Optimizing an xEV in Less Than 1 Day

This is the second blog in a two-part series describing GT-SUITE’s integrated hybrid and electric vehicle design tools. If you have not read Part 1, [CLICK HERE].

The Hybrid Controls Problem:

The recent proliferation of hybrid electric vehicles has greatly complicated the world of vehicle controls engineers. Multiple energy sources and propulsion systems applied to sophisticated hybrid drivetrains necessitate a much more intricate controls strategy than conventionally powered vehicles.

Determining when to distribute power to the engine, motor(s), or both is no simple task, and the time typically taken to develop these controls strategies reflects that. Even developing controls for simple hybrid vehicle models can take precious time away from the rest of the design process, and cutting corners can lead to sub-optimal fuel economy and vehicle performance results during simulation. Fortunately, GT-SUITE’s embedded tools include two different methods to automatically generate optimized, charge-sustaining hybrid controls strategies on a per drive cycle basis:

  • Equivalent Consumption Minimization Strategy (ECMS)
  • Dynamic Programming

Using these tools allows for quick evaluation of a hybrid system’s peak capabilities without the hassle of developing and testing multiple controls options.

Example Walkthrough:

In Part 1 of this blog series, we employed GT-DRIVE+, Integrated Design Optimizer, and JMAG-Express to properly size and characterize an electric motor for a P4 hybrid system in a compact passenger car. These tools streamlined a traditionally time-consuming design process, with model generation and evaluation taking minutes rather than days. The goal was to select a motor for a P4 hybrid to meet the following requirements:

Metric Requirement
Acceleration (0-60 mph) 8.5 seconds
Fuel Economy (City/Highway) 50/52 mpg


This blog will build upon our previous work, applying two of GT-SUITE’s hybrid controls optimization solutions to evaluate the previously selected motor’s impact on drive cycle fuel economy. Applying these tools within our workflow allows us to evaluate estimated fuel economy under optimized control without spending time developing complex hybrid controls.

Figure 1. Hybrid Design Tools Workflow

Previous evaluation of our example model revealed that our 27.5 kW motor selection met the acceleration and highway fuel economy requirements but could not meet the city fuel economy demand. These tests, however, were performed using a rule-based control strategy that was not necessarily optimized for city or highway driving. Applying ECMS and Dynamic Programming to the city drive cycle should provide a better idea of this configuration’s fuel economy capabilities.

Equivalent Consumption Minimization Strategy (Local Optimization):

ECMS in GT-SUITE assigns a “fuel consumption” rate to energy pulled from the vehicle’s battery. Calculation of this energy-equivalent rate is influenced by several user-defined parameters including:

  • Equivalence Factor – this represents the relationship between battery energy and fuel energy
  • Target State of Charge – this sets a target SOC to develop a charge-sustaining strategy
  • Penalty Function Exponent – this influences a penalty function that increasingly penalizes battery energy consumption as the battery deviates farther from the target state of charge

For an ECMS run, the user specifies a variety of independent control variables that are altered at every timestep with the goal of minimizing combined ‘fuel’ consumption from both the engine and the battery. For our example, the following variables were selected:

Variable Values
P4 Motor Torque (27.5 kW motor) -105 Nm to 105 Nm
Transmission Gear Number 1st to 6th Gear
Vehicle Mode Hybrid, Electric, or Conventional


At every timestep, all combinations of the independent control variable values are considered. Any combinations that can meet the drive cycle power demand while obeying the defined constraints are evaluated to determine total fuel consumption. This calculation is heavily influenced by the battery energy-equivalent rate parameters. For example, if the SOC deviates too far from its target, then a larger penalty will be levied on battery consumption to incentivize a charge-sustaining strategy – this means scenarios where more engine power and less motor power is used may be deemed more favorable at that timestep. The variable combination that locally optimizes fuel consumption is then selected, and the process repeats for the remaining timesteps. The process at each timestep is summarized below:

Figure 2. ECMS Process Summary

Applying an ECMS control strategy to our city driving cycle, we will see a significant improvement in fuel economy that meets our initial requirements:


FTP-75 (City) Minimum Fuel Economy Requirement Reported FTP-75 (City) Fuel Economy
Heuristic Control 50 mpg 42.93 mpg
ECMS Local Optimization 50 mpg 58.30 mpg


Figure 3. ECMS and Dynamic Programming runs vary the selected variables at every timestep to minimize fuel consumption


Figure 4. ECMS and Dynamic Programming can be tuned to deliver a charge-sustaining strategy

Despite evaluating 612 different control scenarios at every timestep, this ECMS run completed in less than 3 minutes. After completion, we can see that our motor selection will be sufficient to meet the initial fuel economy requirements – all it needed was a better control strategy. However, optimizing locally at each timestep will likely result in slightly sub-optimal performance over the entire drive cycle.

In other words: This is good, but we can do even better.

Dynamic Programming (Global Optimization):

Dynamic Programming will provide an even clearer picture of our example vehicle’s fuel economy capabilities under optimal control. Dynamic Programming uses similar strategies to minimize fuel consumption but seeks to do so in the context of an entire drive cycle. A global cost function is created and minimized using similar parameters to those defined for ECMS. The run begins at the end of the drive cycle and marches backwards in time to the initial state, where the fuel costs for all possible states and controls are calculated and saved. By referencing these saved values, a controls solution is determined by computing the ‘optimal cost-to-go’. This may not necessarily minimize fuel consumption at every timestep but will produce a solution that cumulatively has the lowest fuel consumption from start to finish.

Applying dynamic programming to our city driving cycle, we will see fuel economy further improve to 62.3 mpg:

Figure 5. City fuel economy Comparison between different controls techniques


Figure 6. Map of Optimal Cost To Go produced by Dynamic Programming Run

Wrapping Up:

This blog series has demonstrated 5 different GT-SUITE tools that will significantly streamline your design process. In our motor sizing example, this increased efficiency was apparent:

  • GT-DRIVE+ instantly generated a P4 HEV vehicle model to use for evaluation – 5 minutes
  • Integrated Design Optimizer automatically selected the correct motor size to meet our acceleration requirements – 20 minutes
  • JMAG-Express instantly created an efficiency map from our selected motor characteristics – 10 minutes
  • Optimization Tools generated controls for our drive cycles to understand motor/vehicle performance under optimal control – 2 hours

One iteration of this design process could conceivably take less than one day. If we are unhappy with the results after evaluating this final design, we can easily iterate through again – tweaking our initial model and motor characteristics and applying all the tools again with relatively little time lost. If you are interested in learning more about any of these tools, feel free to contact us for additional information!

Written by Lloyd Adler-Lombardi