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This paper presents a bifuel hydrogen–gasoline internal combustion engine (ICE) as an effective strategy for extending the electric vehicle's ranges. The electric power produced by the proposed ICE linked with a generator is a nonlinear function of the engine speed and the proportions of hydrogen and gasoline mixed fuel can be approximated around operating conditions. This nonlinear function is approximated by the Taylor series and a comparative study between the obtained results and the experimental data showed the effectiveness of the proposed approach. Furthermore, we observed that the Taylor series approach can achieve less than 7% error, while the modeling with an artificial neural network or a recursive least square method results in more than 8% error. To enable the ICE operation with maximum efficiency, a nonlinear optimization method is used. The proposed maximum efficiency tracking approach is compared with that of the most used industrial methods based on constant speed. The results show that the proposed approach can result in more than 7% of saving in energy, compared to that of the industrial method. [ABSTRACT FROM AUTHOR]

This paper addresses a hydrogen-based generator (genset) adaptive control used as a range extender of a battery electric vehicle. Based on a commercially available gasoline genset, this hydrogen-based generator can use a mixture of gasoline and hydrogen in which the proportion of gasoline varies between 0% and 100%. This hybrid energy system is controlled by an onboard energy management system that splits the electric power demand between the battery and the genset. Given the genset power profile, a maximum efficiency tracking module was designed to provide optimal operating conditions (engine speed and electric power) to a real-time controller. To tackle the genset nonlinearities, an adaptive controller based on the internal model control approach is designed and successfully validated. In addition, a comparative study with an industrial-based control method indicates that the proposed approach is effective and can achieve significant improvement in genset efficiency. [ABSTRACT FROM AUTHOR]

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 $K$ -mean-based NIALM method. [ABSTRACT FROM PUBLISHER]

Cold weather is an important matter for electric and hybrid-electric vehicles (EV/HEVs), because the electro-chemical process is slowed at low temperatures, hence inducing a loss of power and energy. To prevent it from happening, battery packs are heated via a battery thermal management system (BTMS). However, in some cases, HEVs/EVs are not connected to the grid, so the BTMS uses energy from the battery pack, thus impacting total autonomy. Strategies exist, but must be optimized. Therefore, this paper proposes the design of a HEV strategy that is parked outside and unplugged from the electrical grid. Its objective is to find a compromise between the cost in energy contained in the battery and the aging of the cells. The optimization is based on dynamic programming and utilizes an electro-thermal battery model including aging. Simulation results are then compared with other strategies, underlining that parking outside at work or overnight without plugging in are issues with the actual strategies of common EVs/HEVs, mainly because aging as a result of cold temperatures is not considered. [ABSTRACT FROM PUBLISHER]

Lithium-ion (Li-ion) batteries widely used in electric vehicles (EVs) and hybrid EVs (HEVs) are insufficient for vehicle use after they have degraded to 70% to 80% of their original capacity. Battery lifespan is a large consideration when designing battery packs for EVs/HEVs. Aging mechanisms, such as metal dissolution, growth of the passivated surface film layer on the electrodes, and loss of both recyclable lithium ions, affect the longevity of the Li-ion battery at high-temperature operations. Even vehicle maneuvers at low temperatures (T < \mbox0\ ^\circ\mboxC) contribute to battery lifetime degradation, owing to the anode electrode vulnerability to other degradation mechanisms such as lithium plating. Nowadays, only a few battery thermal management schemes have properly considered low-temperature degradation. This is due to the lack of studies on aging of Li-ion batteries at subzero temperature. This paper investigates how load cycle and calendar life properties affect the lifetime and aging processes of Li-ion cells at low temperatures. Accelerated aging tests were used to determine the effect of the ambient temperature on the performance of three 100-Ah LiFeMnP04 Li-ion cells. Two of them were aged through a normalized driving cycle at two temperature tests ( -\mbox20\ ^\circ\mboxC and 25 °C). The calendar test was carried out on one single battery at –20 °C and mid-range of state of charge (50%). Their capacities were continuously measured every two or three days. An aging model is developed and added to a preliminary single-cell electrothermal model to establish, in future works, a thermal strategy capable of predicting how the cell ages. This aging model was then validated by comparing its predictions with the aging data obtained from a cycling test at 0 °C. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]