Implementation of energy management strategy of hybrid power source for electrical vehicle

Marzougui, Hajer; Kadri, Ameni; Martin, Jean‐Philippe; Amari, Mansour; Pierfederici, Serge; Bacha, Faouzi

doi:10.1016/j.enconman.2019.05.037

Cited by 94 publications

(39 citation statements)

References 35 publications

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“…As highlighted in the introduction, the previous works reported in [13][14][15] have been focused on the energy management of electric vehicles based on various nonlinear algorithms (e.g., sliding mode, Lyapunov, fuzzy logic). However, only in [13,14], the developed control algorithms have been validated experimentally on load power profile. Both load power profiles present the same dynamic operations (i.e., three acceleration modes and one braking mode); whereas the chosen load power profile in this work includes one acceleration and braking operation as shown in Figure 12.…”

Section: Comparison Of the Performances Compared To The Previous Workmentioning

confidence: 99%

“…Generally, proportional-integral (PI) compensation is used for energy stability [8][9][10][11][12]. In [13], Marzouguia et al have proposed a control technique of the hybrid network for a hydrogen electric vehicle (fuel cell car) based on three estimation approaches: first, "a fuzzy logic estimation"; second, "a differential flatness control approach" (model-based technique); third "rule-based algorithm", making complex the energy management strategy. Indeed, the fuzzy logic controller is employed to manage the energy flows between the main source (i.e., fuel cell) and storage devices (i.e., battery, SC); whereas the flatness controller is used to regulating the DC bus voltage, allowing ensuring the stability of the microgrid.…”

Section: Introductionmentioning

confidence: 99%

“…Since the hybrid system includes bidirectional DC-DC converters linked to storage devices, the power converter model is nonlinear behavior. Then, it is important to use a nonlinear model-based control approach to the balance of the nonlinearity of the power electronics network [13]. In [14], Song et al have developed an energy management algorithm for an electric vehicle supplied by batteries and SCs.…”

Section: Introductionmentioning

confidence: 99%

“…This theory has lately been utilized in a variety of networks in different scientific domains [17][18][19][20][21][22][23]. Compared to the nonlinear algorithm (i.e., sliding mode, Lyapunov, fuzzy logic) reported in [13][14][15], nonlinear algorithms based on differential flatness require the use of trajectory planning to implement the control laws. This trajectory planning aims at controlling different variables (e.g., currents of converters, stored energy in the DC bus and SC) to manage the energy in an EV while optimizing the performance of the system for any operating point.…”

Section: Introductionmentioning

confidence: 99%

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Differential Flatness-Based Cascade Energy/Current Control of Battery/Supercapacitor Hybrid Source for Modern e–Vehicle Applications

et al. 2020

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This article proposes a new control law for an embedded DC distributed network supplied by a supercapacitor module (as a supplementary source) and a battery module (as the main generator) for transportation applications. A novel control algorithm based on the nonlinear differential flatness approach is studied and implemented in the laboratory. Using the differential flatness theory, straightforward solutions to nonlinear system stability problems and energy management have been developed. To evaluate the performance of the studied control technique, a hardware power electronics system is designed and implemented with a fully digital calculation (real-time system) realized with a MicroLabBox dSPACE platform (dual-core processor and FPGA). Obtained test bench results with a small scale prototype platform (a supercapacitor module of 160 V, 6 F and a battery module of 120 V, 40 Ah) corroborate the excellent control structure during drive cycles: steady-state and dynamics.

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Section: Comparison Of the Performances Compared To The Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differential Flatness-Based Cascade Energy/Current Control of Battery/Supercapacitor Hybrid Source for Modern e–Vehicle Applications

et al. 2020

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“…It is possible to divide EMS methods used in the battery/UC powered EVs as the rule‐based and optimization‐based methods . The rule‐based methods, deterministic and the fuzzy rule‐based methods, are easy for applications, and have tolerances to errors. In the deterministic rule‐based EMS, some scenarios are predetermined for the specific driving profiles.…”

Section: Introductionmentioning

confidence: 99%

Jaya algorithm‐based energy management system for battery‐ and ultracapacitor‐powered ultralight electric vehicle

Demircali

Köroğlu

2020

Int J Energy Res

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Summary To improve the driving performance of the electric vehicles, batteries or ultracapacitors (UCs) are frequently preferred in the energizing systems. In hybrid structures with multiple supply sources, an energy management system (EMS) is needed to improve the system efficiency, and to provide the optimum power sharing between a battery and a UC. The purpose of this study is to investigate the effectiveness of the Jaya optimization method for the urban use of the EMS of an ultralight electric vehicle powered by battery/UC. The performance of the proposed method is compared with dynamic programming (DP) that is one of the global optimization methods and particle swarm optimization (PSO) that is one of the other heuristic methods for real‐time applications. The simulation results show that Jaya‐EMS approached 3.1% to the DP, which yields the optimum result with respect to the total energy loss. In addition, the proposed method yields a loss of less than 1.9% from the PSO‐EMS. If all the above situations are considered, the proposed EMS method has less lossy alternative solution for the real‐time applications.

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Effective energy management strategy based on deep reinforcement learning for fuel cell hybrid vehicle considering multiple performance of integrated energy system

Tan

et al. 2022

Intl J of Energy Research

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To reduce the power burden of the fuel cell system (FCS) in fuel cell hybrid electric vehicles (FCHEVs), battery and supercapacitor are widely used as energy storage system (ESS) in FCHEVs. In this paper, an energy management strategy (EMS) based on deep reinforcement learning is proposed to solve the power allocation problem among three energy sources. Considering that three energy sources increase the complexity of the state action space, this paper proposes an adaptive fuzzy control filter to separate the load demand power to reduce the computational burden of the deep deterministic policy gradient (DDPG) algorithm. Meanwhile, a learning guidance mechanism (LGM) is proposed in this paper to guide the agent in a rational learning direction, and the reward convergence results are presented in this paper. The training results show that the equivalent hydrogen consumption reaches 99.5% of fuzzy logic controlled-based EMS (FLC-EMS), the FCS efficiency improves by 5.9%, and the stability of battery and supercapacitor is also significantly improved. Eventually, three different driving conditions are selected to verify the effectiveness of the proposed EMS, the results show that the proposed EMS in this paper outperforms (FLC-EMS) in all aspects.

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Implementation of energy management strategy of hybrid power source for electrical vehicle

Cited by 94 publications

References 35 publications

Differential Flatness-Based Cascade Energy/Current Control of Battery/Supercapacitor Hybrid Source for Modern e–Vehicle Applications

Differential Flatness-Based Cascade Energy/Current Control of Battery/Supercapacitor Hybrid Source for Modern e–Vehicle Applications

Jaya algorithm‐based energy management system for battery‐ and ultracapacitor‐powered ultralight electric vehicle

Effective energy management strategy based on deep reinforcement learning for fuel cell hybrid vehicle considering multiple performance of integrated energy system

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