Equivalent Consumption Research Articles

Terrain information-involved power allocation optimization for fuel cell/battery/ultracapacitor hybrid electric vehicles via an improved deep reinforcement learning

As for fuel cell hybrid electric vehicles (FCHEV) powered by fuel cell, battery, and ultracapacitor, its complex topology structure and the variable terrain environment bring challenges to the energy management for power sources lifespan and fuel economy. In this paper, a terrain information-involved energy management strategy (EMS) obtained by an improved deep reinforcement learning (DRL) algorithm is proposed to distribute reasonably the power demand for FCHEV. Specially, an action-value-based adaptive noise and an action-screening-mechanism-based priority experience replay are designed for efficient strategy learning with the adopted algorithm. To protect fuel cell and battery from peak power, a hierarchical energy management framework is established by introducing an adaptive fuzzy filter. Then, an equivalent consumption minimization strategy based on multi-objective optimization is constructed and used as the reward of the improved algorithm to achieve the minimum fuel consumption. Meanwhile, based on historical experimental data, the terrain-information-considered transition probability matrix is obtained and applied to the algorithm to reduce computational load and get the optimal EMS. Finally, the effectiveness of the proposed EMS is verified by a series of comprehensive simulations under different driving cycles. The simulation results show that the hierarchical EMS framework can better protect the service life of fuel cell and battery. In addition, compared with the EMS without considering terrain information, the incorporation of terrain information in EMS can enhance the operational efficiency of fuel cell, improve fuel economy by about 8%, and can reach the dynamic programming benchmark level of 89.5%.

하이브리드 철도차량의 ECMS 기반 소비에너지 최적화 연구

In hybrid train it is essential for new technologies to improve energy efficiency such as power distribution between energy sources, SOC(State Of Charge) management, unlike general electric trains driven by single power through catenary lines. This paper suggests an energy management strategy for energy consumption optimization of train. A new energy optimization strategy for hydrogen electric train based on ECMS(Equivalent Consumption Management Strategy) is described using PMP(Pontryagin"s Minimum Principle). The EF(Equivalent Factor) that determines the power distribution between HFC(Hydrogen Fuel Cell) and battery system is changed according to the battery SOC through PI(Proportional-Integral) control. The proposed algorithm is implemented using Matlab/Simulink and verified through co-simulation with Amesim, which includes a vehicle model.

Systemic Nonsteroidal Anti-Inflammatories for Analgesia in Postoperative Critical Care Patients: A Systematic Review and Meta-Analysis of Randomized Control Trials.

We searched Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, trial registries, Google Scholar, and relevant systematic reviews through March 2023. Titles, abstracts, and full texts were reviewed independently and induplicate by two investigators to identify eligible studies. We included randomized control trials (RCTs) that compared NSAIDs alone or as an adjunct to opioids for systemic analgesia. The primary outcome was opioid utilization. In duplicate, investigators independently extracted study characteristics, patient demographics, intervention details, and outcomes of interest using predefined abstraction forms. Statistical analyses were conducted using Review Manager software Version 5.4. (The Cochrane Collaboration, Copenhagen, Denmark). We included 15 RCTs (n = 1,621 patients) for admission to the ICU for postoperative management after elective procedures. Adjunctive NSAID therapy to opioids reduced 24-hour oral morphine equivalent consumption by 21.4 mg (95% CI, 11.8-31.0 mg reduction; high certainty) and probably reduced pain scores (measured by Visual Analog Scale) by 6.1 mm (95% CI, 12.2 decrease to 0.1 increase; moderate certainty). Adjunctive NSAID therapy probably had no impact on the duration of mechanical ventilation (1.6 hr reduction; 95% CI, 0.4 hr to 2.7 reduction; moderate certainty) and may have no impact on ICU length of stay (2.1 hr reduction; 95% CI, 6.1 hr reduction to 2.0 hr increase; low certainty). Variability in reporting adverse outcomes (e.g., gastrointestinal bleeding, acute kidney injury) precluded their meta-analysis. In postoperative critical care adult patients, systemic NSAIDs reduced opioid use and probably reduced pain scores. However, the evidence is uncertain for the duration of mechanical ventilation or ICU length of stay. Further research is required to characterize the prevalence of NSAID-related adverse outcomes.

Multi-objective reinforcement learning-based energy management for fuel cell vehicles considering lifecycle costs

To balance the hydrogen consumption of fuel cell vehicle (FCV), the durability of the fuel cell (FC), and the life of the power battery (PB) to further reduce the whole lifecycle costs of FCV. A multi-objective reinforcement learning-based (MORL-based) energy management strategy (EMS) is proposed in this research. First, the composition mechanism of the FCV lifecycle costs is analyzed, and the equivalent hydrogen consumption model, FC durability degradation model, and PB life decay model are established; Then, a three-dimensional reward function is constructed by integrating the objectives of equivalent hydrogen consumption, FC durability degradation, and PB life decay. And the penalty terms coupled with the decay factors are introduced into the reward function to satisfy the mutual constraint characteristics between the PB and the FC system to ensure the stability of the MORL-based EMS; In addition, the prioritized experience replay technology is introduced into the MORL-based EMS to improve the learning efficiency and convergence of traditional deep Q network (DQN) algorithm; After that, the evaluation and target network of the embedded dueling network are introduced to solve the multi-objective overestimation problem encountered in the training process by generalizing the behavior learning in the presence of similar value behaviors; Finally, the performance of MORL-based EMS and DQN-based EMS is compared by numerical simulation under various driving cycles. The results show that the MORL-based EMS proposed in this paper has better convergence ability, adaptability, and lower lifecycle costs than the DQN-based EMS. In addition, the lifecycle costs of the MORL-based EMS can achieve a 99.2% control effect of the dynamic programming-based EMS.

Design of Two Fuel Cell Buses for Public Transport According to Two Different Operating Scenarios: Urban and Motorway

<div>The transport sector is one of the major parties responsible for carbon dioxide (CO<sub>2</sub>) and pollutants emissions in Europe. For this reason, one of the main commitments of the European Commission is its decarbonization by 2035/2040. To achieve this target, during the last decades, different propulsion technologies were developed such as hybrid electric vehicles (HEVs), plug-in electric vehicles (PHEVs), and battery electric vehicles (BEV). The first two proposals can be considered as bridging technology between the internal combustion engine (ICE) and the BEV because they offer at the same time comparable performance as conventional powertrains and improved efficiency. However, both technologies are struggling with the tightening of pollutants and CO<sub>2</sub> limits. On the other hand, the BEV can offer zero emissions at the tailpipe, but it suffers from limited range capabilities and the lack of fast-charging infrastructures. Within this context, the fuel cell vehicle (FCV) appears as an interesting opportunity because it offers zero tailpipe emissions and equivalent refuelling time of the ICE. This article evaluates through mathematical simulations the performance of two fuel cell electric buses (FCEBs), which are supposed to work respectively in urban and highway driving conditions. The urban bus is equipped with a single fuel cell (FC) module of 85 kW-Net and an electric motor (EM) of 225 kW. The intercity bus is equipped with two FC modules with a total power of 170 kW-Net and two EMs of 225 kW each. A sensitivity to the battery capacity from 20 kWh to 40 kWh was performed for both FECBs. The power split between the FC module and the high-voltage battery was optimized with the Equivalent Consumption Minimization Strategy (ECMS). The two FCEBs were tested considering different portfolios of cycles: in the case of the urban bus in Braunschweig and the Standardized On-Road Test Cycles SORT1 and SORT2 were assumed as a reference, while cycles like the Highway Fuel Economy Test (HWFET), European Transient Cycle (ETC), and cruising at 100 km/h were assumed as reference for the intercity. Simulation results highlighted that the increase of battery capacity in the case of the urban bus from 20 kWh to 30 kWh reduces hydrogen (H<sub>2</sub>) consumption by 11% along the Braunschweig cycle. On the other hand, in the case of the intercity bus, the fuel consumption is less affected by the increase of capacity in the same range. In this case a reduction of 4.7% is estimated for the HWFET cycle, and it is less than 1% in the case of cruising conditions.</div>

Research on Energy Management for Ship Hybrid Power System Based on Adaptive Equivalent Consumption Minimization Strategy

This paper analyzes a hybrid power system containing a fuel cell (FC) and proposes an improved scheme involving the replacement of a single energy storage system with a hybrid energy storage system. In order to achieve a reasonable power distribution between fuel cells and energy storage units and stable operation of the power grid, an efficient energy management system (EMS) based on the equivalent consumption minimization strategy (ECMS) is proposed. To enhance the dynamic response capability of hybrid energy storage systems, a low-pass filter with a variable time constant based on the ultracapacitor SOC feedback is proposed. This paper describes the design of a fuzzy logic controller that adaptively improves the equivalent consumption minimization control strategy, adjusts the equivalent factor in real time, optimizes the operating points of the fuel cell system, and improves system efficiency. The simulation model of the fuel cell hybrid system was established using MATLAB/Simulink. The proposed adaptive equivalent consumption minimization strategy (A-ECMS) was simulated and compared to the state-based and fuzzy logic energy management systems under the simulation of the real operating conditions of the ship. The results show that the proposed strategy can maintain a fuel cell system efficiency above 60% under most operating conditions and can significantly suppress the fluctuation of a fuel cell’s output power. The proposed strategy outperforms the state-based and fuzzy logic-based EMS in terms of stabilizing the hybrid power system and reducing hydrogen consumption. The effectiveness of the proposed strategy has been verified.

Adaptive equivalent consumption minimization strategy based on road grade estimation for a plug-in hybrid electric truck

Road grade plays an important role in deciding power repartition and improving energy management performances. In this paper, instead of predictive energy management strategies where terrain information is obtained from GIS maps, an adaptive equivalent consumption minimization strategy (A-ECMS) considering current road grade information is proposed, aiming to design an instantaneous feedback supervisory controller based on the estimation of current road grade without using external devices. To achieve real-time control for a plug-in hybrid electric truck (PHET), the bounds of the optimal equivalent factor (EF) are analyzed considering comprehensive performances, then the sensitivity of EF is evaluated towards road grade. According to the effect of different road grade scenarios on control performances, the real-time EF adaptation is divided into two conditions, i.e. SOC-based adaptation and Estimation-based adaption. The proposed A-ECMS can achieve good performances on both fuel economy improvement and emission reduction, which approximates the results obtained from the DP algorithm, and the high computing load can be avoided for real-time implementation.

Evaluating a unique enhanced recovery protocol in laparoscopic donor nephrectomy: A single center experience.

Enhanced recovery after surgery (ERAS) protocols have been associated with a reduction in opioid consumption and a hastening in recovery in abdominal surgery. However, their impact on laparoscopic donor nephrectomy (LDN) has not been fully elucidated. The aim of this study is to evaluate opioid consumption and other relevant outcome measures before and after implementation of a unique LDN ERAS protocol. 244 LDN patients were included in this retrospective cohort study. Forty-six underwent LDN prior to implementation of ERAS, whereas 198 patients received ERAS perioperative care. The primary outcome was daily oral morphine equivalent (OME) consumption averaged over the entire postoperative stay. Due to removal of preoperative oral morphine from the protocol partway through the study period, the ERAS group was further subdivided into morphine recipients and non-recipients for subgroup analysis. Secondary outcomes included the incidence of postoperative nausea and vomiting (PONV), length of stay, pain scores, and other relevant measures. ERAS donors consumed significantly fewer average daily OMEs than Pre-ERAS donors (21.5vs. 37.6, respectively; p<.0001).There were no statistically significant differences in OME consumption between morphine recipients and non-recipients. The ERAS group experienced less PONV (44.4% requiring one or more rescue antiemetic postoperatively, vs. 60.9% of Pre-ERAS donors; p=.008). A protocol pairing lidocaine and ketamine with a comprehensive approach to preoperative PO intake, premedication, intraoperative fluid management and postoperative pain control is associated with reduced opioid consumption in LDN.

Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems

This research proposes an improved energy management strategy (EMS) for a fuel cell hybrid power system for an electric aircraft based on a recently developed coyote optimization algorithm (COA). The suggested hybrid system consists of fuel cells and an energy storage system (ESS) to supply the required load in stable conditions. The distribution and performance of the hybrid electrical power system are determined by various energy sources. Consequently, having the best energy management system is essential for completing this work. The suggested EMS’s main objectives are to reduce hydrogen energy utilization and increase power source longevity. The proposed coyote optimization algorithm with external energy maximization strategy (COA-EEMS) and coyote optimization algorithm with equivalent consumption minimisation strategy (COA-ECMS) are tested with the help of the Opal-RT 5700 real-time HIL simulator and MATLAB/Simulink. The proposed algorithms confirm their robustness and higher efficiency by minimizing hydrogen fuel consumption compared to existing algorithms. The merits of the proposed algorithms are presented in detailed and compared with existing algorithms.

Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model

The development of hybrid technology can effectively solve the problems of the high pollution and energy consumption levels of automobiles. Therefore, an energy consumption prediction and control algorithm for hybrid vehicles based on a minimum equivalent fuel consumption model is proposed. The model’s battery power consumption is equivalent to the fuel consumption, and the sum of the engine fuel consumption and the battery equivalent fuel consumption is established as the objective function. By utilizing these factors, an innovative minimum equivalent fuel consumption model was constructed that could be used to measure the energy efficiency of hybrid vehicles. The longitudinal force result of braking force distribution control was obtained, as well as the energy consumption prediction structure of a hybrid electric vehicle. The rolling resistance, air resistance, and climbing resistance of the hybrid electric vehicles were calculated, and the energy consumption control algorithm for hybrid electric vehicles was constructed according to the calculation results. The experimental results indicated that under this research algorithm, the driving energy consumption of hybrid electric vehicles was relatively low and the energy consumption and energy efficiency measurements effectively met the actual demand, and the energy consumption prediction and control results were good.

Utility of Regional Maxillary Nerve Blocks in Improving Cleft Palate Postoperative Outcomes.

Palatoplasty procedures used to repair cleft palates are commonly associated with limiting postoperative pain. Regional anesthetic blocks have been utilized to improve pain outcomes and decrease opioid intake, yet additional data is needed to fully explore its utility in this setting. To explore whether ultrasound-guided suprazygomatic maxillary blocks (SMB) improve postoperative pain, postoperative opioid use, time to oral feeding, and length of stay compared with a palatal field block in cleft palate repair. In this retrospective chart review, 47 patients aged 9 to 25 months who underwent cleft palate repair between 2013 and 2020 were allocated into 2 groups: a control group where patients received only palatal local anesthetic in a field block fashion (N=29), and Maxillary block group who received ultrasound-guided SMB (N=18). Patients were matched by age and cleft Veau type. The primary outcomes were total postoperative morphine equivalent consumption, average pain scores, length of stay, and time to first oral feed. Comparing field block versus SMB groups, there was not a statistical difference in the overall dose of postoperative morphine equivalent opioid administration (11.71 vs. 13.36mg; P =0.483), average pain scores (5.78 vs. 5.27; P =0.194), time to first oral feed [17.21 vs. 14.48 h; P =0.407, 95% CI: (-3.85, 9.32)] or length of stay ( P =0.292). The use of SMBs did not demonstrate a difference in the postoperative outcomes evaluated by this study. Further study is needed to define its utility in cleft palate repair.

Automated mandatory bolus versus basal infusion for maintenance of epidural analgesia in labour.

Automated mandatory bolus versus basal infusion for maintenance of epidural analgesia in labour.

Comparative study between ultrasound-guided erector spinae plane block and thoracic paravertebral block for postoperative analgesia after video-assisted thoracic surgery: an equivalence study

BackgroundIt is advised to use a regional block to lower postoperative opioid usage. Therefore, we aimed to compare the analgesic effect of erector spinae plane block (ESPB) and thoracic paravertebral block (TPVB) on the need for morphine following video-assisted thoracic surgery (VATS).ResultsThe findings revealed that TPVB and ESPB groups were equivalent in analgesic efficacy with postoperative morphine consumption in mg 15.2 ± 2.7 and 14.9 ± 2.4 respectively. statistically significant shorter block time in ESPB was 8.1 ± 1.7 min in contrast to 11.35 ± 1.7 min in TPVB and better patient satisfaction in the ESPB than in TPVB groups (p value < 0.05). No statistically significant differences existed between the two groups in terms of postoperative pain score (VAS) at rest, cough at any time point, or incidence of complications (p value > 0.05). There were significantly lower heart rate (HR) and blood pressure (BP) trends in 15 and 30 min intraoperatively (p value < 0.001) in the TPVB group.ConclusionsESPB has analgesic efficacy equivalent to TPVB from aspects of equivalent postoperative morphine consumption and VAS at rest and during cough with shorter block time and better patient satisfaction.Trial registrationPan African Clinical Trial Registry PACTR202109527452994. Registered on 8 September 2021.

Estimating the welfare gains from anti-retroviral therapy in Sub-Saharan Africa

Since the start of the century, many countries in Sub-Saharan Africa have experienced large gains in life expectancy and average consumption levels. Around the same time, an unprecedented international effort has taken place to combat HIV/AIDS mortality with the expansion of anti-retroviral therapy (ART) across many of the hardest hit countries. In this paper, I estimate the impact of ART on average welfare over time in 42 countries using the equivalent consumption approach. I decompose the change in welfare to isolate the relative contribution of ART-driven improvements in life expectancy and consumption. The results indicate that ART has accounted for around 12% of total welfare growth in SSA between 2000 and 2017. In those countries most affected by HIV/AIDS, this figure rises to around 40%. Moreover, the estimates suggest that welfare in some of the worst-hit countries would have declined over time without the ART expansion.

A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMS

The driving characteristics of a hybrid propulsion system (HPS) depend on operational management strategies. However, the differences in output and response of various types of power sources make real-time management of operating conditions complex. In this study, an operational management strategy, which combines adaptive equivalent consumption minimization strategy (AECMS) and general regression neural network (GRNN), is proposed for optimizing power controls within hybrid power sources. Under this strategy, the optimal function of the AECMS is adaptively calculated by the load profile changes based on GRNN’s prediction results, and it is more suitable for the HPS. Furthermore, the proposed GRNN-AECMS method can minimize fuel consumption and meet requirements for power tracking. From simulation results, fuel consumption under GRNN-AECMS method can be reduced by 2.715 kg, or 31.8%.

Multi-objective real-time energy management for series–parallel hybrid electric vehicles considering battery life

The real-time control of energy management strategy (EMS) is becoming increasingly challenging as the complexity of the model and control strategy increases. To address this issue while ensuring the accuracy of the EMS, a multi-objective real-time EMS based on model predictive control (MPC) that considers battery life is proposed in this paper. Firstly, to balance the accuracy and efficiency of the prediction module, a kernel extreme learning machine based on the whale optimization algorithm is proposed as a short-term speed prediction model. Secondly, an adaptive state of charge (SOC) trajectory planning method is established to plan MPC reference trajectory. Next, to optimize fuel efficiency, electrical energy consumption, and battery aging in real-time, a multi-objective real-time MPC (MOR-MPC) algorithm is proposed. Finally, the effectiveness, real-time performance, and robustness of the proposed strategy are verified. Simulation results demonstrate that the total cost of the strategy is reduced by 6.15% compared to the equivalent consumption minimization strategy (ECMS), with 98.17% of dynamic programming (DP) performance achieved. Real-time performance is improved by 97.89% compared to DP-MPC. Hardware-in-the-loop (HIL) testing is also carried out to evaluate the proposed strategy.

Patient-controlled subcutaneous analgesia with hydromorphone versus oral oxycodone for opioid titration of cancer pain: A prospective multicenter randomized trial.

12124 Background: Studies have shown that oral oxycodone sustained-release (SR) tablets can be used for opioid titration. The European Society for Medical Oncology (ESMO) guidelines for adult cancer pain recommend opioid titration through the parenteral route, usually the intravenous or subcutaneous route. Patient-controlled subcutaneous analgesia (PCSA) with hydromorphone needs further evaluation for opioid titration. This prospective multicenter study was designed to compare the efficacy and safety of hydromorphone PCSA with oral oxycodone SR tablets for opioid titration of cancer pain. Methods: Eligible patients with cancer pain were randomly assigned in a 1:1 ratio to the PCSA group or the oxycontin group for dose titration. Different titration methods were given in both groups depending on whether the patient had an opioid tolerance. The primary endpoint of this study was time to successful titration (TST). Results: A total of 256 patients completed this study. The PCSA group had a significantly lower TST compared with the oxycontin group (median [95% confidence interval (CI)], 5.5 [95%CI: 2.5-11.5] hours vs. 16.0 [95%CI: 11.5-22.5] hours; p&lt; 0.001). PCSA reduced the frequency of breakthrough pain (Btp), especially in patients with opioid tolerance. The number of Btp was 121±28 times and 186±31 times in the PCSA and oxycontin groups, respectively ( p&lt; 0.001). The 12-hour numeric rating scale (NRS) score was significantly lower in the PCSA group than that in the oxycontin group (median [95%CI], 2.5 [95%CI: 1.4-2.9] vs. 4.4[95%CI: 3.2-5.8]) ( p&lt; 0.001). Similar equivalent morphine consumption and significantly improved quality of life were observed in the two groups. No between-group difference in the incidence of opioid-related adverse effects was observed. Conclusions: Compared with oral oxycodone SR tablet, the use of PCSA with hydromorphone achieved faster pain relief and a shorter titration duration for patients with cancer pain without increasing adverse events. Clinical trial information: ChiCTR2000037845 .

Energy Management Strategy Considering Energy Storage System Degradation for Hydrogen Fuel Cell Ship

Abstract A hybrid energy system (HES) including hydrogen fuel cell systems (FCS) and a lithium-ion (Li-ion) battery energy storage system (ESS) is established for hydrogen fuel cell ships to follow fast load transients. An energy management strategy (EMS) with hierarchical control is presented to achieve proper distribution of load power and enhance system stability. In the high-control loop, a power distribution mechanism based on a particle swarm optimization algorithm (PSO) with an equivalent consumption minimization strategy (ECMS) is proposed. In the low-level control loop, an adaptive fuzzy PID controller is developed, which can quickly restore the system to a stable state by adjusting the PID parameters in real time. Compared with the rule-based EMS, hydrogen consumption is reduced by 5.319%, and the stability of the power system is significantly improved. In addition, the ESS degradation model is developed to assess its state of health (SOH). The ESS capacity loss is reduced by 2% and the daily operating cost of the ship is reduced by 1.7% compared with the PSO-ECMS without considering the ESS degradation.

Equivalent Consumption Minimization Strategy of Hybrid Electric Vehicle Integrated with Driving Cycle Prediction Method

Hybrid electric vehicles that can combine the advantages of traditional and new energy vehicles have become the optimal choice at present in the face of increasingly stringent fuel consumption restrictions and emission regulations. Range-extended hybrid electric vehicles have become an important research topic because of their high energy mixing degree and simple transmission system. A compact traditional fuel vehicle is the research object of this study and the range-extended hybrid system is developed. The design and optimization of the condition prediction energy management strategy are investigated. Vehicle joint simulation analysis and bench test platforms were built to verify the proposed control strategy. The vehicle tracking method was selected to collect real vehicle driving data. The number of vehicles in the field of view and the estimation of the distances between the front and following vehicles are calculated by means of the mature algorithm of the monocular camera and by computer vision. Real vehicle cycle conditions with driving environment and slope information were constructed and compared with all driving data, typical working conditions under NEDC, and typical working conditions under UDDS. The BP neural network and fuzzy logic control were used to identify the road conditions and the driver’s intention. The results showed that the equivalent fuel consumption of the control strategy was lower than that of the fixed-point power following control strategy and vehicle economy improved.

Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method

Fuel cell lifetime is strongly affected by dynamic conditions. Most existing energy management works only focus on the fuel cell durability protection from the perspective of output power slope, without deeply considering the influence of the important parameters inside the stack. However, considering the variation of stack internal parameters (mechanism analysis) is more significant for fuel cell lifetime evaluation. In this paper, a health-aware model predictive control (HA-MPC) energy management strategy is proposed for fuel cell electric vehicle. A fuel cell health state model is established from the perspective of stack hydrogen excess ratio (HER), oxygen excess ratio (OER) and humidity through the hybrid modeling method. The fuel cell mechanism model and the low-dimensional data-driven model are established through the grey-box model estimation method and genetic algorithm-based radial basis function (GA-RBF) neural network. Then the objective function of energy management strategy is developed considering the total equivalent hydrogen consumption and stack improper parameter changes of HER, OER and humidity. Comparing with model predictive control strategy based on the typical power cost function, the HA-MPC can effectively reduce the steep drop of HER and OER in low power region by 3.58% and 4.41%, which can protect the fuel cell system lifetime.