Fuel Cell electrifield heavy duty vehicle.pptx

Fuel cell electrified vehicle

1. Update: PhD Research on "An Integrated Energy Management System of a Fuel Cell Electrified Heavy Duty Vehicle" Supervisors Prof. Santanu Prasad Datta Prof. Bahman Shabani Raviteja Nimmala

2. Contents Introduction to the Project Conceptual Understanding of IEMS Research Approach & Methodology Literature Review Initial Work & Progress Timeline for Next Month

3. Introduction to the Project Fuel cell Heavy Duty Vehicles are promising for reducing emissions and improving energy efficiency, but the development of effective Integrated Energy Management Systems that handle diverse operational and environmental conditions is still evolving. While existing Integrated Energy Management Systems tend to focus on optimizing energy use under specific conditions or ideal scenarios , real-world applications involve a wide range of operational and environmental factors that can affect performance. This includes varying loads, driving patterns, road conditions, and environmental factors such as temperature and humidity.

4. Conceptual Understanding of IEMS An Integrated Energy Management System (IEMS) in the context of a fuel cell electrified heavy-duty vehicle refers to the control strategy and framework designed to efficiently manage energy flow between multiple energy sources (such as fuel cells, batteries, and regenerative braking systems) and energy consumers (such as motors and auxiliary systems).

5. Conceptual Understanding of IEMS Control Strategy : IEMS uses an advanced control strategy that dynamically adjusts the energy flow. For example: During high-power demands (e.g., acceleration or uphill driving), both the fuel cell and battery may work together to supply power. During low-power conditions (e.g., steady cruising), the system may primarily rely on the fuel cell, while using the battery minimally or recharging it. In braking or deceleration, the system captures and stores energy through regenerative braking, improving the overall energy efficiency. Challenges & Considerations: System Efficiency: Balancing power output to ensure optimal fuel cell efficiency and prevent excessive battery wear. Durability: Prolonging the lifespan of fuel cells and batteries by managing their power cycles effectively. Emissions & Environmental Impact: Reducing overall emissions by optimizing the use of hydrogen fuel cells, which emit only water, and minimizing reliance on traditional energy sources.

6. Research Approach & Methodology While existing Integrated Energy Management Systems tend to focus on optimizing energy use under specific conditions or ideal scenarios, real-world applications involve a wide range of operational and environmental factors that can affect performance. This includes varying loads, driving patterns, road conditions, and environmental factors such as temperature and humidity. Research Gap: There is a need to develop and refine IEMS that are not only optimized for fuel cell efficiency but are also robust and adaptive to mixed operational and environmental conditions. Specific aspects of this gap include: Adaptive Algorithms: Research on adaptive algorithms that can dynamically adjust energy management strategies based on real-time data from various sensors and external inputs, such as load fluctuations and environmental changes. Multivariate Optimization: Exploring optimization techniques that consider multiple variables simultaneously, including fuel cell performance, battery state-of-charge, thermal management, and energy recovery systems. Real-World Data Integration: Utilizing real-world data to create more accurate models of energy consumption and efficiency. This involves incorporating data from diverse operational scenarios and environmental conditions to train and validate IEMS algorithms. Fault Detection and Recovery: Developing advanced fault detection and recovery mechanisms that ensure the IEMS can handle unexpected situations or failures without compromising vehicle performance or safety. Human Factors and Behaviour Modelling: Investigating how driver behaviour and decision-making affect energy management and how these factors can be integrated into IEMS for better performance and efficiency.

7. Objectives Develop an integrated energy management system including a fuel cell, battery, electrical machine, and cabin for a heavy-duty vehicle and optimize its performance for different drive cycles. Explore the energy efficiency and temperature stability of the proposed thermal management strategy on large-scale battery packs for both hot and cold ambient conditions. Develop a regenerative and dynamic air humidification cycle by utilizing the pure wastewater from the fuel cell stack to optimize the reaction rate and energy output to drive the powertrain. Expose the optimally designed electrical powertrain under different faulty conditions imitating the on-road accidental conditions like overcharging, overheating, collision, etc., and study the corresponding thermal runaway alongside the multi-state reliability of the entire subsystem.

8. Literature Review- Fuel Cell as Series Configuration S.NO Paper Title Reference(s) Key Contributions Methodology Findings/Results Limitations Personal Notes/Insights 1 Digital Twin of a Hydrogen Fuel Cell Hybrid Electric Vehicle: Effect of the Control Strategy on Energy Efficiency. Link: Lorenzo Bartolucci, et al. 2023 IJHE Developed a digital twin to evaluate the effect of control strategies (Range Extender vs. Fuzzy Control) on energy efficiency in FCHEVs. Simulation-based study using MATLAB/Simulink to model FCHEV powertrain and auxiliaries over WLTP driving cycles. Fuzzy logic control increased vehicle range by 4.5% and improved H2-to-wheel efficiency by 48.1%. Temperature and thermal effects were critical. Simulations may not fully account for all real-world variables, and only two control strategies were explored. The use of fuzzy logic control appears promising for optimizing FCHEV performance in diverse environmental conditions. 2 Effect of differential control and sizing on multi-FCS architectures for heavy-duty fuel cell vehicles. Link: R. Novella, et al 2023 ECM Investigated the impact of differential control and sizing strategies for multi-FCS heavy-duty vehicles on fuel consumption and durability. Simulations using a heavy-duty fuel cell vehicle model with different FCS sizes and current density control strategies. A 471% durability increase was achieved with a 3.8% increase in hydrogen consumption. Low dynamics control improves durability significantly. Study focused on heavy-duty driving conditions and specific current density rates. Results might vary in different conditions. Differential control of multi-FCS systems offers a promising approach for enhancing both performance and durability in heavy-duty applications.

9. Literature Review- Fuel Cell as Series Configuration S.NO Paper Title Reference(s) Key Contributions Methodology Findings/Results Limitations Personal Notes/Insights 3 Online Adaptive Energy Management Strategy for Fuel Cell Hybrid Vehicles Based on Improved Cluster and Regression Learner. Link: Mince Li, et al 2023 ECM Developed an online adaptive energy management strategy (EMS) for fuel cell hybrid vehicles using driving pattern recognition (DPR) and machine learning (ML) regression to minimize hydrogen consumption. Improved k-means cluster approach for driving pattern recognition, combined with regression learners to predict optimal energy management based on different driving conditions. Validated using two dynamic test-driving cycles. The proposed EMS reduced hydrogen consumption by up to 5.66% compared to other algorithms. Regression learners showed improved adaptability to complex driving conditions. The study focused on short-term driving conditions without considering fuel cell aging or thermal effects in the EMS. The combination of ML regression with driving pattern recognition shows promise for improving energy efficiency in fuel cell hybrid vehicles, particularly in varying driving conditions. 4 A New Approach to Battery Powered Electric Vehicles: A Hydrogen Fuel-Cell-Based Range Extender System. Link: Roberto Álvarez Fernández, et al 2016 IJHE Proposed a novel powertrain architecture combining a battery electric vehicle (BEV) with a hydrogen fuel-cell-based range extender (ERFC-EV) to overcome limitations in range and refueling infrastructure. Developed a MATLAB/Simulink model to simulate different configurations of ERFC-EV and analyzed the energy consumption and performance under the New European Driving Cycle (NEDC) and real-world driving conditions. Achieved an extended all-electric range of nearly 600 km with an ERFC-EV, compared to 100 km in pure battery mode. The range extender allowed rapid hydrogen refueling and home electric charging, providing flexibility for different driving scenarios. The study focused on theoretical modeling; real-world validation is needed. The impact of different driving conditions and environmental factors on the ERFC-EV's performance requires further investigation. The ERFC-EV concept offers a promising solution to range anxiety in electric vehicles, potentially bridging the gap between BEVs and FCEVs in the transition to sustainable transportation.

10. Literature Review- Fuel Cell as Parallel Configuration S.NO Paper Title References(s) Key Contributions Methodology Findings/Results Limitations Future Work Suggested Personal Notes/Insights 1 Different strategies in an integrated thermal management system of a fuel cell electric bus under real driving cycles in winter. Link: Alberto Broatch, et al 2023 ECM Developed a global model for fuel cell electric buses (FCEB) and evaluated thermal management strategies under winter conditions. Simulation-based study using a real driving cycle of public transport in Valencia, Spain. Tested different thermal management strategies. Energy savings achieved using residual heat for heating the cabin (7%) and preheating the battery (4%). A hybrid solution offered 10% savings. Focused on winter conditions; results may vary in other climates. Not all subsystems were fully integrated in the base configuration. More research needed on fully integrated thermal management systems across different weather conditions and driving cycles. The hybrid strategy shows promise for improving efficiency. Further research could focus on balancing heating demands more dynamically. 2 Adaptive ECMS based on speed forecasting for the control of a heavy-duty fuel cell vehicle for real-world driving. Link: M. Piras, et al 2023 ECM Developed an adaptive energy management strategy (A-ECMS) using speed forecasting with a neural network for fuel cell vehicles. LSTM neural network used to predict vehicle speed for energy management, validated through simulations in GT-Suite. A-ECMS improved battery charge sustenance with 76% SoC fluctuation reduction compared to standard ECMS. Hydrogen consumption increased by 3.76% to 11.40%. Speed forecasting error increases with longer forecasting horizons. Some inefficiencies with real-time FC control. Analyze FC dynamic limits and degradation in future studies to further improve the energy management system. A-ECMS shows great promise for real-world applications in heavy-duty FCVs, particularly in realistic driving conditions.

11. Literature Review- Fuel Cell as Parallel Configuration S.NO Paper Title References(s) Key Contributions Methodology Findings/Results Limitations Personal Notes/Insights 3 Multi-parameter and Multi-objective Optimization of Dual-Fuel Cell System Heavy-Duty Vehicles: Sizing for Serial Development. Link: Zhendong Zhang, et al 2024 Energy Proposed a multi-objective and multi-parameter optimization method using the Multi-Objective Jellyfish Swarm (MOJS) algorithm for sizing dual-fuel cell systems in heavy-duty vehicles, optimizing equivalent hydrogen consumption, mass, and dynamic performance. Developed a dual-layer optimization framework combining Pareto theory and Dynamic Programming (DP) to achieve optimal sizing of fuel cell and battery systems. Performance was evaluated across different vehicle weight levels from 18 tons to 49 tons. The MOJS algorithm provided a more uniform and broader Pareto front compared to other algorithms (NSGA-II, PESA-II, SPEA-II). The optimized configurations reduced equivalent hydrogen consumption by up to 2.66% and improved other performance metrics. High computational cost; the approach requires significant computational resources (13.33 days for the full optimization process). Further research needed to refine models and explore additional driving cycles and fuel cell configurations. The dual-layer optimization approach offers a robust method for improving the efficiency and performance of heavy-duty fuel cell vehicles, particularly for serial development in varying weight classes. 4 Fuel Cell Electric Vehicle Characterisation under Laboratory and In-Use Operation. Link: Giuseppe Di Pierro, et al 2024 ER Characterized a light-duty fuel cell electric vehicle (FCEV) in both laboratory and real-world conditions to measure efficiency and hydrogen consumption. Developed an extended vehicle efficiency metric. Characterized a light-duty fuel cell electric vehicle (FCEV) in both laboratory and real-world conditions to measure efficiency and hydrogen consumption. Developed an extended vehicle efficiency metric. Hydrogen consumption ranged from 0.975 to 1.506 kg/100 km, with vehicle efficiency between 50% and 60%. The extended efficiency metric considered fuel, electrical, and mechanical energy inputs. Results based on limited driving conditions and cycles; further studies needed under extreme conditions and on a wider variety of vehicles. The extended efficiency metric could be useful for future benchmarking. FCEVs are a promising alternative to conventional vehicles with competitive energy consumption.

12. Literature Review- Fuel Cell as Parallel Configuration with Super Capacitor S.NO Paper Title References(s) Key Contributions Methodology Findings/Results Limitations Personal Notes/Insights 1 An Artificial Intelligence and Improved Optimization-Based Energy Management System of Battery-Fuel Cell-Ultracapacitor in Hybrid Electric Vehicles. Link: Harsh Jondhle, et al 2023 JES Proposed a hybrid energy management system (EMS) incorporating artificial intelligence (CNN) and an improved optimization algorithm (PP-SSO) for battery-fuel cell-ultracapacitor hybrid electric vehicles (HEVs). Utilized Convolutional Neural Networks (CNN) for driving pattern recognition and Predator Probability-Based Squirrel Search Optimization (PP-SSO) for optimal weight tuning in energy management. Simulations conducted in MATLAB/Simulink. The proposed EMS reduced the mean absolute error (MAE) and mean square error (MSE) significantly compared to other models like NN, CNN, and SSA-CNN. Efficiency improvements of up to 87.68% were achieved with the optimized model. High computational complexity due to advanced optimization and AI techniques. Further real-world validation is needed to confirm simulation results. The integration of AI and advanced optimization techniques in EMS can significantly improve the efficiency and performance of hybrid electric vehicles, making them more sustainable and practical for future applications. 2 Assessing the Performance of Vehicles Powered by Battery, Fuel Cell, and Ultra-Capacitor: Application to Light-Duty Vehicles and Buses. Link: Shemin Sagaria, et al 2021 ECM Developed a flexible vehicle simulation model to assess energy consumption, range, and performance of vehicles powered by batteries, fuel cells, and ultra-capacitors, applied to light-duty vehicles and buses. MATLAB/Simulink model simulating various powertrain configurations (BEV, FCEV, UC) across different certified driving cycles. Compared energy consumption and range in real-world conditions for light-duty vehicles and buses. BEVs showed the lowest energy consumption (23%) compared to ICE vehicles. Combining a fuel cell with a battery increased range by 10%. Ultra-capacitors extended battery life by 10% but were less effective for light-duty vehicles. UC showed better results for buses, particularly for short trips with frequent stops. The study mainly focused on simulation; real-world validation and diverse driving conditions were limited. Ultra-capacitors were less effective in light-duty vehicles compared to buses. The combination of batteries, fuel cells, and ultra-capacitors provides a promising avenue for optimizing energy efficiency in various vehicle segments, particularly for public transportation.

13. Literature Review- Fuel Cell as Parallel Configuration with Super Capacitor S.NO Paper Title Reference(s) Key Contributions Methodology Findings/Results Limitations Personal Notes/Insights 3 Energy Management of a Fuel Cell/Ultra-Capacitor Hybrid Electric Vehicle Under Uncertainty Based on CO-SNN Method. Link: P. Satheesh Kumar, et al 2024 JES Introduced a hybrid energy management strategy combining Cheetah Optimizer (CO) and Spiking Neural Network (SNN) to manage energy in FC/UC hybrid electric vehicles under uncertain conditions. Developed a MATLAB-based simulation integrating CO for optimization and SNN for adaptive control under varying conditions. The method was validated with two driving cycles (EUDC, HWEFT). The CO-SNN method reduced hydrogen consumption by optimizing fuel cell efficiency and managing ultra-capacitor power. It performed better than existing methods like WHO, BCO, and GPC in robustness and computational efficiency. High computational complexity and the need for real-world validation. The study was limited to simulated driving cycles, which may not cover all real-world scenarios. The integration of optimization and neural computation offers a promising avenue for improving energy management in hybrid vehicles, particularly in dynamic and uncertain environments. 4 Improving Fuel Economy and Performance of a Fuel-Cell Hybrid Electric Vehicle (Fuel-Cell, Battery, and Ultra-Capacitor) Using Optimized Energy Management Strategy. Link: Saman Ahmadi, et al 2018 ECM Developed an optimized energy management strategy (EMS) for fuel-cell hybrid electric vehicles (FCHEVs) using fuzzy logic control (FLC) and genetic algorithms to improve fuel economy and performance. The proposed EMS uses a combination of fuzzy logic control and power track control (PTC), with genetic algorithms optimizing FLC parameters over combined city/highway driving cycles. Simulations conducted using ADVISOR over 22 different driving cycles. The EMS improved fuel economy, vehicle performance, and battery charge sustainability. Acceleration times improved by 18.8%–26.1%, and fuel economy gains were achieved compared to previous strategies. Battery life was extended by reducing deep charge/discharge cycles. The study focused on simulations; real-world validation is needed. Further research should address the economic feasibility of large-scale implementation in heavy-duty applications. The combination of FLC and genetic algorithms proves highly effective in optimizing the EMS for FCHEVs, with significant improvements in both fuel economy and vehicle dynamics.

14. Proposed Fuel Cell powertrain Battery

15. Block Diagram of IEMS-MIMO