Yves Dubé

Two-Layer Energy-Management Architecture for a Fuel Cell HEV Using Road Trip Information

This paper investigates the design of a two-layer energy-management system for a fuel cell hybrid electric vehicle (HEV). The first layer (upper layer) deals with the vehicle energy consumption, whereas the second layer (lower layer) deals with the power splitting between the fuel cell and the battery. The upper layer aims at providing the globally optimal energy consumption profile by considering the road-trip information and the vehicle dynamics. This energy profile is independent of the number and type of power sources on the vehicle. Therefore, it can be used to assist the real-time power splitting algorithm implemented into the lower layer. This layer design goal is mainly to share the vehicle power demand between the fuel cell and the battery while minimizing the hydrogen consumption. In addition, the splitting method takes into account the fuel cell efficiency map and the hydrogen/electricity relative pricing while imposing a smooth behavior on the fuel cell. This smooth behavior is desirable to preserve the fuel cell life and reduce the oxygen starvation phenomenon. The proposed energy-management system has been successfully implemented and validated on an HEV test bench. The experiments and simulations using several standard driving cycles suggest that the approach can reduce the hydrogen consumption up to 10% compared to a rule-based method and a depleting-sustaining method while preserving at the same time the battery pack from overdischarging.

Online System Identification and Adaptive Control for PEM Fuel Cell Maximum Efficiency Tracking

This paper addresses an adaptive control approach for a maximum efficiency tracking of a proton exchange membrane fuel cell. The adaptive control has an online identification system that estimates the fuel cell efficiency behavior parameters and an optimization module that is responsible for providing the best operating conditions. The proposed approach does not require a cell-precise model and is not sensitive to the initial operating conditions and has been successfully tested on a 500-W proton exchange membrane fuel cell. A comparative study with the airflow control strategy shows that this adaptive control can improve the fuel cell efficiency up to 10%. Since the proposed adaptive control requires little knowledge of the fuel cell model, it can be easily used for similar fuel cells.

Hybrid electric vehicle power management strategy including battery lifecycle and degradation model

The following work is concerned with the inclusion of battery degradation mechanisms in power management strategies applied to current-generation hybrid electric vehicles (HEV). Using a real, physical electric vehicle as a basis for validation, a simulation model was realized to describe the behavior of the HEV and its components, including a lead-acid battery bank, an internal combustion engine (ICE) generator and a polymer electrolyte membrane fuel cell (PEMFC). This model is to be used in conjunction with a newly developed battery degradation model and included as part of a power management strategy aimed at reducing operating costs via an associated function, with the objective of studying the effects of battery lifecycle in such a strategy. As a first step towards the realization of this goal, this paper is aimed at describing the model of the HEV and its validation.

Approach in Nonintrusive Type I Load Monitoring Using Subtractive Clustering

In this paper, a low-sampling-rate and nonintrusive appliance loads monitoring (NIALM), which required only few tuning parameters and which is little sensitive to the grid power noise, is presented. Using transformed active power transitions as features, the proposed approach is based on the subtractive clustering and the maximum likelihood classifier. In order to validate the NIALM, a 1 Hz sampling rate experimental data from the reference energy disaggregation dataset is selected. The validation results with six commonly found ON/OFF residential appliances indicate that the proposed approach is effective. In addition, the obtained results from a Monte Carlo simulation suggest that this approach is less sensitive to power grid noise than a

Low Temperature Discharge Cycle Tests for a Lithium Ion Cell

K

Experimental evaluation of PEM Fuel Cell systems efficiency

-mean-based NIALM method.

Low temperature aging tests for lithium-ion batteries

As all drivers in cold countries know, operating HEV/EVs at cold temperature is rather difficult. Indeed, cold weather increases the internal resistance of the battery system creating a high opposing force while operating the battery: slowdown of Li+ diffusivity and decrease of ionic conductivity of electrolyte. Thereby, it limits the amount of energy extracted and reduces cell energy and power capability. Therefore, Li-Ion sensitivity to temperature remains one of the major obstacles to HEV/VEs market penetration. In fact, until now, investigations of low-temperature behaviors of Li-ion cells barely provide suitable information because they have only been extended to small battery capacities or non-currently used HEV/VEs batteries. Therefore, a complete thermal characterization of an actual HEV/VEs battery is missing. This characterization is described in this paper. Indeed; a 100 Ah lithium LiFePO4Mn HEV battery was tested under various operating conditions. The experimental process includes charging at ambient temperature, and discharging under extreme cold weather. The experimentations was conducted at four different temperatures to study the effect of seasonal changes in temperature.

Long-Trip Optimal Energy Planning With Online Mass Estimation for Battery Electric Vehicles

Nowadays, the Proton Exchange Membrane Fuel Cell (PEMFC) is considered as one of the most promising sources of energy with zero emissions. Therefore, maintaining the PEMFC system in its optimal operating conditions is one of the most important research orientations in the domain. In fact, the systems efficiency depends highly on the electric power generated, the amount of hydrogen used and the power consumed by the air compressor to operate the fuel cell stack. In this work, the experimental efficiency of the PEMFC system has been evaluated for different values of the PEMFC parameters such as purges frequency, stack temperature, air relative humidity, pressures and mass flow rates of reagents. Then the experimental results show that for a given load current, an optimal efficiency can be obtained by choosing the right values for the parameters.

PEMFC low temperature startup for electric vehicle

This paper presents the aging test results of a 100Ah prismatic LiFeMnPO4 battery cell at low temperatures. Accelerated aging tests have been carried out on 4 cells to address the effect of cold temperatures on Lithium-ion (Li-ion) batteries. Three of them were aged through a normalized driving cycle at three temperature tests (-20°C, 0°C and 25°C). The calendar test was carried out on one single battery at -20°C and mid-range of SOC (50%). Their capacities were continuously measured every two or three days. From the analysis, this paper shows that it is mandatory to consider the impact of cold temperatures on a battery pack lifespan. Therefore, there is a need for having a proper thermal strategy in order to keep the battery cell in the most appropriate operating condition and thus reduce the cold effect on the battery performances.

Thermal Management of a Hybrid Electric Vehicle in Cold Weather

This paper addresses the optimal battery charging schedule for a long trip. Starting with a fully charged battery, the electric vehicle (EV) must stop at least once for battery charging before reaching its destination. Since the battery lifespan is thoroughly related to its depth-of-discharge and since the charging time can be long, it is therefore useful to provide a feasible battery charging schedule during the long trip. Minimizing a cost function, which includes the charging energy cost, battery degradation, and the charging duration, an optimal charging schedule is proposed and successfully validated with small-pickup EV data. In addition, this method takes into account the possible mass change, as well as wind effect on the predicted energy consumption. A comparative study with the commonly used maximum depleting and charging schedule suggests that the proposed approach is efficient, and it contributes to reducing the overall trip duration while reducing, at the same time, the battery degradation.