Adaptive Energy Management Strategy for Extended-Range Electric Vehicle Based on Micro-Trip Identification

Wu, Xiaodong; Gu, Yanhao; Xu, Min

doi:10.1109/access.2020.3026419

Cited by 14 publications

(3 citation statements)

References 23 publications

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“…Presently, commonly employed control strategies include rule-based approaches (involving logical thresholds and self-judgment segmentation), intelligent strategies (such as fuzzy logic and neural networks), and offline optimization methods (such as dynamic programming and simulated annealing algorithms) [7,8]. Wu X, Fakir, C. E et al introduced an adaptive nonlinear control approach aimed at enhancing the overall efficiency of power systems and motors [9][10][11]. Silva and Jie Li adopted a multi-objective optimization control method to ameliorate energy conversion efficiency, prevent power battery degradation, and achieve the real-time optimization of energy consumption [12,13].…”

Section: Prefacementioning

confidence: 99%

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

Zhang,

Tang,

Zhang

et al. 2024

Energies

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In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles.

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Section: Prefacementioning

confidence: 99%

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

Zhang,

Tang,

Zhang

et al. 2024

Energies

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Section: Introductionmentioning

confidence: 99%

“…[ 19 ] Lee et al [ 20 ] adjusted the values of the control parameters of the PMP according to the SOC of the battery, which effectively reduced the cost of driving. Wu et al [ 21 ] chose the optimal covariance variable of Pontryagin's principle of minima through microtravel identification, which enabled online control. Overall, the global optimization strategy optimizes well, but the calculation speed is slow and the applicability is poor.…”

Section: Introductionmentioning

confidence: 99%

Research on Energy Management Strategy for Extended‐Range Electric Vehicle Based on Bargaining Game

Ding,

Li,

Zhao

et al. 2024

Energy Tech

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To improve the fuel economy and battery life of extended‐range electric vehicles (EREVs), an energy management strategy (EMS) based on the bargaining game (BG) is proposed. This strategy optimizes the overall economy by optimizing power allocation. First, a bargaining model for EREVs is developed. The model considers the auxiliary power unit (APU) and the battery as two parties of the game. The power allocation between the APU and the battery optimizes both the fuel consumption and the battery life. And it also improves the overall benefit of the vehicle. Second, the effect of the discount factor on the control results is analyzed to obtain the best optimization results. Finally, compared to the thermostat strategy and the dynamic programming‐based strategy, the proposed BG‐based strategy reduces fuel consumption by 27.6% and 1.4% under the World Light Vehicle Test Procedure and reduces the effective ampere‐hour fluxes by 62.7% and 14.6%. Under the China Light‐duty Vehicle Test Cycle, it reduces fuel consumption by 32.5% and 0.5% and reduces the effective ampere‐hour fluxes by 56.8% and 27.5%. The simulation results manifest the excellent performance of the proposed BG‐based EMS.

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Adaptive Energy Management Strategy for Extended-Range Electric Vehicle Based on Micro-Trip Identification

Cited by 14 publications

References 23 publications

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

Research on Energy Management Strategy for Extended‐Range Electric Vehicle Based on Bargaining Game

Research on the energy management strategy of extended range electric vehicles based on a hybrid energy storage system

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