Long-term stochastic model predictive control for the energy management of hybrid electric vehicles using Pontryagin’s minimum principle and scenario-based optimization

Ritter, Andreas; Widmer, Fabio; Duhr, Pol; Onder, Christopher H.

doi:10.1016/j.apenergy.2022.119192

Cited by 28 publications

(8 citation statements)

References 28 publications

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“…In order to solve this problem, seawater desalination units [5], drinking water, and drinking water storage resources provide the possibility to manage the cost of drinking water supply in the energy hub system. On the other side, with the significant development of Electric Vehicles Charging Stations (EVCSs) technology and the reduction of their disadvantages, they can be considered as one of the distributed energy sources connected to the grid in the energy hub [6,7]. The presence of EVs at EVCS has complex uncertainties that can be attributed to factors such as holidays, weather conditions, and religious occasions.…”

Section: Aims and Motivationsmentioning

confidence: 99%

“…Alpha cuts of possibility set are obtained using alpha cuts of input variable according to Equation (7). Finally, Equation (8) shows that the output variable is calculated by summing the alpha cuts of the membership function.…”

Section: Definition 1 Production Of a Set Of Z-number Evsmentioning

confidence: 99%

“…On the other side, with the significant development of Electric Vehicles Charging Stations (EVCSs) technology and the reduction of their disadvantages, they can be considered as one of the distributed energy sources connected to the grid in the energy hub [6, 7]. The presence of EVs at EVCS has complex uncertainties that can be attributed to factors such as holidays, weather conditions, and religious occasions.…”

Section: Introductionmentioning

confidence: 99%

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Probability modelling of storage based‐smart energy hub considering electric vehicles charging stations performance and demand side management

Javid,

Derakhshan,

Hakimi

2023

IET Renewable Power Gen

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Nowadays, researchers are studying the energy discharging of electric vehicles (EVs) in charging stations. Furthermore, these stations can help maintain the balance of electric energy and reduce costs in the energy hub in the discharging mode. In this regard, this article proposes a probability and possibility approach by the Z‐number method to investigate the uncertainty of the EV owners’ behaviour at charging stations during the discharge of electric energy to the energy hub. Also, here, the uncertainty of electric energy demand, thermal energy, drinking water, and the price of electric energy, thermal energy, drinking water, and electric energy production from a solar power plant has been done by the scenario‐base method. The seawater desalination unit has been modelled to meet the drinking water needs. To check the final results of the proposed method, the mixed integer linear programming (MILP) model has been selected for energy management in the energy hub in the CPLEX optimization solver. The final results show that modelling the behaviour of EV owners in the charging stations using the Z‐number method leads to reduced consumption of some energy carriers in comparison with the fuzzy method in the proposed energy hub.

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Section: Aims and Motivationsmentioning

confidence: 99%

Section: Definition 1 Production Of a Set Of Z-number Evsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probability modelling of storage based‐smart energy hub considering electric vehicles charging stations performance and demand side management

Javid,

Derakhshan,

Hakimi

2023

IET Renewable Power Gen

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show abstract

“…(2) Time-resolved speed, torque, and braking pressure signals were used for the development of the model-based vehicle mass and road grade estimation method 13 . (3) The spatio-temporal nature of the power request signal was used to quantify the relation between grid and battery energy usage on certain road segments, which then served to derive a stochastic model predictive control approach 14 . (4) The optimal design and control of a thermal energy buffer in an electric city bus was studied based on the passenger loads, velocity and elevation profiles 15 .…”

Section: Background and Summarymentioning

confidence: 99%

ZTBus: A Large Dataset of Time-Resolved City Bus Driving Missions

Widmer,

Ritter,

Onder

2023

Sci Data

Self Cite

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This paper presents the Zurich Transit Bus (ZTBus) dataset, which consists of data recorded during driving missions of electric city buses in Zurich, Switzerland. The data was collected over several years on two trolley buses as part of multiple research projects. It involves more than a thousand missions across all seasons, each mission usually covering a full day of operation. The ZTBus dataset contains detailed information on the vehicle’s power demand, propulsion system, odometry, global position, ambient temperature, door openings, number of passengers, dispatch patterns within the public transportation network, etc. All signals are synchronized in time and include an absolute timestamp in tabular form. The dataset can be used as a foundation for a variety of studies and analyses. For example, the data can serve as a basis for simulations to estimate the performance of different public transit vehicle types, or to evaluate and optimize control strategies of hybrid electric vehicles. Furthermore, numerous influencing factors on vehicle operation, such as traffic, passenger volume, etc., can be analyzed in detail.

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“…In Zeng et al, 21 an optimization‐oriented adaptive ECMS and iterative predictor were presented to guarantee fuel economy and shorten the convergence time of the algorithm. Ritter et al 22 integrated Pontryagin's minimum principle with the scenario approach to improving the computational efficiency of the energy management controllers. Compared with the global optimization‐based strategy, the economy of the proposed strategy increased by only 1.4%.…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

Wang

Tang

et al. 2023

Circuit Theory & Apps

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SummaryThe design of energy management strategy (EMS) plays a vital role in the power performance and economy of battery–ultracapacitor for electric vehicles. A reinforcement learning (RL)‐based EMS is proposed to obtain an optimal power allocation strategy for battery–ultracapacitor electric vehicle, and its robustness is verified at different temperatures. First of all, the dynamic characteristic experiments of the battery and ultracapacitor were performed at 10°C, 25°C, and 40°C to obtain mechanism characteristics at different temperatures. Secondly, a genetic algorithm is selected to identify the parameters of the battery and ultracapacitor model. Next, the RL‐based strategy takes the minimum energy loss of the hybrid energy storage system as the reward function and solves the optimal policy based on Markov theory. The simulation results show that the economy of the RL‐based strategy correspondingly improved by 3.05%, 3.20%, and 3.15% at different temperatures in comparison with the fuzzy‐based strategy, and the economic gap between the RL‐based strategy and the DP‐based strategy is further narrowed down to 7.30%, 3.88%, and 8.40% at different temperatures, respectively. Finally, the proposed strategy is validated under different driving conditions, which indicate that the RL‐based strategy can effectively reduce energy consumption and has good robustness at different temperatures.

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Long-term stochastic model predictive control for the energy management of hybrid electric vehicles using Pontryagin’s minimum principle and scenario-based optimization

Cited by 28 publications

References 28 publications

Probability modelling of storage based‐smart energy hub considering electric vehicles charging stations performance and demand side management

Probability modelling of storage based‐smart energy hub considering electric vehicles charging stations performance and demand side management

ZTBus: A Large Dataset of Time-Resolved City Bus Driving Missions

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

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