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Communication-Free Distributed Charging Control for Electric Vehicle Group
Systems and Control · Submitted 22 Jun 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The results show that this method can reduce the probability of transformer overload caused by out-of-order EV charging after a communication failure.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
The disordered charging of electric vehicles (EVs) in residential areas leads to a rapid increase of the peak load, causing transformer overload, but the charging control of EV group can effectively alleviate this phenomenon. However, existing charging control methods need reliable two-way communication infrastructure, which brings high operation costs and security risks. To offer a backup strategy for charging control of EVs after communication facilities fail, this paper proposes a communication-free charging control scheme to provide a decentralized on-site charging strategy for EV group. First, an uncontrollable EV group baseline estimation considering charging behaviors enabled by Gaussian mixture model (GMM) is proposed to acquire the capacity margin forecasting for controllable EVs. Next, this paper proposes a probabilistic distributed control method to assist users formulate the charging plan autonomously. Here, the charging behavior of EV group is regulated from an optimization with uncertain boundary conditions to a sampling with uncertain feasible regions expressed by a probability distribution. Finally, the scheme is verified via real-world EV charging data from a residential area in Hangzhou, China. The results show that this method can reduce the probability of transformer overload caused by out-of-order EV charging after a communication failure.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The disordered charging of electric vehicles (EVs) in residential areas leads to a rapid increase of the peak load, causing transformer overload, but the charging control of EV group can effectively alleviate this phenomenon. | background 1.00 | 0.07 | 0.04 | asserted 1.00 |
| s1 | However, existing charging control methods need reliable two-way communication infrastructure, which brings high operation costs and security risks. | gap 0.80 | 0.05 | 0.02 | asserted 1.00 |
| s2 | To offer a backup strategy for charging control of EVs after communication facilities fail, this paper proposes a communication-free charging control scheme to provide a decentralized on-site charging strategy for EV group. | contribution 0.99 | 0.76 | 0.08 | asserted 0.97 |
| s3 | First, an uncontrollable EV group baseline estimation considering charging behaviors enabled by Gaussian mixture model (GMM) is proposed to acquire the capacity margin forecasting for controllable EVs. | method 0.82 | 0.91 | 0.03 | asserted 1.00 |
| s4 | Next, this paper proposes a probabilistic distributed control method to assist users formulate the charging plan autonomously. | method 0.72 | 0.90 | 0.04 | asserted 0.99 |
| s5 | Here, the charging behavior of EV group is regulated from an optimization with uncertain boundary conditions to a sampling with uncertain feasible regions expressed by a probability distribution. | method 0.97 | 0.79 | 0.05 | asserted 0.99 |
| s6 | Finally, the scheme is verified via real-world EV charging data from a residential area in Hangzhou, China. | method 0.95 | 0.94 | 0.05 | asserted 0.94 |
| s7 | The results show that this method can reduce the probability of transformer overload caused by out-of-order EV charging after a communication failure. | result 0.97 | 0.95 | 0.89 | asserted 0.94 |
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The paper
Original abstract
The disordered charging of electric vehicles (EVs) in residential areas leads to a rapid increase of the peak load, causing transformer overload, but the charging control of EV group can effectively alleviate this phenomenon. However, existing charging control methods need reliable two-way communication infrastructure, which brings high operation costs and security risks. To offer a backup strategy for charging control of EVs after communication facilities fail, this paper proposes a communication-free charging control scheme to provide a decentralized on-site charging strategy for EV group. First, an uncontrollable EV group baseline estimation considering charging behaviors enabled by Gaussian mixture model (GMM) is proposed to acquire the capacity margin forecasting for controllable EVs. Next, this paper proposes a probabilistic distributed control method to assist users formulate the charging plan autonomously. Here, the charging behavior of EV group is regulated from an optimization with uncertain boundary conditions to a sampling with uncertain feasible regions expressed by a probability distribution. Finally, the scheme is verified via real-world EV charging data from a residential area in Hangzhou, China. The results show that this method can reduce the probability of transformer overload caused by out-of-order EV charging after a communication failure.
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