Dynamic Characteristics of the Electric Vehicle

28TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION DOI: 10.2507/28th.daaam.proceedings.062 DYNAMIC CHARACTERISTICS OF THE ELECTRIC VEHICLE Adnan Masic, Boran Pikula & Dzevad Bibic This Publication has to be referred as: Masic, A[dnan]; Pikula, B[oran] & Bibic, D[zevad] (2017). Dynamic Characteristics of the Electric Vehicle, Proceedings of the 28th DAAAM International Symposium, pp.0446-0451, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-902734-11-2, ISSN 1726-9679, Vienna, Austria DOI: 10.2507/28th.daaam.proceedings.062 Abstract In this paper, a method for analysis of dynamic characteristics of an electric vehicle is presented. The developed method is completely independent from vehicle’s on-board systems and there is no interaction with vehicle’s hardware or software. The test vehicle was a semi-custom electric light truck. Dynamic characteristics from realistic test scenario are presented and discussed. Special attention is dedicated to the energy consumption, kinetic energy recuperation and battery behaviour. Some suggestions for future research are proposed as well. Keywords: Electric vehicle; Data acquisition system; Dynamic characteristics; Energy recuperation 1. Introduction There is little doubt that the future of the road transportation belongs to the electric vehicles. Yet, it is not known how long will the transition take, nor what are the consequences. Therefore, it is very important to collect and analyse the relevant data, before the decisions are made. Hybrid vehicles are intermediate step, and they are already on the roads in millions. Croitorescu and Jiga [1] analysed the model of a hybrid vehicle consisting of an internal combustion engine and an electric motor, while Abouelkheir and Vu [2] performed the simulation of the dual clutch transmission in the hybrid vehicle using the MATLAB and Simulink. Pikula [3] discussed the justification of electric and hybrid vehicles use in urban areas. The aim of this research is twofold: 1. to develop an independent and reliable method for analysis of the dynamic characteristics of the electric vehicles, 2. to collect locally relevant data using the developed method. Main research question is: what kind of the relevant data can be obtained using this method and to what extent? For this purpose, we have acquired an electric vehicle: small truck powered by the three-phase asynchronous motor, EcoCarrier. Dimension of the vehicle are given in the Fig. 1, and some basic specification are: • Power of the electric motor: 15 kW • Max. velocity: 80 km/h • Mass of the vehicle (including the battery, excluding the driver): 1140 kg • Tyres: 195/65 R 15 - 0446 - 28TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION • Brakes: ventilated disc brakes (front) / drum brakes (rear) The mass of the vehicle is surprisingly low for its dimensions, because its chassis is made of aluminium alloy and body parts like the engine hood, doors and roof are made of the thermoplastic polymers. The vehicle is equipped with all necessary systems, making it legal to drive on public roads. Furthermore, it has the (electric) power steering, the vacuum servo brakes and the air suspension (rear)! We have assembled battery for the vehicle using the individual components. Keeping the costs within the budget, AGM (Absorbent Glass Mat) type of the lead acid chemistry was selected. Some specifications of the battery are: • Nominal voltage: 84 V • Capacity: 120 Ah (10.08 kWh) • Mass of the battery: 200 kg Fig. 1. Dimensions of the vehicle (all values are given in mm) 2. Method of data acquisition Our goal was to develop a method of data acquisition which is independent from the vehicle’s on-board system. The software in the vehicle is not aware that the test is underway, and all possible “software optimizations” (not to say cheats) are impossible. Detailed explanation of the methodology for testing the electric vehicles will be published in another manuscript soon. The platform for data acquisition has been developed in-house. It has its roots in the applications related to unmanned aerial vehicles and atmospheric measurements [4], [5]. The platform for data acquisition is based on open projects, such as Arduino [6]. The most challenging part was accurate and reliable measurement of the electric current, which is typically very high in case of electric vehicles. A suitable sensor is successfully adapted and installed on the vehicles main battery. The voltage is measured by means of custom voltage divider and a high performance ADC (Analog to Digital Converter). The list of important sensors is given in the Table 1. Sensor Allegro ACS770 [7] Bosch BMP085 [8] Bosch BMA180 [9] DHT22 [10] Ublox NEO-6M [11] Measured Quantity Electric current Air pressure Acceleration Air temperature and humidity GNSS coordinates Table 1. Sensors Simultaneously with the in-house developed data acquisition system (in-house DAS in further text), we have used a proprietary logger, Racelogic VBOX Sport (VBOX in further test), which is based on the high quality, 20 Hz GPS receiver. Basic specifications of the VBOX are listed in the Table 2. VBOX Sport: Accuracy Resolution Velocity 0.1 km/h 0.01 km/h Acceleration 0.5 % 0.01 g Table 2. VBOX specifications - 0447 - 28TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION 3. Test track The test track (Figure 2) was carefully chosen such that it simulates real driving scenarios, as much as possible. It is 15.5 km long with three characteristic segments: • long straight path (1.8 km), • cruising around the village (6.6 km) and • mixed conditions with frequent stop-and-go cycles (7.1 km). During the hole test, the vehicle consumed 2.48 kWh from the battery, which means that the average consumption was 16.0 kWh/100km. That is not surprising, taking into account mass and type of the vehicle. Taking into account this result, the fuel consumption of the similar vehicles with the internal combustion engine and domestic prices of the electric energy and the diesel fuel, we conclude that the electric version is more affordable by the factor of five, at least! Fig. 2. The test track visualisation in Google Earth 4. Test results In order to check for the consistency between two data loggers, we compare velocity of the vehicle recorded with two systems, on a drive segment throughout the village (Figure 3). From the Figure 3 we can see near perfect agreement of two independent data loggers! Proprietary DAS was extensively tested in various scenarios and we wanted to check if our in-house developed DAS can show at least similar performance. It is important to note that the in-house developed DAS offers almost endless possibilities (we can change the hardware, the software and generally any parameter), which is not the case with the proprietary solution. - 0448 - 28TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION Fig. 3. Comparison of the results from two data loggers In the Figure 4 voltage and current values are presented for the same drive segment. By convention, negative current corresponds to the battery drain, while positive current corresponds to the battery charge. From the Figure 4 we can see that the voltage of the main battery oscillates between 65V and 90V depending on the current intensity and the mode (draining or charging the battery). This wide interval of voltage values is not desirable and indicates significant internal resistance of the battery. In order to evaluate the internal resistance of the battery, we need to record the voltage of the battery under the strong current drain, i.e. full scale volt-ampere characteristic of the battery. Fig. 4. Voltage and current readings from the in-house DAS Volt-ampere characteristic of the vehicle’s battery is presented in the Figure 5. The range of current intensity goes from about 200A drain to 60A charge, which is practically full scale test (taking into account specifications of the vehicle and the current sensor). No-load voltage was about 84V, just as expected. Under the load of 200A, however, the voltage drops by almost 20V! Using the linear regression fit from the Figure 5, and the Ohm’s law, we find the internal resistance of the battery to be 90.5 mi.e. slope of the linear fit. Clearly, this is not a high-performance battery, and another one, with lower internal resistance, would represent the improvement. Lithium-ion batteries would be the best choice, but their price exceeded the budget of this research project significantly. We hope that we will have an opportunity to test the performance of this electric vehicle equipped with the lithium-ion battery in the future. - 0449 - 28TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION Fig. 5. Volt-ampere characteristic of the battery Figure 6 shows the power-time dependence for the same track segment. Part of the graph “above zero” corresponds to the energy recuperation. By integrating power over time we get 300 Wh in the negative part of the graph and 26.6 Wh in the positive part, which means that 8.9 % of spent energy was recuperated back to the battery during the deceleration of the vehicle. Fig. 6. Power consumption during drive on the test track Finally, in the Figure 7 typical acceleration of the vehicle from zero to about 45 km/h is presented. The velocity profile was measured with the Race Logic VBOX. Maximum acceleration of the vehicle was 2.3 m/s2, which is very good result, taking into account small declared power of the electric motor (15 kW only). This was achieved thanks to the single speed gearbox, specifically constructed for the electric vehicle. The velocity profile in the Figure 7 is very similar to the dynamic characteristic of the vehicle of the same size that is powered by the internal combustion engine. Fig. 7. Velocity profile during acceleration on the test track - 0450 - 28TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION 5. Conclusion Comprehensive method for testing dynamic and electric characteristics of the electric vehicle has been developed and successfully tested. The method was able to collect all relevant data (position of the vehicle, speed, acceleration, voltage and current) independently from vehicle’s on-board electronic systems. The test track was chosen so that it reflects realistic driving scenarios. Special attention has been devoted to the performance of the battery and the energy recuperation. Internal resistance of the battery has been determined (90.5 m. The amount of energy recuperated from the motor during the test was about 8 %, which fits to the range of expectancy. The average consumption of electric energy of the vehicle during the entire test was 16.0 kWh/100km. Taking into account local electricity price, this is at least five times cheaper than comparable diesel vehicle. In all, general dynamic characteristics of the electric vehicle are found to be similar to the characteristics of the comparable vehicles with the internal combustion engine. It is necessary, however, to educate drivers and encourage them to adapt the style of driving, so that maximal possible amount of energy can be recuperated. The main limitation of the in-house developed DAS is the current sensor (limited to ±200A). This was sufficient for our particular vehicle, but may not suffice for some other vehicles. However, the in-house developed platform is very flexible and integration of new sensors is straight forward (once they are available). Further tests are expected in the near future. They should include more test tracks, more driving scenarios, and (most important) different climate conditions. The test was performed during the summer, with very favourable weather. It would be interesting to re-do the test during the cold winter period. Battery behaviour should be carefully analysed under the extremely low temperatures (common for this geographical area). Finally, we can give the answer to the main research question: this platform can provide all relevant data for determination of dynamic characteristics of the electric vehicle in realistic driving scenarios and can be easily adapted for tests of other electric vehicles (significantly smaller or bigger than the particular vehicle in this test). 6. Acknowledgments Donation of the electric vehicle from Volkswagen Sarajevo Ltd is greatly acknowledged. So is the material support from the Faculty of Mechanical Engineering (Department for IC Engines), University of Sarajevo. Last, but not least, we would like to thank to Mr. Nijaz Delalic, for his support and collaboration on this project! 7. References [1] Croitorescu, V. & Jiga, G. (2010). Influence of Using a Hybrid Electric Powertrain Combined with an ICE Variable Displacement in Automotive Architecture. Annals of DAAAM for 2010 & Proceedings of the 21st International DAAAM Symposium, Volume 21, No. 1, ISSN 1726-9679 ISBN 978-3-901509-73-5, Editor B. Katalinic, Published by DAAAM International, Vienna, Austria, EU, 2010 [2] Abouelkheir M. & Vu T. M. (2016). Modeling and Simulation of Dual Clutch Transmission and Hybrid Electric Vehicles. 11th International DAAAM Baltic Conference “INDUSTRIAL ENGINEERING” 20-22nd April 2016, Tallinn, Estonia [3] Pikula B. (2011). Justification of Electric and Hybrid Vehicles Use in Urban Areas. Sarajevo Green Design Conference, Sarajevo, September 2011, ISBN 978-90-365-3451-2, pp 156-160 [4] Masic, A.; Musemic, R. & Dzaferovic-Masic, E. (2016). Temperature Inversion Measurements in Sarajevo Valley Using Unmanned Aerial Vehicles, Proceedings of the 27th DAAAM International Symposium, pp.0423-0427, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-902734-08-2, ISSN 1726-9679, Vienna, Austria, DOI: 10.2507/27th.daaam.proceedings.062 [5] Masic, A. (2015).Unmanned Aerial Vehicle as Data Acquisition System. Journal of Trends in the Development of Machinery and Associated Technology. Vol. 19, No. 1, 2015, ISSN 2303-4009 (online), p.p. 181-184. [6] Bujdei, C. & Moraru S. A. (2011). A Design for a Network End Node, Based on Arduino and Xbee. Annals of DAAAM for 2011 & Proceedings of the 22nd International DAAAM Symposium, Volume 22, No. 1, ISSN 17269679, ISBN 978-3-901509-83-4, Editor B. Katalinic, Published by DAAAM International, Vienna, Austria, EU, 2011 [7] http://www.allegromicro.com/~/media/Files/Datasheets/ACS770Datasheet.ashx?la=en?sc_camp=64EB2DD6B3FE4C088C07DB87D5D9B6EF, (2017). Accessed on: 2017-08-24 [8] https://ae-bst.resource.bosch.com/media/_tech/media/datasheets/BST-BMP180-DS000-121.pdf, (2017). Accessed on: 2017-08-24 [9] http://www.geeetech.com/Documents/BST-BMA180-FL000-03.pdf, (2017). Accessed on: 2017-08-24 [10] https://www.sparkfun.com/datasheets/Sensors/Temperature/DHT22.pdf, (2017). Accessed on: 2017-08-24 [11] https://www.u-blox.com/sites/default/files/products/documents/NEO-6_DataSheet_(GPS.G6-HW-09005).pdf, (2017). Accessed on: 2017-08-24 - 0451 -