Temperature control system for range optimization in electric vehicle

In this paper, we propose a mechanism to optimize range of EV by integrating some of the best methods to estimate rotor position, efficient temperature control modules and machine learning algorithms that analyze the vehicle’s environment and driving pattern.
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Temperature control system for range optimization in electric vehicleInternational Journal of Mechanical Engineering and Technology (IJMET)Volume 10, Issue 03, March 2019, pp. 1117-1126. Article ID: IJMET_10_03_114Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3ISSN Print: 0976-6340 and ISSN Online: 0976-6359© IAEME Publication Scopus Indexed TEMPERATURE CONTROL SYSTEM FOR RANGE OPTIMIZATION IN ELECTRIC VEHICLE R. Angeline Department of Computer Science and Engineering, Faulty of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India Sahitya. PDepartment of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India Swathi. SDepartment of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India Chethan. T. SDepartment of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India Shivani. LDepartment of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India ABSTRACT In this paper, we propose a mechanism to optimize range of EV by integrating some of the best methods to estimate rotor position, efficient temperature control modules and machine learning algorithms that analyze the vehicle’s environment and driving pattern. A simulation of an EV model with the above-mentioned modules is presented through Simulink. The result of this simulation is compared with the result of simulation with the same modules but with Machine Learning Algorithms integrated. A comprehensive comparison analysis is then presented to show how range of an EV improves as the machine learns. Keywords: Simulink, Reinforced Learning, Electric Vehicle, Q Learning, TD(λ)Learning. http://www.iaeme.com/IJMET/index.asp 1117 editor@iaeme.com R. Angeline, Sahitya. P, Swathi. S, Chethan. T. S and Shivani. L Cite this Article: R. Angeline, Sahitya. P, Swathi. S, Chethan. T. S and Shivani. L, Temperature Control System for Range Optimization in Electric Vehicle, International Journal of Mechanical Engineering and Technology, 10(3), 2019, pp. 1117-1126. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=31. INTRODUCTIONThe exponential depletion of petroleum and natural gas has prompted an aggressive researchand development to manufacture electric vehicles (EV). A major concern in EVs is to optimizerange because of the following reasons 1. Infrastructure to charge batteries is insufficient. 2. Ittakes 30-40mins to charge a vehicle. 3. Resources required to manufacture the battery unit arealso limited. Hence the need to optimize range is required to ensure longevity of the EV’sbattery. Several methods have been proposed to estimate rotor position, thus, eliminating the needto have heavy mechanical sensors. One of the methods implements Machine Learning toestimate position of rotors in EV by using phase current and voltage as their data points [3].Other methods concentrate on carrier signal injection based sensorless control techniques atzero and low speeds or on improved dynamic models of Permanent Magnet Synchronous Motor(PMSM) drives. The former methods are hindered by saturation effects and signal injectionleads to unwelcomed torque ripples. In latter methods, the dependency on back-emf results ininaccurate estimation of rotor position at very low and zero speeds. The influence of environment temperature on battery of EVs has been extensively studiedthrough a number of tests like Electrochemical Impedance Spectroscopy test and DynamicDriving test thus, reaching a conclusion that at low temperature, a number of events like DCinternal resistance and polarization effect are the main factors that limits the batteryperformance [4]. Experiments on 50A.h Lithium-iron phosphate batteries under temperaturerange of minus 40°C - 40°C have been carried out to analyze the influence of environmenttemperatures on voltages, internal resistance, efficiency and life cycle of battery while chargingand discharging [5]. Research has also been conducted to dynamically equalize the temperature of all batterycells (especially Li-ion batteries) in a package because focusing on average temperature of thebattery package rather than each battery cell in the package has resulted in wear-out, uneventemperature distribution and has affected the safety standards of the battery cells [6].Furthermore, temperature variation degrades torque accuracy and efficiency of IPMSMmachines and hence, compensation control algorithm is shown to save energy and improveefficiency. In this paper, we propose a mechanism to optimize range of Electric Vehicle by integratingsome of the best methods that focus on controlling battery temperature and ML algorithms thatanalyze the road conditions, driving pattern, ambient temperature and wind speed to estimatean accurate torque for the EV. ML algorithm is also proposed to estimate the power required tocool down the battery, which further increases the range in EVs. A simulation of an EV modelthat integrates battery temperature control module is presented through Simulink. The result ofthis simulation is compared with the result of that with ML algorithms integrated. Acomprehensive comparison analysis is then presented to show how range of an EV improves asthe machine learns. http://www.iaeme.com/IJMET/index.asp ...