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ECU Map Optimization Support Tool
ORANGE Navigator & Optimizer

Easily set up a suitable operation plan.
We support compliance work efficiently through various approaches.

To reduce CO2 emissions, the electrification of vehicles such as EVs, HEVs, and PHEVs is progressing. The control parameters of automobiles, such as engine control, motor control, and battery management, are increasing exponentially. Furthermore, multi-objective optimization work is required to reduce exhaust emissions while simultaneously reducing fuel consumption. Response surface methodology is an efficient method for multi-objective optimization. ORANGE Navigator performs measurements for model creation using automated driving. Conditions related to the experimental design, such as monitoring of measured values and control settings for external devices, can be set. ORANGE Optimizer can create the optimal ECU map using response surface methodology based on the collected data. Because optimization can be performed with an approach suited to the situation at hand, ECU maps can be created efficiently.

Steady-state adaptation

Features

  • By integrating and managing the operation schedule and experiment plan, and linking with the FAMS system, autonomous operation becomes possible.

  • It supports various Design of Experiments (DoE) methods and enables the planning of high-dimensional (up to 20 dimensions) compatibility tests.

  • Conformance testing can be performed incorporating monitoring and control of temperature conditions, commands for measuring instrument configuration, and other parameters.

  • Equipped with a limit point search function for ECU parameters and boundary planning.

  • Up to 10 local multi-objective optimizations, global optimizations, and smooth ECU map exploration are possible.

  • Reflect general-purpose driving patterns in the driving simulation optimization.

  • Achieves high-dimensional (up to 20 dimensions) in-boundary optimization.

  • Steady-state adaptation

By combining multiple steady-state models to create a large-scale model and inputting driving conditions into the model, driving simulations can be performed. While this simulation is not perfectly accurate because the model does not include a lag component, it provides a good indication of the optimization results.

Transient adaptation options

Features

  • To create a transient simulation model based on a steady-state model, we make effective use of steady-state fitted data.

  • Because smaller element models can be combined to create larger models, it is possible to grow the plant model.

  • Even in tests for creating transient models, ECU communication is implemented using the conventional standard (ASAP3). High-speed ECU communication is not required.

  • Transient operation follows a repeating trapezoidal pattern (sweep + stabilization), making it measurable with existing equipment.

  • It allows for project management of multiple data sets, and data transfer from ORANGE Navigator to ORANGE Optimizer is smooth.

  • The driving patterns to be simulated can be arbitrarily set, making it possible to obtain transient optimization results tailored to the destination.

  • ① Reflect transient changes during operation in the plant model. ② Link control by the ECU with the plant model.
    ① Reflecting transient changes during operation in the plant model
    ②: Linking control via ECU with the plant model

By connecting state variables such as lag components and temperature to the steady-state model, the accuracy of transient simulations can be improved. Furthermore, by connecting ECU control lags to the plant model, accuracy can be further improved, and control constants can be optimized.

The concept of transient adaptation

We will add transient elements to a steady-state model.
Transient simulation models can simulate time-series data by inputting time-series data.
For time-series simulations, it is important to reproduce delays in temperature and exhaust emissions.

  • The concept of transient adaptation
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