Rule-based Control and Fuzzy Control for Power Management Strategies for Hybrid Vehicles

Corrêa, Fernanda Cristina; Eckert, Jony Javorski; Silva, Ludmila C. A.; Martins, Marcella Scoczynski Ribeiro; Baroncini, Virginia; Santiciolli, Fabio Mazzariol; Gonçalves, Cristhiane; Dedini, Franco Giuseppe

doi:10.1109/colcaci50549.2020.9247872

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…In [26], a hybrid vehicle system was examined, and fuzzy logic was employed for voltage and power control. However, the complexity of the fuzzy rule led to increased oscillations during sudden changes in operating conditions, potentially causing system instability.…”

Section: Introductionmentioning

confidence: 99%

PSO-ANFIS-Based Energy Management in Hybrid AC/DC Microgrid along with Plugin Electric Vehicle

Ashokkumar,

Venkatramanan

2023

International Journal of Photoenergy

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This study proposes a hybrid AC/DC microgrid with plugin EVs, leveraging PSO-tuned ANFIS for voltage and power control. With the existing control, which faced challenges such as instability and complexity, the proposed approach is aimed at simplifying control through PSO, efficient power sharing, and reduced sample requirements. This innovative method contributes to improved energy management in hybrid microgrids, bridging existing research gaps. This approach streamlines neural transmission in microgrid control, addressing challenges in distributed generation power, load demand, energy storage system SOC, and AC grid power integration. Notably, the proposed PSO-ANFIS simplifies electric vehicle power references using distinct inputs for each mode, trained through PSO. This methodology is tailored for microgrids with varying power profiles, presenting a promising solution for efficient energy management. The proposed EMS was experimentally verified using MATLAB simulations of a small-scale hybrid AC/DC microgrid for every operating mode. The financial dynamics of a microgrid’s power exchange with the main grid are examined through three distinct methodologies: fuzzy logic, ANFIS (adaptive neurofuzzy inference system), and PSO-ANFIS (ANFIS optimized using particle swarm optimization). In case 1, the PSO-ANFIS approach demonstrates its superiority by achieving the lowest grid purchase power cost of 1995.24 Rs/day compared to fuzzy (2243.63 Rs/day) and ANFIS (2150.45 Rs/day), while also yielding the highest revenue from power selling to the microgrid: PSO-ANFIS (668.84 Rs/day) surpassing fuzzy (536.12 Rs/day) and ANFIS (575.35 Rs/day). Similarly, in case 2, PSO-ANFIS proves its efficiency with the lowest net price of 8619.192 Rs/day, showcasing its effectiveness in optimizing financial dynamics. Furthermore, in case 3, the revenue aligns precisely with net prices, indicating the PSO-ANFIS method’s financial advantage, generating the highest revenue of 6544.0224 Rs/day compared to fuzzy (6025.36 Rs/day) and ANFIS (6153.214 Rs/day). These findings underscore the potential utility of the PSO-ANFIS approach in optimizing microgrid operations and enhancing cost-effectiveness across various scenarios.

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Section: Introductionmentioning

confidence: 99%

PSO-ANFIS-Based Energy Management in Hybrid AC/DC Microgrid along with Plugin Electric Vehicle

Ashokkumar,

Venkatramanan

2023

International Journal of Photoenergy

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“…For example, Li et al [17] used the optimization results of DP to improve the rule-based EMS and achieved better energy-saving effect. Correa FC et al [18] used DP to find the optimal power distribution principle between engine and motor, and designed a fuzzy logic controller on the basis of considering SOC balance and driving style recognition. Although DP can obtain the global optimal solution, it needs to obtain future working conditions in advance and can only be realized in offline manner [19].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Aware Energy Management Strategy for Hybrid Electric Vehicle Using Hybrid Deep Learning Method

Zhang¹,

Zhao²,

Sun³

et al. 2022

IEEE Access

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Energy management strategy (EMS) is important to ensure energy-saving performance of hybrid electric vehicle (HEV). However, the power coupling property between different power sources, together with stochastic power demand fluctuation poses great challenges for EMS to achieve desirable performance in real-world scenario. This paper presents an uncertainty-aware energy management strategy for HEV. A speed predictor combining convolutional neural network and long short-term memory neural network is proposed to extract temporal features that could reveal speed change mechanism. Then an online self-adaptive transition probability matrix is constructed to estimate the speed prediction uncertainty. Tube model predictive control (tube-MPC) is finally used to solve the optimization control problem in a receding horizon manner. The robust set introduced in the tube-MPC greatly enhances the optimality and robust-ness of the control sequence under the scenario with speed prediction uncertainty. Simulations are conducted to verify the effectiveness of the proposed method. Results show that the speed prediction accuracy is 47.4% and 23.1% higher than exponential decay rate prediction model and autoregressive integrated moving average model respectively. Compared with traditional rule-based and MPC method, the proposed tube-MPC method could achieve 10.7% and 3.0% energy-saving performance improvement in average.

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“…This method is not efficient because it does not consider the accumulated power received by the AC grid and the SOC of the ESS. Additionally, it has the disadvantage that the algorithm for operating the EMS is complicated [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network

et al. 2021

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This paper proposes an artificial neural network (ANN)-based energy management system (EMS) for controlling power in AC–DC hybrid distribution networks. The proposed ANN-based EMS selects an optimal operating mode by collecting data such as the power provided by distributed generation (DG), the load demand, and state of charge (SOC). For training the ANN, profile data on the charging and discharging amount of ESS for various distribution network power situations were prepared, and the ANN was trained with an error rate within 10%. The proposed EMS controls each power converter in the optimal operation mode through the already trained ANN in the grid-connected mode. For the experimental verification of the proposed EMS, a small-scale hybrid AD/DC microgrid was fabricated, and simulations and experiments were performed for each operation mode.

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Rule-based Control and Fuzzy Control for Power Management Strategies for Hybrid Vehicles

Cited by 5 publications

References 12 publications

PSO-ANFIS-Based Energy Management in Hybrid AC/DC Microgrid along with Plugin Electric Vehicle

PSO-ANFIS-Based Energy Management in Hybrid AC/DC Microgrid along with Plugin Electric Vehicle

Uncertainty-Aware Energy Management Strategy for Hybrid Electric Vehicle Using Hybrid Deep Learning Method

Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network

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