Vehicle Level Simulation is a rapidly expanding technique which most OEMs are exploring to help reduce testing cost and time. To be effective, these simulations must accurately represent the vehicle being simulated. This can be achieved in two steps, first by collecting the data required for modeling and feeding it into a simulation tool to create a virtual replica. The second step is to make sure that the model is well validated or correlated. In this blog, we are going to highlight some tips which simplifies model validation process.
Conventional Vehicle (ICEV) Correlation Procedure:
Let us consider that user-A is working on a conventional vehicle and trying to extract Fuel Economy output. User fed all the inputs required by the vehicle model which included some assumptions due to lack/absence of data. He found that mileage of the vehicle is having slight mismatch when compared to test results. To give you some background, mileage is an output from simulation which indicates how much distance a vehicle travels on an average per unit volume of fuel being consumed. Typically, in the units of mpg or kmpl. Final outcome (Mileage) from the model depends on various quantities, few of them involves BSFC/Fuel-Rate map input (for a map-based engine) and engine operating points which indirectly depends on drag coefficient, frontal area, rolling resistance, tire-rolling-radius, gear reductions, driveline efficiencies, inertias, effective mass, GVW, shift pattern, etc. to name some of them. It might be hard to identify which of these quantities is the culprit for mismatch with respect to test results. Hence, we have come up with a procedure that can help you in eliminating few parameters at a time to make your model correlation task easier with an ultimate goal of creating a virtual replica of your actual system/subsystem. Additionally, calibrated GT-Predictive Engine (GT-POWER) models can be used to generate engine maps like BMEP, BSFC, etc.
Battery Electric Vehicle (BEV) Correlation Procedure:
On the other hand, let us consider that user-B is working on an electrified vehicle and trying to extract current and voltage response of a Battery to a standard drive cycle like IDC/NEDC. He found that there is a mismatch for current and voltage results in simulation when he compares it with test data. To give you some background about typical vehicle model workflow, driver decides the power demand based on the target drive cycle and corresponding resistive forces (aerodynamic drag, rolling resistance, road grade, driveline inefficiency, etc.). Motor speed primarily depends on gear reductions, tire rolling radius and vehicle speed at that instance. Based on power demand and current operating speed, torque demand can be derived which the motor is asked to deliver to meet cycle demands. Power delivered by motor is known as brake power. Due to motor and inverter efficiency, there will be some losses and a summation of these losses, aux loads, and Brake power is what the battery needs to deliver which is also known as Electrical power. Finally based on the OCV-IR maps defined for an ECM (Equivalent Circuit Model), using fundamental equations of Electrical circuits, we arrive at current and voltage. Hence you can clearly see that current and voltage response is one of the final outcomes which depends on many other factors, hence it’s hard to predict the exact reason for mismatch of simulation results when compared to test data. Similarly, as we explained for an ICEV, we have listed a procedure which could simplify your task and help you eliminate and verify few parameters at a time.
As described in the correlation flowcharts, at an initial stage of vehicle development, users might not have all the data required for vehicle modeling. Hence to assist them, we have many options available within our tool GT-SUITE. Some of the relevant ones are discussed below:
- Characterization: for ECM (Equivalent Circuit Model) model creation using test data.
- ECM Database: for ECM model creation in absence of test data created using GT-Autolion database.
- Static Analysis: for gear shift pattern generation.
GT offers a quick and easy to use tool which is capable of parameter estimation for Electrical Equivalent Circuit models including 0 to 3 RC branches. We call it Characterization tool. Glimpse shown below:
Electrical Equivalent Model aka ECM Database:
Starting with v2022B1, you can find a database of 30-coin cells comprising of different chemistries and applications. This was developed using actual test data and is quite reliable in absence of data at initial stages of development process. Database comprises of 10 different chemistry combinations and 3 variants of each based on application (power dense, energy dense or balanced). This can be easily scaled up to a cylindrical/pouch/prismatic cell and even up to a pack following instructions mentioned in the template help of our electrical equivalent battery template. This comes as a part of installation and can be located in the following directory: %GTIHOME%\v2022\resrc\BatteryLibrary\ElecEq.
GT offers two modes for vehicle level simulations which are mainly dynamic and kinematic. Kinematic or static mode is used to perform these following tasks:
- Imposed speed analysis for drive cycle demand calculations.
- Grade Climbing Ability: Gradeability analysis over different gears, Tractive Force Calculations (gross and net), Tractive power calculations (gross and net), N-V curve, Acceleration potential, and others.
- Shift Strategy Generation and Optimization: Generation of shift strategy based on acceleration potential curves and drivability involving additional FE constraints.
- Controls Optimization for HEVs: ECMS, DP and Dynamic ECMS.
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- RPM: Revolutions Per Minute
- MPG: Miles Per Gallon
- KMPL: Kilometers Per Liter
- BSFC: Brake Specific Fuel Consumption
- GVW: Gross Vehicle Weight
- BMEP: Brake Mean Effective Pressure
- IDC: Indian Drive Cycle
- NEDC: New European Driving Cycle
- OCV: Open Circuit Voltage
- IR: Internal Resistance
- VKA: Vehicle Kinematic Analysis
- ECM: Equivalent Circuit Models (Resistive/Thevenin)
- N-V: Motor/Engine RPM vs Vehicle Speed curve
- FE: Fuel Economy
- HEV: Hybrid Electric Vehicles
- ECMS: Equivalent Consumption Minimization Strategy
- DP: Dynamic Programming
- RC: Resistance and Capacitance
- FDR: Final Drive Ratio or Sprocket and Chain Ratio
- PGR: Primary Gear Reduction if present
- GR: Gear Ratio of transmission if present