Electric Vehicle Charging Management Based on Deep Reinforcement Learning

Li, Sichen; Hu, Weihao; Cao, Dengqing; Tomislav, Dragicevic; Huang, Qi; Chen, Zhe; Blaabjerg, Frede

doi:10.35833/mpce.2020.000460

Cited by 75 publications

(24 citation statements)

References 31 publications

(43 reference statements)

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“…Renewable energy has attracted much attention because of sustainable development issues [1][2][3][4]. LIBs are widely used in both military and civil fields because of their high energy density, high rated voltage, strong adaptability to high and low temperatures and low cost.…”

Section: Introductionmentioning

confidence: 99%

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Guo

Yang

Zhang

et al. 2022

Prot Control Mod Power Syst

113

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The estimation of state of health (SOH) of a lithium-ion battery (LIB) is of great significance to system safety and economic development. This paper proposes a SOH estimation method based on the SSA-Elman model for the first time. To improve the correlation rates between features and battery capacity, a method combining median absolute deviation filtering and Savitzky–Golay filtering is proposed to process the data. Based on the aging characteristics of the LIB, five features with correlation rates above 0.99 after data processing are then proposed. Addressing the defects of the Elman model, the sparrow search algorithm (SSA) is used to optimize the network parameters. In addition, a data incremental update mechanism is added to improve the generalization of the SSA-Elman model. Finally, the performance of the proposed model is verified based on NASA dataset, and the outputs of the Elman, LSTM and SSA-Elman models are compared. The results show that the proposed method can accurately estimate the SOH, with the root mean square error (RMSE) being as low as 0.0024 and the mean absolute percentage error (MAPE) being as low as 0.25%. In addition, RMSE does not exceed 0.0224 and MAPE does not exceed 2.21% in high temperature and low temperature verifications.

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Section: Introductionmentioning

confidence: 99%

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Guo

Yang

Zhang

et al. 2022

Prot Control Mod Power Syst

113

View full text Add to dashboard Cite

show abstract

“…An LSTM block is constructed by a cell state and three gates, i.e., input gate, forget gate, and output gate. The general architecture of an LSTM block is described in detail in [48], [56]- [58]. When the hidden layers are formed with LSTM blocks, the calculation method is almost the same as the LSTM block.…”

Section: A Lstmmentioning

confidence: 99%

Outlier Reconstruction Based Distribution System State Estimation Using Equivalent Model of Long Short-term Memory and Metropolis-Hastings Sampling

Xia

Sun

Chen

2022

Journal of Modern Power Systems and Clean Energy

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The accuracy of distribution system state estimation (DDSE) is reduced when phasor measurement unit (PMU) measurements contain outliers because of cyber attacks or global positioning system spoofing attacks. Therefore, to enhance the robustness of DDSE to measurement outliers, approximate the target distribution of Metropolis-Hastings (MH) sampling, and judge the prediction of the long short-term memory (LSTM) network, this paper proposes an outlier reconstruction based state estimation method using the equivalent model of the LSTM network and MH sampling (E-LM model), motivated by the characteristics of the chronological correlations of PMU measurements. First, the target distribution of outlier reconstruction is derived using a kernel density estimation function. Subsequently, the reasons and advantages of the E-LM model are explained and analyzed from a mathematical point of view. The proposed LSTM-based MH sampling can approximate the target distribution of MH sampling to decrease the number of the futile iterations. Moreover, the proposed MH-based forecasting of the LSTM can judge each LSTM prediction, which is independent of its true value. Finally, simulations are conducted to evaluate the performance of the E-LM model by integrating the LSTM network and the MH sampling into the outlier reconstruction based DDSE.

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“…For an EV taxi fleet, Tang et al [124] define user comfort as customer waiting times. [153], [154], [119], Li &Wan [132] define a user requirement for the SoC and minimize the charging cost within this constraint.…”

Section: Management Of User Discomfortmentioning

confidence: 99%

“…EV charging optimization targets include the following: reducing peak load [29], reducing charging costs for the EV [153], [154], [119], [132], [139], reducing both charging cost and waiting time [123], reducing charging cost based on knowledge of user behavior [156], minimizing the cost for the charging station with a PV and battery storage [155], minimizing the cost of several such stations [131], and aggregating several stations within a local market operated by an aggregator [135].…”

Section: B) Charging I) Carmentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.

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Electric Vehicle Charging Management Based on Deep Reinforcement Learning

Cited by 75 publications

References 31 publications

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

Outlier Reconstruction Based Distribution System State Estimation Using Equivalent Model of Long Short-term Memory and Metropolis-Hastings Sampling

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

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