Investigation of the battery degradation impact on the energy management of a fuel cell hybrid electric vehicle

This paper studies the influence of battery degradation over the performance of a fuel cell hybrid electric vehicle (FCHEV). For this purpose, an optimized fuzzy strategy based on the costs of battery and fuel cell degradations as well as fuel consumption and battery recharging is employed. Simulations are done by two driving cycles for three scenarios based on battery state of health (SOH) and validity of feedback signal. Simulation results prove that battery aging has a considerable impact on the total cost of a FCHEV. Moreover, tuning of the EMS parameters according to the battery SOH decreases the defined cost

Design of an Incentive-based Demand Side Management Strategy for Stand-Alone Microgrids Planning

Demand Side Management Strategies (DSMSs) can play a significant role in reducing installation and operational costs, Levelized Cost of Energy (LCOE), and enhance renewable energy utilization in Stand-Alone Microgrids (SAMGs). Despite this, there is a paucity in literature exploring how DSMS affects the planning of SAMGs. This paper presents a methodology to design an incentive-based DSMS and evaluate its impact on the planning phase of a SAMG. The DSMS offers two kinds of incentives, a discount in the flat tariff to increase the electrical energy consumption of the users, and an extra payment added to the fare to penalize it. The design of the methodology integrates the optimal energy dispatch of the energy sources, the tariff design, and its sizing. In this regard, the main contribution of this paper is the design of an incentive-based DSMS using a Disciplined Convex approach, and the evaluation of its potential impacts over the planning of SAMG. The methodology also computes how the profits of the investors are modified when the economic incentives vary. A study case shows that the designed DSMS effectively reduces the size of the energy sources, the LCOE, and the payments of the customers for the purchased energy

Guest Editorial Special Section on Design, Modeling, and Control of Hybrid and Multi-source Vehicles

ADVANCED traction and propulsion systems have been developed in order to ensure better energy performance, reduced operating cost, and higher lifetime of future transportation systems like road vehicles, as well as more electric trains, subways, ships, and airplanes. Such a powertrain integrates several complex subsystems (including power or energy sources, electric machines, power electronics, mechanical transmission), and it becomes mandatory to consider the whole system in order to reach the best performance

A novel online energy management strategy for multi fuel cell systems

This paper addresses the design of an energy management strategy (EMS) for a multi-stack fuel cell system (MFCS). In this regard, firstly, two power allocation strategies, namely Daisy Chain and Equal Distribution have been developed and compared in characteristics terms. Subsequently, a novel adaptive strategy is proposed to split the power between the fuel cells and the battery by utilizing the demanded power, state of charge (SOC) of the battery, maximum power and efficiency point of each fuel cell. In a MFCS, each fuel cell shows variable performances in different operating conditions depending on its specific ageing, material, and external factors. The purpose of this study is to ensure an equal level of degradation for each fuel cell and to make them operate in an efficient zone, with the assistance of an online identification method as well as an adaptive power strategy. Simulations have been conducted in Matlab-Simulink environment. In this work, a mechanistic fuel cell model is employed to imitate the behaviour of a real MFCS and a semi-empirical model, coupled with an adaptive recursive least square (ARLS) to predict the maximum power (MP) and maximum efficiency (ME). The results of the proposed strategy show noticeable improvements in the fuel economy

Deep reinforcement learning energy management system for multiple battery based electric vehicles

In recent years, energy management systems have become an emerging research topic. This concept allows the distribution of energy-intensive loads among various energy sources. An appropriate resource allocation scheme is necessary for the controller to efficiently allocate its energy resources in different operating conditions. Recent advances in artificial intelligence are instrumental to solve complex energy management problems by learning large repertoires of behavioral skills. This consists of hand-engineered policy and human-like expertise representations. In this paper, a deep reinforcement learning based resource allocation scheme is proposed for electric vehicles avoiding to work at the level of complex vehicle dynamics. Using multiple energy storage devices, like batteries, in parallel increases their maintenance due to their different behavior in various operating conditions. Thus, the proposed strategy aims to learn optimal policies to equilibrate the state of charge (SOC) of all batteries extending their lifespan and reducing their frequent maintenance

An Adaptive State Machine Based Energy Management Strategy for a Multi-Stack Fuel Cell Hybrid Electric Vehicle

This paper aims at designing an online energy management strategy (EMS) for a multi-stack fuel cell hybrid electric vehicle (FCHEV) to enhance the fuel economy as well as the fuel cell stacks (FCSs) lifetime. In this respect, a two-layer strategy is proposed to share the power among four FCSs and a battery pack. The first layer (local to each FCS) is held solely responsible for constantly determining the real maximum power and efficiency of each stack since the operating conditions variation and ageing noticeably influence stacks' performance. This layer is composed of a FCS semi-empirical model and a Kalman filter. The utilized filter updates the FCS model parameters to compensate for the FCSs' performance drifts. The second layer (global management) is held accountable for splitting the power among components. This layer uses two inputs per each FCS, updated maximum power and efficiency, as well as the battery state of charge (SOC) and powertrain demanded power to perform the power sharing. The proposed EMS, called adaptive state machine strategy, employs the first two inputs to sort the FCSs out and the other inputs to do the power allocation. The ultimate results of the suggested strategy are compared with two commonly used power sharing methods, namely Daisy Chain and Equal Distribution. The results of the suggested EMS indicate promising improvement in the overall performance of the system. The performance validation is conducted on a developed test bench by means of hardware-in-the-loop (HIL) technique

An Online Energy Management Strategy for a Fuel Cell/Battery Vehicle Considering the Driving Pattern and Performance Drift Impacts

Energy management strategy (EMS) has a profound influence over the performance of a fuel cell hybrid electric vehicle since it can maintain the energy sources in their high efficacy zones leading to efficiency and lifetime enhancement of the system. This paper puts forward an online multi-mode EMS to efficiently split the power among the components while embracing the effects of the driving conditions and performance degradation of the fuel cell system. In this regard, firstly, a self-organizing map (SOM) is trained to cluster the driving patterns. The SOM competitive layer in this work is composed of ten driving features as inputs and it classifies the driving patterns into three classes in the output. Subsequently, a three-mode fuzzy logic controller (FLC) is designed and optimized offline by the genetic algorithm for each driving pattern. Unlike the other similar works, the output membership function of the FLC is designed based on the online identification of the maximum power and efficiency of the fuel cell system which change over time. Finally, the SOM is utilized to recognize the driving mode at each sequence and accordingly activate the most sui