Research Designs of LEV
Date: September 10, 2017
Potential areas of research identified in the discussion is to be a paper evaluating a number of articles. An explanation of how this analysis merits the research method used and how to one evaluates the ways the research methods are used in the study. For example, in the script for a quantitative theoretical perspective section, a researcher tests a theory, then tests the hypotheses, then defines and operationalizes the variables, and finally measures the variables using an instrument (Creswell, 2014). This theoretical perspective is based on the potential areas of research from previous discussion of light electric vehicles (LEV) along with two quantitative journal articles.
Light Electric Vehicles (LEV) Description
In this topic of light electric vehicles (LEV), the focus is electric bicycles (eBikes) where one pedals to initiate the amount of amount of power to an electric motor mounted on the bike. An electric battery supplies the energy needed to power the eBike along with the pedaling force of the rider resulting in a greater total applied force accelerating the rider forward depending on the power of the motor. The system consists of controllers receiving information by a network of sensors from pedaling, braking, temperature, battery, and motor where this information can be sent to cloud to extend functionality as Software as a Service (SaaS). From this description, a research method best suited where some historical precedent exists for viewing is a quantitative theoretical approach. In quantitative studies, the researcher uses theory deductively with objective of testing or verifying the theory (Creswell, 2014). Therefore, two quantitative journal articles are to show variables referring to characteristic or attributes that can be measured or observed.
Motor Analysis and Evaluation
In the first article, a comparison of electric vehicle (EV) motors of induction motor (IM) drives and permanent magnet brushless dc motor (BDCM) drives. In addition, a switched reluctance motor (SRM) has been proposed as an alternative solution for EV propulsion but have not been found in EV drives. However, due to their low cost, compact size and high reliability, SRM may be an alternative for EVs.
A quantitative comparison of these attributes such as drives efficiency, power device, complexity, power density (kW/kg), size (l/kW), cost ($US/kW), and max speed are evaluated for the drives. Experts evaluated the EV propulsion systems following the criteria of cost, efficiency, reliability/maintenance, high speed and range. 17% of the respondent regarded the BDCM drives suited for EV because of their compactness, low weight, high efficiency and high controllability while only 11% of the respondents chose SRM for EV propulsion because their high reliability, low cost, simplicity and high-speed potential. 72% of the respondents prefer IMs because of the low cost, high reliability, and low torque. This quantitative comparison of EV drives from a survey of experts’ opinions conducted concluded induction motor drives are preferred for EVs.
The merits of this study give EV designers of high performance electric vehicles value based quantitative comparison where a theory for fitting the best motor to eBike design requirements. To evaluate these merits, BDCM motors for eBike designs is currently used in the marketplace and inductive motors is currently used in Tesla Motors EVs.
Battery Analysis and Evaluation
The second article, another important component of LEV is the battery. Since the battery is a significant portion of the cost, weight, safety, and power to EVs, much research and development activity has been seen in the marketplace such as the Tesla’s Giga Factory (Struthers, 2016). A quantitative study of the performance of various batteries for battery electric vehicles (BEV) is compared on the short and longer term (Gerssen-Gondelach, Faaij, 2012). This study presents quantitative data of various batteries; however, the focus of high performance batteries includes Nickel-metal-hydride (NiMH), Lithium-ion (Li-ion), high temperature sodium-beta batteries (NaS and ZEBRA), Lithium Metal Polymer (LMP), Lithium-sulfur (Li-S) along with lithium-air (Li-air) and zinc-air (Zn-air), and developmental conversion, organic, nickel-lithium and lithium-copper batteries. The data attributes present the types of batteries Li-ion, ZERBA, Li-S, Zn-air, Li-air and compares each type with performance such as specific energy (Wh/kg), specific power (W/kg), efficiency (%), cycle life (# cycles), lifetime (years’), operating temperature, and safety. In addition, performance projections are for 2015, 2025, and beyond are given.
These performance and cost projections relative to production is of merit to BEV designers by comparing and contrasting types of battery while promoting environmentally and economically competitive product. For example, the well-to-wheel (WTW) energy consumption and emission are the lowest for Li-ion with 314-374 Wh/km and 76-90 gCO2 eq/km. In the medium term (5-20years) as opposed to short (<5 years) and long term (>20 years), lithium-based batteries have the potential to reach high specific energy comparatively, while batteries that do not contain lithium have better economic perspectives. For example, 83% of Li-ion battery costs can drop below 300 $/kWh by 2020. ZEBRA batteries comparatively can attain cost levels of 200-300 $/kWh (Gerssen-Gondelach, Faaij, 2012). The evaluation of data for Li-ion batteries from the Boston Consultancy Group (BCG) and Deutsche Bank gave the low and high-volume estimates for future high-volume production costs where cell production costs can be reduced scaling high volume production along with material costs (cathode, anode, electrolyte, separator, current collectors). For example, the cost of Tesla batteries was reduced (Struthers, 2016) by using a graphite (20 $2010/kg) anode as opposed to a more expensive nickel metallic lithium (61-128 $2010/kg) anode used in ZEBRA battery (Gerssen-Gondelach, Faaij, 2012).
The script for a quantitative theoretical perspective section, a researcher tests a theory, then tests the hypotheses, then defines and operationalizes the variables, and finally measures the variables using an instrument (Creswell, 2014). Based on the analysis gathered from the two quantitative articles, types of motors and the other types of batteries for BEV, the researcher test a theory. The theory applied to eBikes where different categories of bikes and various performance levels in a cost sensitive marketplace is similar to the theory applied to electric cars. For example, in a high-performance bike category that applies to eBikes has characteristics of speed (45km/hr), torque (90 Nm), and battery energy (11.5 – 20 Ah) where these attributes translates to rider feeling of speed, acceleration and climbing hills, and duration and power of the ride relative to time. In theory, a BDCM motor and Li-ion battery should satisfy these requirements. To test this theory, a researcher constructs an eBike with these components and measures the results.
Instruments to test speed, torque, and battery power is needed to verify objectives and test theory. Instruments such as a dynamometer to test speed of the eBike according to European standard EN 15194:2009-06 (Maturo, n.d.). A motor lab with that includes EV analysis software capable of synchronizing readouts of mechanical and electrical test signals, digital torque sensor, current probes, and voltage probes to measure motor torque under various conditions (Hoyer, 2013). A battery lab with a battery analyzer to measure the energy a battery holds specified in Ah by applying a full discharge of the battery (Battery University, 2016).
In this paper, potential areas of research identified in the LEV paper evaluating a number of articles where the analysis merits the quantitative research method used and the ways the research methods are used in eBikes. A script for a quantitative theoretical perspective section where a researcher tests a theory, then tests the hypotheses, then defines and operationalizes the variables, and finally measures the variables using an instrument (Creswell, 2014). This theory of a BDCM motor and Li-ion battery is based on data from two articles about BEV electric motors and batteries. These performance characteristics such as speed, torque, and battery power is measured by instruments such as speedometer, motor lab dynamo, and battery analyzer. The results of these tests then would indicate whether the theory meets these high-performance eBike category with characteristics of speed (45km/hr), torque (90 Nm), and battery energy from battery (11.5 – 20 Ah).
Battery University. (2016, January). BU-909: Battery Test Equipment. Retrieved from http://batteryuniversity.com/learn/article/battery_test_equipment.
Chang, L. (1994). Comparison of ac drives for electric vehicles-A report on experts’ opinion survey. IEEE Aerospace and Electronic Systems Magazine, 9(8), 7-11.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Thousand Oaks, CA: SAGE.
Gerssen-Gondelach, S. J., & Faaij, A. P. (2012). Performance of batteries for electric vehicles on short and longer term. Journal of power sources, 212, 111-129.
Hoyer, M. (2013, November). The Misconceptions of EV Motor Testing. Retrieved from http://www.machinedesign.com/motorsdrives/misconceptions-ev-motor-testing.
Maturo. (n.d.). Dynamometer for E-Bikes According to new EPAC/EMC standard. Retrieved from file:///Users/silentblade/Downloads/DYN_E-Bike.pdf
Struthers, R. (2016, October). Tesla Giga Factory, Among Others, May Cause Lithium or Graphite Shortages. Retrieved from https://seekingalpha.com/article/4010559-tesla-giga-factory-among-others-may-cause-lithium-graphite-shortages.