Amol Patil email@example.com linkedin.com/in/amol-a-patil 269-***-****
Experienced Mechanical Engineer with research focus on development, simulation, optimization and testing of hybrid electric vehicle powertrain control. Highly proficient with vehicle powertrain control algorithms development including modeling, simulation and optimization in MATLAB/Simulink for optimal energy management of hybrid-electric vehicles. EDUCATION
MS in Mechanical Engineering (Western Michigan University) GPA: 3.75 Dec’19 BS in Mechanical Engineering (University of Pune, India) GPA: 3.50 July’16 SKILLS
• Vehicle Dynamics: Engine, Battery, Transmission, Motors.
• Control Strategies: Global (DP), Adaptive (MPC), Robust.
• Product design: GD&T, DFMEA, DOE, Root-cause Analysis.
• HEV Powertrain Modeling & Simulation in MATLAB/Simulink.
• Perception: LSTM, CNN, RNN in Python and MATLAB.
• CATIA V5, Solid Works, Auto CAD, Microsoft Office Suite. WORK EXPERIENCE
Graduate Research Assistant, Western Michigan University (MCO Lab, Dept. of Mechanical and Aerospace) Aug’18 - Present
• Developed a high-fidelity, control-oriented hybrid-electric vehicle model using MATLAB/Simulink.
• Performed modeling and simulation of IC engine, motor, generator, battery and transmission models of HEV powertrain.
• Developed optimal control algorithms in MATLAB using “dynamic programming” that reduced fuel consumption by 4.3%.
• Analyzed effects of 1D, 2D, and 3D grids coupled with interpolation methods on optimal control results by convergence theory.
• Successfully maintained SOC of battery for whole drive cycle (charge sustention) to achieve battery stability.
• Developed computationally efficient optimal EMS algorithm by using “Model Predictive Control (MPC)” that improved fuel efficiency by 3.4% (practical considerations).
• Participated in development of autonomous vehicle by introducing camera, LiDAR, NVIDIA PX2 as a perception system.
• Participated in vehicle emissions model development using MOVES and MATLAB.
• Participated in algorithms development for vehicle velocity prediction using LSTM, CNN, and RNN in Python, and MATLAB.
• Analyzed the effect of different signals (GPS, ego vehicle parameters, V2X, and CAN data) on velocity prediction accuracy. Mechanical Engineer, Sigmarq Technologies Pvt. Ltd. Jan’17 – Dec’17
• Efficiently designed and assembled paper printing machine parts using AutoCAD, Solid Works and Catia V5.
• Assisted senior engineers to prepare, update and control the operating manuals, spare part manuals and operating part manuals.
• Developed DVP&R, GD&T, Man management techniques, documentation methods, risk analysis and management.
• Interact with PPC department to keep updating BOM by changing ECN of drawing to fulfill customers requirement. Mechanical Engineering Intern, Kunal Aggrotech July’16 – Dec’16
• Created designs of rota-razor components (e.g. base cutter, frame, chopper) using AutoCAD and PRO-E.
• Performed root cause analysis on existing products and worked on alternate designs to meet customer requirements.
• Assisted senior design engineers by analyzing and compiling data using statistical analysis software. ACADEMIC PROJECTS
National Go - Kart Championship: Participated in a team of 15 members and facilitated the design of low cost go – kart chassis using CATIA and analyzed the optimum strength and rigidity of chassis using ANSYS. Dionysius Catapult: Designed and developed a catapult prototype (from concept to prototype) and analyzed it’s working efficiency. Tools used: DFMEA, GD&T, Cost Analysis, PDS, QFT, Root-cause Analysis, DOE (by JMP), Robustness Assessment, BOM. Catapult Design Using DOE and Regression Analysis: Designed a full factorial (2k) and a half factorial (2k-1) experiment with 5 factors with each operating at 3 levels, to maximize the distance of a projectile. Clean Diesel Combustion – Future Challenges and Emissions (Combustion): Investigated combustion and exhaust emissions characteristics by applying various split injection strategies to get optimal injection which improves FE and exhaust emissions. PUBLICATIONS
• Identification and Review of the Research Gaps Preventing Fuel Efficient Vehicle Control from Optimal Energy management.
• Comparison of Optimal Energy Management Strategies Using Dynamic Programming, Model Predictive Control, and Constant Velocity Prediction (Manuscript Submitted).