP

  • User-friendly environment
  • Easy engine model calibration
  • Automatic, robust controller tuning
  • Possibility of controller tuning in time/frequency domain
  • Real-time optimization algorithms deployable to the ECU or rapid prototyping environment
  • Small ECU memory demand (e.g. standard 2×2 control problem needs less than 70kB)
  • Fixed real-time algorithm structure parameterized only by engine calibration specific data
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