Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle

Wei, Changyin; Sun, Xiuxiu; Chen, Yong; Zang, Libin; Bai, Shujie

doi:10.1016/j.energy.2021.120858

Cited by 32 publications

(8 citation statements)

References 36 publications

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“…However, the layout is more complex and the design costs are higher, which result in higher requirements for the control strategies. It is currently the most popular configuration on the market for achieving optimum power performance and fuel economy [37,38]. The structure is displayed in Figure 3.…”

Section: Series-parallel Hybrid Electric Vehiclementioning

confidence: 99%

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

2023

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With the world’s energy reserves under strain and the requirements of national carbon emission regulations, the fuel efficiency and environmental friendliness of automobiles are becoming increasingly important. Due to the combination of long cruising range and energy efficiency, hybrid electric vehicles (HEVs) have been adopted as a reliable option for improving fuel economy and reducing emissions. In order to fully exploit the advantages of hybrid electric vehicles, energy management and torque distribution have become the focus of control strategies for HEVs, while also ensuring battery life and meeting requirements for fuel consumption, emissions and driving performance. Therefore, a great deal of research has been carried out on energy management strategies and many approaches have been offered in the literature. This review provides a comprehensive assessment of the literature, highlighting its contributions and making a complete reference for scholars interested in hybrid vehicle development, control, and optimization.

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Section: Series-parallel Hybrid Electric Vehiclementioning

confidence: 99%

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

2023

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“…The fuzzy control, based on fuzzy rule base, is an intelligent control that simulates human control experience and knowledge. [12][13][14] But a fuzzy control strategy with a good control effect is difficult to design directly under various driving conditions. 15 Thus, the fuzzy control is normally combined with other methods to better distribute the power output between different energy sources than a single fuzzy control strategy.…”

Section: Rule-based Energy Management Strategiesmentioning

confidence: 99%

“…Therefore, the fuzzy control‐based strategy was proposed to solve this problem. The fuzzy control, based on fuzzy rule base, is an intelligent control that simulates human control experience and knowledge 12–14 . But a fuzzy control strategy with a good control effect is difficult to design directly under various driving conditions 15 .…”

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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“…In addition to external parameters and external planning factors, it is necessary to take into consideration the specifics of the vehicles themselves. In terms of innovative systems, care must be taken to ensure that electric vehicles in operation are equipped with a suitable battery management system [40,41]. One of the important parameters that need to be taken into account when purchasing vehicles is the size and capacity of the traction battery [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Research into the Impacts of Driving Cycles and Load Weight on the Operation of a Light Commercial Electric Vehicle

et al. 2021

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The European Parliament has adopted Directive 2019/1161 on the promotion of environmentally friendly and energy-efficient road transport vehicles, which also defines the obligations and forms of support for the procurement of environmentally friendly vehicles in urban logistics. The increase in the number of shipments delivered within e-commerce, which is also the result of the COVID-19 pandemic, requires a transition to a sustainable logistics system. New research questions are being raised in the preparation of new projects for the introduction of small electric commercial vehicles in particular. One of the main research questions about deployment itself is whether light commercial electric vehicles are able to fully replace conventionally powered vehicles. What operating conditions are optimal for the operation of them? How does load weight affect the energy efficiency of operating a light commercial electric vehicle? The authors decided to carry out research into the impacts of weight and the nature of a driving cycle under laboratory conditions to eliminate all external factors that could distort individual measurements and their results. In order to simulate driving cycles, an urban driving cycle was designed on the basis of the course of speed, acceleration, deceleration and slope conditions of roads in the selected regional city of Žilina (Slovakia). In the case of the operation of an electrically powered light commercial vehicle, the impact of load weight on the range of the vehicle is low, and is below the level of the theoretical maximum range of the vehicle in urban logistics applications. The operation of electrically powered vehicles in hilly terrains with relatively longer gradients and steeper slopes increases electricity consumption and, thereby, reduces their range.

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Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle

Cited by 32 publications

References 36 publications

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles

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

Research into the Impacts of Driving Cycles and Load Weight on the Operation of a Light Commercial Electric Vehicle

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