Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan

Fu, Zhumu; Zhu, Longlong; Tao, Fazhan; Si, Pengju; Sun, Lifan

doi:10.1016/j.ijhydene.2020.01.017

Cited by 110 publications

(31 citation statements)

References 29 publications

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“…Optimization-based strategies (OBS) generally calculate (near) optimal results by using a strategy that minimizes the sum of objective functions over time (global optimization) or by instantly minimizing an objective function (local optimization). However, they can hardly be applied in real-time for the high dependency on driving cycles or online computational complexity [10]- [13]. To find a better trade-off technique to compromise optimization and computation load, a feasible solution is presented in this paper.…”

Section: A Literature Reviewmentioning

confidence: 99%

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

et al. 2021

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This paper introduces an energy management strategy design method for a plug-in hybrid electric vehicle based on the data-driven approach and online signal analysis. It includes two parts, mode division strategy design, and power distribution strategy design. Using the random forest in data mining technology to analyze optimization results of dynamic programming can quickly extract key information and establish optimal and understandable mode division strategy with high precision and stability directly. Besides, integrating the classic "Engine optimal operating curve control" strategy with wavelet transform and Markov prediction, which not only enhances the adaptability of the strategy to different driving conditions but also improves the fuel economy by reducing the impact of transient power on the engine operation. At the same time, to improve the prediction accuracy of the algorithm without increasing the computational complexity, this paper adds a prediction result correction function to the first-order Markov prediction model to reduce the impact of slow update of the probability matrix on prediction accuracy. The simulation results show the average prediction error of the improved Markov prediction model is reduced by 5.3% and the new energy management strategy designed reduces fuel consumption by 8.28% at the cost of a small increase in electricity consumption.INDEX TERMS Plug-in hybrid electric vehicle, dynamic programming, random forest, Markov chain, wavelet transform.

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Section: A Literature Reviewmentioning

confidence: 99%

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

et al. 2021

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“…Classification Control Algorithm [7,10] Rule-based Operating mode control [8,9] Thermostat control [11] State machine control [10,[12][13][14] Fuzzy control [16] Optimization-based Particle swarm optimization [17,18] ECMS [19,20] DP [21] PMP…”

Section: Refmentioning

confidence: 99%

“…The rule-based strategies have strong practicability and high reliability [7][8][9][10][11][12][13][14][15]. However, most of them are based on the engineering experience, and the results are dependent on the design of the rules.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Energy Management Strategies Considering Power System Durability for PEMFC-LIB Hybrid Logistics Vehicle

Liang

Jia

et al. 2021

Energies

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For commercial applications, the durability and economy of the fuel cell hybrid system have become obstacles to be overcome, which are not only affected by the performance of core materials and components, but also closely related to the energy management strategy (EMS). This paper takes the 7.9 t fuel cell logistics vehicle as the research object, and designed the EMS from two levels of qualitative and quantitative analysis, which are the composite fuzzy control strategy optimized by genetic algorithm and Pontryagin’s minimum principle (PMP) optimized by objective function, respectively. The cost function was constructed and used as the optimization objective to prolong the life of the power system as much as possible on the premise of ensuring the fuel economy. The results indicate that the optimized PMP showed a comprehensive optimal performance, the hydrogen consumption was 3.481 kg/100 km, and the cost was 13.042 $/h. The major contribution lies in that this paper presents a method to evaluate the effect of different strategies on vehicle performance including fuel economy and durability of the fuel cell and battery. The comparison between the two totally different strategies helps to find a better and effective solution to reduce the lifetime cost.

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“…Such challenging optimization problems are those specific to the optimal (parameter) tuning of fuzzy (logic) controllers, where both the process and the controller are nonlinear and deterministic algorithms are not successful. The following metaheuristic algorithms have been applied most recently to the optimal tuning of fuzzy controllers in representative examples: adaptive weight Genetic Algorithm (GA) for gear shifting control [3], GA-based multiobjective optimization for electric vehicle powertrain control [4], GA for hybrid power systems control [5], engines control [6], energy management in hybrid vehicles [7], servo system control [2], wellhead back pressure control systems [8], micro-unmanned helicopter control [9], Particle Swarm Optimization (PSO) algorithm with compensating coefficient of inertia weight factor for filter time constant adaptation in hybrid energy storage systems control [10], set-based PSO algorithm with adaptive weights for optimal path planning of unmanned aerial vehicles [11], PSO algorithm for zinc production [12] and inverted pendulum control [13], hybrid PSO-Artificial Bee Colony algorithm for frequency regulation in microgrids [14], Imperialist Competitive Algorithm for human immunodeficiency control [15], Grey Wolf Optimizer (GWO) algorithms for sun-tracker systems [16] and servo system control [2], PSO, Cuckoo Search and Differential Evolution (DE) for gantry crane systems position control [17], Whale Optimization Algorithm (WOA) for vibration control of steel structures [18], Grasshopper Optimization Algorithm for load frequency control [19], DE for electro-hydraulic servo system control [20], Gravitational Search Algorithm (GSA) and Charged System Search (CSS) for servo system control [2].…”

Section: Introductionmentioning

confidence: 99%

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

Precup¹,

David²,

Roman³

et al. 2021

IJCIS

107

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This paper suggests five new contributions with respect to the state-of-the-art. First, the optimal tuning of cost-effective fuzzy controllers represented by Takagi-Sugeno-Kang proportional-integral fuzzy controllers (TSK PI-FCs) is carried out using a fresh metaheuristic algorithm, namely the Slime Mould Algorithm (SMA), and a fuzzy controller tuning approach is offered. Second, a relatively easily understandable formulation of SMA is offered. Third, a real-world application of SMA is given, focusing on the optimal tuning of TSK PI-FCs for nonlinear servo systems in terms of optimization problems that target the minimization of discrete-time cost functions defined as the sum of time multiplied by squared control error. Fourth, using the concept of improving the performance of metaheuristic algorithms with information feedback models, proposed by Wang and Tan, Improving metaheuristic algorithms with information feedback models, IEEE Trans. Cybern. 49 (2019), 542-555, Gu and Wang, Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization, Fut. Gen. Comput. Syst. 107 (2020), 49-69, and Zhang et al., Enhancing MOEA/D with information feedback models for large-scale many-objective optimization, Inf. Sci. 522 (2020), 1-16, new metaheuristic algorithms are introduced in terms of inserting the model F1 in SMA and other representative algorithms, namely Gravitational Search Algorithm (GSA), Charged System Search (CSS), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA). Fifth, the real-time validation of the cost-effective fuzzy controllers and their tuning approach is performed in the framework of angular position control of laboratory servo system. The comparison with other metaheuristic algorithms that solve the same optimization problem for optimal parameter tuning of cost-effective fuzzy controllers suggestively highlights the superiority of SMA. Experimental results are included.

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Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan

Cited by 110 publications

References 29 publications

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

Comparison of Two Energy Management Strategies Considering Power System Durability for PEMFC-LIB Hybrid Logistics Vehicle

Slime Mould Algorithm-Based Tuning of Cost-Effective Fuzzy Controllers for Servo Systems

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