Optimal Charging Navigation Strategy for Electric Vehicles

Guo, Xu; Liu, Jiaoyu; Hao, Fan

doi:10.1109/iciicii.2016.0061

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Most existing literature used modeling methods to focus on the optimization design of vehicle performance and technical innovation. For example, research studies on HVs and EVs focused on the optimal location of charging infrastructures [24,25], optimal route scheduling and navigation strategy [26,27], and vehicle scheduling problem [28]. Besides, some scholars also studied the auto industry using the system dynamics (SDs) method.…”

Section: Introductionmentioning

confidence: 99%

China’s Auto Industry Upgrade Process Based on Aging Chain and Coflow Model

Zhang

Zhou

et al. 2021

Complexity

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To protect energy resources and alleviate environmental pollution, many countries attach great importance to the transformation of traditional industries into clean energy industries. In this paper, fuel vehicles (FVs), hybrid vehicles (HVs), and electric vehicles (EVs) are included in the research. Then, based on the aging chain and coflow theory of SDs, we construct a dynamic matching model of the auto industry upgrade process and its energy consumption attributes. The simulation results of China’s auto industry show that (1) the upgrading of the auto industry is an evolutionary process from high energy consumption and high pollution to low energy consumption and no pollution and the transition from FVs and HVs to EVs will undergo two adjustments; (2) simply reducing energy supply does not have the expected impact on vehicle size and vehicle energy consumption intensity and only by adjusting the energy supply and upgrade ratios together, energy utilization efficiency can be improved; (3) market screening time has an impact on auto industry upgrade speed by affecting vehicle market share and dwell time; (4) China’s auto industry upgrade process should adhere to “problem-oriented” and strengthen consumer guidance, technology innovation, and infrastructure construction. The conclusions can provide references for industrial policy adjustment and industrial structure optimization.

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Section: Introductionmentioning

confidence: 99%

China’s Auto Industry Upgrade Process Based on Aging Chain and Coflow Model

Zhang

Zhou

et al. 2021

Complexity

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“…Thus, to take full advantage of the potential of each HEV, the development of an operational strategy is essential. An appropriate selection of the navigation mode (SM or DM), efficient battery recharge, and optimal scheduling of deliveries should be considered as part of this strategy [4], [6]. Therefore, this paper aims at developing an intelligent tool to ensure the optimal selection of navigation modes (OSNMs) of HEVs considering time constraints, speed limits on urban roads, and energy requirements of the HEVs for different levels of CO 2 emissions reduction.…”

mentioning

confidence: 99%

“…This model was used to evaluate the performance of a fleet of EVs considering the fast and ultra-fast charging rates during operation. A multiobjective model to find the optimal charging and navigation strategy considering the traffic congestion, power network operation, and the number of charging stations at each node was developed in [6]. In [7], a proper framework was proposed for plug-in HEVs to optimally allocate the limited charging stations among metropolitan areas via adequate interactions among public charging availability, electricity prices, and destination and route choices.…”

mentioning

confidence: 99%

Optimal Selection of Navigation Modes of HEVs Considering CO₂ Emissions Reduction

Cerna

Pourakbari‐Kasmaei

Contreras

et al. 2019

IEEE Trans. Veh. Technol.

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In this paper, a mixed-integer linear programming (MILP) model is proposed to optimize hybrid electric vehicle (HEV) navigation modes on the city map, namely the problem of the optimal selection of navigation modes (OSNMs). The OSNMs problem of the HEV as part of the operating strategy is obtained considering a constraint set related to CO2 emissions reduction, efficient battery charging, and the optimal scheduling of deliveries. Uncertainties in the HEV navigation on urban roads are modeled using probability values assigned to an established set of traffic density values according to the levels of service (LOS). The model is implemented in AMPL and solved using the commercial solver CPLEX. The case study considers real data related to the Prius Prime technology and shows the effectiveness of automating the HEV navigation modes considering CO2 emissions reduction levels during an operating strategy. Index Terms-Efficient battery charging, optimal selection navigation modes, optimal scheduling of deliveries, operating strategy. NOMENCLATURE A. Sets , Average speed value in the navigation of the HEV in urban road ki for delivery d (km/h).

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