Browsing by Author "Pramanik, Sourav"

Now showing 1 - 5 of 5
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    Charge optimization of lithium-ion batteries for electric-vehicle application
    (2015-03-02) Pramanik, Sourav; Anwar, Sohel; Wasfy, Tamar; Li, Lingxi
    In recent years Lithium-Ion battery as an alternate energy source has gathered lot of importance in all forms of energy requiring applications. Due to its overwhelming benefits over a few disadvantages Lithium Ion is more sought of than any other Battery types. Any battery pack alone cannot perform or achieve its maximum capacity unless there is some robust, efficient and advanced controls developed around it. This control strategy is called Battery Management System or BMS. Most BMS performs the following activity if not all Battery Health Monitoring, Temperature Monitoring, Regeneration Tactics, Discharge Profiles, History logging, etc. One of the major key contributor in a better BMS design and subsequently maintaining a better battery performance and EUL is Regeneration Tactics. In this work, emphasis is laid on understanding the prevalent methods of regeneration and designing a new strategy that better suits the battery performance. A performance index is chosen which aims at minimizing the effort of regeneration along with a minimum deviation from the rated maximum thresholds for cell temperature and regeneration current. Tuning capability is provided for both temperature deviation and current deviation so that it can be tuned based on requirement and battery chemistry and parameters. To solve the optimization problem, Pontryagin's principle is used which is very effective for constraint optimization with both state and input constraints. Simulation results with different sets of tuning shows that the proposed method has a lot of potential and is capable of introducing a new dynamic regeneration tactic for Lithium Ion cells. With the current optimistic results from this work, it is strongly recommended to bring in more battery constraints into the optimization boundary to better understand and incorporate battery chemistry into the regeneration process.
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    Look Ahead Based Control Strategy for Hydro-Static Drive Wind Turbine Using Dynamic Programming
    (MDPI, 2020-10) Pramanik, Sourav; Anwar, Sohel; Mechanical and Energy Engineering, School of Engineering and Technology
    This research paper presents a look-ahead optimal control strategy for a Hydro-static Drive Wind Turbine when look ahead wind speed information is available. The proposed predictive controller is a direct numerical optimizer based on the well established principles of Hamilton-Jacobi-Bellman (Dynamic Programming). Hydro-static transmission based, non-linear model of wind turbine is used in this optimization work. The optimal behavior of the turbine used the non-linearity of aerodynamic maps and hydro-static drive train by a convex combination of state space controller with measurable generator speed and hydraulic motor displacement as scheduling parameters. A comparative analysis between a optimal controller based on Maximum Power Point Tracking (MPPT) algorithm as published in literature and the proposed look ahead based predictive controller is presented. The simulation results show that proposed look ahead strategy offered optimal operation of the wind turbine by closely tracking the optimal tip-speed ratio to maximize capacity factor while also maintaining the hydraulic motor speed close to the desired value to ensure that the frequency of electrical output is constant. It is observed from the simulation results that the proposed predictive controller provided around 3.5% better performance in terms of improving total system losses and harvesting energy as compared to the MPPT algorithm.
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    Predictive Energy Management of Mild-Hybrid Truck Platoon Using Agent-Based Multi-Objective Optimization
    (IEEE, 2023-07-11) Pramanik, Sourav; Anwar, Sohel; Mechanical and Energy Engineering, School of Engineering and Technology
    The objective of this paper is to formulate and analyze the benefits of a predictive non-linear multi objective optimization method for a platoon of mild-hybrid line haul trucks. In this study a group of three trucks with hybrid electric powertrain are considered in a platoon formation where each truck has a predictive optimal control to save fuel with out any loss of trip time. While the controller on each truck uses the look ahead knowledge of the entire route in terms of road grade, the overall platoon controller used a multi agent method (Metropolis algorithm) to define coordination between the trucks. While the individual trucks, showed significant improvement in fuel economy when running on predictive mode, the true savings came from the entire platoon and showed promising results in terms of absolute fuel economy without trading off on total trip time. The proposed algorithm also proved to be significantly emission efficient. A platoon of 3 trucks achieved an average of 10% fuel savings while cutting back 13% on engine out NOx emissions for engine off coasting and 9.3% fuel saving with 8% emissions reduction for engine idle coast configuration when compared to non-predictive non-platoon configuration.
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    Predictive Optimal Control of Mild Hybrid Trucks
    (MDPI, 2022-10-11) Pramanik, Sourav; Anwar, Sohel; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that a vehicle operates in its true optimal operating region, given a variety of constraints such as road grade, load, gear shifts, battery state of charge (for hybrid vehicles), etc. In this paper, a research study is conducted to evaluate the fuel economy and subsequent emission benefits when applying predictive control to a mild hybrid line-haul truck. The problem is solved using a combination of dynamic programming with backtracking and model predictive control. The specific fuel-saving features that are studied in this work are dynamic cruise control, gear shifts, vehicle coasting and torque management. These features are evaluated predictively as compared to a reactive behavior. The predictive behavior of these features is a function of road grade. The result and analysis show significant improvement in fuel savings along with NOx benefits. Out of the control features, dynamic cruise (predictive) control and dynamic coasting showed the most benefits, while predictive gear shifts and torque management (by power splitting between battery and engine) for this architecture did not show fuel benefits but provided other benefits in terms of powertrain efficiency.
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    Predictive Optimal Control of Mild Hybrid Trucks
    (MDPI, 2022) Pramanik, Sourav; Anwar, Sohel; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    Numerous per- and polyfluoroalkyl substances (PFASs) occur in consumer food packaging due to intentional and unintentional addition, despite increasing concern about their health and environmental hazards. We present a substance flow analysis framework to assess the flows of PFASs contained in plant fiber-based and plastic food packaging to the waste stream and environment. Each year between 2018 and 2020, an estimated 9000 (range 1100–25 000) and 940 (range 120–2600) tonnes per year of polymeric PFASs were used in 2% of food packaging in the U.S. and Canada, respectively. At least 11 tonnes per year of non-polymeric PFASs also moved through the food packaging life cycle. Approximately 6100 (range 690–13 000) and 700 (range 70–1600) tonnes per year of these PFASs were landfilled or entered composting facilities in the U.S. and Canada, respectively, with the potential to contaminate the environment. The results suggest that minimal food packaging contains intentionally added PFASs which, nonetheless, has the potential to contaminate the entire waste stream. Further, this indicates that PFASs are not needed for most food packaging. These results serve as a benchmark to judge the effectiveness of future industry and government initiatives to limit PFAS use in food packaging.