Alexandre Ravey

Energy-Source-Sizing Methodology for Hybrid Fuel Cell Vehicles Based on Statistical Description of Driving Cycles

This paper describes a new methodology based on the statistical description of driving cycles to size the energy source of a hybrid vehicle. This methodology is applied to a fuel-cell-based collection truck for very specific driving patterns. Based on experimental data, random driving cycles are then generated, allowing the distribution of the average powers and energies to be computed. The analysis proves that a 20-kW fuel cell stack is sufficient for a 13 000-kg truck. The results show that the fuel cell system could be downsized, compared with classical solutions, where much larger fuel cells are required.

Control Strategies for Fuel-Cell-Based Hybrid Electric Vehicles: From Offline to Online and Experimental Results

This paper describes two different control strategies for a fuel-cell-based hybrid electric vehicle (FCHEV). The offline strategy is based on dynamic programming, and the online strategy is based on an optimized fuzzy logic controller. These two strategies are then compared. Finally, the fuzzy logic controller is validated using a real FCHEV.

Combined optimal sizing and energy management of hybrid electric vehicles

This paper describes a new methodology for sizing energy sources in hybrid electric vehicles, that enables obtaining the minimal sizing required for a given driving cycle, independently of the chosen energy management strategy. The methodology is based on two combined optimization loops: one for sizing the energy sources, using a genetic algorithm, and another one for computing the optimal energy management strategy for a specific driving cycle, using dynamic programming. Results show that the algorithm can find the best sizing of sources for the best fuel consumption, with a 6.5kW fuel cell and a 75Wh battery for the ECE driving cycle and a 9.0kW fuel cell and a 72Wh battery for the LA92 cycle. Compared to results obtained through the mean sizing power method, the algorithm shows that the hydrogen consumption can be reduced by up to 70% and the size of the battery by up to 67 %. The proposed methodology can thus help optimize the sizing of hybrid vehicles used for given driving cycles.

Design and Development of a Smart Control Strategy for Plug-In Hybrid Vehicles Including Vehicle-to-Home Functionality

Plug-in hybrid electric vehicles (PHEVs) are seen to be a step forward in transportation electrification, to replace internal combustion engine (ICE)-based conventional vehicles. However, to consider the vehicle-to-home (V2H) and home-tovehicle (H2V) capabilities, new energy control strategy has to be developed to avoid new peaks consumption. This paper presents a novel controller based on fuzzy logic, which integrates an objective state-of-charge (SoC) for V2H application. The V2H capability is used when the PHEV is connected to the home to help the grid to meet the household loads during peak period. The SoC objective is the minimum SoC that the PHEV has to have when the driver connects the PHEV to the home. The proposed controller is applied on fourth different scenario.

Online Estimation of Lithium Polymer Batteries State-of-Charge Using Particle Filter-Based Data Fusion With Multimodels Approach

In this paper, a robust model-based battery state-of-charge (SOC) estimating algorithm is proposed with a novel approach based on combination of multimodels data-fusion technique and particle filter (PF). The proposed method is particularly adapted for SOC estimation under real-time conditions and the presence of measurement noise. In this innovative approach, multiple battery models have been used in order to accurately estimate a battery SOC. During the estimation process, the measured battery terminal voltage is compared with the multiple battery models output to generate individual residual, which is then used to calculate the weight of estimated value from each battery model. This weight, which represents the accuracy of observation equation of each battery model, is inversely proportional to the residual. The estimated SOC values from different models are then fused and the weights of estimated values from each battery model are adjusted dynamically using PF and weighted average methodology, in order to calculate the final SOC estimation of the battery. For each proposed battery model, the corresponding parameter-tuning strategies are also presented. In addition, the proposed method has been validated by experimental results. The results demonstrate that the proposed multimodels-based algorithm can be implemented effectively for real-time application, and achieve better accuracy than single model-based methods.

Dynamic Phenomena Coupling Analysis and Modeling of Proton Exchange Membrane Fuel Cells

Dynamic variable coupling analysis is an important method to properly design a control structure for complex multivariable and multi-physical dynamic systems, such as fuel cells. The fuel cell is an electrochemical energy conversion device which includes different inter-coupled dynamic phenomena in electrical, fluidic, and thermal domains. In order to achieve optimized fuel cell performance, different operation variables, such as fuel cell temperature, inlet air flow rate, hydrogen pressure, and membrane water content, need to be properly controlled. In this paper, variable coupling analyses of fuel cell dynamic behaviors are presented and discussed based on a proton exchange membrane fuel cell (PEMFC) dynamic model, which considers in particular the transient phenomena in both fluidic and thermal domain. The analyses of dynamic phenomena step responses are conducted using the relative gain array for various control input variables. Quantitative analyses of coupling effects in different physical domains are shown and discussed. The analysis results can be used to optimize the controller design for fuel cell system.

Energy sources sizing for hybrid fuel cell vehicles based on statistical description of driving cycles

This paper describes a new methodology to size the energy source of a hybrid vehicle based on statistical description of driving cycles. This methodology is applied to a fuel cell based collection truck for which the driving pattern is very specific. Randoms driving cycles are then generated allowing the distribution of the average powers and energies to be computed. The analysis shows that even for a 2,500 kg truck, a 3,5kW fuel cell stack is sufficient, allowing the fuel cell system to be downsized compared to classical solutions using several tens of kilowatts fuel cell systems.

On-line estimation of lithium polymer batteries state-of-charge using particle filter based data fusion with multi-models approach

In this paper, a robust model-based battery state of charge (SOC) estimating algorithm is proposed with a novel approach based on multi-models data fusion technique and particle filter (PF). The proposed method is particularly adapted for SOC estimation under conditions of sharp current variations and presence of measurement noise. In this innovative approach, multiple battery models have been used in order to accurately estimate a battery SOC. The measured battery terminal voltage is compared with the multiple battery models output to generate a residual, which is then used to calculate the weight of estimated value from each battery model. This weight, which represents the accuracy of observation equation of each battery model, is inversely proportional to the residual. The estimated SOC values from different models are then fused and the weights of estimated values from each battery model are adjusted dynamically using particle filter and weighted average methodology, in order to calculate the final SOC estimation of the battery. In addition to the simulation, the proposed method has been validated by experimental results. The results demonstrate that the proposed multi-models based algorithm can achieve better accuracy than single model-based methods.

Control strategy of fuel cell hybrid electric vehicle based on driving cycle recognition

This paper describes a novel control strategy based on driving cycle recognition. A Driving Cycle Recognition Algorithm (DCRA) is firstly presented. It allows switching between three driving modes: urban, suburban and highway. A real-time control strategy is then defined based on fuzzy logic with DCRA. Results are drawn and compared to fuzzy logic controllers parametrized for urban or highway cycles.

Degradation prediction of PEM fuel cell stack based on multi-physical aging model with particle filter approach

In this paper, a novel prediction approach for proton exchange membrane fuel cell (PEMFC) performance degradation is proposed based on a multi-physical aging model with particle filter approach. The proposed multi-physical aging model uses aging coefficients to describe fuel cell different physical aging phenomena over time, including membrane conductivity losses, reduction of reactants mass transfer and reaction activity losses. In order to accurately model the activation loss, the implicit Butler-Volmer equation is used. The initial values of the aging parameters are tuned by fitting the fuel cell polarization curve at the beginning of life. Based on the initialized aging model, the first step of prediction approach is to estimate all the aging parameters using Bayesian Monte Carlo-based Particle Filter (PF) during the learning phase of experimental aging test. The suitable fitting curve function is then selected to satisfy the degradation behavior of each trained aging parameter, and further provide the extrapolated values of aging parameters in the validation phase. By applying these extrapolated aging parameters into aging model, the prediction result of fuel cell output voltage in the validation phase can be obtained. The results demonstrate that the proposed approach have good prediction performance for fuel cell degradation. In addition, each obtained aging parameters provides an insight into the different degree of physical aging process over time during the fuel cell operating, which is important to understand degradation mechanisms.