Optimal charging of electric vehicle fleets: Minimizing battery degradation and grid congestion using Battery Storage Systems

Geerts, David; Medina, Róbinson; Sark, Wilfried van; Wilkins, Steven

doi:10.1109/smart55236.2022.9990120

Cited by 2 publications

(7 citation statements)

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“…An example of the dependency of one of these parameters on SoC and temperature is found in Figure 1b. All the other parameter dependencies are found in the previous version of this work [11]. The current and voltage at pack level is computed by scaling up the cell current and voltage, i.e.,…”

Section: Battery Modelmentioning

confidence: 99%

“…The optimization objective maximizes the RUL of a single vehicle. This is captured by the following optimization problem: 12) Equation ( 13), (11) where I is the charging current profile of the vehicle, further defined by…”

Section: Optimizing the Charging Profile Of One Vehiclementioning

confidence: 99%

“…The batteries are assumed to be Li(NiMnCo)O2 18650 lithium-ion batteries. The complete set of battery model parameters is shown in the previous work [11]. An overview of some key model parameters is shown in Table 2.…”

Section: Case Studymentioning

confidence: 99%

“…In comparison with the earlier conference paper on which this publication is based [11], step 2 and 3 have not been published in that paper. Note that the first step could have been used directly for the third step (skipping the ML step).…”

mentioning

confidence: 91%

“…An earlier version of this paper has been published [11], which optimized the charging scheme of a single vehicle as well as a fleet of vehicles, while focusing on cost effectively reducing the load on the electricity grid. This paper extends such a version by creating a ML model that predicts the degradation speed of a battery while taking into account internal variability of batteries.…”

Section: Introductionmentioning

confidence: 99%

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Charge Scheduling of Electric Vehicle Fleets: Maximizing Battery Remaining Useful Life Using Machine Learning Models

Geerts,

Medina,

van Sark

et al. 2024

Batteries

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Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e. the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet.

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Section: Battery Modelmentioning

confidence: 99%

Section: Optimizing the Charging Profile Of One Vehiclementioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

mentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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