AIM

To understand and use the ADVISOR tool in MATLAB.

INTRODUCTION

1. VEHICLE MODEL:

[1] Electric motorcycles and scooters are plug-in electric vehicles with two or three wheels. The electricity is stored on board in a rechargeable battery, which drives one or more electric motors. Electric scooters (as distinct from motorcycles) have a step-through frame. Most electric motorcycles and scooters as of May 2019 are powered by rechargeable lithium-ion batteries, though some early models used nickel-metal hydride batteries. Alternative types of batteries are available. Z Electric Vehicle has pioneered use of a lead/sodium silicate battery (a variation on the classic lead acid battery invented in 1859, still prevalent in automobiles) that compares favorably with lithium batteries in size, weight, and energy capacity, at considerably less cost.

2. ADVISOR:

[2] ADVISOR stands for ADvanced VehIcle SimulatOR. It is a set of model, data, and script text files for use with MATLAB and Simulink.  It is designed for rapid analysis of the performance and fuel economy of conventional, electric, and hybrid vehicles.  ADVISOR also provides a backbone for the detailed simulation and analysis of user defined drivetrain components, a starting point of verified vehicle data and algorithms from which to take full advantage of the modeling flexibility of Simulink and analytic power of MATLAB.

ADVISOR was preliminarily written and used in November 1994.  Since then, it has been modified as necessary to help manage the US DOE Hybrid Vehicle Propulsion System subcontracts.  Only in January 1998 was a concerted development effort undertaken to clean up and document ADVISOR.

You may benefit from using ADVISOR if you want to:

• estimate the fuel economy of vehicles that have not yet been built.
• learn about how conventional, hybrid, or electric vehicles use (and lose) energy throughout their drivetrains.
• compare relative tailpipe emissions produced on a number of cycles.
• evaluate an energy management strategy for your hybrid vehicle’s fuel converter.
• optimize the gear ratios in your transmission to minimize fuel use or maximize performance, etc.

The models in ADVISOR are:

• mostly empirical, relying on drivetrain component input/output relationships measured in the laboratory, and
• quasi-static, using data collected in steady state (for example, constant torque and speed) tests and correcting them for transient effects such as the rotational inertia of drivetrain components.

ADVISOR will allow the user to answer questions like:

• Was the vehicle able to follow the speed trace?
• How much fuel and/or electric energy were required in the attempt?
• How does the state-of-charge of the batteries fluctuate throughout a cycle?
• What were the peak powers delivered by the drivetrain components?
• What was the distribution of torques and speeds that the piston engine delivered?
• What was the average efficiency of the transmission?

 

By iteratively changing the vehicle definition and/or driving cycle, the user can go on to answer questions such as:

• At what road grade can the vehicle maintain 55 mph indefinitely?
• What is the smallest engine I can put into this vehicle to accelerate from 0 to 60 mph in 12 s?
• What is the final drive ratio that minimizes fuel use while keeping the 40 to 60 mph time below 3 s?
• What is the fuel economy sensitivity to mass, aerodynamic drag, or other vehicle or component variations?

ADVISOR’s GUI and other script files answer many of these questions automatically, while others require some custom programming on the user’s part. Because ADVISOR is modular, its component models can be relatively easily extended and improved.  For example, an electrochemical model of a battery, complete with diffusion, polarization, and thermal effects, can easily be put into a vehicle to cooperate with a motor model that uses a measured efficiency map.  Of course, developing new, detailed models of drivetrain components (or anything else, for that matter) requires an intimate familiarity with the environment, MATLAB/Simulink. ADVISOR was developed as an analysis tool, and not originally intended as a detailed design tool.  Its component models are quasi-static and cannot be used to predict phenomena with a time scale of less than a tenth of a second or so.  Physical vibrations, electric field oscillations and other dynamics cannot be captured using ADVISOR, however recent linkages with other tools such as Saber, Simplorer, and Sinda/Fluint allow a detailed study of these transients in those tools with the vehicle level impacts linked back into ADVISOR.

As an analysis tool, ADVISOR takes the required/desired speed as an input, and determines what drivetrain torques, speeds, and powers would be required to meet that vehicle speed.  Because of this flow of information back through the drivetrain, from tire to axle to gearbox and so on, ADVISOR is what is called a backward-facing vehicle simulation. Forward-facing vehicle simulations include a model of a driver, who senses the required speed and responds with an accelerator or brake position, to which the drivetrain responds with a torque.  This type of simulation is well suited to the design of control systems, for example, down to the integrated circuit and PC card level—the implementation level.

ADVISOR is well suited to evaluate and, by iterative evaluation, design control logic and energy management strategies.  By this, we mean something like “When the engine torque output is low and the battery state of charge is high, turn off the engine.”  The control logic, with which ADVISOR can work, is about what you want the vehicle to do.  The detailed control system, getting into details of how you would implement this control logic in hardware, is about how to make the vehicle do what you want and is not the original intention of ADVISOR’s application. In electrical components’ communication with each other, ADVISOR deals in power, and not in voltage and current.  Linkages to other tools, such as Saber and Simplorer let the user work with a voltage bus. The vehicle dynamics calculations required for traction control and the wheel slip model assume that the front axle is the only drive axle.  Simple steps can be taken to correct the weight transfer calculation if you wish to model a rear-drive vehicle, and an example wheel file that accomplishes this is included.  Modeling a four-wheel drive vehicle requires involved Simulink reprogramming.

3. FTP DRIVE CYCLE:

[3] The EPA Federal Test Procedure, commonly known as FTP-75 for the city driving cycle, are a series of tests defined by the US Environmental Protection Agency (EPA) to measure tailpipe emissions and fuel economy of passenger cars (excluding light trucks and heavy-duty vehicles). The testing was mandated by the Energy Tax Act of 1978 in order to determine the rate of the guzzler tax that applies for the sales of new cars. The current procedure has been updated in 2008 and includes four tests: city driving (the FTP-75 proper), highway driving (HWFET), aggressive driving (SFTP US06), and optional air conditioning test (SFTP SC03).

The "city" driving program of the EPA Federal Test Procedure is identical to the UDDS plus the first 505 seconds of an additional UDDS cycle. Then the characteristics of the cycle are:

• Distance travelled: 17.77 km (11.04 miles)
• Duration: 1874 seconds
• Average speed: 34.1 km/h (21.2 mph)

The procedure is updated by adding the "hot start" cycle that repeats the "cold start" cycle of the beginning of the UDDS cycle. The average speed is thus different, but the maximum speed remains the same as in the UDDS. The weighting factors are 0.43 for the cold start and transient phases together and 0.57 for the hot start phase. Though it was originally created as a reference point for fossil fueled vehicles, the UDDS and thus the FTP-75, are also used to estimate the range in distance travelled by an electric vehicle in a single charge.

Figure 1. EPA FTP-75 driving cycle.

 

4. GRADEABILITY:

[3] The ‘grade’ (also known as slope, incline, gradient, main fall, pitch or rise) of a physical feature or landform refers to the tangent of the angle of that surface to the horizontal. Gradeability is a special case of the slope, where zero indicates horizontality. It is measured either in degrees(°) or percentage(%). A larger number indicates a higher or steeper degree of "tilt". Often slope is calculated as a ratio of "rise" to "run", or as a fraction ("rise over run") in which ‘run’ is the horizontal distance (not the distance along the slope) and ‘rise’ is the vertical distance. Grades are typically specified for new linear constructions (such as roads, landscape grading, roof pitches, railroads, aqueducts, and pedestrian or bicycle circulation routes). While aligning a highway too, the gradient is decided for designing the vertical curve.

Gradeability by definition is the ability of a commercial vehicle to negotiate a grade(slope/acclivity) in Gross Vehicle Weight (GVW) condition and it can vary from 0% to 45% (maximum). A 45° gradient is equivalent to 100%. In other words, gradeability is the highest grade a vehicle can ascend maintaining a particular speed.

Example: A truck with a gradeability of 7% at 60 mph can maintain 60 mph on a grade with a rise of 7%.

Figure 2. Gradeability Measurement

Gradeability is dependent on engine power, drivetrain type, gear ratio, weight distribution, vehicle's center of gravity, and traction. For off-road vehicles, gradeability equates to the steepest hill (grade) a truck can climb when running at peak engine torque in its lowest transmission gear (and lowest rear-axle ratio if the axle has a double reduction type gearbox). A double reduction gearbox system is one in which the engine output speed is reduced by two times.

OBJECTIVES

• To understand and work on FTP drive cycle and check if the electrical vehicle under consideration is capable of travelling 45km.
• To understand the output of the electrical vehicle running on FTP cycle when battery capacity varies.
• To perform gradeability test on a car PRIUS which is of Japan model.

PROBLEM SPECIFICATION AND SOLVING:

PROBLEM STATEMENT I:

For EV_defaults_in file, if cargo mass is 500 kg with all other default conditions, can the vehicle travel for 45 km with FTP drive cycle? Conclude your observations.

EXPLANATION AND OBSERVATION:

• ADVISOR tool is downloaded from the following link as provided by the instructor.

https://sourceforge.net/projects/adv-vehicle-sim/

• The tool is downloaded and aligned along the path the MATLAB works in the system of the user.
• m is a function in MATLAB and is loaded in editor window and ‘advisor’ is typed in command window and to execute it, f5 is clicked.
• ADVISOR tool opens as follows:

Figure 3. Layout of ADVISOR tool

• A window opens where, by default parallel orientation of the vehicle’s data is presented on the screen. It is necessary to change the file name and drivetrain configuration as follows:

Figure 4. Changing file name to ‘EV_defaults_in’

Figure 5. Changing Drivetrain Configuration to ‘ev’.

• The following is the layout with default data provided by the ADVISOR> The default cargo mass is 136 kg as shown in the following figure.

Figure 6. Layout of the basic default configuration.

• The cargo mass is to be changed to 500 kg as needed from the problem statement I. To do so, the default value is selected and 500 is entered. The value, by default, is in kilograms.

Figure 7. Layout of the required configuration.

• To know the layout of the system under consideration, its block diagram can be understood by clicking on ‘view Block Diagram’. The layout of the block diagram is as follows:

Figure 7. Layout of the required configuration in block diagram format.

• For further modelling, it is required to click on ‘continue’ and a new window appears that asks the user to feed the appropriate drive cycle and number of times the cycle is to be calculated.
• In the new window, the drive cycle to be selected is ‘FTP.’
• Initially, the number of cycles is left as 1. And eventually it is increased to 2 and 3 in different simulations, respectively.

 

Figure 8. Selection of CYC_FTP drive cycle.

• The CYC_FTP drive cycle is as follows:

Figure 9. Layout of CYC_FTP drive cycle.

• The default distance travelled by the vehicle running on this drive cycle is 11.04 miles. And the layout of the simulation is as follows:

Figure 10. Layout of CYC_FTP drive cycle and other information.

• By leaving the remaining parameters under default conditions, the simulation of the so far detailed model is RUN.
• The results are obtained in a new window which gives the detailed description of each and every result obtained as follows:

Figure 11. Layout of results for 1 FTP drive cycle.

Figure 12. Layout of results for 2 FTP drive cycles.

• The warnings for this model are obtained as follows:

Missed Trace by > 2 mph (3.2 km/h)

Trace Miss Analysis:

1. Absolute average difference: 0.037937 mph
2. Percent of time with trace miss greater than 2 mph (absolute): 0.66613%
3. Greatest percent difference based on max. cycle speed requested is 11.9007% at 4651 seconds, (array index 4652)
4. Greatest percent difference is 18.4446% at 4904 seconds, (array index 4905)
5. Greatest absolute difference is 6.7477 mph at 4651 seconds, (array index 4652)

Figure 13. Layout of results for 3 FTP drive cycles.

• The warnings for this model are obtained as follows:

Missed Trace by > 2 mph (3.2 km/h)

Required distance exceeded EV range.

Trace Miss Analysis:

1. Absolute average difference: 0.15983 mph
2. Percent of time with trace miss greater than 2 mph (absolute): 2.615%
3. Greatest percent difference based on max. cycle speed requested is 21.0205% at 5159 seconds, (array index 5160)
4. Greatest percent difference is 26.1368% at 5409 seconds, (array index 5410)
5. Greatest absolute difference is 11.9186 mph at 5159 seconds, (array index 5160)

• The distance travelled in the all the three cases are 11 miles, 22 miles and 25.7 miles, respectively.

PROBLEM STATEMENT II:

In the above case as mentioned in Problem Statement I, try changing the battery capacity and repeat the simulation.

EXPLANATION AND OBSERVATION:

• All the parameters are kept unaltered as mentioned in the problem statement I. The number of modules i.e., ‘# of mod’ are changed to vary the battery voltage. The following consists of variation of battery voltage.

 

Figure 14. Varying the number of modules to 26.

Figure 15. Layout of results for 2 FTP drive cycles – 26 modules.

• The warnings for this model in the Figure 15 are obtained as follows:

Missed Trace by > 2 mph (3.2 km/h)

Trace Miss Analysis:

1. Absolute average difference: 0.02175 mph
2. Percent of time with trace miss greater than 2 mph (absolute): 0.46427%
3. Greatest percent difference based on max. cycle speed requested is 8.3483% at 4651 seconds, (array index 4652)
4. Greatest percent difference is 15.0822% at 4904 seconds, (array index 4905)
5. Greatest absolute difference is 4.7335 mph at 4651 seconds, (array index 4652)

Figure 16. Layout of results for 3 FTP drive cycles – 26 modules

• The warnings for this model in the Figure 16 are obtained as follows:

Missed Trace by > 2 mph (3.2 km/h)

Required distance exceeded EV range.

Trace Miss Analysis:

1. Absolute average difference: 0.10945 mph
2. Percent of time with trace miss greater than 2 mph (absolute): 2.0583%
3. Greatest percent difference based on max. cycle speed requested is 18.8655% at 5158 seconds, (array index 5159)
4. Greatest percent difference is 23.3893% at 5156 seconds, (array index 5157)
5. Greatest absolute difference is 10.6968 mph at 5158 seconds, (array index 5159)

Figure 17. Varying the number of modules to 27.

Figure 18. Layout of results for 2 FTP drive cycles – 27 modules.

• The warnings for this model in the Figure 18 are obtained as follows:

Missed Trace by > 2 mph (3.2 km/h)

Trace Miss Analysis:

1. Absolute average difference: 0.010708 mph
2. Percent of time with trace miss greater than 2 mph (absolute): 0.26241%
3. Greatest percent difference based on max. cycle speed requested is 5.4205% at 4904 seconds, (array index 4905)
4. Greatest percent difference is 11.6418% at 4904 seconds, (array index 4905)
5. Greatest absolute difference is 3.0734 mph at 4904 seconds, (array index 4905)

 

• The warnings for this model in the Figure 19 are obtained as follows:

Missed Trace by > 2 mph (3.2 km/h)

Required distance exceeded EV range.

Trace Miss Analysis:

1. Absolute average difference: 0.067902 mph
2. Percent of time with trace miss greater than 2 mph (absolute): 1.2542%
3. Greatest percent difference based on max. cycle speed requested is 16.6429% at 5158 seconds, (array index 5159)
4. Greatest percent difference is 20.7755% at 5156 seconds, (array index 5157)
5. Greatest absolute difference is 9.4365 mph at 5158 seconds, (array index 5159)

 

Figure 19. Layout of results for 3 FTP drive cycles – 27 modules.

Figure 20. Varying the number of modules to 28.

• The warnings for this model in the Figure 21 are obtained as follows:

Missed Trace by > 2 mph (3.2 km/h)

Trace Miss Analysis:

1. Absolute average difference: 0.0042125 mph
2. Percent of time with trace miss greater than 2 mph (absolute): 0.040371%
3. Greatest percent difference based on max. cycle speed requested is 3.7928% at 4904 seconds, (array index 4905)
4. Greatest percent difference is 8.1459% at 4904 seconds, (array index 4905)
5. Greatest absolute difference is 2.1505 mph at 4904 seconds, (array index 4905)

Figure 21. Layout of results for 2 FTP drive cycles – 28 modules.

Figure 22. Layout of results for 3 FTP drive cycles – 28 modules.

• The warnings for this model in the Figure 22 are obtained as follows:

Missed Trace by > 2 mph (3.2 km/h)

Required distance exceeded EV range.

Trace Miss Analysis:

1. Absolute average difference: 0.040721 mph
2. Percent of time with trace miss greater than 2 mph (absolute): 0.59563%
3. Greatest percent difference based on max. cycle speed requested is 13.8771% at 5157 seconds, (array index 5158)
4. Greatest percent difference is 17.4431% at 5156 seconds, (array index 5157)

 

Figure 23. Varying the number of modules to 29.

Figure 24. Layout of results for 2 FTP drive cycles – 29 modules.

• The warnings for this model in the Figure 24 are obtained as follows:

None.

• The warnings for this model in the Figure 25 are obtained as follows:

Missed Trace by > 2 mph (3.2 km/h)

Required distance exceeded EV range.

Trace Miss Analysis:

1. Absolute average difference: 0.022915 mph
2. Percent of time with trace miss greater than 2 mph (absolute): 0.41577%
3. Greatest percent difference based on max. cycle speed requested is 10.4887% at 5156 seconds, (array index 5157)
4. Greatest percent difference is 14.8132% at 5409 seconds, (array index 5410)
5. Greatest absolute difference is 5.9471 mph at 5156 seconds, (array index 5157)

Figure 25. Layout of results for 3 FTP drive cycles – 29 modules.

 

PROBLEM STATEMENT III:

Perform gradeability test with PRIUS_Jpn_defaults_in file. Compare your results in table and conclude.

EXPLANATION AND OBSERVATION:

• The vehicle under consideration is Toyota Prius, japan model. The model is selected from LoadFile in the start window of ADVISOR as follows:

 

Figure 26. Changing file name to ‘PRIUS_JPN_defaults_in’.

• The layout of the Prius japan model is as follows:

Figure 27. Layout of the required configuration.

• To know the layout of the system under consideration, its block diagram can be understood by clicking on ‘view Block Diagram’. The layout of the block diagram is as follows:

Figure 27. Layout of the required configuration in block diagram format.

• The parameters are not altered, and the simulation is proceeded to next step where gradeability can be tested using certain parameters.
• The default drive cycle for this simulation is FTP and the number of drive cycles is kept at 3.
• To perform gradeability test, an option named ‘Gradeability test’ is to be selected and to vary the parameters, ‘Grade options’ is selected as shown in the figure 28. In this tests, all the systems are enabled.

Figure 28. Grade test advanced options.

• The values of velocity are varied from 15mph to 70mph and the gradeability is identified. The following are the results of the gradeability test:

Figure 29. Result of gradeability test for vehicle speed of 15mph.

Figure 30. Result of gradeability test for vehicle speed of 20mph.

Figure 31. Result of gradeability test for vehicle speed of 25mph.

• The gradeability of the vehicle when the speed range was maintained from 15mph to 25 mph are as follows:

For 15 mph, the gradeability is 24.4%

For 20 mph, the gradeability is 19.8%

For 25 mph, the gradeability is 16.5%

• The SOC levels used up for 15 mph, 20 mph and 25 mph are ≈50%.

 

Figure 32. Result of gradeability test for vehicle speed of 30 mph.

Figure 33. Result of gradeability test for vehicle speed of 35 mph.

• The gradeability of the vehicle when the speed range was maintained as 30 mph and 35 mph are as follows:

For 30 mph, the gradeability is 14.7%

For 35 mph, the gradeability is 13%

• The SOC levels used up for 30 mph and 35 mph are ≈50%.

Figure 34. Result of gradeability test for vehicle speed of 40 mph.

Figure 35. Result of gradeability test for vehicle speed of 45 mph.

• The gradeability of the vehicle when the speed range was maintained as 40 mph and 45 mph are as follows:

For 40 mph, the gradeability is 11.7%

For 45 mph, the gradeability is 10.7%

• The SOC levels used up for 40 mph and 45 mph are ≈50%.

Figure 36. Result of gradeability test for vehicle speed of 50 mph.

Figure 37. Result of gradeability test for vehicle speed of 55 mph.

• The gradeability of the vehicle when the speed range was maintained as 50 mph and 55 mph are as follows:

For 50 mph, the gradeability is 9.7%

For 55 mph, the gradeability is 8.9%

• The SOC levels used up for 50 mph and 55 mph are ≈50%.

  

RESULTS

• By changing the cargo mass to 500kg the total weight summed up to 1552 kg and by varying the drive cycles for default number of modules i.e., 25, the following results are obtained:

Therefore, from the above statistics, the vehicle driving with FTP drive cycle cannot travel 45 km even if we increase the number of drive cycles and the SOC gets exhausted in the third drive cycle.

The maximum distance that the vehicle is expected to drive at the end of third drive cycle is 41.36 km.

• As per the requirements of problem Statement II, the battery voltage is varied to identify if the vehicle can travel 45km and the results are tabulated below:

From the above statistics, it can be understood that irrespective of the battery voltage, the distance travelled for 2 FTP drive cycles is in the order of 35.5 km, a constant value. For two FTP drive cycles, the SOC decreases as the voltage of the battery increases. The third FTP drive cycle uses 100% of SOC. To reach 45km, it is mandatory to use 3 FTP drive cycles with 28 battery modules, having the voltage of 345 V. This is the good bargain considering the weight and the battery usage. Although, the same can be achieved by 29 battery modules but that adds additional 9kg of weight to the vehicle which is undesired. Therefore, the optimum value of voltage required to travel 45 km is 345V achieved by 28 modules of battery.

• The gradeability test is performed on the Toyota Prius, Japan model by varying the speed. It is assumed that time taken to attain any particular velocity takes 10 seconds and is kept constant throughout. Therefore, by changing the velocity from 15 mph to 55 mph, the following data is obtained:

The distance travelled by the vehicle irrespective of the velocity is 33.1 miles which is equal to 53.26 km. The SOC level used under all the cases is approximated to nearly being 50%.

From the above tabulated results according to the problem statement III, it can be understood that as the velocity increases, the gradeability decreases i.e., they hold an inverse relationship. The least velocity under consideration has the highest gradeability of 24.4% whilst the largest velocity under consideration i.e., 55 mph has the least gradeability of 8.9%.

This evaluation is not accurate but an idea of gradeability test can be achieved. Not all the velocities fall under same gear application. The gear application changes for different values of velocity that the vehicle attains.

CONCLUSION

The required problems have been solved and justified with appropriate results. The cargo mass change resulted in the change in the overall weight of the system and initial battery voltage is not sufficient to travel 45 km and therefore, the voltage of the battery was increased to 345 V to reach the desired distance, but this is a compromise with the overall weight. Since, to achieve the distance the weight of the system has to be increased. Gradeability test on Prius is conducted by varying the velocities from 15mph to 55 mph and is observed that the velocity and gradeability are inversely proportional to each other.

BIBLIOGRAPHY

1. https://en.wikipedia.org/wiki/Electric_motorcycles_and_scooters
2. http://adv-vehicle-sim.sourceforge.net/advisor_ch1.html
3. https://en.wikipedia.org/wiki/FTP-75
4. https://skill-lync.freshdesk.com/support/solutions/articles/43000572450-gradeability-of-commercial-vehicles#:~:text=Gradeability%20by%20definition%20is%20the,to%2045%25%20(maximum).&text=In%20other%20words%2C%20gradeability%20is,ascend%20maintaining%20a%20particular%20speed.