Fuel Cell Hybrid Electric Vehicle Research Articles

Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization

It is crucial to accurately calculate the cost function of the energy management strategy (EMS) of the hybrid powertrain to improve the hydrogen economy of the system. This paper proposes an EMS for fuel cell hybrid electric vehicles (FCHEV) based on improved dynamic programming (DP) and air supply optimization to improve economy and reliability. Taking the maximum net power output of the FC system as the target, the optimal oxygen excess ratio (OER) and cathode pressure of the FC system under different current densities are solved by using PSO. A velocity prediction method based on Bi-LSTM is developed to predict short-term velocity changes in real time. The DP algorithm is introduced and the EMS of the DP algorithm based on short-term velocity prediction is developed for real-time hybrid powertrain optimization and management. Based on the results of energy allocation and optimal gas supply conditions of FCs, the cost function of EMS is modified to reallocate the power of the FC system and battery. The results demonstrate that the proposed method achieves the lowest hydrogen consumption compared to the other two algorithms. Remarkably, it reduces the fuel cost by up to 8.85 % compared to the commonly used online DP algorithm.

Energy management strategies based on soft actor critic reinforcement learning with a proper reward function design based on battery state of charge constraints

The environmental concerns have pushed our society to find solutions to reduce the emission of transportation systems. One promising solution is the fuel cell hybrid electric vehicle (FCHEV), with a proton exchange membrane fuel cell (PEMFC) and a lithium-ion battery. The presence of two different energy sources requires a smart energy management strategy (EMS) which must not only optimize energy consumption (conventional approaches), but also mitigate component’s degradation (New approaches : Health-conscious strategies). For that, a wide range of techniques has been proposed in the literature, and the most recent techniques are based on machine learning. Reinforcement learning (RL) is usually the most chosen strategy, but as in the optimization techniques, a strong design of the objective function (called reward function in RL) is required. The main challenge in the reward function is to encourage a proper battery state of charge (SOC) management, while reducing the energy consumption and avoiding the factors that accelerate the FC aging. The current advancement in the literature shows satisfactory results, but the reward function design have great improvement potential in the chosen approach. Indeed, most approaches in RL for SOC management with continuous actions are based on the reference SOC principle, which reduces the EMS ability to optimize the other part of the reward function. This paper reveals the challenges associated with the introduction of SOC limits in the reward function and proposes an approach, based on SOC boundaries in the reward function. The contribution allows to better consider the parts to optimize, as it gives more freedom than previous reference SOC techniques all reducing the consumption by 12.9%.

Hierarchical Energy Management Strategy Based On M-DQN for Fuel Cell Hybrid Electric Vehicle

For fuel cell hybrid electric vehicles equipped with a fuel cell (FC), battery (BAT), and supercapacitor (UC), this paper proposes a hierarchical energy management strategy (EMS) based on the improved M-DQN algorithm to reduce hydrogen consumption, enhance the efficiency of the FC, and maintain the state of charge (SoC) of the BAT. Firstly, an adaptive fuzzy low-pass filter is employed to achieve the frequency decoupling of power demand, enabling the UC to provide/absorb the peak power demand. Secondly, an energy management framework based on M-DQN is designed, and the concept of an equivalent consumption minimization strategy is applied to construct the reward function, which includes penalty factors related to the efficiency of the FC and SoC deviation of the BAT, aiming at optimizing the power allocation between the FC and BAT. Simulations and platform tests were conducted under various typical driving cycles. The results indicate that, compared with the traditional M-DQN-based EMS, the proposed EMS can significantly maintain the SoC of the BAT, improve the efficiency of the FC, reduce hydrogen consumption, and even achieve an average improvement of 5.2% in fuel economy.

MLD Modeling and MPC-Based Energy Management Strategy for Hydrogen Fuel Cell/Supercapacitor Hybrid Electric Vehicles

Energy management strategies for hydrogen fuel cell hybrid electric vehicles (FCHEVs) are a key factor in achieving real-time vehicle energy optimization control, vehicle driving economy, and fuel cell durability. In this paper, for an FCHEV equipped with a fuel cell and supercapacitor, the quantitative information, logic rules, and operational constraints are transformed into linear integer inequalities according to its different operating modes, and the Hysdel language is used to establish its mixed logic dynamic model (MLD). Then, the energy management strategy based on model predictive control (MPC) is developed using the MLD model as the prediction model and the equivalent hydrogen consumption and the performance degradation of the fuel cell as the optimization performance indexes. Finally, under the World Light Vehicle Test Cycle, a joint simulation was carried out with Advisor and Simulink to verify the proposed strategy’s superiority by comparing it with the power following control strategy (PFCS) and the compound fuzzy control strategy (CFCS). The results show that the strategy not only ensures real-time FCHEV energy control, but also reduces hydrogen consumption by 10.98% and 1.98% and the number of start/stop times of a fuel cell by six and four, compared to PFCS and CFCS, respectively, which improves the economy of the whole vehicle as well as the durability of the fuel cell.

Towards a fossil-free urban transport system: An intelligent cross-type transferable energy management framework based on deep transfer reinforcement learning

Deep reinforcement learning (DRL) is now a research focus for the energy management of fuel cell vehicles (FCVs) to improve hydrogen utilization efficiency. However, since DRL-based energy management strategies (EMSs) need to be retrained when the types of FCVs are changed, it is a laborious task to develop DRL-based EMSs for different FCVs. Given that, this article introduces transfer learning (TL) into DRL to design a novel deep transfer reinforcement learning (DTRL) method and then innovatively proposes an intelligent transferable energy management framework between two different urban FCVs based on the designed DTRL method to achieve the reuse of well-trained EMSs. To begin, an enhanced soft actor-critic (SAC) algorithm integrating prioritized experience replay (PER) is formulated to be the studied DRL algorithm in this article. Then, an enhanced-SAC based EMS of a light fuel cell hybrid electric vehicle (FCHEV) is pre-trained by using massive real-world driving data. After that, the learned knowledge stored in the FCHEV's well-trained EMS is captured and then transferred into the EMS of a heavy-duty fuel cell hybrid electric bus (FCHEB). Finally, the FCHEB's EMS is fine-tuned in a stochastic environment to ensure adaptability to real driving conditions. Simulation results indicate that, compared to the state-of-the-art baseline EMS, the proposed DTRL-based EMS accelerates the convergence speed by 91.55% and improves the fuel economy by 6.78%. This article contributes to shortening the development cycle of DRL-based EMSs and improving the utilization efficiency of hydrogen energy in the urban transport sector.

Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehicles

The automotive sector is experiencing rapid evolution, with the next-generation emphasizing clean energy sources such as fuel-cell hybrid electric vehicles (FCHEVs) due to their energy efficiency, eco-friendliness, and extended driving distance. Implementing effective energy management strategies play a critical role in optimizing power flow and electrical efficiency in these vehicles. This study proposes an optimized energy management strategy (EMS) for FCHEVs. The suggested EMS introduces a hybridization between the equivalent consumption minimization strategy (ECMS) and the Artificial Hummingbird Algorithm (AHA). The Federal Test Procedure for Urban Driving (FTP-75) is employed to evaluate the performance of the proposed EMS. The results are assessed and validated through comparison with outcomes obtained by other algorithms. The findings demonstrate that the proposed EMS surpasses other optimizers in reducing fuel consumption, potentially achieving a 48.62% reduction. Moreover, the suggested EMS also yields a 15.45% increase in overall system efficiency.

Bi-LSTM predictive control-based efficient energy management system for a fuel cell hybrid electric vehicle

Energy management systems (EMS) in hybrid electric vehicles play a vital role in achieving optimal energy utilization, improved fuel efficiency, lower emissions, and improved vehicle performance. Thus, this paper proposes a Bidirectional Long-Term Memory (Bi-LSTM) model based on an efficient EMS for a hybrid electric vehicle that manages the powertrain elements of the IC engine, electric motor(s), fuel cell, and energy storage system. The proposed EMS also continuously monitors various parameters, including vehicle speed, driver inputs, battery state of charge, engine load, and environmental conditions, to make real-time decisions on power distribution and operation modes. Further, the proposed system leverages the capabilities of the Bi-LSTM model to capture intricate temporal dependencies and bidirectional context in driving patterns using the real-time dataset. Empirical studies are conducted to substantiate the efficacy of the proposed energy management strategy vis-à-vis diverse controllers, encompassing Model Predictive Control and Sliding Mode Control. Real-world driving data is employed as the basis for these investigations, aiming to provide a robust assessment of the proposed energy management approach in practical scenarios. Results demonstrate the efficacy of the suggested methodology compared to traditional EMS approaches, showcasing improvements in energy utilization and reduced environmental impact. Finally, this research emphasizes a comparative analysis of the proposed topology with the existing approaches in a real-world fuel cell hybrid electric vehicle system. The findings provide insights into the feasibility of deploying intelligent EMS strategies for improved sustainability and efficiency in a modern energy-efficient environment.

Effects of Fuel Cell Size and Dynamic Limitations on the Durability and Efficiency of Fuel Cell Hybrid Electric Vehicles under Driving Conditions

In order to enhance the durability of fuel cell systems in fuel cell hybrid electric vehicles (FCHEVs), researchers have been dedicated to studying the degradation monitoring models of fuel cells under driving conditions. To predict the actual degradation factors and lifespan of fuel cell systems, a semi-empirical and semi-physical degradation model suitable for automotive was proposed and developed. This degradation model is based on reference degradation rates obtained from experiments under known conditions, which are then adjusted using coefficients based on the electrochemical model. By integrating the degradation model into the vehicle simulation model of FCHEVs, the impact of different fuel cell sizes and dynamic limitations on the efficiency and durability of FCHEVs was analyzed. The results indicate that increasing the fuel cell stack power improves durability while reducing hydrogen consumption, but this effect plateaus after a certain point. Increasing the dynamic limitations of the fuel cell leads to higher hydrogen consumption but also improves durability. When considering only the rated power of the fuel cell, a comparison between 160 kW and 100 kW resulted in a 6% reduction in hydrogen consumption and a 10% increase in durability. However, when considering dynamic limitation factors, comparing the maximum and minimum limitations of a 160 kW fuel cell, hydrogen consumption increased by 10%, while durability increased by 83%.

Dynamic programming solutions extracted SOC-trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle

The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future driving cycles. To utilize the solutions of DP, a SOC-trajectory online learning generation algorithm based approximate global optimization energy management control strategy is proposed. Initially, the global optimality of DP is used to extract the optimal SOC gradients for diverse driving scenarios. Real-time generation of optimal gradient factors for SOC trajectories is facilitated through the training of a backpropagation neural network with DP solutions. Subsequently, the deterministic rules are designed to plan SOC under actual driving conditions, with a dynamically updated threshold by the trained agents. Finally, based on the above, the optimal calculation of energy allocation is performed by combining sequence quadratic programming. Numerical verification, inclusive of hardware-in-the-loop experiments, show the effectiveness of the proposed strategy. The results demonstrate that the proposed strategy improves fuel economy by 7.39% compared to ECMS. Additionally, it reduces the cost of fuel cell life loss by 32.09% and achieves over 90% optimization of global driving cost.

An energy management strategy for fuel cell hybrid electric vehicle based on a real-time model predictive control and pontryagin’s maximum principle

ABSTRACT In order to maintain the battery SOC, the fuel cell power will fluctuate dramatically, as well as frequent start-stop, which will greatly increase the life attenuation of the fuel cell and reduce the durability. An optimization-based energy management strategy with a real-time model predictive control and pontryagin’s maximum principle for FCHEV is proposed in this paper, both the fuel economy and the fuel cell durability are considered in the optimization. A novel model predictive control is studied to achieve energy distribution. After the calculation of predicted speed sequence through back propagation neural network, pontryagin’s maximum principle is introduced to solve the optimal control problem in each prediction horizon and obtain the ideal control strategy. In addition, the fuel cell degradation model is introduced in the modeling process, the minimum power point of the fuel cell system is designed to improve the fuel economy and durability of the fuel cell. Compared with the rule-based strategy, the proposed MPC strategy has better performance to reduce the total equivalent hydrogen consumption, which can save up to 8.44% in the test case of the mid-size fuel cell passenger car while maintaining the stability of the battery’s state of charge.

Optimized Design of a H2-Powered Moped for Urban Mobility

Micro-mobility plays an increasingly important role in the current energy transition thanks to its low energy consumption and reduced contribution to urban congestion. In this scenario, fuel cell hybrid electric vehicles have several advantages over state-of-the-art battery electric vehicles, such as increased driving ranges and reduced recharge times. In this paper, we study the conversion of a commercial electric moped (Askoll eS3®) into a fuel cell hybrid electric vehicle by finding the optimal design of the components through an optimization methodology based on backward dynamic programming. This optimal design and operation strategy can also be implemented with a rules-based approach. The results show that a system composed of a 1 kW proton exchange membrane fuel cell, a 2000 Sl metal hydride hydrogen tank, and a 240 Wh buffer battery can cover the same driving range as the batteries in an electric moped (119 km). Such a hybrid system occupies considerably less volume (almost 40 L) and has a negligibly higher mass. The free volume can be used to extend the driving range up to almost three times the nominal value. Moreover, by using a high-pressure composite tank, it is possible to increase the mass energy density of the onboard energy storage (although compression can require up to 10% of the hydrogen’s chemical energy). The fuel cell hybrid electric vehicle can be recharged with green hydrogen that is locally produced. In detail, we analyze a residential scenario and a shared mobility scenario in the small Italian city of Viterbo.

Energy Management Strategy Based on Optimal System Operation Loss for a Fuel Cell Hybrid Electric Vehicle

Energy Management Strategy Based on Optimal System Operation Loss for a Fuel Cell Hybrid Electric Vehicle

Efficient training for energy management in fuel cell hybrid electric vehicles: An imitation learning-embedded deep reinforcement learning framework

Deep reinforcement learning (DRL)-based methods have become predominant in the field of energy management strategy (EMS) today. However, DRL methods are often burdened by extensive training periods and a predisposition towards local optima. This work uses Imitation Learning (IL) to fully exploit the enormous training-guide potential of optimization-based methods, as a way to address the aforementioned disadvantages of DRL, building an IL-embedded DRL framework for EMS. Firstly, the off-line globally optimal trajectory is extracted, and then IL is utilized to imitate the trajectory. Subsequently, the network learned from IL is used as the initial policy network for the DRL algorithm to start training. The EMS based on the framework considers hydrogen consumption, fuel cell degradation, and power battery aging, with the goal of reducing the total driving cost. Behavioral Cloning and Proximal Policy Optimization (PPO) are used as the algorithms of IL and DRL parts, respectively in this study, to experiment the effect of the framework. Simulation results show that, compared to standalone PPO, the proposed framework can reduce the training steps by 51.69% while improving the total reward by 4.57%. Within the test cycle, the proposed framework attains 95.59% of the global optimal driving cost, exceeding standalone PPO by 5.79%.

Breaking New Ground in HEV Energy Management: Kinetic Energy Utilization and Systematic EMS Approaches based on robust drive control

The issues faced by hybrid electric vehicles (HEVs) include locating and managing free energy to preserve resource dynamics and constraints while preserving prolonged autonomy. This study assessed a hybrid electric vehicle (HEV) equipped with a fuel cell (FC), battery, direct current generators (DCGs), and supercapacitor (SC) to meet the power needs of an automobile utilizing variable power converters. This study examines four HEV energy management strategies (EMSs), increasing clean environmental power penetration by utilizing the HEV's kinetic energy, as a new contribution. Strategies for Proportional-Integral (PI), State-Machine (SM), Artificial Neural Network (ANN), and Adaptive Neural Fuzzy Inference System (ANFIS) EMSs are discussed. In addition to implementing direct torque control with a space vector modulation-based ANFIS controller (ANFIS-DTC-SVM), this study proposes to insert DCGs in the front wheels of HEVs for free energy production. Simulations of EMSs yielded approximative findings, achieving a 22.2 (%) free-exploited kinetic energy. The ANN-based EMS surpassed the competition, yielding the highest energy efficiency 98.2 (%) and the lowest fuel consumption 48.68 (SI). As a result of maximizing battery utilization and limiting fuel consumption, the examined HEV's dependability and stability were confirmed and reached, highlighting the importance of kinetic energy.

A reinforcement learning energy management strategy for fuel cell hybrid electric vehicles considering driving condition classification

The energy management strategy is one of the core technologies to ensure the safe and efficient operation of the fuel cell vehicle. In this paper, a reinforcement learning energy management strategy for fuel cell and lithium-ion battery hybrid powertrain based on driving condition recognition is proposed. Specifically, an unsupervised clustering strategy is used for the classification of vehicle driving conditions. Subsequently, the classified driving conditions are used for training different agents in deep Q-network learning. The deep Q-network learning strategy with a reward value function that integrates fuel consumption and fuel cell life decay is developed. The proposed method is validated by a driving condition test under real vehicle operation. Through comparative simulations and experiments, our method demonstrates its effectiveness in reducing fuel cell lifetime degradation, minimizing battery state of charge variations, and enhancing the overall performance of the hybrid power system. The method also reduces hydrogen fuel consumption by 48% compared with the commonly used rule-based method.

Model predictive control energy management strategy integrating long short-term memory and dynamic programming for fuel cell vehicles

This paper proposes a novel energy management strategy satisfying real-time and highly efficient energy management strategies for fuel cell hybrid electric vehicles (FCHEVs). The strategy is based on model predictive control (MPC), which integrates long short-term memory (LSTM) and dynamic programming (DP). A high-precision powertrain model of the investigated FCHEV is established for subsequent simulations. After training under several typical working conditions, LSTM is designed to forecast future power demands of the entire vehicle. Using the prediction results, the DP algorithm calculates the control scheme based on model predictive control. Considering the economy and durability of power sources, the results of four different control strategies are compared: thermostat, power following, traditional DP, and MPC. The MPC proposed in this paper reduces the total usage cost per 100 km on the test set by 9%, 33.5%, and −4.6%.

Advanced Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles

This paper aims to develop an Advanced Equivalent Consumption Minimization Strategy (AECMS) for efficient fuel cell hybrid electric vehicles (FCHEV). The Equivalent Consumption Minimization Strategy (ECMS) is one of the most feasible solutions for hybrid electric vehicles as it has less complexity and low computational cost in making the optimal control decision. However, typical ECMS designs with fixed gain use could not enhance an optimal energy control when dealing with multi decisive factors for highly non-linear and uncertain systems like FCHEVs. To address this control challenge, the AECMS is developed as the combination of the ECMS and an adaptive equivalence factor to optimally split the energy flow between the fuel cell and battery in the FCHEV. First, a cost function is formulated based on two key objectives which are power consumptions of the fuel cell and battery, the equivalence factor and a set of soft and hard constraints, considering the number of fuel cell start/stop, fuel cell optimal operating range, state of charge (SoC) range, temperature ranges and power limits. Second, the adaptive equivalence factor is newly designed as the combination of states and dynamics of the fuel cell and battery and corresponding weighting factors. Third, fuzzy inferences are derived to adjust online the weighting factors in an adaptive manner according to the working conditions of the energy sources, subsequently improving the overall vehicle energy efficiency and maintaining the battery SoC while prolonging their lifetime. Numerical simulations using a FCHEV model have been carried out under UDDS and HWFET drive cycles to illustrate the superior performance of the AECMS against the traditional ECMS.

Co-optimization of Energy Management and Eco-Driving Considering Fuel Cell Degradation Via Improved Hierarchical Model Predictive Control

An advanced eco-driving technology is widely recognized as having enormous potential to reduce the vehicle fuel consumption. However, most research on eco-driving focuses on the stability and safety for vehicle operating while disregarding its comfort and economy. To meet the requirements for safety and comfort, at the same time, enhance the economic performance of the vehicles, an improved hierarchical model predictive control cooperative optimization strategy is proposed for fuel cell hybrid electric vehicle with car-following scenario. Specifically, the upper-level model predictive controller controls the velocity, inter-vehicle distance and acceleration to guarantee safety and comfort for driving. According to the velocity information obtained from the upper model predictive controller, the lower-level improved model predictive controller considers the impact of disturbance changes on vehicle economy and aims to minimize the vehicle operating cost considering fuel cell degradation, so as to allocate energy rationally. Finally, the enhancement of economic performance of proposed strategy is verified with the results of comparative study that 3.09% economic improvement on the premise of assuring safety and comfort of driving.

Improved Supervisory Controller Design for a Fuel Cell Hybrid Electric Vehicle

Improved Supervisory Controller Design for a Fuel Cell Hybrid Electric Vehicle

Safety and Longevity-Enhanced Energy Management of Fuel Cell Hybrid Electric Vehicle with Machine Learning Approach

Safety and Longevity-Enhanced Energy Management of Fuel Cell Hybrid Electric Vehicle with Machine Learning Approach