Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

Zheng, Yongliang; Feng, He; Shen, Xinze; Xuesheng, Jiang

doi:10.3390/en13020426

Cited by 23 publications

(12 citation statements)

References 36 publications

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“…There are two ways of fault diagnosis: online and offline 269 . The controller area network (CAN)‐based systems are commonly used in FCHEVs for control and communication 271 . Due to the need for diagnostics of the FCHEV's electronic control system, they are critical in terms of vehicle safety and reliability 272 .…”

Section: Issues and Challengesmentioning

confidence: 99%

Optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles

Luo

et al. 2021

Int J Energy Res

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Summary The transportation sector consumes a large amount of fossil fuels consequently exacerbating the global environmental and energy crisis. Fuel‐cell hybrid electric vehicles (FCHEVs) are promising alternatives in the continuous transition to clean energy. This article summarizes the recent advances pertaining to the optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles, especially the fuel cell + battery hybrid topology, and discusses current technological bottlenecks hindering the commercialization of FCHEVs. The development of HEVs, markets, environmental and economic benefits, components, topologies, energy management strategies, degradation mechanisms, and safety standards of FCHEVs are reviewed. Proton exchange membrane fuel cells constitute the mainstream and most mature fuel cell technology for automobile applications. Battery hybridization is currently favored among the available FCHEV topological designs to improve the dynamic response and recover the braking energy. Energy management strategies encompassing logic rule‐based simple methods, intelligent control methods, global optimization strategies, and local optimization strategies are described, and issues and challenges encountering FCHEVs are discussed. In addition to promoting the construction of hydrogen supply facilities, future efforts are expected to focus on solving problems such as the high cost, durability of fuel cells, cold start, lifetime of batteries, security and comfort, system optimization, energy management systems, integration, and diagnosis of faults. This review serves as a reference and guide for future technological development and commercialization of FCHEVs. Highlights Advances of the optimization and cutting‐edge design of FCHEVs are reviewed. Battery hybridization is currently favored among the available topological designs. Benefits, components, topologies, and energy management strategies are described. Markets, degradation mechanisms, and safety standards of FCHEVs are introduced. Technological bottlenecks hindering the commercialization of FCHEVs are discussed.

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Section: Issues and Challengesmentioning

confidence: 99%

Optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles

Luo

et al. 2021

Int J Energy Res

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“…To combine the superiority of rule-based and optimization-based methods and improve the environment adaptability of EMS in further, vehicle driving patterns are then utilized. Fuzzy logic classifier and DP-based adaptive EMS (Zhang et al, 2015), Bayesian probability estimation–based fuzzy EMS (Zhou et al, 2017), neural networks–based fuzzy EMS (Zhang et al, 2019), k-nearest neighbor–based MPC EMS (Li et al, 2020), and support vector machine–based fuzzy EMS (Zheng et al, 2020) have recognized the driving patterns in terms of vehicle speed information to improve the performance of EMSs. In our previous work, a neural network driving pattern recognition–based fuzzy EMS was proposed, which achieved real-time control and up to 8.89% fuel consumption has been saved (Zhang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Q-Learning-based fuzzy energy management for fuel cell/supercapacitor HEV

Tao

Zhang

Zhijun³

et al. 2022

Transactions of the Institute of Measurement and Control

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A novel fuzzy energy management strategy (EMS) based on improved Q-learning controller and genetic algorithm (GA) is proposed for the real-time power split between fuel cell and supercapacitor of hybrid electric vehicle (HEV). Different from driving pattern recognition–based method, Q-Learning controller takes actions by observing the driving states and compensates to fuzzy controller, that is, no need to know the driving pattern in advance. Aimed to prolong the fuel cell lifetime and decrease its energy consumption, the initial values of Q-table are optimized by GA. Moreover, to enhance the environment adaptation capability, the learning strategy of Q-learning controller is improved. Two adaptive energy management strategies have been compared, and simulation results show that current fluctuation can be reduced by 6.9% and 41.5%, and H2 consumption can be saved by 0.35% and 6.08%, respectively. Meanwhile, state of charge (SOC) of supercapacitor is sustained within the desired safe range.

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“…In [30], a novel transit flow prediction model was proposed on the basis of LSSVM, with the result showing little difference between the prediction value and the actual value. In [31,32], the working conditions were identified by LSSVM as optimized by grid search and cross validation (CV). Unfortunately, the parameters used to optimize LSSVM are time consuming.…”

Section: Introductionmentioning

confidence: 99%

A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN

et al. 2021

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Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.

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Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

Cited by 23 publications

References 36 publications

Optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles

Optimization and cutting‐edge design of fuel‐cell hybrid electric vehicles

Q-Learning-based fuzzy energy management for fuel cell/supercapacitor HEV

A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN

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