Optimization of Charging Strategies for Battery Electric Vehicles Under Uncertainty

Huber, G. M.; Bogenberger, Klaus; Lint, Hans van

doi:10.1109/tits.2020.3027625

Cited by 8 publications

(5 citation statements)

References 39 publications

(55 reference statements)

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“…As a solution method, they showed an adapted Dijkstra algorithm [18]. In further work, Huber et al [26] showed the integration of a dynamic energy buffer to react to unforeseen effects, which locally mean higher energy consumption.…”

Section: Single-route Charging Optimizationmentioning

confidence: 99%

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Time vs. Capacity—The Potential of Optimal Charging Stop Strategies for Battery Electric Trucks

et al. 2022

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The decarbonization of the transport sector, and thus of road-based transport logistics, through electrification, is essential to achieve European climate targets. Battery electric trucks offer the greatest well-to-wheel potential for CO2 saving. At the same time, however, they are subject to restrictions due to charging events because of their limited range compared to conventional trucks. These restrictions can be kept to a minimum through optimal charging stop strategies. In this paper, we quantify these restrictions and show the potential of optimal strategies. The modeling of an optimal charging stop strategy is described mathematically as an optimization problem and solved by a genetic algorithm. The results show that in the case of long-distance transport using trucks with battery capacities lower than 750 kWh, a time loss is to be expected. However, this can be kept below 20 min for most battery capacities by optimal charging stops and sufficient charging infrastructure.

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Section: Single-route Charging Optimizationmentioning

confidence: 99%

“…We define PoIs here as rest sites both with and without charging possibility. Analogous to Huber et al [18,26] and Cussigh et al [27], we assume a defined route, which is described by the total distance and a constant average velocity. Along the defined route, the PoIs are set through the infrastructure model.…”

Section: Simulation Environmentmentioning

confidence: 99%

Time vs. Capacity—The Potential of Optimal Charging Stop Strategies for Battery Electric Trucks

et al. 2022

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“…Scheubner et al used a multi-linear regression (MLR) model to compute a stochastic velocity prediction, which is then used to predict a probability distribution for the energy consumption using a physical model and a sequential Monte Carlo simulation [5]. Furthermore, the uncertainty of EDPs has been considered in BEV routing applications [24], [25], [26], [27].…”

Section: Background and Related Workmentioning

confidence: 99%

“…A disadvantage of a constant, deterministic safety margin is that is frequently too large. An unnecessarily large safety margin reduces the effective driving range and reduces the possibilities for feasible routing and charge planning strategies [27].…”

Section: A Safety Marginmentioning

confidence: 99%

Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles With Federated Learning

Thorgeirsson

Scheubner

Funfgeld³

et al. 2021

IEEE Open J. Veh. Technol.

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Today's drivers of battery electric vehicles must deal with limited driving range in a sparse charging infrastructure. An accurate prediction of energy demand and driving range is therefore important and enables reliable routing and charge planning applications. Predictions of energy demand entail uncertainty, which can be considered directly with the use of probabilistic prediction algorithms. Machine learning algorithms are frequently applied in this context, but data used to train these algorithms are often distributed over a fleet of connected vehicles. Federated learning can be applied in this setting, but predictive uncertainty is typically not considered. We apply an extension of the federated averaging algorithm to learn probabilistic neural networks and linear regression models in a communication-efficient and privacy-preserving manner. We demonstrate the performance advantage of probabilistic prediction models over deterministic prediction models using proper scoring rules. Furthermore, we show that federated learning can improve the standard, driver-individual learning. Using probabilistic predictions, variable safety margins based on destination attainability can be applied, leading to increased effective driving range and reduced travel time.

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“…To date, there have been extensive works developing viable charging strategies. A majority of these approaches integrate charging stops into conventional routing problems and minimize the travel time or energy required on the route, as in [6], [7], [8]. The authors in [9] propose an optimal driving and charging strategy for electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Rollout-Based Charging Strategy for Electric Trucks With Hours-of-Service Regulations

Bai

Johansson

et al. 2023

IEEE Control Syst. Lett.

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Freight drivers of electric trucks need to design charging strategies for where and how long to recharge the truck in order to complete delivery missions on time. Moreover, the charging strategies should be aligned with drivers' driving and rest time regulations, known as hours-of-service (HoS) regulations. This letter studies the optimal charging problems of electric trucks with delivery deadlines under HoS constraints. We assume that a collection of charging and rest stations is given along a pre-planned route with known detours and that the problem data are deterministic. The goal is to minimize the total cost associated with the charging and rest decisions during the entire trip. This problem is formulated as a mixed integer program with bilinear constraints, resulting in a high computational load when applying exact solution approaches. To obtain real-time solutions, we develop a rollout-based approximate scheme, which scales linearly with the number of stations while offering solid performance guarantees. We perform simulation studies over the Swedish road network based on realistic truck data. The results show that our rollout-based approach provides near-optimal solutions to the problem in various conditions while cutting the computational time drastically.

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Optimization of Charging Strategies for Battery Electric Vehicles Under Uncertainty

Cited by 8 publications

References 39 publications

Time vs. Capacity—The Potential of Optimal Charging Stop Strategies for Battery Electric Trucks

Time vs. Capacity—The Potential of Optimal Charging Stop Strategies for Battery Electric Trucks

Probabilistic Prediction of Energy Demand and Driving Range for Electric Vehicles With Federated Learning

Rollout-Based Charging Strategy for Electric Trucks With Hours-of-Service Regulations

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