Annual energy consumption of electric vehicle air conditioning in China

Zhang, Ziqi; Liu, Cichong; Chen, Xiaoning; Zhang, Chengquan; Chen, Jiangping

doi:10.1016/j.applthermaleng.2017.07.032

Cited by 61 publications

(19 citation statements)

References 23 publications

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“…To embody the dispersion characteristic of charging load in a whole area l (i.e. residential area (RA), commercial area (CA) or working area (WA)), index I l DCP is proposed and can be identified via (17) and (18) as follows:…”

Section: E Impact Of P J+1st Jm On the Spatial-temporal Distributimentioning

confidence: 99%

“…Despite the effectiveness and advantages of the EV travel models presented in [14]- [17], various environmental factors, including temperature, traffic congestion and subjective charging willingness, have not been considered in the prediction of EVs' charging load. It should be noted that on high temperature day, EV users probably turn on air conditioning, causing at least 20% increment in electricity consumption [18]. Furthermore, traffic congestion can affect charging behaviors and lead to the variance of connection periods [19].…”

Section: Introductionmentioning

confidence: 99%

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Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

et al. 2020

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Integration of large-scale cluster electric vehicles (EVs) and their spatial-temporal transfer randomness are likely to affect the safety and economic operation of the distribution network. This paper investigates the spatial-temporal distribution prediction of EVs' charging load and then evaluates the reliability of the distribution network penetrated with large-scale cluster EVs. To effectively predict the charging load, trip chain technology, Monte Carlo method, and Markov decision process (MDP) theory are employed. Moreover, a spatial-temporal transfer model of EVs is established, and based on which, an EV energy consumption model and a charging load prediction model are constructed with consideration of temperature, traffic condition and EV owner's subjective willingness in different scenarios. With the application of sequential Monte Carlo method, the paper further evaluates distribution network reliability in various charging scenarios. In the evaluation, indices including per unit value (PUV), fast voltage stability index (FVSI), loss of load probability (LOLP), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and expected energy not supplied (EENS) are incorporated. To validate the proposed prediction model and evaluation method, a series of numerical simulations are conducted on the basis of taking the traffic-distribution system of a typical city as an example. The result demonstrates that the proposed spatial-temporal transfer model is more practical in charging load prediction than the popularly used Dijkstra's shortest path algorithm. Moreover, high temperature, congestion and the increment of EV penetration rate will further weaken distribution network reliability. INDEX TERMS Electric vehicle, reliability evaluation, trip chain technology, Markov decision process, sequential Monte Carlo method, spatial-temporal transfer randomness NOMENCLATURE ABBREVIATIONS EV Electric vehicle MDP Markov decision process SOC State of charge TNN Traffic network node DNN Distribution network node DCP Dispersion of charging power PARAMETERS μ Mean of the beginning time of each trip  Variance of the beginning time of each trip v i,j max Maximum speed allowed at the road (i, j) x EV's position in the travel path D x Distance between the origin of the trip and x C EV Capacity of EV battery

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Section: E Impact Of P J+1st Jm On the Spatial-temporal Distributimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

et al. 2020

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“…Compared to other waste heat activated refrigeration means, absorption refrigeration has advantages of large cooling capacity, high heat and mass transfer coefficient, and good flexibility to heat source variation. It is estimated that about 35% of fuel consumption in vehicle refrigeration are used for air conditioning, food cold storage, or other cooling requirements . The feasibility of waste heat activated absorption refrigerator used on vehicles were verified by the former researchers .…”

Section: Introductionmentioning

confidence: 96%

Dynamic simulation and experimental verification on absorption refrigeration driven by vehicle waste heat

Liu

Han

et al. 2020

Int J Energy Res

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Waste heat exhausted from vehicle engine has significant influences on the performances of absorption refrigeration driven by vehicle exhaust heat (VARS), especially for the transient response of VARS to the exhaust temperature and flow rate. Moreover, ambient temperature and flow rate of solution pump also affect its performances. Based on these, a dynamic mathematical model of VARS was proposed and established to observe its dynamic performances in this work. Further, the dynamic model was solved, and the simulation results were compared to the experimental data of a prototype in the same operating conditions. Finally, the transient response of VARS to exhaust temperature and flow rate, ambient temperature, and flow rate of solution pump were analyzed. The simulation results showed that the response time is about 2000 seconds when a disturbance was imposed. The research findings can supply some guidance for a reliable design, optimization, and control strategy for the actual application of the VARS. K E Y W O R D Sbubble absorber, dynamic simulation, spiral coil generator, vehicle driven absorption refrigeration

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“…In fact, the continuous rise of temperature will trigger the increase in the opening rate of air conditioning (AC) in EVs, which can lead to at least 20% increment in EVs' energy consumption. 16 Besides, traffic congestion can change the driving routes of EV owners and influence EVs connection periods. 17 The second deficiency is that the application of EVs' spatialtemporal distribution in References 11-15 mainly focuses on the charging load forecasting and the planning and reliability evaluation of distribution network, which ignores the impacts of EVs' spatial-temporal distribution on the scheduling of CSS's operation.…”

Section: Introductionmentioning

confidence: 99%

“…The first deficiency is that EVs' transfer randomness, temperature, and traffic condition are not given full consideration in the simulation of EVs' transfer process, which reduces the prediction accuracy of EVs spatial‐temporal distribution. In fact, the continuous rise of temperature will trigger the increase in the opening rate of air conditioning (AC) in EVs, which can lead to at least 20% increment in EVs' energy consumption 16 . Besides, traffic congestion can change the driving routes of EV owners and influence EVs connection periods 17 .…”

Section: Introductionmentioning

confidence: 99%

Decentralized scheduling optimization for charging‐storage station considering multiple spatial‐temporal transfer factors of electric vehicles

2020

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To comprehensively consider the actual spatial-temporal transfer process of electric vehicles (EVs) and enhance the computation efficiency of scheduling, this article proposes a spatial-temporal transfer model of EVs and an improved Lagrange dual relaxation method (ILDRM) for the decentralized scheduling of a charging-storage station (CSS). Specifically, with the application of trip chain technology, Monte Carlo, and Markov decision process (MDP), the spatialtemporal transfer model of EVs is constructed, taking into account multiple factors including temperatures, traffic conditions, and transfer randomness. Subsequently, by introducing ILDRM, a decentralized optimization model is proposed which converts the traditional centralized optimization model into a set of sub-problems. Moreover, the optimization model aims to maximize the profit of CSS under the constraints of vehicle-to-grid behavior and the operation of both CSS and distribution network. To validate the proposed spatialtemporal transfer model and the decentralized optimization method for CSS, a series of simulations in various scenarios are performed regarding the load curve and computation efficiency. The comprehensive and systematical study indicates that the proposed spatial-temporal transfer model enables to reflect EVs transfer randomness and it is more factually practical than the classical Dijkstra algorithm. Besides, ILDRM can provide a high computationally efficient solution to the operation of CSS.

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Annual energy consumption of electric vehicle air conditioning in China

Cited by 61 publications

References 23 publications

Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

Dynamic simulation and experimental verification on absorption refrigeration driven by vehicle waste heat

Decentralized scheduling optimization for charging‐storage station considering multiple spatial‐temporal transfer factors of electric vehicles

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