Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models

Pelletier, Samuel; Jabali, Ola; Laporte, Gilbert; Veneroni, Marco

doi:10.1016/j.trb.2017.01.020

Cited by 358 publications

(196 citation statements)

References 59 publications

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“…Hence, charging speed only depends on the current SoC, but not on the arrival SoC. These conditions are met by realistic physical models of charging stations [44,46,62]. Moreover, exploiting the shifting property, it is possible to represent the (bivariate) charging function cf v using a univariate function c cf v : R ≥0 → [0, M ] with c cf v (τ ) := cf v (0, τ ); see Figure 2 and our explanation further below.…”

Section: Modeling Charging Stopsmentioning

confidence: 99%

Shortest feasible paths with charging stops for battery electric vehicles

Baum

Dibbelt

Gemsa

et al. 2015

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

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We study the problem of minimizing overall trip time for battery electric vehicles in road networks. As battery capacity is limited, stops at charging stations may be inevitable. Careful route planning is crucial, since charging stations are scarce and recharging is time-consuming. We extend the Constrained Shortest Path problem for electric vehicles with realistic models of charging stops, including varying charging power and battery swapping stations. While the resulting problem is N P-hard, we propose a combination of algorithmic techniques to achieve good performance in practice. Extensive experimental evaluation shows that our approach (CHArge) enables computation of optimal solutions on realistic inputs, even of continental scale. Finally, we investigate heuristic variants of CHArge that derive high-quality routes in well below a second on sensible instances. Related Work. Classic route planning approaches apply Dijkstra's algorithm [18] to a graph representation of the transportation network, using fixed scalar arc weights corresponding to, e. g., driving time. For faster queries, speedup techniques have been proposed, with different benefits in terms of preprocessing time and space, query speed, and simplicity [2]. A* search [32] uses vertex potentials to guide the search towards the target. A successful variant, ALT (A*, Landmarks, Triangle Inequality) [27], obtains good potentials from precomputed distances to selected landmark vertices. Contraction Hierarchies (CH) [24], on the other hand, iteratively contract vertices in increasing order of (heuristic) importance during preprocessing, maintaining distances between all remaining vertices by adding shortcut arcs where necessary. The CH query is then bidirectional, starting from source and target, and proceeds only from less important to more important vertices. Combining both techniques, Core-ALT [4] contracts all but the most important vertices (e. g., the top 1 %), performing ALT on the remaining core graph. This approach can also be extended to more complex scenarios, such as edge constraints to model, e. g., maximum allowed vehicle height or weight [23]. More recently, techniques (including variants of CH and ALT) were introduced that allow an additional customization after preprocessing, to account for dynamic or user-dependent metrics [11,17,19]. Also, approaches towards extended scenarios exist, such as batched shortest paths [12] or shortest via paths [1,14]. Here, a common approach is to make use of a relatively fast target selection phase, precomputing distances to relevant points of interest to enable faster queries. However, these techniques were only evaluated for single-criterion search, where the distance between two vertices is always a unique scalar value. For multi-criteria scenarios, on the other hand, problem complexity and solution sizes increase significantly, and practical approaches are only known for basic problem variants [21,23,56]. For a more complete overview of techniques and combinations, see Bast et al. [2].Regarding rout...

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Section: Modeling Charging Stopsmentioning

confidence: 99%

Shortest feasible paths with charging stops for battery electric vehicles

Baum

Dibbelt

Gemsa

et al. 2015

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

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“…In the charge and discharge cycle, lithium metal embedded in the carbon material is ionized and travels among the electrolyte, the cathode material, and the anode material through the porous medium to release or accept electrons. From the microscopic point of view, the main factors affecting the aging degradation of LIBs are metal deposition, formation of passivation film, crack propagation, and dissolution of active material in the electrode …”

Section: Battery Modelsmentioning

confidence: 99%

A review on battery management system from the modeling efforts to its multiapplication and integration

2019

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Summary Progress in battery technology accelerates the transition of battery management system (BMS) from a mere monitoring unit to a multifunction integrated one. It is necessary to establish a battery model for the implementation of BMS's effective control. With more comprehensive and faster battery model, it would be accurate and effective to reflect the behavior of the battery level to the vehicle. On this basis, to ensure battery safety, power, and durability, some key technologies based on the model are advanced, such as battery state estimation, energy equalization, thermal management, and fault diagnosis. Besides, the communication of interactions between BMS and vehicle controllers, motor controllers, etc is an essential consideration for optimizing driving and improving vehicle performance. As concluded, a synergistic and collaborative BMS is the foundation for green‐energy vehicles to be intelligent, electric, networked, and shared. Thus, this paper reviews the research and development (R&D) of multiphysics model simulation and multifunction integrated BMS technology. In addition, summary of the relevant research and state‐of‐the‐art technology is dedicated to improving the synergy and coordination of BMS and to promote the innovation and optimization of new energy vehicle technology.

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“…In our problem, several companies jointly invest in purchasing and installing CSs. To this end, we consider a set of potential locations for the shared CSs and multiple charging technologies (e.g., slow, moderate, and fast) with a nonlinear charging function (Pelletier et al., ). Each company runs a distribution system involving (a) a homogeneous and unlimited fleet of EVs and (b) a depot with a preassigned set of customers with service times.…”

Section: Problem Definition and Formulationmentioning

confidence: 99%

The electric vehicle routing problem with shared charging stations

Koç¹,

Jabali

Mendoza

et al. 2018

Int Trans Operational Res

Self Cite

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We introduce the electric vehicle routing problem with shared charging stations (E-VRP-SCS). The E-VRP-SCS extends the electric vehicle routing problem with nonlinear charging function (E-VRP-NL) by considering several companies that jointly invest in charging stations (CSs). The objective is to minimize the sum of the fixed opening cost of CSs and the drivers cost. The problem consists of deciding the location and technology of the CSs and building the routes for each company. It is solved by means of a multistart heuristic that performs an adaptive large neighborhood search coupled with the solution of mixed integer linear programs. It also contains a number of advanced efficient procedures tailored to handle specific components of the E-VRP-SCS. We perform extensive computational experiments on benchmark instances. We assess the competitiveness of the heuristic on the E-VRP-NL and derive 38 new best known solutions. New benchmark results on the E-VRP-SCS are presented, solved, and analyzed.

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Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models

Cited by 358 publications

References 59 publications

Shortest feasible paths with charging stops for battery electric vehicles

Shortest feasible paths with charging stops for battery electric vehicles

A review on battery management system from the modeling efforts to its multiapplication and integration

The electric vehicle routing problem with shared charging stations

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